diff --git a/.claude/skills/train-model/SKILL.md b/.claude/skills/train-model/SKILL.md new file mode 100644 index 0000000..896c715 --- /dev/null +++ b/.claude/skills/train-model/SKILL.md @@ -0,0 +1,277 @@ +--- +name: train-model +description: End-to-end agent for training LTX-2 models. Probes filesystem and GPU, picks the right conditioning mode from the user's intent, prepares the dataset (scenes, captions, references), preprocesses, autotunes, launches, and monitors training. Use when the user wants to train, fine-tune, LoRA, or otherwise produce a custom LTX-2 model. +argument-hint: [optional source path or run name] +user-invocable: true +allowed-tools: Bash, Read, Grep, Glob, Edit, Write, Agent, AskUserQuestion, TodoWrite +--- + +# Train Model — Orchestrator + +Take the user from "I want to train something" to a running, monitored training job — automating the mechanical glue (dataset layout, captioning, preprocessing, config patching, launch, monitoring) without making silent decisions on their behalf. + +> **Source of truth for the trainer:** [`packages/ltx-trainer/docs/`](../../../packages/ltx-trainer/docs/). This skill orchestrates those scripts; it does not duplicate their reference content. + +## Hard Invariants + +These are non-negotiable. Re-read them before every action that touches the filesystem or starts a process. + +1. **No file mutation outside the run workspace without explicit user approval.** The workspace is `./projects//`. Never overwrite, move, delete, or modify any user file or directory outside it without surfacing an explicit ask. Hours of dataset work must never be silently destroyed. +2. **No heavy work before plan approval.** Captioning, preprocessing, training, and autotune do **not** start until the user approves `plan.md` (Phase 4). Probing the filesystem and running `nvidia-smi` is fine; encoding videos or downloading models is not. +3. **No silent assumptions.** Every non-trivial default appears under "Assumptions" in `plan.md`. +4. **No code changes to the trainer package without explicit consent.** If the user's intent doesn't map to a supported configuration, follow the **Escape Hatch** section below — do not unilaterally edit `packages/ltx-trainer/`. +5. **No fabricated claims about training outcomes or data sufficiency.** Do not assert how well something *will* train, whether a dataset is "too small," how many samples/seconds of audio are "enough," which modality will "learn better," expected quality, or any similar prediction — you have no grounded basis for these, they're frequently wrong, and they mislead users. Stick to facts you can substantiate: what the trainer/docs actually say, observed numbers (loss, step time, VRAM), counts, and the user's own stated goals. If the user asks for a recommendation that depends on such judgment, you may share it **only** as an explicitly-flagged uncertainty ("I'm not sure — you'd have to try it"), never as authoritative fact. When in doubt, say less. + +## Keep the User Informed + +Most users don't know how this skill works under the hood — they don't know what "preprocessing," "a one-sample sanity check," or "autotune" mean or why they're happening. Narrate the run in plain language so it never feels like a black box: + +- **Entering a phase:** one or two sentences on *what you're about to do and why* — in user terms, not jargon. +- **Leaving a phase:** one line on *what came out of it* (e.g. "captioned 9 clips," "found the fastest stable config: batch 1, ~3s/step"). +- **Explain the non-obvious phases explicitly** — these are the ones that confuse people: + - *Sanity check (Phase 6):* "Before the full run, I do a quick dry run on a single clip at your target resolution. It catches out-of-memory or config problems in a couple of minutes instead of failing hours into training." + - *Autotune (Phase 6):* "Then I try a few configuration variants on that one clip to pick the fastest one that still fits your GPU — so the full run is as fast as it can be." + - *Preprocess (Phase 7):* "I'm encoding your videos into the compressed latents the trainer reads. One-time step; the trained model never sees the raw videos directly." +- Keep it concise — a sentence or two per transition, not walls of text or raw logs. This is running commentary, not a replacement for the upfront plan (Phase 4) or the status reports (Phase 8). +- Long-running steps (preprocess, training): say roughly how long it'll take and that you'll report back, so silence doesn't read as "stuck." +- **Describe what you're doing — don't editorialize about how it'll turn out.** Narration covers *what's happening*; it must not drift into unfounded predictions about training quality or data sufficiency (e.g. "26s of audio is too little," "voice won't learn well"). Those are fabricated claims — see Hard Invariant #5. State facts and the user's choices; leave the "will it be good?" judgment to the user watching the results. + +## Phase 0 — Set Up + +Create the workspace and todos. + +1. Pick a workspace root in this order (use first writable): + - `$LTX_TRAININGS_DIR` + - `/data/ltx-trainings/` + - `/workspace/ltx-trainings/` + - `./projects/` (repo-relative — preferred default in this repo) +2. Derive a tentative `` from the user's words; finalise after Phase 1 once the mode is known. Format: `--`. +3. Create `//` and seed empty subdirs: `dataset/`, `outputs/`, `overfit/`. +4. Create a todo list covering Phases 1–9 so the user can see progress. + +## Phase 1 — Intent + +Ask one question, framed in user terms (not jargon): + +> What do you want the model to learn? Examples: "generate videos from text," "make a LoRA of a specific style," "extend a video forward in time," "add sound effects to a silent video," "fill in masked regions of a video." + +Map the answer to one or more conditioning modes via `references/mode-selector.md`. If the requested capability has no mapping, go to **Escape Hatch**. + +### Plain concept/style LoRA → ask how it'll be used, default to I2V + +A "train a LoRA on X" request (a character/style/concept LoRA, no specific conditioning task) maps to either T2V or I2V. These aren't locked to inference: LoRA weights are pipeline-agnostic (the same checkpoint loads in both T2V and I2V inference), and the `i2v_lora` config applies first-frame conditioning with **`probability: 0.5`** — so it learns **both** conditioned (I2V) and unconditioned (T2V) generation in one run, and the first frame is taken automatically from each training clip (no extra data prep). I2V is therefore a versatile superset. + +Ask how they intend to use the result: + +> Will you generate videos from **text alone** (T2V), from a **starting image** (I2V), or **both / not sure**? + +- **Both / not sure (default):** use `i2v_lora` (probabilistic first-frame) — works for both at inference. +- **I2V:** `i2v_lora`. +- **Text only:** `t2v_lora`. + +(This only applies to plain concept/style LoRAs. A specific task — extension, inpainting, foley, IC-LoRA, etc. — maps directly to its mode via `mode-selector.md`; no usage question needed.) + +### Confirm the mode before proceeding + +Once the mode is determined, **state it plainly and confirm it** before doing any probing or work — a wrong inference is cheap to fix here and expensive later: + +> "Got it — I'll train an **I2V LoRA** (usable for both image-to-video and text-to-video at inference). Sound right?" + +The mode also appears in the plan (Phase 4), but confirm it here so the rest of the flow isn't built on a wrong guess. + +## Phase 2 — Probe + +No questions in this phase. Inspect what's already there. Use `references/onboarding.md` as the source of truth for the prerequisite checklist and what to do when something is missing. + +### Filesystem probe +- If the user pointed at a path, classify: directory of raw videos, single long video, directory with a metadata file (CSV/JSON/JSONL), existing `.precomputed/`, partial outputs from a prior run. +- For metadata files, identify columns: `video`/`media_path`, `caption`, `audio`, `reference_video`, `video_mask`, etc. (see `packages/ltx-trainer/docs/dataset-preparation.md`). +- **Clip lengths (small datasets only):** for datasets up to a few hundred clips, `ffprobe` each clip's frame count and note the **minimum**. Clips shorter than the target frame bucket are silently skipped by `process_dataset.py`, so the shortest clip caps the achievable frame count — feed this into the Phase 3 resolution/frame-count choice (pick a bucket the clips support, or plan multi-bucket). Skip this per-clip probe for large datasets (too slow); rely instead on the post-preprocess reconciliation in Phase 7, which flags any dropped clips regardless of dataset size. +- Check for an existing `//` and whether `outputs/checkpoints/` contains a prior checkpoint (`lora_weights_step_*.safetensors` or `model_weights_step_*.safetensors`, plus a matching `training_state_step_*.pt` when resume state is enabled). This is a **resume candidate** — but note the trainer does *not* auto-resume from the output dir; resuming requires explicitly setting `model.load_checkpoint` in `config.yaml` to that checkpoint path. See `phases/launch-and-monitor.md` for the resume flow. +- Check disk space at the workspace root. Preprocessed latents, checkpoints, and validation samples add up across a run; surface the available space alongside a rough sense of what one run consumes (one preprocessed bucket scales with sample count and resolution; each checkpoint is several GB), and warn the user if free space looks tight given their dataset size. + +### Hardware probe +- `nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader` → GPU model, count, VRAM. Stop the run if no CUDA GPU. +- W&B login state — use wandb's **own** credential resolution (source-agnostic: covers env var, netrc, and the wandb settings file), not a hand-rolled netrc grep and **not** `wandb status` (which misleadingly reports `api_key: null` even when logged in): + + ```bash + uv run python -c "import wandb; print(bool(wandb.Api().api_key))" # True => logged in + ``` + + `True` → W&B is available, enable it. `False` → genuinely not logged in. If the check errors or is ambiguous, **ask the user** rather than silently disabling — a wrong "W&B off" assumption can incorrectly disable expected tracking. +- Apply `references/hardware-profiles.md` to derive defaults (32GB / 40–60GB / 80GB+ VRAM tier). + +### Prerequisite probe (first-run sanity) +- `uv` installed (`command -v uv`). +- Workspace synced (lockfile present + `ltx-trainer` import works). +- LTX-2 `.safetensors` and Gemma text encoder dir present in `/models/`, `~/models/`, or `$LTX_MODELS_DIR`. +- Captioner availability: Gemini auth (`GEMINI_API_KEY`/`GOOGLE_API_KEY` or gcloud/Vertex) OR a ≥40 GiB GPU to host the Qwen3-Omni-30B vLLM server (FP8; bf16 needs ≥66 GiB). On typical consumer GPUs (24/32 GB), Gemini is effectively the only local-free option — see `references/onboarding.md`. + +For any missing prerequisite, **do not silently fail in a later phase**. Present the finding in chat with the specific next step from `references/onboarding.md`. The skill may offer to auto-install / auto-download missing pieces — but only ever after asking the user explicitly, one item at a time (model downloads are tens of GB each). Never auto-modify shell rc files or system config without consent. + +When everything (or what the user agreed to set up) is in place, fold the resolved paths into the plan's Assumptions section. + +### Pre-existing artifacts in the run dir + +If the run dir (or a user-supplied path) already contains artifacts from a prior session, classify each into one of three buckets — only the third prompts the user: + +1. **Deterministically verifiable → verify, then reuse silently (or stop).** `.precomputed/` latents: load a sample, check tensor shapes + modality coverage against the target (Phase 7). Match → reuse, no question. Mismatch/incomplete → stop and ask (reuse-at-old-spec / re-preprocess to a new dir / abort). +2. **Cheap, fully-derived intermediates → regenerate silently.** `overfit/`, eval renders, sanity/temp configs, one-sample metadata. Delete and redo; don't ask. +3. **Expensive AND not deterministically verifiable → ask.** Captions (`dataset.json`) and trained checkpoints/outputs. We can't programmatically decide whether existing captions or a half-finished run are what the user wants now, so surface what was found (counts, and how/when produced if knowable) and ask: reuse vs regenerate (for checkpoints: resume vs fresh). + +Principle: only ask when reuse-vs-regenerate is a genuine judgment call with cost either way. Never silently delete user-supplied data (hard invariant #1). + +## Phase 3 — Ask (minimum viable set) + +Ask only what cannot be inferred. Use `AskUserQuestion` with multiple-choice when possible. Typical questions: + +- **Target resolution / frame count** — propose a default per mode (e.g., `768x512x49` for T2V LoRA on consumer GPUs); offer overrides. +- **Training steps** — if dataset size doesn't pin it, propose a default (e.g., 2000 for small LoRA datasets). +- **LoRA trigger word / concept name** — only for style/concept LoRAs. Ask **only** for the word itself (or whether they want one). **Never** ask or mention *how* it's injected — it's always the `--lora-trigger` flag (passed to `process_dataset.py`, which forwards it to `process_captions.py` where the prepend happens); this is a fixed implementation detail. Presenting caption-injection as an option creates unnecessary confusion. +- **Captioner backend** — only if more than one path is viable (e.g. a ≥40 GiB GPU can host the Qwen3-Omni-30B server *and* Gemini auth is available). On typical consumer GPUs, default to `gemini_flash` and surface that Gemini auth is required rather than asking. +- **Model paths** — only if not found in the probe. + +Never ask anything answerable by `ls`, `nvidia-smi`, or the W&B credential check above. + +## Phase 4 — Plan + +Write the plan to `//plan.md` using `references/plan-template.md`. Present it to the user in chat (don't just dump the file path). Wait for explicit approval before proceeding. + +If the user requests changes, edit the plan and re-present. Do not start Phase 5 until approval. + +## Phase 5 — Prepare Dataset + +If captioned metadata already exists with all required columns for the chosen mode, skip this phase. Otherwise follow `phases/prepare-dataset.md` — re-read it before acting. + +**Captioning gate:** caption a 3-sample spot-check first, show the captions in full, and **STOP for explicit user approval** before captioning the full set. The user must approve or give tuning instructions — never auto-proceed to the full pass. (Details in `phases/prepare-dataset.md`.) + +**Conditioning-inputs gate:** modes that need a reference (V2V/A2A/AV2AV IC-LoRA) or a mask (video/audio inpainting) require a per-sample input that encodes the user's specific idea. **Ask the user to provide it** — never invent the method (no defaulting to Canny/depth/generic masks). Only help generate it if the user explicitly asks, following *their* approach. Don't enter preprocessing for these modes without the input present. (Details in `phases/prepare-dataset.md` Step 4.) + +## Phase 6 — Sanity Check + Autotune (always run) + +**Tell the user what this phase is before starting it** — it's the most opaque to someone who doesn't know the design (see "Keep the User Informed"). In plain terms: a quick single-clip dry run at the target resolution to catch OOM/config errors cheaply, followed by trying a few config variants to pick the fastest stable one. + +Run **at the full target resolution** on **one sample** before the full preprocess. Purpose: +1. Catch OOM / config errors before paying the full preprocessing cost. +2. Empirically pick the fastest stable config via a small sweep. + +Steps: +1. Pick one sample from the dataset metadata. Preprocess just that sample to `//overfit/.precomputed/` (see `phases/preprocess-dataset.md` — use it in "one-sample" mode). +2. Generate a temp config matching the planned full-run config but with `data.preprocessed_data_root: overfit/.precomputed`, `optimization.steps: 50`, `validation.interval: 50`, `checkpoints.interval: null`. +3. Run the **baseline trial**: the conservative config from the matched VRAM tier (32GB tier = `t2v_lora_low_vram.yaml` defaults; 80GB+ tier = `t2v_lora.yaml` defaults — see `references/hardware-profiles.md`). +4. **Success criteria** (all required): + - No OOM, no NaN loss, no crash. + - All 50 training steps complete. + - Validation sample at step 50 generates successfully (validation pass is a real OOM risk — do not skip). + - **For audio runs:** the one-sample `audio_latents/` is non-empty (the trainer log should report audio enabled). A joint/audio run that silently produced no audio latents is a failure even if steps complete — see the audio gate in `phases/preprocess-dataset.md`. + - **Loss is NOT a success criterion.** Loss can be non-monotonic even when training is healthy. +5. **Autotune sweep** — incremental, capped at 5 trials total. Each trial = current best + one change. Stop on OOM (revert), no step-time improvement, or 5 trials: + - Trial 2: `quantization: null` (disable transformer quantization) if VRAM headroom allows. + - Trial 3: `optimizer_type: adamw` (disable 8-bit optimizer) if headroom allows. + - Trial 4: `batch_size` up (1 → 2 → 4). Adjust `gradient_accumulation_steps` proportionally to keep effective batch constant. **Note:** batch size can't be meaningfully tested on the one-sample set — defer it (test on the full set later, or just keep `batch_size: 1`, which is preferable for small concept-LoRA datasets anyway). + - **Do not sweep:** resolution (user decision), `acceleration.load_text_encoder_in_8bit` (one-time, no step-time impact), `enable_gradient_checkpointing` (the trainer's example configs ship with it on; on the 80GB+ tier you *may* try it off, but for the 22B model it usually OOMs even with tens of GB of apparent headroom — don't expect a win; never turn it off on the 32GB tier). +6. Collect per trial: step time and peak VRAM. **Prefer the trainer's own end-of-run stats** (it prints total time / step time and peak GPU memory) — no external timing tool is needed (`/usr/bin/time` is often not installed). Append results to `//autotune.log`. +7. Winning trial's deltas are patched into the main `config.yaml`. Summarise the sweep to the user (one line per trial + winner). + +If the baseline trial fails, consult `references/troubleshooting.md`, propose a fix, re-run. Never push forward to the full preprocess after a failed sanity check. + +## Phase 7 — Full Preprocess + +Follow `phases/preprocess-dataset.md`. Re-read it before acting. + +**If `.precomputed/` already exists at the target path** (user-supplied or prior run), the phase verifies shapes and modality coverage before reuse. On mismatch it stops and asks — never silently overwrites. + +## Phase 8 — Launch & Monitor + +Follow `phases/launch-and-monitor.md`. Re-read it before acting. Surface the W&B URL (if enabled) and produce periodic status reports. At completion, the phase writes `//outputs/run-summary.md`. + +## Phase 9 — Post-Train Validate + +After training finishes, follow `phases/post-train-validate.md`. Re-read it before acting. The phase renders the final LoRA against in-distribution, out-of-distribution, and held-out prompts; surfaces the MP4 paths to the user and exits. + +**Important constraint:** the post-train validate phase does not solicit a verdict and does not coach on causes for "soft" failures. Soft training quality has no reliable if/then rule book — the user inspects the renders and decides for themselves whether to ship, iterate, or change course. The orchestrator's job ends after Phase 9; iteration is a new invocation with a new run-name. + +## Monitor-Only Entry + +If invoked against an existing `//` that already has a training process running or completed, **skip to Phase 8 in monitor-only mode** instead of restarting anything: report current step, recent loss, ETA, checkpoint list, W&B URL, and the resume command. Distinguish "live process" vs "stopped run with checkpoints" and offer the appropriate next action. + +## Escape Hatch: Unsupported Modes + +If Phase 1 intent doesn't map to any combination of supported `flexible`-strategy conditions: + +1. Stop. Do not edit `packages/ltx-trainer/` on your own. +2. Explain what's missing in concrete terms: "you want X. The trainer supports A, B, C via conditions D, E, F. X requires a new condition / strategy." +3. Identify the code change needed (typically a new `Condition` subclass in `ltx_trainer/training_strategies/flexible.py` plus schema wiring in `config.py`). +4. Ask the user explicitly: "Proceed with the code change, do it yourself, or abort?" +5. Only on explicit consent, drop out of this skill's orchestrator and edit code as a normal agent task. After the change lands and is tested, return here. + +## Ask-vs-Assume Cheat Sheet + +| Decision | How | +|----------|-----| +| Precision, quantization, optimizer, grad checkpointing | Assume from matched VRAM tier. List in plan's "Assumptions". | +| Checkpoint/validation interval, seed, W&B project name, output dir | Assume sensible defaults. List in "Assumptions". | +| Target resolution / frame count | Ask if not given; propose mode-appropriate default. | +| Step count | Ask if dataset size doesn't pin it. | +| LoRA trigger word / concept name | Ask for the word only (style/concept LoRAs). Never ask *how* it's injected — always `--lora-trigger`. | +| Captioner backend | Ask only if multiple backends are viable. | +| Anything answerable by `ls`, `nvidia-smi`, or the W&B credential check | Never ask. Probe. | + +## The Two `load_text_encoder_in_8bit` Flags + +Same name, different layers — do not conflate: + +| Flag | Layer | Effect | +|------|-------|--------| +| `process_dataset.py --load-text-encoder-in-8bit` | Preprocessing CLI | Memory during caption-embedding precompute (Phase 7). One-time per dataset. | +| `acceleration.load_text_encoder_in_8bit` (trainer YAML) | Trainer config | Memory during validation-sample prompt-embedding caching at training start (Phase 8). One-time per run. | + +Both are one-time costs and neither affects per-step training speed. Default per the trainer's shipped configs: **ON** on the 32GB tier (matches `t2v_lora_low_vram.yaml`), **OFF** on the 80GB+ tier (matches `t2v_lora.yaml`). No measured guidance for the 40–60GB tier beyond starting from the 32GB tier and autotuning. + +## Workspace Layout + +``` +// + plan.md # the approved plan + config.yaml # generated training config (NOT in packages/ltx-trainer/configs/) + autotune.log # per-trial sweep results + dataset/ + dataset.json # captions + media paths (training split) + holdout.jsonl # held-out split (if reserved) + videos/ # source media copies (NO derived files here) + .precomputed/ # latents/ audio_latents/ conditions/ (+ references/masks per mode) + outputs/ + checkpoints/ # training checkpoints + states + samples/ # in-training validation samples (step_*) + eval/ # Phase 9: in-distribution/ out-of-distribution/ held-out/ + prompts.json + run-summary.md # written at completion + logs/ # all run logs + overfit/ # Phase 6 scratch (one-sample preprocess + sanity/autotune runs) +``` + +`` default: `--`. Surface in the plan; user may rename. + +### Workspace hygiene (keep it clean) + +- **Don't create undocumented directories** (e.g. an ad-hoc `scratch/`). Intermediates belong under `overfit/` (sanity/autotune scratch) or a `/tmp` tempdir — not loose in the run dir or the repo root. +- **Never write derived files into `dataset/videos/`** (the source media dir). Latents go under `.precomputed/`; one-sample/eval metadata goes under `overfit/`, not `dataset/`. +- **One canonical manifest per artifact** — don't leave duplicate `*_prompts.json` / metadata copies. +- **Clean up phase byproducts:** the Phase 9 eval must delete its validate-only trainer cruft (see `phases/post-train-validate.md`); `overfit/` is scratch and may be removed after a successful run. The final tree should look like the layout above — no stray `.pt`/`.wav` files, no `eval/checkpoints/`, no duplicate manifests. + +## References + +- `references/mode-selector.md` — user intent → conditioning mode mapping, LoRA rank guidance (read in Phase 1). +- `references/onboarding.md` — first-run prerequisite checklist, model download paths, captioner graceful degradation (read in Phase 2). +- `references/hardware-profiles.md` — GPU VRAM → tier + config defaults (read in Phase 2). +- `references/config-patching.md` — safe YAML edits + schema constraints (read whenever editing `config.yaml`). +- `references/troubleshooting.md` — OOM, NaN, validation failures, resume (read on any failure). +- `references/plan-template.md` — exact plan.md format (read in Phase 4). + +## Phase Procedures + +These are procedure documents the orchestrator reads when entering each phase. They are not standalone skills — they're never invoked by Claude's skill-discovery system. The orchestrator opens them via the `Read` tool and follows the instructions inline. + +- `phases/prepare-dataset.md` — Phase 5: scenes, captioner iteration, IC-LoRA references, metadata, holdout split. +- `phases/preprocess-dataset.md` — Phases 6 (one-sample) & 7 (full): `process_dataset.py` orchestration, existing-data verification. +- `phases/launch-and-monitor.md` — Phase 8: launch command, accelerate, W&B, status reports, run-summary writing. +- `phases/post-train-validate.md` — Phase 9: render the final LoRA against three prompt categories; surface paths only. diff --git a/.claude/skills/train-model/phases/launch-and-monitor.md b/.claude/skills/train-model/phases/launch-and-monitor.md new file mode 100644 index 0000000..dcb0ac7 --- /dev/null +++ b/.claude/skills/train-model/phases/launch-and-monitor.md @@ -0,0 +1,235 @@ +# Phase 8 — Launch & Monitor + +Procedure document for the `train-model` orchestrator (Phase 8 + monitor-only re-entry). Read this file in full before acting on launch/monitor. + +Goal: start the training job, surface the W&B URL, produce periodic status reports, write `run-summary.md` at completion. + +The orchestrator's hard invariants apply (see `../SKILL.md`). + +## Launch — Single GPU + +```bash +cd packages/ltx-trainer +uv run python scripts/train.py "//config.yaml" +``` + +## Launch — Multi-GPU + +Use Accelerate. For LoRA, DDP (default) is fine. For full FT, use FSDP. + +```bash +cd packages/ltx-trainer + +# DDP (LoRA, multi-GPU) +uv run accelerate launch scripts/train.py "//config.yaml" + +# FSDP (full FT, multi-GPU) +uv run accelerate launch \ + --config_file configs/accelerate/fsdp.yaml \ + scripts/train.py "//config.yaml" +``` + +**Pass `--disable-progress-bars` to `train.py` whenever stdout is redirected to a log file** (every background run) or running multi-GPU. The Rich progress bar rewrites a single line with carriage returns and does **not** flush parseable newlines to a redirected log, so without this flag the log shows no step/loss lines and you're forced to poll `nvidia-smi`. With it, step/loss lines are written normally and the log is greppable. + +## Pre-Launch Checks (every launch) + +1. Run the self-check from `references/config-patching.md` (paths exist, frame/resolution constraints, generated modalities have matching latents dirs, references/masks dirs present for conditional modes). +2. Confirm `nvidia-smi` shows expected GPUs available (not occupied by another process). +3. If `wandb.enabled: true`, confirm credentials still resolve: `uv run python -c "import wandb; print(bool(wandb.Api().api_key))"` → `True`. (Don't use `wandb status`.) If `False`, surface to the user before launching — they may want to `wandb login` or run without tracking. + +## Run In Background, Monitor Foreground + +Long training runs should not block the agent's response loop, and they must survive past the launching turn. + +**Prefer the agent's managed/native background-shell mechanism** (the harness facility for long-running background commands — output streaming + PID/exit tracking, survives across turns). It's the reliable way to launch training: it stays alive, streams to a log the agent can poll, and reports completion. Launch the training command through that mechanism, writing to `//logs/train.log` with `--disable-progress-bars`, using **absolute paths** for the config and log (`uv run --directory packages/ltx-trainer` changes the cwd, so a relative config path won't resolve). + +**`nohup ... &` is a last-resort fallback only.** Detached jobs can be harder to track and may not survive environment/session cleanup, so only use it if no managed background mechanism is available, and verify the PID is still alive afterward: + +```bash +# Fallback ONLY — prefer the managed background shell above. +mkdir -p "//logs" +nohup uv run --directory packages/ltx-trainer python scripts/train.py \ + "//config.yaml" --disable-progress-bars \ + > "//logs/train.log" 2>&1 & +echo $! > "//logs/train.pid" +``` + +## Status Report + +Produce on user request (or every automatically). Pull from: + +- **W&B run URL:** First lines of `train.log` after init, or `wandb.run.url` from a `wandb` Python snippet. Surface as a clickable URL. +- **Latest step:** `tail -n 200 "//logs/train.log" | grep -oE "step [0-9]+" | tail -1`. +- **Recent loss:** `tail -n 200 "//logs/train.log" | grep -oE "loss[: ]+[0-9.]+" | tail -5`. +- **Checkpoints saved:** `ls -1t "//outputs/checkpoints/" 2>/dev/null`. +- **Validation samples:** `ls -1t "//outputs/samples/" 2>/dev/null`. +- **GPU utilization snapshot:** `nvidia-smi --query-gpu=name,utilization.gpu,memory.used,memory.total --format=csv,noheader`. +- **ETA:** only report one **grounded in real numbers the trainer has actually emitted** — do not invent or "educated-guess" an ETA before the trainer has produced per-step timings. Compute as `(total_steps - current_step) * recent_avg_step_time`, where `recent_avg_step_time` is measured from **steady-state training steps** (the trainer's reported per-step time or log timestamps), **excluding** one-time setup that doesn't repeat per step: model loading, the step-0 validation pass, and periodic validation passes. The trainer's own early ETA projection is skewed high by the slow step-0 validation and settles after a few steps — wait for it to settle rather than quoting the inflated early figure. Until real step timings exist, say "measuring step time…" rather than guessing a duration. + +Format the report tightly — one block, no fluff: + +``` +Step 1240 / 2000 (62%) — loss ~0.072 (last 5: 0.071, 0.073, 0.069, 0.075, 0.072) +ETA: ~1h 24m | GPU: 91% util, 39.2 / 48.0 GB +Latest checkpoint: lora_weights_step_01000.safetensors +Latest validation: samples/step_01200_*.mp4 +W&B: https://wandb.ai///runs/ +``` + +## Monitor-Only Mode + +Invoked when the orchestrator detects an existing `//` with checkpoints or a running process. + +1. Check if the training process is live: `[ -f .../logs/train.pid ] && kill -0 $(cat .../logs/train.pid) 2>/dev/null && echo LIVE || echo STOPPED`. +2. Produce the same status report as above. +3. If STOPPED: + - Compute step from last checkpoint. + - Find the latest checkpoint pair (`lora_weights_step_*.safetensors` or `model_weights_step_*.safetensors`, plus a matching `training_state_step_*.pt` when resume state is enabled) under `//outputs/checkpoints/`. + - Patch `model.load_checkpoint` in `config.yaml` to point at that checkpoint file (this is the only way the trainer knows to resume — there's no auto-detection from `output_dir`). + - Surface the resume command: `uv run python scripts/train.py "//config.yaml"`. + - Ask the user to confirm both the patch and the launch before applying. + +## Resume + +The trainer **does not auto-resume from `output_dir`**. To resume an interrupted run: + +1. Set `model.load_checkpoint` in `config.yaml` to the latest checkpoint file (e.g. `//outputs/checkpoints/lora_weights_step_02000.safetensors`). +2. Launch normally. The trainer loads those weights, then looks for a matching `training_state_step_*.pt` **next to the loaded checkpoint** and restores optimizer/scheduler/step state from it. If the state file is missing, weights load but training starts from step 0. +3. To load the weights but skip the state restore (e.g. for branching off into a new run from a known-good checkpoint), set `checkpoints.no_resume: true`. + +Always ask the user before patching `model.load_checkpoint` or setting `no_resume` — checkpoints are precious. + +## Failure During Training + +If the training process exits non-zero: + +1. Tail the log and identify the error type. +2. Cross-reference `references/troubleshooting.md`. +3. Propose a config fix (with the exact diff to `config.yaml`). +4. Ask the user before applying. Then resume with the patched config. + +Never silently restart a failed training run without acknowledging the failure to the user. + +## After Training Completes + +1. Write a **run summary** to `//outputs/run-summary.md` (see below) so the user can find their bearings months later without rereading the trainer docs. +2. Show final checkpoint path and step count. +3. Show W&B URL if enabled. +4. Return control to the orchestrator (Phase 9 — post-train validate runs next). + +### Writing `run-summary.md` + +The summary is the **landing page** for this run. Anyone (including the user months from now) should be able to read it and understand: what was trained, on what data, with what config, where everything lives, how to use the result, and how to continue. The trainer doesn't produce this — the skill does. + +Template (fill from `plan.md`, `config.yaml`, `autotune.log`, dataset metadata, training log): + +```markdown +# + +**Trained:** on +**Final checkpoint:** `outputs/checkpoints/.safetensors` (step ) + +## What this LoRA does + + + +## Trigger word + +`` — include in prompts at inference time. (Omit this section if no trigger word.) + +## Mode + + (). Conditioning: . + +## Dataset + +- Source: `` +- Captioning: <`qwen_omni` (Qwen3-Omni-30B via vLLM) | `gemini_flash` | "user-supplied"> + - Captioner instruction used: +- Samples: + at xx +- Preprocessed to: `dataset/.precomputed/` + +## Training config + +Final values after autotune (deltas from baseline noted in `autotune.log`): + +| Field | Value | +|-------|-------| +| Optimizer | <...> | +| Mixed precision | | +| Quantization | <...> | +| Gradient checkpointing | | +| Batch size × grad accum | × (effective ) | +| LoRA rank / alpha | / (or "full FT") | +| LoRA target modules | (or "n/a") | +| Steps | | +| Learning rate | | +| Step time (final) | ~s | +| Peak VRAM | ~ GB | + +Full config: `//config.yaml` + +## Outputs + +- Checkpoints: `outputs/checkpoints/` +- Validation samples (during training): `outputs/samples/` +- Post-train eval renders (if Phase 9 ran): `outputs/eval/` +- W&B run: + +## How to use this checkpoint + +For inference, point `packages/ltx-pipelines/` at the final checkpoint. Example invocation: + +\`\`\`bash +# (Minimal sketch — adapt to the pipeline you're using.) +# load base LTX-2 model + apply this LoRA from outputs/checkpoints/.safetensors +\`\`\` + +## How to continue training + +To resume from the final checkpoint (e.g. more steps, different LR), edit `config.yaml`: + +\`\`\`yaml +model: + load_checkpoint: ".safetensors>" +optimization: + steps: # trainer resumes optimizer/scheduler/step from the training_state_step_*.pt sitting next to the checkpoint above +\`\`\` + +Then re-launch with the same command in the "Launched with" section below. + +## How this was launched + +\`\`\` + +\`\`\` + +## Reproducibility + +- Seed: +- Workspace: `` +- Repo commit at launch: `` +- LTX-2 model: `` +- Text encoder: `` +``` + +Write the file using the `Write` tool. Don't embed it in a heredoc — the markdown nested in this skill is illustrative; fill the template with real values from the run's artifacts. + +### Next steps (surface to user) + +After writing the summary, point the user at: +- The summary file path. +- The final checkpoint path. +- The W&B URL if enabled. +- The upcoming Phase 9 (post-train validate) — the orchestrator handles the transition. + +Suggest, but don't run: +- Test inference with `packages/ltx-pipelines/`. +- Push to HF Hub via the trainer's `hub.push_to_hub` config (a separate, lightweight re-launch). +- Continue training from the final checkpoint (see summary's "How to continue training" section). + +## Do Not + +- Do not modify `output_dir` contents after a run completes — checkpoints belong to the user now. +- Do not start a second training run into the same `output_dir` without explicit user approval. Resume requires patching `model.load_checkpoint` (the trainer does not auto-detect prior checkpoints); a true fresh-start from the same dir additionally needs `checkpoints.no_resume: true`. +- Do not auto-restart a failed run without diagnosis and user approval. diff --git a/.claude/skills/train-model/phases/post-train-validate.md b/.claude/skills/train-model/phases/post-train-validate.md new file mode 100644 index 0000000..fb8bdfd --- /dev/null +++ b/.claude/skills/train-model/phases/post-train-validate.md @@ -0,0 +1,171 @@ +# Phase 9 — Post-Train Validate + +Procedure document for the `train-model` orchestrator (Phase 9). Read this file in full before acting on post-train validation. + +Goal: render the final checkpoint against three prompt categories so the user can inspect the result and form their own judgement. Save outputs in an organized layout. **Do not** prompt for pass/fail verdicts, do not infer causes for failures, do not suggest fixes — soft training failures don't have a clean if/then rule book, and pretending otherwise wastes the user's time. + +The orchestrator's hard invariants apply (see `../SKILL.md`). + +## What this phase does + +1. Collect prompts for three categories. +2. Render the final LoRA against all collected prompts. +3. Save outputs under `//outputs/eval//`. +4. Print the paths and exit. + +## Categories + +### 1 — In-distribution + +A few captions from the training set itself. Tests whether the model learned what it was shown. + +- Default: 3 random captions from the dataset metadata (seed 42 for reproducibility). +- Source: `//dataset/dataset.json` (the captions used for training, after the held-out split). + +### 2 — Out-of-distribution + +Prompts the model has never seen, but in the same domain. Tests whether the model generalizes the concept beyond memorized phrasings. + +Ask the user once: + +> "For out-of-distribution validation, paste 2–3 prompts you'd realistically want to generate at inference time. (If you don't have any specific ones in mind, reply 'default' — I'll use a few generic prompts that include the trigger word.)" + +- On `default`: synthesize 3 short prompts using the LoRA's trigger word and a generic scene context (e.g., " walking in a forest at dawn"). Note these are generic — they're better than nothing, but real user-style prompts make a stronger test. +- Otherwise: use the user's prompts verbatim. +- **For a run with a generated audio modality, the synthesized prompts must describe the audio** — matching how the training captions describe it (inspect a few from `dataset.json` first). A prompt with no audio direction leaves the audio branch unguided and the generated audio comes out poor. E.g. for the talking-head case include spoken-voice/room-tone direction; for music/ambience/foley describe the sound character. (Categories 1 and 3 reuse the real captions verbatim, so they already carry audio description — this only applies to the synthesized Category 2 prompts.) If the user pasted their own prompts and it's an audio run, and they omitted audio direction, note that the audio may be weak without it. + +### 3 — Held-out + +Captions from samples that were never seen during training. Tests true generalization, not memorization. + +- Source: `//dataset/holdout.jsonl` (written by `prepare-dataset` Step 5). +- Use all entries if there are ≤5; otherwise sample 5 with seed 42. +- **If holdout doesn't exist** (dataset was too small, or user-skipped during prepare): print a clear note in the output summary — *"Held-out evaluation skipped: no holdout set was reserved for this run. The post-train eval only covers Categories 1 and 2."* Don't synthesize substitutes. + +## Rendering mechanism + +Use the trainer's existing validation infrastructure rather than wiring up `ltx-pipelines` from scratch. Create a temporary "validate-only" config and run the trainer with it. + +### Step 1 — Build eval config + +Copy `//config.yaml` to `//eval-config.yaml`. Patch: + +```yaml +model: + load_checkpoint: ".safetensors>" + +optimization: + steps: 1 # we don't want to train; we want validation to fire + # Keep batch_size/grad_accum at the run's autotuned values to match its VRAM footprint. + +validation: + skip_initial_validation: false + interval: 1 # run validation at step 0 (and at the only training step) + samples: + # Inject all collected prompts here, tagged by category in the prompt itself + # so the output filenames make the category obvious. + - prompt: "[CAT1-IND] " + # ... repeat for each prompt in all three categories + # Keep video_dims, frame_rate, guidance/STG settings as the trained config. + # Keep generate_audio consistent with the trained modality config. + +checkpoints: + interval: null # do not save more checkpoints + no_resume: true # load the LoRA's weights but do not restore optimizer/scheduler/step state + +output_dir: "//outputs/eval" +``` + +The `[CAT1-IND]`, `[CAT2-OOD]`, `[CAT3-HELDOUT]` tags in the prompt strings make the output MP4 filenames self-describing in the trainer's validation sample directory. + +**Attach the mode's conditions to each sample.** A bare `prompt` only validates a pure text-to-X mode (T2V, T2A). For any conditioned mode, the trained model expects the same conditioning at validation time — a prompt with no conditions tests a different task than what was trained, and conditioned modes may fail outright. Add the `conditions` list that matches the run's mode (the trained `config.yaml` `training_strategy` and the example config for the mode are the reference): + +| Mode | Add to each sample | +|------|--------------------| +| I2V | `conditions: [{type: first_frame, image_or_video: }]` | +| Video extension / suffix | `conditions: [{type: prefix|suffix, ...}]` | +| V2V / AV2AV IC-LoRA | `conditions: [{type: reference, ...}]` (point at a held-out reference) | +| V2A (foley) | `conditions: [{type: video_to_audio, ...}]` | +| A2V | `conditions: [{type: audio_to_video, ...}]` | +| Inpainting (video/audio) | `conditions: [{type: mask, ...}]` | +| Outpainting | `conditions: [{type: spatial_crop, ...}]` | +| A2A IC-LoRA | `conditions: [{type: reference, ...}]` (held-out reference audio) | +| T2V, T2A | none — a bare `prompt` is correct | + +Mirror the condition shapes used in the mode's example config under `packages/ltx-trainer/configs/`. For held-out (Category 3) and OOD (Category 2) samples on conditioned modes, draw the conditioning media from the held-out set so the eval stays out-of-distribution. + +### Step 2 — Run the trainer in validate-only mode + +```bash +cd packages/ltx-trainer +uv run python scripts/train.py "//eval-config.yaml" +``` + +The trainer will load the LoRA, run initial validation against all the prompts, do one trivial training step (which we discard), and exit. Validation samples land in `//outputs/eval/samples/`. + +### Step 3 — Organize outputs and clean up trainer cruft + +The validate-only run is a trainer run, so it inevitably writes throwaway artifacts: an indexed `samples/` dir, a forced final checkpoint (the trainer **always** saves one at the end, regardless of `checkpoints.interval`), and a `training_config.yaml`. Don't leave these around or duplicate the renders. + +1. **Move** (don't copy) each generated MP4 from the trainer's indexed `samples/` dir into the category layout, naming by category + a slug of the prompt. Use the index→category mapping you built when constructing `validation.samples`. +2. **Delete the trainer cruft** from the eval dir once the renders are moved: the indexed `samples/` dir, the forced `checkpoints/` dir, and `training_config.yaml`. (These are byproducts of the validate-only hack — there's no config flag to suppress the final-checkpoint save, so clean it up here.) +3. Write **one** manifest, `outputs/eval/prompts.json` (filename → full prompt + category). Don't leave a second copy elsewhere. +4. Remove the temporary `eval-config.yaml` (or keep it under the run's scratch, not in `outputs/`). + +Final eval layout — exactly this, nothing else: + +``` +//outputs/eval/ + in-distribution/ _.mp4 ... + out-of-distribution/ _.mp4 ... + held-out/ _.mp4 ... # only if a holdout set existed + prompts.json # filename -> full prompt + category (single manifest) +``` + +No `eval/samples/`, no `eval/checkpoints/`, no `eval/training_config.yaml`, no duplicate manifest. + +### Step 4 — Surface paths + +Print a tight block, no judgement, no follow-up question: + +``` +Post-train evaluation complete. + +In-distribution renders ( samples): + //outputs/eval/in-distribution/ + +Out-of-distribution renders ( samples): + //outputs/eval/out-of-distribution/ + +Held-out renders ( samples): + //outputs/eval/held-out/ # or: "(skipped — no holdout set)" + +Open the MP4s and decide for yourself whether the model is good. Soft +training quality is judged by watching the videos, not by a checklist — +there's no substitute for your own eyes here. +``` + +Return control to the orchestrator. The orchestrator's run is now complete. + +## What this phase does NOT do + +- Does not ask "is this good?" / "pass / partial / fail?". +- Does not infer failure causes. +- Does not suggest fixes, follow-up runs, hyperparameter changes, dataset changes. +- Does not write any verdict to `run-summary.md` or elsewhere. +- Does not delete or modify training checkpoints. +- Does not push to any remote / cloud / registry. + +The user looks at the videos and makes their own call. If they want to iterate, they re-invoke the orchestrator with a new run-name. + +## Failure modes + +- **Final checkpoint missing.** Surface and stop. Don't render against an intermediate checkpoint without explicit user consent. +- **`load_checkpoint` OOM at inference time.** Lower `validation.video_dims` in the eval config (smaller renders are still useful for a sanity look). Retry once. If still OOM, surface the failure and let the user run inference manually via `packages/ltx-pipelines/`. +- **All renders look broken/black.** May be an inference-pipeline-side issue rather than a training failure. Mention in the output block: *"If renders look broken across all categories, try `packages/ltx-pipelines/` directly to rule out a pipeline issue."* Then exit. Do not investigate further. + +## Do not + +- Do not skip Category 1 or 2. They're cheap and informative. +- Do not invent a held-out set if `holdout.jsonl` is missing — the prepare-dataset step decides that. +- Do not coach the user on what "good" means for their use case. diff --git a/.claude/skills/train-model/phases/prepare-dataset.md b/.claude/skills/train-model/phases/prepare-dataset.md new file mode 100644 index 0000000..89d6e9f --- /dev/null +++ b/.claude/skills/train-model/phases/prepare-dataset.md @@ -0,0 +1,245 @@ +# Phase 5 — Prepare Dataset + +Procedure document for the `train-model` orchestrator (Phase 5). Read this file in full before acting on the prepare-dataset phase. + +Goal: produce a captioned, complete dataset metadata file at `//dataset/dataset.json` consumable by `process_dataset.py`. Idempotent — re-runs skip work already done. + +The orchestrator's hard invariants apply (see `../SKILL.md`), especially: **no file mutation outside the workspace without explicit user approval.** + +## Inputs + +The orchestrator passes: +- Source path (directory of videos / single video / pre-existing metadata file). +- Target mode (T2V, I2V, V2V IC-LoRA, V2A, etc.) — determines which columns are required. +- Captioner backend choice (Qwen3-Omni local vLLM server / Gemini Flash cloud / skip). +- Workspace path `//`. + +## Required Columns by Mode + +`process_dataset.py` detects columns by convention and resolves each to a role. The media column may be `video` **or** `audio`. When a dataset has a `video` column with an audio track and no separate `audio` column, audio is **auto-extracted** from the video (unless `--skip-audio`), so an explicit `audio` column is only needed when the audio lives in separate files. + +**Video-generating modes** (need a `video` column): + +| Mode | Required | Optional | +|------|----------|----------| +| T2V, I2V, video extension/suffix | `video`, `caption` | `audio` (else auto-extracted) | +| Video outpainting | `video`, `caption` | | +| Video inpainting | `video`, `caption`, `video_mask` | | +| V2A (foley) | `video`, `caption` | `audio` (target; else auto-extracted) | +| A2V | `video`, `caption` | `audio` (else auto-extracted from video) | +| V2V IC-LoRA | `video`, `caption`, `reference_video` | | +| AV2AV IC-LoRA | `video`, `caption`, `reference_video`, `reference_audio` | `audio` (else auto-extracted) | + +**Audio-only modes** (no `video` column — the media column is `audio`): + +| Mode | Required | Optional | +|------|----------|----------| +| T2A | `audio`, `caption` | | +| Audio extension/suffix | `audio`, `caption` | | +| Audio inpainting | `audio`, `caption`, `audio_mask` | | +| A2A IC-LoRA | `audio`, `caption`, `reference_audio` | | + +Aliases: `media_path` for `video`, `ref_media_path` for `reference_video`. + +## Workflow + +### Step 1 — Classify source + +```bash +# If source is a file, identify type: +file "" +# If source is a directory, count media: +find "" -maxdepth 1 -type f \( -name "*.mp4" -o -name "*.mov" -o -name "*.webm" \) | wc -l +``` + +Cases: +- **Pre-existing metadata file** (CSV/JSON/JSONL) → copy to `//dataset/dataset.json`, audit columns. Skip to Step 4. +- **Directory of short scenes** → skip Step 2, go to Step 3. +- **Directory containing long videos** → run Step 2. +- **Single long video** → run Step 2. + +**Stage the media under `dataset/` before captioning — don't discover this by failing.** Both `caption_videos.py` and `process_dataset.py` reference media by paths **relative to the metadata file's own directory**, so the media must live under `//dataset/`. Stage it up front into `dataset/videos/` (and write metadata paths relative to `dataset/`, e.g. `videos/1.mp4`): +- Prefer **symlinks** (instant, no disk cost): `ln -s / //dataset/videos/`. Symlinks pointing at the original source location work correctly. +- Use a **copy** instead if the workspace and source are on different filesystems or the source may move/change. +- Never caption or preprocess directly against an out-of-tree source path (e.g. `/path/to/source-videos`) — it will fail the relative-path resolution. The original source is left untouched either way. +(Scene-split output in Step 2 already lands under `dataset/scenes/`, which satisfies this.) + +### Step 2 — Scene splitting (only for long videos) + +`split_scenes.py` takes **one video file** at a time (`video_path` and `output_dir` are both positional arguments). When the source is a directory of long videos, iterate over each file. To drop scenes shorter than 2 seconds, use `--filter-shorter-than 2s` (the `--min-scene-length` option is an integer **frame** count, not seconds — don't pass a float). + +```bash +cd packages/ltx-trainer +# Single file: +uv run python scripts/split_scenes.py \ + "" \ + "//dataset/scenes" \ + --filter-shorter-than 2s + +# Directory of long videos — iterate: +for f in ""/*.mp4 ""/*.mov ""/*.webm; do + [ -e "$f" ] || continue + uv run python scripts/split_scenes.py "$f" \ + "//dataset/scenes" \ + --filter-shorter-than 2s +done +``` + +Result: scenes saved to `//dataset/scenes/`. Pass that directory to Step 3. + +### Step 3 — Captioning + +Skip entirely if a metadata file with all required `caption` entries already exists. + +**Use the captioner's default instruction.** `caption_videos.py` ships a well-tuned default caption prompt — use it as-is (do **not** pass `--instruction`). Captioning runs in two phases: a small **spot-check pass** so the user can confirm the captions look sane, then a **full pass** on the rest. + +A custom `--instruction` is the exception, not the norm. Only use one when: +- the **nature of the dataset genuinely demands it** (e.g. a narrow domain the default prompt won't describe well), or +- the **user, after seeing the spot-check captions, explicitly asks** for a change (e.g. "too much background detail"). + +Do not invent a custom instruction pre-emptively, and in particular **do not bake a subject name / trigger word into the captions via `--instruction`** — the trigger word is handled separately at preprocessing (see "Trigger word" below). + +#### Choosing a backend + +Two backends, with very different hardware needs: + +- **`qwen_omni` (local, default):** Qwen3-Omni-30B-A3B-Thinking served by a local vLLM HTTP server (`serve_captioner.py`). ~65 GiB model download. Default **FP8** quantization uses ~31 GiB of weights and **fits on a 40 GiB GPU** (plus KV cache); **bf16** uses ~60 GiB and needs **≥66 GiB free VRAM**. +- **`gemini_flash` (cloud):** Google `gemini-3.5-flash`. No local model, runs anywhere, parallelisable with `--num-workers`. + +**Steer modest hardware to Gemini.** If the GPU is below ~40 GiB (i.e. typical consumer cards — 24 GB / 32 GB), it can't host even the FP8 server, so the local captioner isn't an option — recommend `gemini_flash` and tell the user they'll need Gemini auth: either a `GEMINI_API_KEY`/`GOOGLE_API_KEY` (get one at ) or working gcloud/Vertex AI credentials. If they can't or won't set that up and the hardware can't run Qwen3, the only remaining path is bringing their own captions in the dataset metadata (skip captioning entirely). On a 40 GiB+ GPU the local server is viable (FP8); bf16 needs an 80GB-class card. + +#### Qwen server prerequisite (qwen_omni only) + +The local backend talks to a vLLM server that must already be running. Launch it once in a **separate terminal** (it stays loaded across captioning runs): + +```bash +cd packages/ltx-trainer +uv run python scripts/serve_captioner.py # FP8 by default, serves on http://127.0.0.1:8001/v1 +# bf16 (needs >= 66 GiB free VRAM): --quantization bf16 +# different port/interface: --port 9000 --host 0.0.0.0 +``` + +First launch downloads the model (~65 GiB). `caption_videos.py` reaches it via `--vllm-url` (default `http://127.0.0.1:8001/v1`). Skip this entirely when using `gemini_flash`. + +#### 3a — Spot-check pass (3 samples) + +Caption 3 samples with the **default prompt** (no `--instruction`) to confirm the captioner is producing sane output before committing to the whole set. + +```bash +cd packages/ltx-trainer +# qwen_omni (server from the previous step must be running): +uv run python scripts/caption_videos.py \ + "//dataset/videos/" \ + --output "//dataset/preview-captions.json" \ + --captioner-type qwen_omni + # Point at 3 staged clips under dataset/videos/ (a small subdir or 3 explicit files) — not the out-of-tree source. + # Optional: --vllm-url http://127.0.0.1:9000/v1 (if the server uses a non-default port) +``` + +Print the 3 captions **in full** to the user, then **STOP and wait** for their explicit verdict: + +> "Here are sample captions from the default prompt. Please review them — reply 'good' to caption the rest, or tell me what to change." + +**This is a hard gate. Do NOT caption the full set until the user explicitly approves the samples.** Do not auto-proceed, do not assume "looks fine," do not batch this with other questions. The user must either approve or give tuning instructions first — the whole point of the spot-check is to let them judge caption quality and content before paying for the full pass. + +If the user requests changes, introduce a custom `--instruction` (or switch backend), re-run the spot-check on the same 3 samples, show the new captions, and **stop for approval again**. Loop until the user approves. If a custom instruction still isn't converging after a few rounds, switch captioner backend or have the user supply a few manual captions as examples — but still don't proceed to the full set without their OK. + +#### 3b — Full pass + +Run on the **staged media dir** (`dataset/videos/` from Step 1, or `dataset/scenes/` from Step 2) with the **default prompt** (or the same `--instruction` only if one was explicitly agreed in 3a). Caption the staged in-tree media — not the original out-of-tree source path. + +**Qwen3-Omni (local — server must be running):** + +```bash +cd packages/ltx-trainer +uv run python scripts/caption_videos.py \ + "//dataset/videos" \ + --output "//dataset/dataset.json" \ + --captioner-type qwen_omni +``` + +**Gemini Flash (cloud — runs anywhere, parallelisable):** + +```bash +# Auth: GEMINI_API_KEY / GOOGLE_API_KEY env var, or gcloud / Vertex AI credentials. +cd packages/ltx-trainer +uv run python scripts/caption_videos.py \ + "//dataset/videos" \ + --output "//dataset/dataset.json" \ + --captioner-type gemini_flash \ + --num-workers 5 +``` + +Output: JSON list of `{caption, media_path}` with paths **relative to the output file location**. The 3 spot-check captions can be merged in to avoid re-captioning them. + +#### Trigger word (handled at preprocessing, not in captions) + +For style/concept LoRAs the trigger word is **not** written into the captions here. It is prepended to every caption at preprocessing: pass `--lora-trigger ""` to `process_dataset.py` in Phase 7, which forwards it to the caption-processing step (`process_captions.py`, where the prepend actually happens). That is the canonical mechanism — keep the captions describing what's actually on screen (via the default prompt), and let the trigger flag bind the concept to the token. Record the chosen trigger word in the plan so Phase 7 passes it through. Do not also bake the word into captions (it would double up). + +**The injection mechanism is a fixed implementation detail — never make it a user-facing question.** The *only* trigger-word thing to ask the user is the **word itself** (or whether they want a trigger word at all). Do **not** ask, mention, or present as an option *how* it gets injected (e.g. "inject into the caption vs via `process_dataset`") — it is always `--lora-trigger`, full stop. Surfacing the method as a choice creates unnecessary confusion. + +### Step 4 — Conditioning inputs (modes that need references or masks) + +Some modes need a per-sample conditioning input beyond the video/audio and caption: + +| Mode | Required extra input | Column | +|------|----------------------|--------| +| V2V IC-LoRA | reference video | `reference_video` (alias `ref_media_path`) | +| AV2AV IC-LoRA | reference video + reference audio | `reference_video`, `reference_audio` | +| A2A IC-LoRA | reference audio | `reference_audio` | +| Video inpainting | per-frame video mask | `video_mask` | +| Audio inpainting | audio mask | `audio_mask` | + +These inputs encode **the user's specific idea** for the LoRA (what the reference represents, which regions the mask covers). There is no universal recipe, so **do not invent or default to a particular method** (e.g. don't assume Canny edges, depth, pose, or some generic box/border mask). The agent must not pick the conditioning semantics for the user. + +Workflow when the chosen mode needs one of these and the dataset doesn't already provide it: + +1. **Check first** — if the user already supplied the column (and the files exist), use it as-is and move on. +2. **Otherwise, ask the user to provide it**, explaining concretely what's needed: the column name, that it's one file per sample aligned to each clip, and that the *content/semantics are their call* (what the reference should depict, what the mask should cover). Make clear this reflects their specific use-case — you won't guess it. +3. **Help generate only if the user asks.** If they say "can you generate the references/masks by doing X" (X = their described method), then help: write or run a small script for *their* approach, or use a repo tool if it fits. One such tool exists — `scripts/compute_reference.py` generates **Canny edge** reference videos — but only mention/use it if the user specifically wants Canny; never offer it as the default. +4. **Hard gate:** do not proceed to preprocessing for a conditioning mode until the required column is present with real files. Surface clearly if it's missing. + +**Column-naming note (if references are generated):** `compute_reference.py` writes a `reference_video` field, which +`process_dataset.py` detects automatically. Legacy datasets using `ref_media_path` also work. + +### Step 5 — Holdout split + +Reserve a subset of samples as a **held-out set** never seen during training. This is what Phase 9 (post-train validate) renders against to test true generalization rather than memorization. + +Decision tree: + +1. **User already supplied a held-out set** (separate file or directory they explicitly nominated): do not split. Copy/reference their file to `//dataset/holdout.jsonl` and leave `dataset.json` as-is. +2. **Small dataset** (judge qualitatively; tens of samples or fewer): holding samples out meaningfully reduces training capacity. Ask the user: + > "Dataset has samples. Reserving a holdout meaningfully reduces what's available for training. Options: (a) reserve 1–2 for holdout, (b) skip holdout — post-train eval will only test in-distribution. Your call." +3. **Otherwise:** auto-split. Reserve a small fraction (this skill's default: roughly 10% of samples, bounded so the holdout doesn't grow huge — a handful of held-out samples is usually enough). Use seed 42 for the split so it's reproducible. Surface the count and the picked IDs in the plan. + +After splitting, write `//dataset/holdout.jsonl` (one JSON object per line with the same columns as `dataset.json`). **Remove the held-out entries from `dataset.json`** so they don't enter preprocessing or training. + +Always print: +> "Held out of samples for post-train evaluation. Held-out IDs: ." + +If holdout is skipped, surface in the plan: *"Skipping holdout — dataset is too small. Post-train eval will only render in-distribution prompts; true generalization isn't testable for this run."* + +### Step 6 — Audit + +Before returning to the orchestrator, verify the metadata file has all required columns for the chosen mode. Print a one-line summary: + +> "Prepared training samples (+ held out) for . Columns: . Saved to `//dataset/dataset.json` (+ `holdout.jsonl`)." + +## Idempotency + +- If `dataset.json` already exists and all required columns are present: skip captioning. Confirm reuse with the user only if the file was supplied by them outside the workspace (per the orchestrator's file-safety invariant). +- If captioning was partial (some entries missing `caption`), re-run captioning only on the missing entries by filtering the metadata file before passing to `caption_videos.py`. + +## Failure Modes + +- Qwen server won't start / OOMs on launch → use the default `--quantization fp8` (not `bf16`), lower `--gpu-memory-utilization`, or reduce `--max-model-len` on `serve_captioner.py`. If the GPU simply can't host a 30B model, switch to `gemini_flash`. +- `caption_videos.py` can't connect (qwen_omni) → the vLLM server isn't running or `--vllm-url` is wrong. Start `serve_captioner.py` first and confirm the URL/port match. +- Gemini rate-limit → reduce `--num-workers`, retry. +- Scene splitter produces 0 scenes → the detector found no cuts, or `--filter-shorter-than` removed everything. Lower/remove `--filter-shorter-than`, or adjust the detector threshold. (`--min-scene-length` is an integer minimum-frames-per-scene for the detector, not a short-scene filter.) +- IC-LoRA reference compute fails on some frames → script logs the failures; report counts to the user and ask whether to proceed with the remaining samples or stop. + +## Do Not + +- Do not move or rename the user's source files. The skill reads them in place; the workspace contains only **derived** artifacts. +- Do not delete `scenes/` or partial captioning outputs without approval — they may be expensive to regenerate. diff --git a/.claude/skills/train-model/phases/preprocess-dataset.md b/.claude/skills/train-model/phases/preprocess-dataset.md new file mode 100644 index 0000000..f84aa4e --- /dev/null +++ b/.claude/skills/train-model/phases/preprocess-dataset.md @@ -0,0 +1,130 @@ +# Phases 6 (one-sample) & 7 (full) — Preprocess Dataset + +Procedure document for the `train-model` orchestrator. Read this file in full before acting on the preprocess phase. + +Goal: run `process_dataset.py` to produce VAE latents, audio latents, and text embeddings. Two modes: +1. **One-sample** (Phase 6 sanity check) — preprocess a single sample to `//overfit/.precomputed/`. +2. **Full** (Phase 7) — preprocess the whole dataset to `//dataset/.precomputed/`. + +The orchestrator's hard invariants apply (see `../SKILL.md`), especially: **never silently overwrite existing user data.** + +## Required Subdirectories by Mode + +Under `.precomputed/`: + +| Subdir | Required for | +|--------|--------------| +| `latents/` | Video-bearing modes only (T2V, I2V, video extend/inpaint/outpaint, V2V/AV2AV IC-LoRA, A2V, V2A). **Not** produced for audio-only modes. | +| `conditions/` | Always (text embeddings) | +| `audio_latents/` | Any mode with audio: video modes carrying audio, plus all audio-only modes (T2A, audio extend/suffix/inpaint, A2A IC-LoRA) | +| `reference_latents/` | V2V IC-LoRA, AV2AV IC-LoRA | +| `reference_audio_latents/` | A2A IC-LoRA, AV2AV IC-LoRA | +| `video_masks/` | Video inpainting | +| `audio_masks/` | Audio inpainting | + +**Audio-only modes** (T2A, audio extend/suffix, audio inpainting, A2A IC-LoRA) produce `audio_latents/` + `conditions/` (plus `audio_masks/` or `reference_audio_latents/` as applicable) and **no `latents/`**. Do not flag a missing `latents/` as incomplete for these modes. + +## Workflow — Full Preprocess + +### Step 1 — Verify existing `.precomputed/` (if present) + +If `//dataset/.precomputed/` already exists: + +1. List subdirectories present. Confirm all required for the chosen mode are present. +2. Load one sample per modality and check tensor shapes: + ```bash + uv run python -c "import torch; t = torch.load(''); print(t.shape if hasattr(t, 'shape') else {k: v.shape for k, v in t.items()})" + ``` +3. Compare shapes against the target resolution from the plan. + +**On any mismatch or missing subdirectory: STOP. Do not run `process_dataset.py`.** Ask the user via `AskUserQuestion`: +- Reuse the existing data at its current resolution (update plan + config accordingly). +- Re-preprocess to a new directory (`//dataset/.precomputed-v2/` etc.) — preserves the existing data. +- Abort. + +**Never pass `--overwrite` without explicit user approval** for this exact action. + +### Step 2 — Invoke `process_dataset.py` + +```bash +cd packages/ltx-trainer +uv run python scripts/process_dataset.py \ + "//dataset/dataset.json" \ + --resolution-buckets "xx" \ + --model-path "" \ + --text-encoder-path "" \ + --output-dir "//dataset/.precomputed" \ + --load-text-encoder-in-8bit # on 32GB tier (low-VRAM config), per t2v_lora_low_vram.yaml +``` + +Add as needed: +- `--skip-audio` — if mode doesn't use audio (T2V video-only variants). +- `--audio-durations ""` — for T2A from a captions-only file. +- `--lora-trigger ""` — for style/concept LoRAs. +- `--reference-downscale-factor ` — for IC-LoRA modes if downscaled references are desired. +- `--video-column`, `--caption-column` — only if the metadata file uses non-standard column names. + +**Do not pass `--overwrite`** unless re-preprocessing was explicitly approved in Step 1. + +### Step 3 — Audit output + +After completion, verify: + +```bash +ls "//dataset/.precomputed/" +# Expected subdirs per the mode table above. + +# Count files in each: +for d in latents conditions audio_latents reference_latents video_masks audio_masks; do + if [ -d "//dataset/.precomputed/$d" ]; then + echo "$d: $(ls "//dataset/.precomputed/$d" | wc -l)" + fi +done +``` + +Counts in each required subdir should equal the dataset sample count. + +**Reconcile counts — do this for every run, any dataset size.** Compare the `latents/` (and `audio_latents/`) count against the caption/sample count. If fewer latents were produced, `process_dataset.py` **silently skipped** clips — most commonly because they were **shorter than the target frame bucket** (it logs each skip). When counts don't match: + +1. Identify which clips were dropped (grep the preprocess log for skip/"fewer frames" lines, or diff the produced `.pt` stems against the metadata). +2. **Surface it to the user** with the count and the specific clips — never silently proceed on a shrunk dataset. +3. Offer options: re-preprocess at a **smaller frame bucket** the clips support, add a **second (shorter) bucket** to keep the short clips (multi-bucket requires `batch_size: 1`), or accept the loss. Let the user decide. + +**Audio gate (hard stop for audio runs).** For any run with an audio modality (joint audio+video, A2V, V2A, T2A, audio-only modes), verify `audio_latents/` is **present and non-empty** with one `.pt` per sample. `process_dataset.py` **swallows audio-decode errors and continues** — it logs "0 videos with audio" and produces empty `audio_latents/` rather than failing. If an audio run produced no audio latents, **stop** — do not proceed to training (it would silently train audio-free). The usual cause is a broken audio decode path (e.g. missing/incompatible `torchcodec`); confirm `uv run python -c "import torchaudio; torchaudio.load('')"` works (see `references/troubleshooting.md`), fix it, then re-preprocess with `--overwrite`. + +## Workflow — One-Sample (Phase 6) + +Same as full preprocess, but operate on a single-sample metadata file: + +1. Pick the first sample from `//dataset/dataset.json` and write a one-sample metadata file **inside the dataset dir** — e.g. `//dataset/_one_sample.json` — copying the entry **verbatim, keeping its relative `media_path`**. `process_dataset.py` resolves media paths relative to the metadata file's own directory, so the one-sample file must sit beside the real media (i.e. in `dataset/`, the same dir as `dataset.json`). **Do not** place it in `overfit/` and **do not** rewrite the path to an absolute one — an absolute path produces mirrored nested output dirs (`.precomputed/latents/absolute/path/.../x.pt`) instead of a clean `latents/x.pt`. +2. Run `process_dataset.py` on that file with `--output-dir "//overfit/.precomputed"` (output still goes to `overfit/`, only the metadata lives in `dataset/`). +3. Use the **same `--resolution-buckets`** as the planned full run. Critical: a small-shape sanity check is misleading because resolution is the dominant memory factor. +4. Clean up the temporary `dataset/_one_sample.json` afterward (it's scratch; don't leave it in the dataset dir). + +## Decode-and-Verify (optional debug aid) + +If the user reports validation samples look wrong or training diverges, decode one preprocessed sample back to media: + +`decode_latents.py` takes the **latents directory** and an **output directory** as positional arguments (it decodes the whole directory, not a single `.pt` file). Add `--with-audio` and `--audio-latents-dir` if the dataset has audio. + +```bash +cd packages/ltx-trainer +uv run python scripts/decode_latents.py \ + "//dataset/.precomputed/latents" \ + "//dataset/.precomputed/decoded_check" \ + --model-path "" +``` + +If decoded output is garbled, preprocessing itself is suspect (wrong model, wrong VAE). + +## Failure Modes + +- **"shape mismatch" on resume:** Step 1's verification check. Ask user before any mutation. +- **`frames % 8 != 1`** error from process_dataset.py: the requested frame count is invalid; correct in the plan and re-launch. +- **VRAM OOM during preprocessing:** add `--load-text-encoder-in-8bit`. If still OOM, reduce `--batch-size`. +- **Disk full:** preprocessed latents can be large (especially audio). Surface to user with a `du -sh` summary of `.precomputed/`. + +## Do Not + +- Do not delete or overwrite existing `.precomputed/` data without explicit user approval for that exact action. +- Do not preprocess at a smaller resolution to "save time" — the sanity check exists specifically to validate the planned resolution. diff --git a/.claude/skills/train-model/references/config-patching.md b/.claude/skills/train-model/references/config-patching.md new file mode 100644 index 0000000..a9d8bd2 --- /dev/null +++ b/.claude/skills/train-model/references/config-patching.md @@ -0,0 +1,73 @@ +# Config Patching + +How to safely produce `//config.yaml` from an example in `packages/ltx-trainer/configs/`. The trainer's config schema is Pydantic with `extra="forbid"` — unknown fields are rejected. Full field reference: [`packages/ltx-trainer/docs/configuration-reference.md`](../../../../packages/ltx-trainer/docs/configuration-reference.md). + +## Workflow + +1. Copy the example config matching the selected mode (see `mode-selector.md`) to `//config.yaml`. +2. Patch fields as described below. Preserve YAML comments where possible — they help the user audit the run later. +3. **Never** edit the example config in `packages/ltx-trainer/configs/`. That's the user's reference library. + +## Required Patches (every run) + +| Field | Value | +|-------|-------| +| `model.model_path` | Absolute path to local `.safetensors` (from probe or user). | +| `model.text_encoder_path` | Absolute path to local Gemma directory (from probe or user). | +| `data.preprocessed_data_root` | `//dataset/.precomputed` (absolute). | +| `output_dir` | `//outputs` (absolute). | + +## Hardware-Driven Patches + +Apply per the matched VRAM tier in `references/hardware-profiles.md`. After autotune (Phase 6), patch the winning trial's deltas in. + +## Schema Constraints (validate before launch) + +These will cause Pydantic errors or runtime failures; check before invoking the trainer. + +- **Frame count:** `validation.video_dims[2]` must satisfy `frames % 8 == 1` (1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...). +- **Resolution:** `validation.video_dims[0]` and `[1]` must be divisible by 32. +- **Multi-bucket training:** if dataset uses multiple resolution buckets, set `optimization.batch_size: 1`. +- **At least one generated modality:** `training_strategy` must have at least one of `video.is_generated` or `audio.is_generated` set to `true`. +- **Audio condition restrictions:** the audio modality cannot use `first_frame` or `spatial_crop` conditions. +- **Strategy name:** prefer `training_strategy.name: "flexible"`. `text_to_video` and `video_to_video` still work but emit deprecation warnings. + +## LoRA Patches + +For style/concept LoRAs: + +- `lora.rank` and `lora.alpha`: set from the matched VRAM tier and use case. **32GB tier** pins rank 16 per `t2v_lora_low_vram.yaml`; **80GB+ tier** uses rank 32 per `t2v_lora.yaml`. Keep `alpha == rank`. See `mode-selector.md` for use-case-driven rank guidance. +- `lora.target_modules`: short patterns like `"to_k"`, `"to_q"`, `"to_v"`, `"to_out.0"` match all attention modules (video + audio + cross-modal). Add `"ff.net.0.proj"`, `"ff.net.2"` only if user explicitly wants higher capacity. +- **Audio-only LoRA targets** (T2A, audio inpainting): use `"audio_attn1.to_*"`, `"audio_attn2.to_*"` patterns to avoid touching video weights. See `configs/t2a_lora.yaml` for the exact list. + +## Validation Sample Prompts + +The example configs ship with placeholder validation prompts. Validation condition fields are documented in +[`configuration-reference.md#validation-condition-types`](../../../../packages/ltx-trainer/docs/configuration-reference.md#validation-condition-types). +For style/concept LoRAs: +- Replace at least one `validation.samples[].prompt` with a prompt that uses the user's trigger word or describes the target concept. Tells the user something useful at the first validation interval. +- Keep `validation.video_dims` consistent with the training resolution to make samples comparable. +- **Describe the audio, for any run with a generated audio modality** (joint audio+video, T2A, V2A, etc.). The validation prompts must describe the audio the **same way the training captions do** — if the training captions transcribe speech or characterise sound (e.g. *"he says: ‘…’"*, *"calm spoken voice, quiet room tone"*, *"upbeat acoustic guitar"*), the validation prompts must include comparable audio direction. A prompt with no audio description gives the model no guidance for the audio branch and the generated audio comes out poor. This is not speech-specific — any audio (music, ambience, foley) needs describing. Mirror the structure/level of audio detail found in the dataset captions (inspect a few before writing the prompts). + +## W&B Patches + +- If the W&B credential check passes (`uv run python -c "import wandb; print(bool(wandb.Api().api_key))"` → `True`): `wandb.enabled: true`, `wandb.project` = `ltx2-`, `wandb.tags` includes the mode. (Do not use `wandb status` — it falsely reports `api_key: null` when logged in via netrc.) +- If `False`: `wandb.enabled: false`. Surface in plan: *"Not logged in to W&B — run `wandb login` before training to enable tracking."* If the check errored/was ambiguous, ask the user rather than assuming off. + +## Output Dir Behaviour + +The trainer resumes optimizer/scheduler/step state **only when `model.load_checkpoint` is set** to a checkpoint file; it then looks for a matching `training_state_step_*.pt` next to that file. It does **not** auto-detect prior checkpoints in `output_dir/checkpoints/`. To resume, patch `model.load_checkpoint` to the latest checkpoint. To load weights but skip state restore: `checkpoints.no_resume: true`. + +For the orchestrator's resume flow: when resuming an interrupted run, patch `model.load_checkpoint` to the latest checkpoint under `output_dir/checkpoints/`. Leaving it unset starts a fresh run from step 0 even if checkpoints exist on disk. + +## Self-Check Before Launch + +Before any `python scripts/train.py` invocation: + +1. All `model_path`, `text_encoder_path`, `preprocessed_data_root` exist on disk. +2. Frame and resolution constraints satisfied (see above). +3. Generated modalities have matching latents directories under `.precomputed/`. +4. For modes with `reference` condition: `reference_latents/` (and/or `reference_audio_latents/`) exists. +5. For modes with `mask` condition: `video_masks/` (and/or `audio_masks/`) exists. + +A failed check at this point is much cheaper than a failed training start. diff --git a/.claude/skills/train-model/references/hardware-profiles.md b/.claude/skills/train-model/references/hardware-profiles.md new file mode 100644 index 0000000..2eb8692 --- /dev/null +++ b/.claude/skills/train-model/references/hardware-profiles.md @@ -0,0 +1,98 @@ +# VRAM Tiers + +Map probed GPU(s) to a starting training config. The autotune sweep in Phase 6 then empirically improves on this baseline. Use these **tier names** in `plan.md` and user-facing chat — not letter codes. + +Source of truth: the two configs shipped in the trainer repo — +`packages/ltx-trainer/configs/t2v_lora.yaml` (standard) and +`packages/ltx-trainer/configs/t2v_lora_low_vram.yaml` (low VRAM). +Per `packages/ltx-trainer/docs/quick-start.md`, the trainer documents +**80GB recommended** and **32GB minimum**. Anything below 32GB is +unsupported by the project. + +## Probe + +```bash +nvidia-smi --query-gpu=name,memory.total --format=csv,noheader +``` + +Pick the **smallest** VRAM tier across the visible GPUs. Multi-GPU only adds throughput at the same per-GPU memory budget — it doesn't relax per-GPU limits. + +## Minimum Gate + +If per-GPU VRAM is **< 32 GB**, stop the run. Surface to the user: + +> "This GPU has GB VRAM. The LTX-2 trainer requires a minimum of 32GB (see `packages/ltx-trainer/docs/quick-start.md`). Training is unlikely to fit even with maximum memory savings, and we don't ship a tested config below 32GB. Options: (a) abort, (b) try anyway with the low-VRAM config and accept it may OOM — purely at your own risk." + +Do not invent a sub-32GB tier. The trainer team doesn't ship one. + +## 32GB tier — low-VRAM config + +**VRAM range:** 32 GB per GPU (trainer minimum). + +**Typical GPUs:** RTX 5090, V100 32GB. + +Start from `packages/ltx-trainer/configs/t2v_lora_low_vram.yaml` verbatim. Key choices already in that file (do not re-specify in `//config.yaml` — copy the file and patch only the paths from `references/config-patching.md`): + +- `optimizer_type: "adamw8bit"` +- `enable_gradient_checkpointing: true` +- `batch_size: 1`, `gradient_accumulation_steps: 1` +- `quantization: "int8-quanto"` +- `load_text_encoder_in_8bit: true` +- `offload_optimizer_during_validation: true` +- `lora.rank: 16`, `lora.alpha: 16` + +Autotune (Phase 6) will sweep `quantization` off, `optimizer_type` → adamw, and `batch_size` up — but at 32GB the sweep often hits OOM on trial 2 or 3. That's fine; the conservative baseline still works. + +## 80GB+ tier — standard config + +**VRAM range:** 80 GB per GPU and above (trainer recommended). + +**Typical GPUs:** A100 80GB, H100 80GB, H200, B200. + +Start from `packages/ltx-trainer/configs/t2v_lora.yaml` verbatim. Key choices already in that file: + +- `optimizer_type: "adamw"` +- `enable_gradient_checkpointing: true` (autotune may turn it off if headroom allows) +- `batch_size: 1`, `gradient_accumulation_steps: 1` +- `quantization: null` +- `load_text_encoder_in_8bit: false` +- `lora.rank: 32`, `lora.alpha: 32` + +On the 80GB+ tier, the autotune baseline already equals this config (adamw, no quantization), so the quantization/optimizer trials are no-ops; the only real lever is gradient checkpointing off — but for the 22B model that **usually OOMs even with tens of GB of apparent headroom**, so treat a win there as unlikely. The trainer reports its own step-time and peak-VRAM at the end of each run — use those rather than an external timer. + +For ≥140GB GPUs (H200, B200), the same 80GB+ tier baseline applies. FA3/FA4 attention backends are viable on Hopper/Blackwell and can speed up training, but they're optional — the trainer's defaults work on PyTorch SDPA without extra setup. + +## 40–60GB tier — mid-range (autotune from low-VRAM) + +**VRAM range:** 40–60 GB per GPU. The trainer doesn't ship a tested config for this range. + +**Typical GPUs:** A40, A6000 48GB, L40, RTX 6000 Ada. + +Start from the **32GB tier** (low-VRAM config) and let autotune relax `quantization`, `optimizer_type`, and `batch_size` based on actual headroom. Don't pre-bake intermediate YAML values that haven't been measured. Surface this as **40–60GB tier** in the plan. + +## Multi-GPU + +If `nvidia-smi` reports N ≥ 2 GPUs of the same model: + +- Launch with `uv run accelerate launch scripts/train.py `. +- Use `packages/ltx-trainer/configs/accelerate/fsdp.yaml` for full fine-tune. +- DDP (default `accelerate launch` without a config file) is fine for LoRA. +- Effective batch = `batch_size * gradient_accumulation_steps * num_gpus`. Reduce `gradient_accumulation_steps` proportionally to keep the effective batch consistent with the plan. + +## Full Fine-Tune + +If the user chose full fine-tune (`model.training_mode: "full"`): + +- Require multi-GPU + FSDP on 80GB+ tier GPUs. Otherwise warn in the plan that single-GPU full FT is unlikely to fit and propose LoRA instead. +- Set `acceleration.offload_optimizer_during_validation: true` always (optimizer state is huge under full FT). + +## Model Path Constraints + +- `model.model_path`: local `.safetensors` only. No URLs. +- `model.text_encoder_path`: local Gemma model directory. No URLs. + +If probe didn't find these in conventional locations (`/models/`, `~/models/`, `$LTX_MODELS_DIR`), ask the user in Phase 3 (or offer to download per `references/onboarding.md`). + +## Notes on Loss-of-Generality + +The two anchor tiers (32GB and 80GB+) correspond directly to the two configs the trainer ships. The autotune sweep is the empirical layer — if a particular GPU consistently lands on a different stable config, **update the relevant trainer config first**, not this file. This skill follows the trainer's choices, not the other way around. diff --git a/.claude/skills/train-model/references/mode-selector.md b/.claude/skills/train-model/references/mode-selector.md new file mode 100644 index 0000000..ad84f1a --- /dev/null +++ b/.claude/skills/train-model/references/mode-selector.md @@ -0,0 +1,74 @@ +# Mode Selector + +Map the user's stated intent to a `flexible`-strategy configuration. All modes are supported via a single strategy (`training_strategy.name: "flexible"`); the difference is which modality is generated and which `conditions` are attached. + +> Full reference: [`packages/ltx-trainer/docs/training-modes.md`](../../../../packages/ltx-trainer/docs/training-modes.md). This file is the **lookup table** for translating user intent. + +## Decision Table + +| User says (roughly)... | Mode | Example config | Modalities | Conditions | +|------------------------|------|----------------|------------|------------| +| "generate videos from text", "T2V LoRA" | T2V | `configs/t2v_lora.yaml` | video gen, audio gen | none | +| "generate videos from a starting image", "I2V" | I2V | `configs/i2v_lora.yaml` | video gen, audio gen | `first_frame` (video) | +| **plain concept/style LoRA** ("train a LoRA on X", no specific task) | **I2V by default** (see note) | `configs/i2v_lora.yaml` | video gen, audio gen | `first_frame` (video), `probability: 0.5` | +| "extend a video forward in time" | Video extension (prefix) | `configs/video_extend_lora.yaml` | video gen, audio gen | `prefix` (video) | +| "extend a video backward in time" | Video extension (suffix) | `configs/video_suffix_lora.yaml` | video gen, audio gen | `suffix` (video) | +| "fill in masked regions of a video" | Video inpainting | `configs/video_inpainting_lora.yaml` | video gen | `mask` (video) | +| "expand a video beyond its borders" | Video outpainting | `configs/video_outpainting_lora.yaml` | video gen | `spatial_crop` (video) | +| "style transfer from reference video", "IC-LoRA", "depth/pose/canny control" | V2V IC-LoRA | `configs/v2v_ic_lora.yaml` | video gen | `reference` (video) | +| "generate video to match an audio track" | A2V | `configs/a2v_lora.yaml` | video gen, audio frozen | none (audio `is_generated: false`) | +| "add sound effects to silent video", "foley", "V2A" | V2A | `configs/v2a_lora.yaml` | video frozen, audio gen | none (video `is_generated: false`) | +| "generate audio from text", "T2A" | T2A | `configs/t2a_lora.yaml` | audio gen | none | +| "extend audio forward / backward" | Audio extension | `configs/audio_extend_lora.yaml`, `configs/audio_suffix_lora.yaml` | audio gen | `prefix` / `suffix` (audio) | +| "fill in masked regions of audio" | Audio inpainting | `configs/audio_inpainting_lora.yaml` | audio gen | `mask` (audio) | +| "audio style transfer from reference", "A2A IC-LoRA" | A2A IC-LoRA | `configs/a2a_ic_lora.yaml` | audio gen | `reference` (audio) | +| "joint video+audio reference control" | AV2AV IC-LoRA | `configs/av2av_ic_lora.yaml` | video gen, audio gen | `reference` (both) | +| Any of the above with full fine-tune | Full FT variant | as above, set `model.training_mode: "full"` | (mode-specific) | (mode-specific) | + +### Why I2V is the default for a plain concept/style LoRA + +A "train a LoRA on X" request isn't tied to one inference mode: **LoRA weights are pipeline-agnostic** — the same checkpoint loads in both T2V and I2V inference (both use `TI2VidOneStagePipeline`/`TwoStages`). The `i2v_lora` config trains `first_frame` with **`probability: 0.5`**, so the model learns both first-frame-conditioned (I2V) and unconditioned (T2V) generation in one run, and the first frame comes from each training clip automatically (no extra data prep). That makes I2V a versatile **superset** — usable for both at inference at no extra cost — which is why it's the default for a plain LoRA. Ask the user how they'll use it (text-only / from an image / both) and only drop to `t2v_lora` if they're sure it's text-only. (See the orchestrator `SKILL.md` Phase 1.) + +## Disambiguation Questions + +When the user's first answer is ambiguous, ask **one** follow-up: + +- "extend a video" → forward or backward in time? +- "control with a reference" → video reference (depth/pose/canny/etc.) or audio reference? +- "fill in regions" → video regions (masked frames) or audio regions (masked time)? +- "T2V" → joint video+audio (default) or video-only? +- LoRA or full fine-tune? Default to LoRA unless the user has multi-GPU + clear reason for full. + +## Combining Modes + +The flexible strategy allows stacking conditions. Common combinations: + +- **I2V + V2A** (start frame + generate audio for the resulting video) — not directly expressible; would need two passes. +- **Video extension + audio extension** — both modalities generate, both have `prefix` condition. Express in one config. +- **IC-LoRA + I2V** — `first_frame` + `reference` conditions on the video modality. + +If the user asks for a combination not listed, check `packages/ltx-trainer/src/ltx_trainer/training_strategies/flexible.py` for which conditions can co-exist on a modality. Audio modality cannot use `first_frame` or `spatial_crop`. + +## When the Intent Doesn't Map + +If after disambiguation there is no entry in the table and no combination of `flexible` conditions covers the user's request, go to the **Escape Hatch** section in the orchestrator `SKILL.md`. Do not silently pick the closest mode. + +## LoRA Rank by Use Case + +Once the mode is picked, choose `lora.rank` (and matching `lora.alpha`) based on what the LoRA is supposed to capture. These are starting points; autotune doesn't sweep rank because it's a quality knob, not a step-time one. + +| Use case | Suggested rank | Notes | +|----------|----------------|-------| +| Single character, single object, single style | 32–64 | Default for most concept LoRAs. Start at 32; bump to 64 if validation samples underfit. | +| Multi-character world, dense series, complex multi-concept | 96–128 | More capacity for distinguishing several concepts inside one LoRA. | +| Camera move, motion, transition (i.e. behavioural, not visual) | 8–16 | Motion is a thin signal — high ranks just memorise frame content. | +| IC-LoRA control (V2V depth/pose/Canny/etc., A2A audio reference) | 16–32 | Start at 16 for structural control (depth, pose, edges); 24–32 if the reference carries richer style/texture. Video IC-LoRA often lands lower than concept LoRAs. | +| LTX-2 trainer's default if unsure | 32 | Safe baseline. | + +On the **32GB tier** (`hardware-profiles.md`), the low-VRAM config already pins `lora.rank: 16`. If the user picks a higher-rank use case on 32GB, surface the trade-off in the plan but let them decide — the autotune sweep doesn't touch rank, so a too-high rank will simply OOM at training time. + +Keep `alpha == rank` unless the user has a specific reason otherwise (effective scaling = `alpha / rank`). + +## Starting Config + +Copy the matching example config — the exact filename from the **Example config** column of the decision table above (e.g. T2V → `configs/t2v_lora.yaml`, I2V → `configs/i2v_lora.yaml`, A2A IC-LoRA → `configs/a2a_ic_lora.yaml`) — into `//config.yaml` as the starting point. Then patch per `references/config-patching.md`. diff --git a/.claude/skills/train-model/references/onboarding.md b/.claude/skills/train-model/references/onboarding.md new file mode 100644 index 0000000..358b8d5 --- /dev/null +++ b/.claude/skills/train-model/references/onboarding.md @@ -0,0 +1,115 @@ +# First-Run Onboarding + +What the orchestrator's Phase 2 probe checks for, what to do when something is missing, and what the skill is allowed to set up automatically (with explicit user approval). + +## Prerequisites Checked in Phase 2 + +| Prerequisite | How to detect | If missing | +|--------------|---------------|------------| +| CUDA GPU visible | `nvidia-smi` returns ≥1 GPU | Stop. Training requires CUDA — point the user at non-LTX-2 docs. | +| Linux | `uname -s` returns `Linux` | Stop. Trainer uses Triton (Linux-only). | +| `uv` installed | `command -v uv` | Offer to install (see "Auto-setup" below). | +| Workspace synced | `[ -f uv.lock ] && uv pip list \| grep -q ltx-trainer` | Offer to run `uv sync` from repo root. | +| LTX-2 model weights | Search `/models/`, `~/models/`, `$LTX_MODELS_DIR` for a `.safetensors` matching `*ltx*2*` | Offer to download (see "Model downloads"). | +| Gemma text encoder dir | Search same locations for a directory containing Gemma config | Offer to download. | +| Captioner backend | Gemini auth (`GEMINI_API_KEY`/`GOOGLE_API_KEY` or gcloud/Vertex), OR a ≥40 GiB GPU to host the Qwen3-Omni-30B vLLM server (FP8), OR captions already in the dataset | See "Captioner graceful degradation" below. **Check the HF cache for an already-downloaded Qwen model before assuming a download is needed** (see note below the table). | +| W&B login (optional) | `uv run python -c "import wandb; print(bool(wandb.Api().api_key))"` → `True` means logged in. Uses wandb's own credential resolution (env/netrc/settings). **Don't** use `wandb status` (reports `api_key: null` even when logged in). | Not a blocker. If `False`: disabled in config + flagged in plan. If the check errors/ambiguous: ask the user, don't assume off. | +| Disk space | `df -h $WORKSPACE` | Surface available space alongside what a run consumes (preprocessed latents, several-GB checkpoints, validation samples). Flag concerns to the user; don't enforce a hard threshold. | + +Surface findings as a compact table in chat. For each missing item, present the user with a concrete next step (download command, install command, or "skip this — here's the consequence"). + +**Check the HF cache before declaring the local captioner unavailable.** The Qwen3-Omni model is served from the HuggingFace cache, not `~/models/`. Before concluding it must be downloaded (or ruling it out on free-disk grounds), check whether it's already cached: + +```bash +ls -d "${HF_HOME:-$HOME/.cache/huggingface}"/hub/models--Qwen--Qwen3-Omni* 2>/dev/null \ + && du -sh "${HF_HOME:-$HOME/.cache/huggingface}"/hub/models--Qwen--Qwen3-Omni* 2>/dev/null +``` + +If it's cached (~60 GiB, all shards present), no download is needed — don't rule out the local captioner because of low free disk on the *home* partition; the weights already exist. Only the GPU-VRAM constraint (≥40 GiB for FP8) then applies. + +## Auto-Setup (with explicit user approval) + +The skill may, only after the user explicitly says yes, do these setup actions. Each action is a single discrete question. + +### Run `uv sync` + +```bash +cd +uv sync +``` + +Ask: *"Repo not synced. Run `uv sync` now? It will download the project's Python dependencies."* + +### Install `uv` + +Ask: *"`uv` not installed. Install via the official one-liner now?"* + +```bash +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +After install, ask the user to restart their shell or `source ~/.bashrc` before continuing. + +### Model downloads + +Use `huggingface-cli` (comes with `huggingface-hub`, transitively pulled by `uv sync`). Default destination: `$LTX_MODELS_DIR` if set, else `~/models/` (create if missing). Surface destination in the prompt — never download into the repo or into the workspace. + +**LTX-2 base model:** + +```bash +huggingface-cli download Lightricks/LTX-2.3 \ + ltx-2.3-22b-dev.safetensors \ + --local-dir ~/models/ltx-2.3 +``` + +Public reference: . + +**Gemma text encoder:** + +```bash +huggingface-cli download google/gemma-3-12b-it-qat-q4_0-unquantized \ + --local-dir ~/models/gemma-3-12b +``` + +**Qwen3-Omni captioner (only if the GPU can host it):** + +The local captioner is Qwen3-Omni-30B-A3B-Thinking served by a vLLM server (`scripts/serve_captioner.py`), which downloads the model (~65 GiB) on first launch via `uvx vllm` — there's no separate `huggingface-cli` step. Default **FP8** (~31 GiB weights) fits on a **40 GiB** GPU; **bf16** (~60 GiB) needs **≥66 GiB free VRAM**. On a GPU below ~40 GiB (typical consumer 24/32 GB cards), don't use it — use Gemini instead (see "Captioner graceful degradation"). + +The base model + text encoder are large (multi-GB) downloads. Ask the user before each one — `huggingface-cli` reports the actual size at the start of the transfer. Do **not** batch them into a single "yes/no"; the user may want only what's missing. + +### Hugging Face login (if any downloads fail with 401) + +Ask: *"Hugging Face download requires login (some Lightricks models are gated). Run `huggingface-cli login` now? You'll need a token from ."* + +## Captioner Graceful Degradation + +The captioner is the trickiest prerequisite. The local backend (`qwen_omni`) is now a **30B model served by a vLLM server** — ~65 GiB download; FP8 fits on a 40 GiB GPU, bf16 needs ≥66 GiB. Typical consumer cards (24/32 GB) can't host it, so for most users **prefer Gemini**. + +Decision tree: + +1. **User already has captions in their dataset metadata** → skip the captioner entirely (Step 3 of `prepare-dataset` skips when captions are present). +2. **GPU ≥40 GiB (FP8) / ≥66 GiB (bf16)** → `qwen_omni` is viable: launch `scripts/serve_captioner.py` first, then caption. Gemini is still fine here too. +3. **GPU below ~40 GiB (the common consumer case)** → `qwen_omni` is not an option. Recommend **`gemini_flash`** and tell the user they need Gemini auth: a `GEMINI_API_KEY`/`GOOGLE_API_KEY` (get one at ) **or** working gcloud/Vertex AI credentials. +4. **No Gemini auth and can't run Qwen3** → the only remaining path is bringing their own captions: add a `caption` column to the dataset metadata, then re-invoke the skill. + +Wait for the user's choice. Don't pick one automatically — but make the hardware reality explicit so they don't try to run the server on a card that can't host it. + +## What Auto-Setup Does NOT Touch + +- Does not modify the user's shell rc files except by explicit instruction (e.g., "add `export LTX_MODELS_DIR=...` to your `.bashrc`?" with the user agreeing). +- Does not modify `~/.gitconfig`, `~/.ssh/`, or any auth-related files. +- Does not install GPU drivers, CUDA, or system packages. +- Does not delete or move existing model files. If a model is found but at an unexpected path, surface and let the user decide. +- Does not download into the repo or workspace. Models live at `~/models/` (or `$LTX_MODELS_DIR`). + +## Configuration Inheritance + +After downloads, the skill records the resolved paths into the plan's Assumptions section and into `config.yaml`: + +```yaml +model: + model_path: "/home//models/ltx-2.3/ltx-2.3-22b-dev.safetensors" + text_encoder_path: "/home//models/gemma-3-12b" +``` + +Suggest (don't enforce) setting `LTX_MODELS_DIR=~/models` in their shell rc for future runs. diff --git a/.claude/skills/train-model/references/plan-template.md b/.claude/skills/train-model/references/plan-template.md new file mode 100644 index 0000000..d52182f --- /dev/null +++ b/.claude/skills/train-model/references/plan-template.md @@ -0,0 +1,107 @@ +# Plan Template + +Write `//plan.md` following this template. The plan is the user's contract with the agent — it's the gate before any heavy work runs. + +Scale each section to its relevance. Skip subsections that don't apply (e.g., no captioning if data is already captioned), but never collapse "Assumptions" or "Cost/time estimate". + +```markdown +# Training Plan — + +## Goal + + + +## Mode + +**** — . + +- Config base: the concrete example config selected by the mode (e.g. `packages/ltx-trainer/configs/t2v_lora.yaml`) — use the actual filename, not a placeholder +- Conditions: +- Training mode: <`lora` | `full`> + +## Dataset + +- Source: `` () +- Captions: <`already present` | `will be generated with `> +- Audio: <`present` | `absent — using --skip-audio` | `to be paired with --audio-durations`> +- IC-LoRA references: <`present` | `to be generated via compute_reference.py` | `n/a`> + +## Preprocessing + +- Target resolution buckets: `xx` (frames satisfy `frames % 8 == 1`; W,H divisible by 32) +- Estimated time: ~ on detected hardware +- Output: `//dataset/.precomputed/` + +## Training Config + +| Field | Value | +|-------|-------| +| Optimizer | | +| Mixed precision | | +| Quantization | | +| Gradient checkpointing | | +| Batch size | | +| Gradient accumulation | (effective batch = ) | +| Steps | | +| Learning rate | | +| LoRA rank / alpha | (or "full FT") | +| LoRA target modules | (or "n/a") | +| LoRA trigger word | (or "n/a") | +| Validation interval | every steps | +| Checkpoint interval | every steps | + +## Hardware + +- GPU(s): x , GB per GPU +- Launch: <`python scripts/train.py` (single) | `accelerate launch` (multi)> +- VRAM tier: <32GB tier | 40–60GB tier | 80GB+ tier> — + +## Sanity Check + Autotune + +*In plain terms: before committing to the full run, I do a quick dry run on a single clip at your target resolution to catch out-of-memory or config errors in ~2 minutes (rather than failing hours in), then try a few config variants to pick the fastest one that fits your GPU.* + +Mechanics: +- 1 sample, full target resolution, 50 steps + 1 validation pass. +- Autotune sweep: up to 5 trials varying quantization / optimizer / batch size. +- Stops at first OOM or no-improvement. + +## Monitoring + + +- W&B: enabled, project ``, entity ``. URL will be surfaced once training starts. +- W&B: **not logged in** — run `wandb login` before training to enable tracking. Otherwise training proceeds without remote logging. + +## Outputs + +- Training config: `//config.yaml` +- Checkpoints: `//outputs/checkpoints/` +- Validation samples: `//outputs/samples/` +- Autotune log: `//autotune.log` + +## Assumptions + +Defaults the agent chose silently. Override any by replying with the new value. + +- + +## Cost / Time Estimate + +Give only estimates you can ground; label anything not yet measured as rough. Do **not** state a confident training duration before the sanity check has measured a real step time — say "training duration TBD until the sanity check measures step time" and fill it in afterward (per Hard Invariant #5: no fabricated predictions). + +- Captioning: ~ (rough) +- Preprocessing: ~ (rough) +- Sanity check + autotune: ~ (rough) +- Full training: **measured after sanity check** — then ` × ` +- **Total wall-clock estimate:** rough until step time is measured; refine after the sanity check. + +## Approve to Proceed + +Reply "approve" (or with edits) to start. No captioning, preprocessing, autotune, or training will run before approval. +``` + +## Notes on Writing the Plan + +- Show numbers, not adjectives. "~3 hours" beats "fairly long." +- Surface every assumption that, if wrong, would cost the user time. Better to over-list than under-list — the user can skim. +- If a section reveals you need to ask another question, **stop and ask** before finalizing the plan. The plan is the last gate, not the first. +- If the user's hardware can't reasonably support the requested mode (e.g., single-GPU full FT on a 32GB consumer card), say so plainly in the plan and propose the alternative (LoRA, multi-GPU, etc.), rather than silently downgrading. diff --git a/.claude/skills/train-model/references/troubleshooting.md b/.claude/skills/train-model/references/troubleshooting.md new file mode 100644 index 0000000..26acc4b --- /dev/null +++ b/.claude/skills/train-model/references/troubleshooting.md @@ -0,0 +1,84 @@ +# Troubleshooting + +Quick lookup for failures during sanity check, preprocessing, or training. For deeper coverage see `packages/ltx-trainer/docs/troubleshooting.md`. + +## OOM During Training Step + +Order of operations (cheapest first): + +1. `optimization.enable_gradient_checkpointing: true` (if not already on). +2. `optimization.batch_size: 1` and increase `gradient_accumulation_steps` to preserve effective batch. +3. `optimization.optimizer_type: "adamw8bit"`. +4. `acceleration.quantization: "int8-quanto"`. +5. Reduce `lora.rank` (32 → 16 → 8). Alpha follows rank. +6. Reduce target resolution (`validation.video_dims` and re-preprocess the dataset at the new resolution). + +The last option is expensive — flag it clearly to the user before re-preprocessing. + +## OOM During Validation Sample Generation + +The validation pass loads decoders + runs CFG/STG inference; it can OOM even when the training step fits. + +1. `acceleration.load_text_encoder_in_8bit: true` (trainer config, not the dataset script). +2. `acceleration.offload_optimizer_during_validation: true` (especially for full FT or high-rank LoRA). +3. Reduce `validation.video_dims` (smaller validation than training is fine — it's only for visual feedback). +4. Reduce `validation.inference_steps` (e.g. 30 → 20). +5. Increase `validation.interval` to validate less often. + +## NaN Loss + +1. Check `acceleration.mixed_precision_mode`: prefer `"bf16"`. If `"fp16"`, switch. +2. Verify dataset latents are well-formed: `uv run python scripts/decode_latents.py --model-path ` (it decodes a whole latents directory, not a single `.pt`) should reconstruct sensibly. +3. Lower `optimization.learning_rate` by 5x. +4. Add `optimization.max_grad_norm: 1.0` (default; verify it's set). +5. If using `quantization`, try `null` — INT8/INT4 quantization can interact badly with poorly-conditioned LoRA inits at high LR. + +## Validation Samples Look Wrong but Loss Is Fine + +Often not a bug — validation uses simplified inference. For real quality assessment, run a checkpoint through `packages/ltx-pipelines/` after training. + +## Trainer Won't Start: Config Validation Error + +Pydantic `extra="forbid"` means typos in field names fail loudly. Read the error carefully — it names the offending field and path. Fix and re-launch. + +Common offenders: +- `latents_dir` typo or wrong relative path. +- A required field genuinely missing after copying an example (most fields have defaults; check the error message for the exact field path). +- `target_modules` listed at wrong nesting level (must be under `lora:`). + +## Trainer Won't Start: Missing Files + +- `model_path` not found → re-probe `/models/`, `~/models/`, `$LTX_MODELS_DIR`, or ask the user. +- `text_encoder_path` directory missing the Gemma config → ensure the path is to the Gemma model dir, not its parent. +- `preprocessed_data_root` doesn't contain expected subdirs → re-verify Phase 7 ran for the chosen mode (see `phases/preprocess-dataset.md`). + +## Resume Stops Working + +The trainer **does not auto-resume from `output_dir`**. Resume happens only when `model.load_checkpoint` is explicitly set to a checkpoint file; the trainer then loads those weights and looks for a `training_state_step_*.pt` next to that file to restore optimizer/scheduler/step. Common pitfalls: +- `model.load_checkpoint` not set or set to the wrong path → fresh run from step 0 even when `outputs/checkpoints/` is full of artifacts. Patch `model.load_checkpoint` to the latest checkpoint. +- `checkpoints.no_resume: true` is set → weights load but state is discarded. Remove the flag if you want a proper resume. +- `training_state_step_*.pt` missing from next to the loaded checkpoint → weights load but step counter resets. Make sure the state file accompanies the checkpoint. +- `training_state_step_*.pt` corrupted (size 0, fails `torch.load`) → trainer falls back to step 0 with a warning. + +## Autotune Trial Failed Mid-Sweep + +- If trial 2 (quantization off) OOMs: revert to trial 1 and stop the sweep. The 32GB tier baseline is correctly aggressive. +- If trial 3 (adamw) OOMs: revert to adamw8bit. Continue with trial 4 if VRAM headroom allows. +- If trial 4 (batch_size up) OOMs: revert and stop. We've found the ceiling. + +Never carry over a failing trial's deltas. Always revert to the last-known-good before the next change. + +## Captioning Is Slow + +- Local `qwen_omni` is a 30B model served by `serve_captioner.py` (vLLM). If the server won't start or OOMs on launch: keep the default `--quantization fp8` (don't use `bf16` unless ≥66 GiB free VRAM), lower `--gpu-memory-utilization`, or reduce `--max-model-len`. If the GPU can't host a 30B model at all, switch to `gemini_flash`. +- `caption_videos.py --captioner-type qwen_omni` errors connecting → the vLLM server isn't running or `--vllm-url` doesn't match. Start `serve_captioner.py` first and confirm the port. +- For most hardware and for larger datasets, prefer `--captioner-type gemini_flash --num-workers ` (needs Gemini auth: `GEMINI_API_KEY`/`GOOGLE_API_KEY` or gcloud/Vertex) — runs anywhere and parallelises; local Qwen needs a heavy GPU and a running server. + +## Process_Dataset Errors + +- "frames divisible by..." → the video doesn't have enough frames at the requested temporal resolution. Either shorten the requested frame count or use `split_scenes.py` to break long videos. +- "shape mismatch" on existing `.precomputed/` → user requested a different resolution than the existing data. Per the invariants, **stop and ask** — do not overwrite. Offer: reuse at old resolution / re-preprocess to a new dir / abort. + +## When To Give Up and Ask The User + +If a fix isn't obvious from this file or `packages/ltx-trainer/docs/troubleshooting.md` within two attempts, stop and surface the full error + the steps already tried to the user. Don't loop indefinitely on autonomous fixes — the user has context the agent doesn't (which checkpoints are precious, what they care about preserving, etc.). diff --git a/.gitattributes b/.gitattributes index cb8c63e..d1e8eaa 100644 --- a/.gitattributes +++ b/.gitattributes @@ -2,7 +2,9 @@ *.safetensors filter=lfs diff=lfs merge=lfs -text *.sft filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text *.mp4 filter=lfs diff=lfs merge=lfs -text +packages/ltx-pipelines/tests/assets/*.wav filter=lfs diff=lfs merge=lfs -text *.png filter=lfs diff=lfs merge=lfs -text *.jpeg filter=lfs diff=lfs merge=lfs -text *.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore index 82d7f42..2823371 100644 --- a/.gitignore +++ b/.gitignore @@ -17,8 +17,10 @@ checkpoints/ # Other files .DS_Store -tmp .wandb +projects/ +tmp +wandb/ # Model checkpoints *.ckpt @@ -36,6 +38,7 @@ tmp *.json *.m4a *.mov +*.mp3 *.mp4 *.png *.wav @@ -45,5 +48,22 @@ tmp !packages/ltx-pipelines/tests/assets/expected_hdr_ic_lora_exr/frame_*.exr !packages/ltx-pipelines/tests/assets/hdr_ic_lora_test_input.mp4 +# Text-to-audio (T2A) e2e test baseline (checked in via Git LFS) +!packages/ltx-pipelines/tests/assets/expected_t2a_one_stage_ltx2_3.wav + +# ltx-bench Grafana dashboards (source of truth in the repo) +!packages/ltx-bench/grafana/*.json + +# ltx-trainer E2E test dataset (committed via Git LFS). +# The target_audio/ and reference_audio/ subdirectories are populated lazily at test runtime +# (high-bitrate MP3 extracted from target_videos, plus a low-bitrate MP3 reference); their +# contents stay gitignored via the root *.mp3 rule above. +!packages/ltx-trainer/tests/assets/test_dataset/dataset.json +!packages/ltx-trainer/tests/assets/test_dataset/target_videos/*.mp4 +!packages/ltx-trainer/tests/assets/test_dataset/reference_videos/*.mp4 +!packages/ltx-trainer/tests/assets/test_dataset/conditioning/first_frame.jpg +!packages/ltx-trainer/tests/assets/test_dataset/conditioning/video_mask.png +!packages/ltx-trainer/tests/assets/test_dataset/conditioning/audio_mask.pt + # Binary files *.so diff --git a/packages/ltx-core/README.md b/packages/ltx-core/README.md index 33556dd..198377e 100644 --- a/packages/ltx-core/README.md +++ b/packages/ltx-core/README.md @@ -8,6 +8,7 @@ The foundational library for the LTX-2 Audio-Video generation model. This packag - **`conditioning/`**: Tools for preparing latent states and applying conditioning (image, video, keyframes) - **`guidance/`**: Perturbation system for fine-grained control over attention mechanisms - **`loader/`**: Utilities for loading weights from `.safetensors`, fusing LoRAs, and managing memory +- **`block_streaming/`**: Memory-efficient inference that streams transformer blocks through the GPU one at a time (from pinned CPU buffers or directly from disk) - **`model/`**: PyTorch implementations of the LTX-2 Transformer, Video VAE, Audio VAE, Vocoder and Upscaler - **`text_encoders/gemma`**: Gemma text encoder implementation with tokenizers, feature extractors, and separate encoders for audio-video and video-only generation - **`quantization/`**: FP8 quantization backends (FP8-TensorRT-LLM scaled MM, FP8 cast) for reduced memory footprint. @@ -55,6 +56,7 @@ pip install -e packages/ltx-core - **Loader** ([`loader/`](src/ltx_core/loader/)): Model loading from `.safetensors`, LoRA fusion, weight remapping, and memory management - **Quantization** ([`quantization/`](src/ltx_core/quantization/)): FP8 quantization backends for reduced memory footprint and faster inference +- **Block Streaming** ([`block_streaming/`](src/ltx_core/block_streaming/)): Streams transformer blocks through the GPU one block at a time, so the full model runs on machines without enough memory to hold all its weights at once ### Loader @@ -157,6 +159,39 @@ from ltx_core.quantization.fp8_cast import build_policy as build_fp8_cast_policy policy = build_fp8_cast_policy("/path/to/checkpoint.safetensors") ``` +### Block Streaming + +The `block_streaming/` module ([`src/ltx_core/block_streaming/`](src/ltx_core/block_streaming/)) lets the full model run on machines that lack the memory to hold all of its weights at once. It streams the transformer's blocks through a small rolling set of GPU buffers, loading each block's weights just before it runs and recycling them afterwards, so only a few blocks are resident on the GPU at any moment. Construct it with `StreamingModelBuilder`, which returns a `BlockStreamingWrapper` -- an `nn.Module` drop-in for the wrapped model. + +#### Strategies + +The strategy is chosen automatically from `cpu_slots_count` relative to the number of blocks: + +- **RAM streaming** (default, `cpu_slots_count` omitted or `>= num_blocks`): all blocks are pre-loaded into pinned CPU buffers (with LoRA fusion) at build time, then copied to the GPU on demand. Fast; higher CPU memory. +- **Disk streaming** (`cpu_slots_count < num_blocks`): blocks are read from the `.safetensors` file on demand on a background worker thread. Slower; lowest CPU memory. + +#### Basic usage + +```python +import torch +from ltx_core.block_streaming import StreamingModelBuilder + +builder = StreamingModelBuilder( + model_class_configurator=MyModelConfigurator, + model_path="/path/to/model.safetensors", + blocks_attr="transformer_blocks", # dotted path to the nn.ModuleList + blocks_prefix="transformer_blocks", # state-dict key prefix for block weights +) + +# Omit cpu_slots_count for RAM streaming; pass a value < num_blocks for disk streaming. +model = builder.build( + device=torch.device("cuda"), + dtype=torch.bfloat16, + cpu_slots_count=4, + gpu_slots_count=2, +) +``` + For complete, production-ready pipeline implementations that combine these building blocks, see the [`ltx-pipelines`](../ltx-pipelines/) package. --- diff --git a/packages/ltx-core/pyproject.toml b/packages/ltx-core/pyproject.toml index 6dd2edc..654d126 100644 --- a/packages/ltx-core/pyproject.toml +++ b/packages/ltx-core/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ltx-core" -version = "1.1.5" +version = "1.1.6" description = "Core implementation of Lightricks' LTX-2 model" readme = "README.md" requires-python = ">=3.10" diff --git a/packages/ltx-core/src/ltx_core/block_streaming/__init__.py b/packages/ltx-core/src/ltx_core/block_streaming/__init__.py index 88a181a..ee2d5c5 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/__init__.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/__init__.py @@ -5,8 +5,9 @@ CPU-to-GPU copies, caching, and stream synchronization. Two weight source strategies are available: - **RAM streaming** (default): all blocks pre-loaded into pinned CPU buffers with LoRA fusion at build time. Fast, higher CPU memory. -- **Disk streaming** (``cpu_slots < num_blocks``): blocks read from - disk on demand with FIFO eviction. Slower, lower CPU memory. +- **Disk streaming** (``cpu_slots < blocks_number``): blocks are read from + disk on demand by a :class:`DiskWeightSource`, on a background worker + thread. Slower, lower CPU memory. """ from ltx_core.block_streaming.builder import DISK_CPU_SLOTS, StreamingModelBuilder diff --git a/packages/ltx-core/src/ltx_core/block_streaming/block_fetcher.py b/packages/ltx-core/src/ltx_core/block_streaming/block_fetcher.py new file mode 100644 index 0000000..14bfd98 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/block_streaming/block_fetcher.py @@ -0,0 +1,83 @@ +"""BlockFetcher: async disk reads on a worker thread.""" + +from __future__ import annotations + +import logging +import queue +import threading +from dataclasses import dataclass + +import torch + +from ltx_core.block_streaming.disk import DiskBlockReader + +logger = logging.getLogger(__name__) + +Buffer = dict[str, torch.Tensor] + + +@dataclass(slots=True) +class FetchHandle: + """Caller-facing handle for an outstanding read returned by :meth:`BlockFetcher.submit`. + Carries only what the caller needs: a completion event the worker sets and the + read's error. The worker updates these once the read finishes. + """ + + _done: threading.Event + _error: BaseException | None = None + + def wait(self) -> BaseException | None: + """Block until the read finishes; return its error, or ``None`` on success.""" + self._done.wait() + return self._error + + +@dataclass(slots=True) +class _ReadRequest: + """One outstanding read, internal to :class:`BlockFetcher`. + The fetcher's worker reads block ``idx`` into the caller-carved ``buffer`` and + updates ``handle`` (its error, then its event) once the read has finished. + """ + + idx: int + buffer: Buffer + handle: FetchHandle + + +class BlockFetcher: + """Fills caller-supplied buffers on a worker thread.""" + + def __init__(self, reader: DiskBlockReader) -> None: + self._reader = reader + self._request_queue: queue.SimpleQueue[_ReadRequest | None] = queue.SimpleQueue() + self._worker = threading.Thread(target=self._run, name="BlockFetcher-IO", daemon=True) + self._worker.start() + + def submit(self, idx: int, buffer: Buffer) -> FetchHandle: + """Enqueue a read of block *idx* into the caller-carved *buffer*, return its handle.""" + handle = FetchHandle(_done=threading.Event()) + request = _ReadRequest(idx=idx, buffer=buffer, handle=handle) + self._request_queue.put(request) + return handle + + def cleanup(self) -> None: + """Drain pending reads, join the worker, close the reader.""" + self._request_queue.put(None) + self._worker.join() + self._reader.cleanup() + + def _run(self) -> None: + # Pinned buffers are allocated under the caller's inference_mode, so + # in-place copy_ from this thread requires inference_mode here too. + with torch.inference_mode(): + while True: + request = self._request_queue.get() + if request is None: + return + + try: + self._reader.read_into(request.buffer, request.idx) + except Exception as exc: + logger.exception("BlockFetcher: fetch failed for item %d", request.idx) + request.handle._error = exc + request.handle._done.set() diff --git a/packages/ltx-core/src/ltx_core/block_streaming/builder.py b/packages/ltx-core/src/ltx_core/block_streaming/builder.py index 0930e4a..9331f33 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/builder.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/builder.py @@ -2,19 +2,28 @@ from __future__ import annotations +import copy import logging -from dataclasses import dataclass, field, replace -from typing import Generic +from dataclasses import replace +from typing import TYPE_CHECKING, Final, Generic import safetensors import torch from torch import nn +from ltx_core.block_streaming import utils as bs_utils +from ltx_core.block_streaming.block_fetcher import BlockFetcher from ltx_core.block_streaming.disk import DiskBlockReader, DiskTensorReader, LoraSource -from ltx_core.block_streaming.pool import WeightPool +from ltx_core.block_streaming.pool import BufferPool from ltx_core.block_streaming.provider import WeightsProvider -from ltx_core.block_streaming.source import DiskWeightSource, PinnedWeightSource, WeightSource -from ltx_core.block_streaming.utils import allocate_layout_views, derive_layout, make_block_key, resolve_attr +from ltx_core.block_streaming.source import DiskWeightSource, PinnedBlock, PinnedWeightSource, WeightSource +from ltx_core.block_streaming.utils import ( + carve_buffer, + derive_layout, + layout_nbytes, + make_block_key, + resolve_attr, +) from ltx_core.block_streaming.wrapper import BlockStreamingWrapper from ltx_core.loader.fuse_loras import FuseRule, bf16_fuse_rule, fuse_lora_weights from ltx_core.loader.helpers import create_meta_model, load_state_dict, read_model_config @@ -25,23 +34,29 @@ from ltx_core.loader.primitives import ( ModelBuilderProtocol, StateDict, StateDictLoader, + TensorLayout, ) from ltx_core.loader.registry import DummyRegistry, Registry from ltx_core.loader.sd_ops import SDOps from ltx_core.loader.sft_loader import SafetensorsModelStateDictLoader from ltx_core.model.model_protocol import ModelConfigurator, ModelType +if TYPE_CHECKING: + from typing_extensions import Self + logger = logging.getLogger(__name__) DISK_CPU_SLOTS = 2 _DEFAULT_GPU_SLOTS = 2 +_PREFETCH_DEPTH = 2 -@dataclass(frozen=True) class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]): """Immutable builder for :class:`BlockStreamingWrapper`. Reads block weights from safetensors on demand. ``cpu_slots`` and ``gpu_slots`` control the memory/speed trade-off (see :meth:`build`). + The builder is immutable (``with_*`` return modified copies) and exposes + its state via read-only properties backed by private attributes. Args: model_class_configurator: Creates the model from a config dict. model_path: One or more ``.safetensors`` checkpoint paths. @@ -59,30 +74,99 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) (e.g. ``"transformer_blocks"``). """ - model_class_configurator: type[ModelConfigurator[ModelType]] - model_path: str | tuple[str, ...] - model_sd_ops: SDOps | None = None - module_ops: tuple[ModuleOps, ...] = field(default_factory=tuple) - loras: tuple[LoraPathStrengthAndSDOps, ...] = field(default_factory=tuple) - model_loader: StateDictLoader = field(default_factory=SafetensorsModelStateDictLoader) - registry: Registry = field(default_factory=DummyRegistry) - fuse_rule: FuseRule = bf16_fuse_rule + def __init__( + self, + model_class_configurator: type[ModelConfigurator[ModelType]], + model_path: str | tuple[str, ...], + model_sd_ops: SDOps | None = None, + module_ops: tuple[ModuleOps, ...] = (), + loras: tuple[LoraPathStrengthAndSDOps, ...] = (), + model_loader: StateDictLoader | None = None, + registry: Registry | None = None, + fuse_rule: FuseRule = bf16_fuse_rule, + blocks_attr: str = "", + blocks_prefix: str = "", + ) -> None: + # Read-only: typed with the covariant ModelType, so it must not be a mutable attribute. + self._model_class_configurator: Final = model_class_configurator + self._model_path = model_path + self._model_sd_ops = model_sd_ops + self._module_ops = module_ops + self._loras = loras + self._model_loader = model_loader if model_loader is not None else SafetensorsModelStateDictLoader() + self._registry = registry if registry is not None else DummyRegistry() + self._fuse_rule = fuse_rule + self._blocks_attr = blocks_attr + self._blocks_prefix = blocks_prefix - # Streaming-specific - blocks_attr: str = "" - blocks_prefix: str = "" + @property + def model_class_configurator(self) -> type[ModelConfigurator[ModelType]]: + return self._model_class_configurator - def with_sd_ops(self, sd_ops: SDOps | None) -> StreamingModelBuilder: - return replace(self, model_sd_ops=sd_ops) + @property + def model_path(self) -> str | tuple[str, ...]: + return self._model_path - def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> StreamingModelBuilder: - return replace(self, module_ops=module_ops) + @property + def model_sd_ops(self) -> SDOps | None: + return self._model_sd_ops - def with_loras(self, loras: tuple[LoraPathStrengthAndSDOps, ...]) -> StreamingModelBuilder: - return replace(self, loras=loras) + @property + def module_ops(self) -> tuple[ModuleOps, ...]: + return self._module_ops - def with_fuse_rule(self, fuse_rule: FuseRule) -> StreamingModelBuilder: - return replace(self, fuse_rule=fuse_rule) + @property + def loras(self) -> tuple[LoraPathStrengthAndSDOps, ...]: + return self._loras + + @property + def model_loader(self) -> StateDictLoader: + return self._model_loader + + @property + def registry(self) -> Registry: + return self._registry + + @property + def fuse_rule(self) -> FuseRule: + return self._fuse_rule + + @property + def blocks_attr(self) -> str: + return self._blocks_attr + + @property + def blocks_prefix(self) -> str: + return self._blocks_prefix + + def with_sd_ops(self, sd_ops: SDOps | None) -> Self: + clone = copy.copy(self) + clone._model_sd_ops = sd_ops + return clone + + def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> Self: + clone = copy.copy(self) + clone._module_ops = module_ops + return clone + + def with_loras(self, loras: tuple[LoraPathStrengthAndSDOps, ...]) -> Self: + clone = copy.copy(self) + clone._loras = loras + return clone + + def with_registry(self, registry: Registry) -> Self: + clone = copy.copy(self) + clone._registry = registry + return clone + + def with_lora_load_device(self, device: torch.device) -> Self: + # Streaming fuses LoRAs into pinned CPU buffers; no other staging device is meaningful. + raise NotImplementedError("StreamingModelBuilder loads LoRA weights on CPU only.") + + def with_fuse_rule(self, fuse_rule: FuseRule) -> Self: + clone = copy.copy(self) + clone._fuse_rule = fuse_rule + return clone def model_config(self) -> dict: """Read model configuration from the checkpoint metadata.""" @@ -94,16 +178,16 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) def build( self, - target_device: torch.device, - dtype: torch.dtype, + device: torch.device | None = None, + dtype: torch.dtype | None = None, cpu_slots_count: int | None = None, gpu_slots_count: int | None = None, **_kwargs: object, ) -> BlockStreamingWrapper: """Build and return a ready-to-use :class:`BlockStreamingWrapper`. Args: - target_device: GPU device for compute. - dtype: Weight dtype (e.g. ``torch.bfloat16``). + device: GPU device for compute. ``None`` defaults to ``cuda``. + dtype: Weight dtype (e.g. ``torch.bfloat16``). Required. cpu_slots_count: Number of pinned CPU buffer slots. ``None`` = RAM streaming (all blocks pre-loaded with LoRA fusion). gpu_slots_count: Number of GPU buffer slots. @@ -111,6 +195,9 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) """ if not self.blocks_prefix: raise ValueError("blocks_prefix must be non-empty for streaming") + if dtype is None: + raise ValueError("StreamingModelBuilder.build requires an explicit dtype") + device = device if device is not None else torch.device("cuda") config = read_model_config(self.model_path, self.model_loader) meta_model: nn.Module = create_meta_model(self.model_class_configurator, config, self.module_ops) @@ -120,31 +207,44 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) checkpoint_paths = list(self.model_path) if isinstance(self.model_path, tuple) else [self.model_path] block_key_map, non_block_keys = _scan_checkpoint_keys(checkpoint_paths, self.model_sd_ops, self.blocks_prefix) + expected_indices = set(range(len(blocks))) + if set(block_key_map) != expected_indices: + missing = sorted(expected_indices - set(block_key_map)) + extra = sorted(set(block_key_map) - expected_indices) + raise ValueError( + f"Block weights under prefix '{self.blocks_prefix}.' do not match the {len(blocks)} model blocks: " + f"missing indices {missing}, unexpected indices {extra}" + ) cpu_slots_count = cpu_slots_count if cpu_slots_count is not None else len(blocks) gpu_slots_count = gpu_slots_count if gpu_slots_count is not None else _DEFAULT_GPU_SLOTS if cpu_slots_count >= len(blocks): + lora_sd_and_strengths = self._load_lora_sds() source, lora_sources = self._build_pinned_source( - meta_model, target_device, dtype, cpu_slots_count, block_key_map, non_block_keys + blocks, dtype, cpu_slots_count, block_key_map, lora_sd_and_strengths ) + non_block_loras = lora_sd_and_strengths else: reader = DiskTensorReader(checkpoint_paths) source, lora_sources = self._build_disk_source( - meta_model, target_device, dtype, cpu_slots_count, reader, block_key_map, non_block_keys + blocks, dtype, cpu_slots_count, reader, block_key_map, prefetch_depth=_PREFETCH_DEPTH ) + non_block_loras = [src.as_state_dict_with_strength() for src in lora_sources] - copy_stream = torch.cuda.Stream(device=target_device) - gpu_pool = WeightPool( - source.block_layout, + self._load_non_block_weights(meta_model, non_block_keys, device, dtype, non_block_loras) + + copy_stream = torch.cuda.Stream(device=device) + gpu_pool = BufferPool( + source.slot_nbytes, gpu_slots_count, - target_device, + device, reuse_barrier=lambda event: copy_stream.wait_event(event), ) provider = WeightsProvider( gpu_pool, copy_stream, - target_device, + device, source, lora_sources, self.blocks_prefix, @@ -154,24 +254,12 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) model=meta_model, blocks=blocks, provider=provider, - target_device=target_device, + target_device=device, ) - def _build_pinned_source( - self, - meta_model: nn.Module, - target_device: torch.device, - dtype: torch.dtype, - cpu_slots_count: int, - block_key_map: dict[int, list[tuple[str, str]]], - non_block_keys: list[tuple[str, str]], - ) -> tuple[WeightSource, list[LoraSource]]: - """Pre-load all blocks into pinned CPU buffers with LoRA fusion.""" - model_sd = load_state_dict( - self.model_path, self.model_loader, self.registry, torch.device("cpu"), self.model_sd_ops - ) - - lora_sd_and_strengths = [ + def _load_lora_sds(self) -> list[LoraStateDictWithStrength]: + """Load each configured LoRA into a state dict for fusion (pinned path).""" + return [ LoraStateDictWithStrength( load_state_dict([lora.path], self.model_loader, self.registry, torch.device("cpu"), lora.sd_ops), lora.strength, @@ -179,6 +267,25 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) for lora in self.loras ] + def _filtered_sd_ops(self, name_suffix: str, allowed_model_keys: frozenset[str]) -> SDOps: + """``model_sd_ops`` restricted to *allowed_model_keys* (post-rename keys). + The loader skips keys filtered to None before reading them, so a restricted + load never materializes the excluded partition. The distinct ``name`` avoids + a registry cache-id collision with the other partition. + """ + base = self.model_sd_ops if self.model_sd_ops is not None else SDOps("streaming").with_matching() + allowed = allowed_model_keys if base.allowed_keys is None else (allowed_model_keys & base.allowed_keys) + return replace(base, name=f"{base.name}__{name_suffix}", allowed_keys=allowed) + + def _build_pinned_source( + self, + blocks: nn.ModuleList, + dtype: torch.dtype, + cpu_slots_count: int, + block_key_map: dict[int, list[tuple[str, str]]], + lora_sd_and_strengths: list[LoraStateDictWithStrength], + ) -> tuple[WeightSource, list[LoraSource]]: + """Pre-load each block into its own contiguous pinned CPU buffer with LoRA fusion.""" for block_idx in block_key_map: if block_idx >= cpu_slots_count: raise ValueError( @@ -186,87 +293,75 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) f"got block index {block_idx} with only {cpu_slots_count} slots." ) - blocks = resolve_attr(meta_model, self.blocks_attr) - block_tensors: dict[str, torch.Tensor] = {} + # One contiguous pinned buffer per block, carved into per-param views. The + # views (flattened by full key) are filled in place; the source then keeps + # only the contiguous buffer and the layout to re-carve it on read. + pinned_buffers: dict[int, torch.Tensor] = {} + block_layouts: dict[int, TensorLayout] = {} + fill_views: dict[str, torch.Tensor] = {} for block_idx, entries in block_key_map.items(): - block_params = dict(blocks[block_idx].named_parameters()) - for _sft_key, param_name in entries: - key = make_block_key(self.blocks_prefix, block_idx, param_name) - block_tensors[key] = block_params[param_name] - blocks_layout = derive_layout(block_tensors, dtype) - pinned_blocks = allocate_layout_views(blocks_layout, pin_memory=True) + block_state = _block_state(blocks[block_idx]) + layout = derive_layout({param_name: block_state[param_name] for _sft_key, param_name in entries}, dtype) + buffer = bs_utils.alloc_buffer(layout_nbytes(layout), torch.device("cpu"), pin_memory=True) + views = carve_buffer(buffer, layout) + pinned_buffers[block_idx] = buffer + block_layouts[block_idx] = layout + for param_name, view in views.items(): + fill_views[make_block_key(self.blocks_prefix, block_idx, param_name)] = view + + block_sd = load_state_dict( + self.model_path, + self.model_loader, + self.registry, + torch.device("cpu"), + self._filtered_sd_ops("blocks", frozenset(fill_views)), + ) should_sync = False for key, fused in fuse_lora_weights( - model_sd, lora_sd_and_strengths, fuse_rule=self.fuse_rule, preserve_input_device=False + block_sd, lora_sd_and_strengths, fuse_rule=self.fuse_rule, preserve_input_device=False ): - if key in pinned_blocks: - pinned_blocks[key].copy_(fused, non_blocking=True) - model_sd.sd[key] = None - should_sync = True - else: - model_sd.sd[key] = fused + if key not in fill_views: + raise ValueError(f"Block-restricted load produced {key!r}, which is not a pinned block weight") + fill_views[key].copy_(fused, non_blocking=True) + block_sd.sd[key] = None + should_sync = True if should_sync: torch.cuda.synchronize() # Fill remaining pinned keys from the source state dict. - for key in blocks_layout: - if model_sd.sd[key] is None: + for key, view in fill_views.items(): + if block_sd.sd[key] is None: continue - pinned_blocks[key].copy_(model_sd.sd[key]) - model_sd.sd[key] = None - - pinned: dict[int, dict[str, torch.Tensor]] = { - block_idx: { - param_name: pinned_blocks[make_block_key(self.blocks_prefix, block_idx, param_name)] - for _sft_key, param_name in entries - } - for block_idx, entries in block_key_map.items() - } - - non_block_sd: dict[str, torch.Tensor] = { - model_key: model_sd.sd[model_key].to(device=target_device, dtype=dtype) - for _sft_key, model_key in non_block_keys - } - - meta_model.load_state_dict(non_block_sd, strict=False, assign=True) + view.copy_(block_sd.sd[key]) + block_sd.sd[key] = None + pinned = {idx: PinnedBlock(pinned_buffers[idx], block_layouts[idx]) for idx in pinned_buffers} return PinnedWeightSource(pinned), [] def _build_disk_source( self, - meta_model: nn.Module, - target_device: torch.device, + blocks: nn.ModuleList, dtype: torch.dtype, cpu_slots_count: int, reader: DiskTensorReader, block_key_map: dict[int, list[tuple[str, str]]], - non_block_keys: list[tuple[str, str]], + prefetch_depth: int, ) -> tuple[WeightSource, list[LoraSource]]: - """Create a DiskWeightSource backed by a DiskBlockReader for lazy loading. - Derives the shared pool layout from the meta model's block 0 - this - relies on module_ops (e.g. fp8_cast) leaving the meta param dtype in - sync with the post-sd_ops checkpoint dtype. + """Create a DiskWeightSource backed by a DiskBlockReader. + Pool slots are sized to the largest block and carved per block on read, so + heterogeneous blocks (e.g. layers with differing attention layouts) share + one pool. Pool capacity is ``cpu_slots_count + prefetch_depth`` so the + lookahead loop in ``DiskWeightSource.get`` never evicts its own target. + Layouts come from the meta model; assumes module_ops keep the meta param + dtype in sync with the post-sd_ops checkpoint dtype. """ - lora_sources = [LoraSource(lora.path, lora.sd_ops, lora.strength) for lora in self.loras] + block_layouts = _block_layouts(blocks, block_key_map, dtype) + slot_nbytes = max(layout_nbytes(layout) for layout in block_layouts.values()) - self._load_non_block_weights( - reader, - non_block_keys, - meta_model, - target_device, - dtype, - sd_ops=self.model_sd_ops, - lora_sources=lora_sources, - fuse_rule=self.fuse_rule, - ) - - blocks = resolve_attr(meta_model, self.blocks_attr) - layout = derive_layout(dict(blocks[0].named_parameters()), dtype) - - cpu_pool = WeightPool( - layout, - cpu_slots_count, + cpu_pool = BufferPool( + slot_nbytes, + cpu_slots_count + prefetch_depth, torch.device("cpu"), reuse_barrier=lambda event: event.synchronize(), pin_memory=True, @@ -277,43 +372,42 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) sd_ops=self.model_sd_ops, blocks_prefix=self.blocks_prefix, ) - source = DiskWeightSource(cpu_pool, block_reader) + fetcher = BlockFetcher(block_reader) + source = DiskWeightSource( + cpu_pool, + fetcher, + block_layouts, + blocks_number=len(blocks), + prefetch_depth=prefetch_depth, + ) + lora_sources = [LoraSource(lora.path, lora.sd_ops, lora.strength) for lora in self.loras] + return source, lora_sources - @staticmethod @torch.inference_mode() def _load_non_block_weights( - reader: DiskTensorReader, - non_block_keys: list[tuple[str, str]], + self, model: nn.Module, + non_block_keys: list[tuple[str, str]], device: torch.device, dtype: torch.dtype, - sd_ops: SDOps | None = None, - lora_sources: list[LoraSource] | None = None, - fuse_rule: FuseRule = bf16_fuse_rule, + lora_sd_and_strengths: list[LoraStateDictWithStrength], ) -> None: - """Load non-block weights into *model* on *device*, applying ``sd_ops`` and fusing LoRAs. - Fusion goes through :func:`fuse_lora_weights` under *fuse_rule* so the - bf16 rounding pattern matches the non-streaming and block-streaming - paths, and any quantization-specific fuse rule the builder configures - is honored here as well. + """Load the non-block weights onto *device* and fuse LoRAs -- both paths. + Reads through the loader with ``model_sd_ops`` restricted to the + non-block keys, so ``sd_ops`` (incl. kv-ops such as Gemma's ``lm_head`` + duplication) is applied exactly once and block tensors are never read. """ - non_block_sd: dict[str, torch.Tensor] = {} - for sft_key, model_key in non_block_keys: - tensor = reader.get_tensor(sft_key).to(device=device, dtype=dtype) - if sd_ops is not None: - for kv in sd_ops.apply_to_key_value(model_key, tensor): - non_block_sd[kv.new_key] = kv.new_value - else: - non_block_sd[model_key] = tensor + non_block_sd_ops = self._filtered_sd_ops("non_block", frozenset(mk for _sft_key, mk in non_block_keys)) + loaded = load_state_dict(self.model_path, self.model_loader, self.registry, device, non_block_sd_ops) + non_block_sd = {key: tensor.to(dtype=dtype) for key, tensor in loaded.sd.items()} - if lora_sources: - lora_sd_and_strengths = [src.as_state_dict_with_strength() for src in lora_sources] + if lora_sd_and_strengths: non_block_state = StateDict(sd=non_block_sd, device=device, size=0, dtype={dtype}) for key, fused in fuse_lora_weights( non_block_state, lora_sd_and_strengths, - fuse_rule=fuse_rule, + fuse_rule=self.fuse_rule, preserve_input_device=True, ): non_block_sd[key] = fused @@ -321,6 +415,35 @@ class StreamingModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType]) model.load_state_dict(non_block_sd, strict=False, assign=True) +def _block_state(block: nn.Module) -> dict[str, torch.Tensor]: + """Streamed-eligible tensors of a block: parameters then buffers. + Block streaming swaps both params and checkpoint-backed buffers (e.g. Gemma4's + per-layer ``layer_scalar``), so the layout, pinned packing, and meta-ordering + all consult parameters and buffers together. Non-checkpoint (computed) buffers + are harmless here -- only keys present in ``block_key_map`` are ever streamed. + """ + return {**dict(block.named_parameters()), **dict(block.named_buffers())} + + +def _block_layouts( + blocks: nn.ModuleList, + block_key_map: dict[int, list[tuple[str, str]]], + dtype: torch.dtype, +) -> dict[int, TensorLayout]: + """Per-block layout of the streamed tensors, taken from the meta model. + Blocks may differ in shape and even in which tensors they have (e.g. Gemma4's + full-attention layers drop ``v_proj``), so each block gets its own layout in + ``block_key_map`` order. The pinned packing, the disk reader, and the GPU carve + all key off this same per-block layout, so the provider's contiguous H2D copy + is valid for any entry order (no cross-block ordering required). + """ + layouts: dict[int, TensorLayout] = {} + for idx, entries in block_key_map.items(): + state = _block_state(blocks[idx]) + layouts[idx] = derive_layout({param_name: state[param_name] for _sft_key, param_name in entries}, dtype) + return layouts + + def _scan_checkpoint_keys( checkpoint_paths: list[str], sd_ops: SDOps | None, diff --git a/packages/ltx-core/src/ltx_core/block_streaming/disk.py b/packages/ltx-core/src/ltx_core/block_streaming/disk.py index 16e15d5..aca7f4a 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/disk.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/disk.py @@ -128,8 +128,8 @@ class LoraSource: def as_state_dict_with_strength(self) -> LoraStateDictWithStrength: """Return a :class:`LoraStateDictWithStrength` view of the pinned A/B factors. - Lets :func:`fuse_lora_weights` consume disk-streaming LoRAs without - re-reading the safetensors file or re-applying ``sd_ops``. + Lets non-block fusion consume the already-loaded disk-streaming LoRA + without re-reading the safetensors file or re-applying ``sd_ops``. """ sd: dict[str, torch.Tensor] = {} for prefix, (a, b) in self._pinned_ab.items(): diff --git a/packages/ltx-core/src/ltx_core/block_streaming/pool.py b/packages/ltx-core/src/ltx_core/block_streaming/pool.py index 8dac072..e883207 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/pool.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/pool.py @@ -1,4 +1,4 @@ -"""Weight buffer pool for block streaming.""" +"""Raw buffer pool for block streaming.""" from __future__ import annotations @@ -7,69 +7,64 @@ from typing import Callable import torch -from ltx_core.block_streaming.utils import allocate_layout_views -from ltx_core.loader.primitives import TensorLayout +from ltx_core.block_streaming import utils -class WeightPool: - """Fixed pool of pre-allocated weight buffers with event-based reuse. - All slots share a single buffer (CPU or GPU); each slot is a - contiguous slice carved out of it via :func:`allocate_layout_views`. +class BufferPool: + """Fixed pool of pre-allocated raw buffer slots with event-based reuse. + Slots are carved from a single contiguous ``uint8`` buffer; each is + ``slot_nbytes`` long and handed out as a raw 1-D ``uint8`` tensor. Args: - buffer_layout: ``{name: (shape, dtype)}`` for each buffer. - capacity: Number of buffers to pre-allocate. + slot_nbytes: Byte size of each slot. + capacity: Number of slots to pre-allocate. device: Device for allocation. - reuse_barrier: Called with the pending event before a buffer is reused. + reuse_barrier: Called with the pending event before a slot is reused. pin_memory: Pin buffers (for async H2D copies from CPU). """ def __init__( self, - buffer_layout: TensorLayout, + slot_nbytes: int, capacity: int, device: torch.device, reuse_barrier: Callable[[torch.cuda.Event], None], pin_memory: bool = False, ) -> None: - self._buffer_layout = buffer_layout + self._slot_nbytes = slot_nbytes self._capacity = capacity - self._free: deque[dict[str, torch.Tensor]] = deque() + self._free: deque[torch.Tensor] = deque() self._events: dict[int, torch.cuda.Event] = {} self._reuse_barrier = reuse_barrier - memory_layout = { - _make_key(slot, name): (shape, dtype) - for slot in range(capacity) - for name, (shape, dtype) in buffer_layout.items() - } - all_views = allocate_layout_views(memory_layout, device=device, pin_memory=pin_memory) + buffer = utils.alloc_buffer(max(slot_nbytes * capacity, 1), device, pin_memory) for slot in range(capacity): - self._free.append({name: all_views[_make_key(slot, name)] for name in buffer_layout}) + self._free.append(buffer[slot * slot_nbytes : (slot + 1) * slot_nbytes]) @property def capacity(self) -> int: return self._capacity @property - def buffer_layout(self) -> TensorLayout: - return self._buffer_layout + def slot_nbytes(self) -> int: + return self._slot_nbytes - def acquire(self) -> dict[str, torch.Tensor]: - """Take a free buffer, waiting any pending event before returning.""" - weights = self._free.popleft() - event = self._events.pop(id(weights), None) + def acquire(self) -> torch.Tensor: + """Take a free raw slot, waiting any pending event before returning. + Raises :class:`RuntimeError` if every slot is currently in use. + """ + if not self._free: + raise RuntimeError(f"BufferPool exhausted: all {self._capacity} buffers are in use") + buffer = self._free.popleft() + event = self._events.pop(id(buffer), None) if event is not None: self._reuse_barrier(event) - return weights + return buffer - def release(self, weights: dict[str, torch.Tensor], event: torch.cuda.Event | None = None) -> None: - """Return a buffer to the free list. - If *event* is given it is waited on the next :meth:`acquire` - of this buffer, ensuring the prior operation has completed. + def release(self, buffer: torch.Tensor, event: torch.cuda.Event | None = None) -> None: + """Return a raw slot to the free list. + The *buffer* must be the exact tensor object returned by :meth:`acquire` + (reuse is keyed on its identity). If *event* is given it is waited on the + next :meth:`acquire` of this slot, ensuring the prior operation finished. """ if event is not None: - self._events[id(weights)] = event - self._free.append(weights) - - -def _make_key(slot: int, name: str) -> str: - return f"{slot}/{name}" + self._events[id(buffer)] = event + self._free.append(buffer) diff --git a/packages/ltx-core/src/ltx_core/block_streaming/provider.py b/packages/ltx-core/src/ltx_core/block_streaming/provider.py index fb79e39..879cc56 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/provider.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/provider.py @@ -3,37 +3,28 @@ from __future__ import annotations from collections import OrderedDict +from typing import NamedTuple import torch from ltx_core.block_streaming.disk import LoraSource -from ltx_core.block_streaming.pool import WeightPool +from ltx_core.block_streaming.pool import BufferPool from ltx_core.block_streaming.source import WeightSource +from ltx_core.block_streaming.utils import carve_buffer, layout_nbytes from ltx_core.loader.fuse_loras import FuseRule, aggregate_lora_products, bf16_fuse_rule from ltx_core.loader.primitives import StateDict _EMPTY_STATE_DICT = StateDict(sd={}, device=torch.device("cpu"), size=0, dtype=set()) -def _contiguous_byte_view(weights: dict[str, torch.Tensor]) -> torch.Tensor | None: - """Return a ``uint8`` view spanning every tensor in *weights*, or ``None`` if - they don't share one contiguous storage region.""" - tensors = list(weights.values()) - if not tensors: - return None - storage = tensors[0].untyped_storage() - storage_ptr = storage.data_ptr() - start = end = tensors[0].storage_offset() * tensors[0].element_size() - for t in tensors: - if t.untyped_storage().data_ptr() != storage_ptr or not t.is_contiguous(): - return None - offset = t.storage_offset() * t.element_size() - nbytes = t.numel() * t.element_size() - start = min(start, offset) - end = max(end, offset + nbytes) - view = torch.empty(0, dtype=torch.uint8, device=tensors[0].device) - view.set_(storage, start, (end - start,), (1,)) - return view +class CachedBlock(NamedTuple): + """A cached GPU block: the raw pool slot plus the carved per-key views. + The raw slot is what is returned to the pool on eviction; the views are + what callers consume. + """ + + raw: torch.Tensor + views: dict[str, torch.Tensor] class WeightsProvider: @@ -51,7 +42,7 @@ class WeightsProvider: def __init__( self, - pool: WeightPool, + pool: BufferPool, copy_stream: torch.cuda.Stream, target_device: torch.device, source: WeightSource, @@ -61,7 +52,7 @@ class WeightsProvider: ) -> None: self._copy_stream = copy_stream self._pool = pool - self._cache: OrderedDict[int, dict[str, torch.Tensor]] = OrderedDict() + self._cache: OrderedDict[int, CachedBlock] = OrderedDict() self._events: dict[int, torch.cuda.Event] = {} self._target_device = target_device self._source = source @@ -72,40 +63,45 @@ class WeightsProvider: def get(self, idx: int) -> dict[str, torch.Tensor]: """Return GPU weights for block *idx*. Does H2D copy on miss.""" if idx in self._cache: - return self._cache[idx] + return self._cache[idx].views # Evict oldest GPU buffer if at capacity. if len(self._cache) >= self._pool.capacity: - evicted_idx, evicted_weights = self._cache.popitem(last=False) - self._pool.release(evicted_weights, event=self._events.pop(evicted_idx, None)) + evicted_idx, evicted = self._cache.popitem(last=False) + self._pool.release(evicted.raw, event=self._events.pop(evicted_idx, None)) - gpu_weights = self._pool.acquire() - cpu_weights = self._source.get(idx) + layout = self._source.block_layout(idx) + raw = self._pool.acquire() + gpu_weights = carve_buffer(raw, layout) + cpu_buffer = self._source.get(idx) - h2d_event = self._copy_to_gpu(idx, gpu_weights, cpu_weights) + h2d_event = self._copy_to_gpu(idx, raw, gpu_weights, cpu_buffer, layout_nbytes(layout)) self._source.release(idx, event=h2d_event) - self._cache[idx] = gpu_weights + self._cache[idx] = CachedBlock(raw, gpu_weights) return gpu_weights def _copy_to_gpu( self, idx: int, + raw: torch.Tensor, gpu_weights: dict[str, torch.Tensor], - cpu_weights: dict[str, torch.Tensor], + cpu_buffer: torch.Tensor, + nbytes: int, ) -> torch.cuda.Event: """Enqueue H2D copy + LoRA fusion on the copy stream and wait on compute. - The wait is intentionally inside this method so callers -- and - instrumentation regions wrapping it -- observe the full transfer time. + *cpu_buffer* is one contiguous source buffer carved by the same layout as + *raw*, so a single byte copy of its leading *nbytes* reproduces every view + in *gpu_weights*. The wait is intentionally inside this method so callers -- + and instrumentation regions wrapping it -- observe the full transfer time. """ + if not cpu_buffer.is_contiguous() or cpu_buffer.dtype != torch.uint8 or cpu_buffer.numel() < nbytes: + raise ValueError( + f"source buffer for block {idx} must be a contiguous uint8 buffer of >= {nbytes} bytes, " + f"got {cpu_buffer.dim()}-D {cpu_buffer.dtype} with {cpu_buffer.numel()} elements" + ) with torch.cuda.stream(self._copy_stream): - gpu_view = _contiguous_byte_view(gpu_weights) - cpu_view = _contiguous_byte_view(cpu_weights) - if gpu_view is not None and cpu_view is not None and gpu_view.numel() == cpu_view.numel(): - gpu_view.copy_(cpu_view, non_blocking=True) - else: - for name, gpu_tensor in gpu_weights.items(): - gpu_tensor.copy_(cpu_weights[name], non_blocking=True) + raw[:nbytes].copy_(cpu_buffer[:nbytes], non_blocking=True) if self._lora_sources: self._fuse_block_loras(idx, gpu_weights) h2d_event = torch.cuda.Event() diff --git a/packages/ltx-core/src/ltx_core/block_streaming/source.py b/packages/ltx-core/src/ltx_core/block_streaming/source.py index 62d0d42..2d15693 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/source.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/source.py @@ -2,31 +2,38 @@ from __future__ import annotations -from collections import OrderedDict -from typing import Protocol +from typing import NamedTuple, Protocol import torch -from ltx_core.block_streaming.disk import DiskBlockReader -from ltx_core.block_streaming.pool import WeightPool +from ltx_core.block_streaming.block_fetcher import BlockFetcher, FetchHandle +from ltx_core.block_streaming.pool import BufferPool +from ltx_core.block_streaming.utils import carve_buffer, layout_nbytes from ltx_core.loader.primitives import TensorLayout class WeightSource(Protocol): """Provides pinned CPU weights for a given block index. - Assumes all buffers share an identical layout across all block indices. + Blocks may be heterogeneous: each has its own layout, so the source exposes a + per-block layout and the byte size of the largest block (which sizes the + pool slots -- a smaller block is carved into the front of a max-sized slot). + The source is the single source of truth for each block's layout. """ + def block_layout(self, idx: int) -> TensorLayout: + """Per-block buffer layout (shape + dtype for each param).""" + ... + @property - def block_layout(self) -> TensorLayout: - """Shared per-block buffer layout (shape + dtype for each param).""" + def slot_nbytes(self) -> int: + """Byte size of the largest block; sizes a pool slot (16-byte aligned).""" ... - def get(self, idx: int) -> dict[str, torch.Tensor]: - """Return CPU weights for block *idx*.""" + def get(self, idx: int) -> torch.Tensor: + """Return one contiguous CPU buffer for block *idx*.""" ... - def release(self, idx: int, event: torch.cuda.Event) -> None: + def release(self, idx: int, event: torch.cuda.Event | None) -> None: """Signal that an async operation using these weights is guarded by *event*.""" ... @@ -35,68 +42,133 @@ class WeightSource(Protocol): ... -class DiskWeightSource(WeightSource): - """Reads block weights from disk into pinned CPU buffers on demand.""" +class _Scheduled(NamedTuple): + """A scheduled (possibly in-flight) read: the raw pool slot and its fetch handle.""" + + raw: torch.Tensor + status: FetchHandle + + +class DiskWeightSource(WeightSource): + """WeightSource that streams blocks from disk via a :class:`BlockFetcher`. + ``get(idx)`` must be paired with a ``release(idx)`` before *idx* is fetched + again; getting a block that is already in flight raises. Each read acquires a + raw pool slot, carves it to block *idx*'s layout, and hands the carved views to + the fetcher to fill, so one max-sized slot can serve blocks of differing shapes. + ``get`` returns that same contiguous slot. + """ + + def __init__( + self, + pool: BufferPool, + fetcher: BlockFetcher, + block_layouts: dict[int, TensorLayout], + blocks_number: int, + prefetch_depth: int = 0, + ) -> None: + if blocks_number <= 0: + raise ValueError(f"blocks_number must be > 0, got {blocks_number}") + if prefetch_depth < 0: + raise ValueError(f"prefetch_depth must be >= 0, got {prefetch_depth}") + max_layout_nbytes = max((layout_nbytes(layout) for layout in block_layouts.values()), default=0) + if pool.slot_nbytes < max_layout_nbytes: + raise ValueError( + f"pool slot is too small for the largest block: slot {pool.slot_nbytes} bytes < {max_layout_nbytes}" + ) - def __init__(self, pool: WeightPool, reader: DiskBlockReader) -> None: self._pool = pool - self._cache: OrderedDict[int, dict[str, torch.Tensor]] = OrderedDict() - self._events: dict[int, torch.cuda.Event] = {} - self._reader = reader + self._blocks_number = blocks_number + self._prefetch_depth = prefetch_depth + self._fetcher = fetcher + self._block_layouts = block_layouts + self._scheduled: dict[int, _Scheduled] = {} + self._in_flight: dict[int, torch.Tensor] = {} + + def block_layout(self, idx: int) -> TensorLayout: + return self._block_layouts[idx] @property - def block_layout(self) -> TensorLayout: - return self._pool.buffer_layout + def slot_nbytes(self) -> int: + return self._pool.slot_nbytes - def get(self, idx: int) -> dict[str, torch.Tensor]: - """Return CPU weights for block *idx*. Reads from disk on miss.""" - if idx in self._cache: - return self._cache[idx] + def get(self, idx: int) -> torch.Tensor: + if idx in self._in_flight: + raise RuntimeError(f"Block {idx} is already in flight; release it before getting it again") - if len(self._cache) >= self._pool.capacity: - evicted_idx, evicted_weights = self._cache.popitem(last=False) - self._pool.release(evicted_weights, event=self._events.pop(evicted_idx, None)) + scheduled = self._scheduled.pop(idx, None) + if scheduled is None: + scheduled = self._schedule(idx) + error = scheduled.status.wait() + if error is not None: + self._pool.release(scheduled.raw) + raise error - weights = self._pool.acquire() - self._reader.read_into(weights, idx) - self._cache[idx] = weights - return weights + self._in_flight[idx] = scheduled.raw + for k in range(1, self._prefetch_depth + 1): + self._ensure_scheduled((idx + k) % self._blocks_number) + return scheduled.raw - def release(self, idx: int, event: torch.cuda.Event) -> None: - """Attach an H2D event -- waited before this buffer is recycled.""" - self._events[idx] = event + def release(self, idx: int, event: torch.cuda.Event | None) -> None: + raw_buffer = self._in_flight.pop(idx) + self._pool.release(raw_buffer, event=event) def cleanup(self) -> None: - """Clear cache and close the disk reader.""" - self._cache.clear() - self._events.clear() - self._reader.cleanup() + self._fetcher.cleanup() + while self._in_flight: + _, raw_buffer = self._in_flight.popitem() + self._pool.release(raw_buffer) + while self._scheduled: + _, scheduled = self._scheduled.popitem() + scheduled.status.wait() + self._pool.release(scheduled.raw) - def __len__(self) -> int: - return len(self._cache) + def _ensure_scheduled(self, idx: int) -> None: + """Schedule a read for *idx* if one is not already pending.""" + if idx not in self._scheduled: + self._scheduled[idx] = self._schedule(idx) + + def _schedule(self, idx: int) -> _Scheduled: + """Acquire a raw slot, carve it to block *idx*, enqueue a read, return the handle. + The raw slot and its fetch status are returned together so the caller can + track both as one unit; the fetcher only receives the carved views to fill. + """ + raw_buffer = self._pool.acquire() + carved = carve_buffer(raw_buffer, self._block_layouts[idx]) + status = self._fetcher.submit(idx, carved) + return _Scheduled(raw_buffer, status) + + +class PinnedBlock(NamedTuple): + """A pre-loaded pinned block: its single contiguous buffer and the layout to carve it with.""" + + buffer: torch.Tensor + layout: TensorLayout class PinnedWeightSource(WeightSource): - """Pre-loaded pinned CPU weights.""" + """Pre-loaded pinned CPU weights, one contiguous (possibly heterogeneous) buffer per block.""" - def __init__(self, weights: dict[int, dict[str, torch.Tensor]]) -> None: - if not weights: + def __init__(self, blocks: dict[int, PinnedBlock]) -> None: + if not blocks: raise ValueError("PinnedWeightSource requires at least one block") - self._weights = weights + self._blocks = blocks + self._slot_nbytes = max(layout_nbytes(block.layout) for block in blocks.values()) + + def block_layout(self, idx: int) -> TensorLayout: + return self._blocks[idx].layout @property - def block_layout(self) -> TensorLayout: - first_block = self._weights[min(self._weights)] - return {name: (t.shape, t.dtype) for name, t in first_block.items()} + def slot_nbytes(self) -> int: + return self._slot_nbytes - def get(self, idx: int) -> dict[str, torch.Tensor]: - return self._weights[idx] + def get(self, idx: int) -> torch.Tensor: + return self._blocks[idx].buffer - def release(self, idx: int, event: torch.cuda.Event) -> None: + def release(self, idx: int, event: torch.cuda.Event | None) -> None: pass def cleanup(self) -> None: - self._weights.clear() + self._blocks.clear() def __len__(self) -> int: - return len(self._weights) + return len(self._blocks) diff --git a/packages/ltx-core/src/ltx_core/block_streaming/utils.py b/packages/ltx-core/src/ltx_core/block_streaming/utils.py index c99cc6d..6e7289e 100644 --- a/packages/ltx-core/src/ltx_core/block_streaming/utils.py +++ b/packages/ltx-core/src/ltx_core/block_streaming/utils.py @@ -5,7 +5,7 @@ from __future__ import annotations import math import weakref from dataclasses import dataclass -from typing import Any +from typing import Any, NamedTuple import torch from torch import nn @@ -84,7 +84,7 @@ def _alloc_pinned_exact(nbytes: int) -> torch.Tensor | None: return buf -def _alloc_buffer(nbytes: int, device: torch.device | None, pin_memory: bool) -> torch.Tensor: +def alloc_buffer(nbytes: int, device: torch.device | None, pin_memory: bool) -> torch.Tensor: """Allocate one ``uint8`` buffer for :func:`allocate_layout_views`. For pinned host buffers, prefer ``cudaHostRegister`` to dodge the caching allocator's power-of-2 rounding. Falls back to the caching allocator if @@ -112,6 +112,47 @@ class _TensorSlice: return math.prod(self.shape) * self.dtype.itemsize +class LayoutSlices(NamedTuple): + """Per-key tensor slices of a layout plus the total aligned buffer size.""" + + slices: dict[str, _TensorSlice] + nbytes: int + + +def _layout_slices(layout: TensorLayout) -> LayoutSlices: + """Compute the byte offset of each key in *layout* and the total aligned size. + The size is at least one byte so empty layouts still produce a valid buffer. + """ + slices: dict[str, _TensorSlice] = {} + cursor = 0 + for key, (shape, dtype) in layout.items(): + cursor = _align_up(cursor, _BUFFER_ALIGN) + slices[key] = _TensorSlice(offset=cursor, shape=shape, dtype=dtype) + cursor += slices[key].size() + return LayoutSlices(slices, max(_align_up(cursor, _BUFFER_ALIGN), 1)) + + +def layout_nbytes(layout: TensorLayout) -> int: + """Byte size of one contiguous, 16-byte-aligned buffer holding *layout* (>= 1).""" + return _layout_slices(layout).nbytes + + +def carve_buffer(buffer: torch.Tensor, layout: TensorLayout) -> dict[str, torch.Tensor]: + """Carve per-key tensor views for *layout* into the front of *buffer*. + *buffer* is a 1-D ``uint8`` tensor at least :func:`layout_nbytes` long. Each + returned tensor is a non-overlapping slice of its leading bytes reinterpreted + at the requested shape and dtype; any trailing bytes are left unused. That + slack is what lets one max-sized pool slot hold a smaller (heterogeneous) + block. The views keep *buffer*'s storage alive via PyTorch refcounting. + """ + if buffer.dtype != torch.uint8 or buffer.dim() != 1: + raise ValueError(f"carve_buffer expects a 1-D uint8 buffer, got {buffer.dim()}-D {buffer.dtype}") + slices, nbytes = _layout_slices(layout) + if buffer.numel() < nbytes: + raise ValueError(f"buffer too small to carve layout: need {nbytes} bytes, got {buffer.numel()}") + return {key: buffer[s.offset : s.offset + s.size()].view(s.dtype).view(s.shape) for key, s in slices.items()} + + def allocate_layout_views( layout: TensorLayout, device: torch.device | None = None, @@ -123,12 +164,5 @@ def allocate_layout_views( requested shape and dtype. The views keep the underlying storage alive via PyTorch refcounting — drop them all to release the memory. """ - slices: dict[str, _TensorSlice] = {} - cursor = 0 - for key, (shape, dtype) in layout.items(): - cursor = _align_up(cursor, _BUFFER_ALIGN) - slices[key] = _TensorSlice(offset=cursor, shape=shape, dtype=dtype) - cursor += slices[key].size() - # Allocate at least one byte so empty layouts still produce a valid buffer. - buffer = _alloc_buffer(max(_align_up(cursor, _BUFFER_ALIGN), 1), device, pin_memory) - return {key: buffer[s.offset : s.offset + s.size()].view(s.dtype).view(s.shape) for key, s in slices.items()} + buffer = alloc_buffer(layout_nbytes(layout), device, pin_memory) + return carve_buffer(buffer, layout) diff --git a/packages/ltx-core/src/ltx_core/components/guiders.py b/packages/ltx-core/src/ltx_core/components/guiders.py index ee68eab..af09df6 100644 --- a/packages/ltx-core/src/ltx_core/components/guiders.py +++ b/packages/ltx-core/src/ltx_core/components/guiders.py @@ -253,6 +253,11 @@ class MultiModalGuider: and as scale * (cond - uncond) for stg, steering the denoising process away from the unconditioned prediction. """ + dtype = cond.dtype + cond = cond.float() + uncond_text = uncond_text.float() if isinstance(uncond_text, torch.Tensor) else uncond_text + uncond_perturbed = uncond_perturbed.float() if isinstance(uncond_perturbed, torch.Tensor) else uncond_perturbed + uncond_modality = uncond_modality.float() if isinstance(uncond_modality, torch.Tensor) else uncond_modality pred = ( cond + (self.params.cfg_scale - 1) * (cond - uncond_text) @@ -265,7 +270,7 @@ class MultiModalGuider: factor = self.params.rescale_scale * factor + (1 - self.params.rescale_scale) pred = pred * factor - return pred + return pred.to(dtype) def do_unconditional_generation(self) -> bool: """Returns True if the guider is doing unconditional generation.""" diff --git a/packages/ltx-core/src/ltx_core/components/noisers.py b/packages/ltx-core/src/ltx_core/components/noisers.py index 2db66d8..39122b0 100644 --- a/packages/ltx-core/src/ltx_core/components/noisers.py +++ b/packages/ltx-core/src/ltx_core/components/noisers.py @@ -17,18 +17,20 @@ class GaussianNoiser(Noiser): def __init__(self, generator: torch.Generator): super().__init__() - self.generator = generator - def __call__(self, latent_state: LatentState, noise_scale: float = 1.0) -> LatentState: - noise = torch.randn( + def _sample_noise(self, latent_state: LatentState) -> torch.Tensor: + return torch.randn( *latent_state.latent.shape, device=latent_state.latent.device, dtype=latent_state.latent.dtype, generator=self.generator, ) - scaled_mask = latent_state.denoise_mask * noise_scale - latent = noise * scaled_mask + latent_state.latent * (1 - scaled_mask) + + def __call__(self, latent_state: LatentState, noise_scale: float = 1.0) -> LatentState: + noise = self._sample_noise(latent_state) + latent = torch.lerp(latent_state.latent.float(), noise.float(), noise_scale) + latent = torch.lerp(latent_state.clean_latent.float(), latent, latent_state.denoise_mask) return replace( latent_state, latent=latent.to(latent_state.latent.dtype), diff --git a/packages/ltx-core/src/ltx_core/conditioning/__init__.py b/packages/ltx-core/src/ltx_core/conditioning/__init__.py index 58b82d0..8d3eb9f 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/__init__.py +++ b/packages/ltx-core/src/ltx_core/conditioning/__init__.py @@ -7,6 +7,7 @@ from ltx_core.conditioning.types import ( ConditioningItemAttentionStrengthWrapper, VideoConditionByKeyframeIndex, VideoConditionByLatentIndex, + VideoConditionByMask, VideoConditionByReferenceLatent, ) @@ -17,5 +18,6 @@ __all__ = [ "ConditioningItemAttentionStrengthWrapper", "VideoConditionByKeyframeIndex", "VideoConditionByLatentIndex", + "VideoConditionByMask", "VideoConditionByReferenceLatent", ] diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py b/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py index 77cfd0b..bd8d962 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py @@ -3,6 +3,7 @@ from ltx_core.conditioning.types.attention_strength_wrapper import ConditioningItemAttentionStrengthWrapper from ltx_core.conditioning.types.keyframe_cond import VideoConditionByKeyframeIndex from ltx_core.conditioning.types.latent_cond import VideoConditionByLatentIndex +from ltx_core.conditioning.types.mask_cond import VideoConditionByMask from ltx_core.conditioning.types.reference_audio_cond import AudioConditionByReferenceLatent from ltx_core.conditioning.types.reference_video_cond import VideoConditionByReferenceLatent @@ -11,5 +12,6 @@ __all__ = [ "ConditioningItemAttentionStrengthWrapper", "VideoConditionByKeyframeIndex", "VideoConditionByLatentIndex", + "VideoConditionByMask", "VideoConditionByReferenceLatent", ] diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/keyframe_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/keyframe_cond.py index df75fd8..ba009cd 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/keyframe_cond.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/keyframe_cond.py @@ -10,8 +10,9 @@ from ltx_core.types import LatentState, VideoLatentShape class VideoConditionByKeyframeIndex(ConditioningItem): """ Conditions video generation on keyframe latents at a specific frame index. - Appends keyframe tokens to the latent state with positions offset by frame_idx, - and sets denoise strength according to the strength parameter. + Appends keyframe tokens to the sequence with positions offset by frame_idx: the keyframe + latents become clean-latent tokens (placeholder zeros in the noisy latent) and the denoise + mask is set from the strength parameter. To add attention masking, wrap with :class:`ConditioningItemAttentionStrengthWrapper`. Args: keyframes: Keyframe latents [B, C, F, H, W]. @@ -75,7 +76,7 @@ class VideoConditionByKeyframeIndex(ConditioningItem): ) return LatentState( - latent=torch.cat([latent_state.latent, tokens], dim=1), + latent=torch.cat([latent_state.latent, torch.zeros_like(tokens)], dim=1), denoise_mask=torch.cat([latent_state.denoise_mask, denoise_mask], dim=1), positions=torch.cat([latent_state.positions, positions], dim=2), clean_latent=torch.cat([latent_state.clean_latent, tokens], dim=1), diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/latent_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/latent_cond.py index c036273..00c1816 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/latent_cond.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/latent_cond.py @@ -9,8 +9,8 @@ from ltx_core.types import LatentState class VideoConditionByLatentIndex(ConditioningItem): """ Conditions video generation by injecting latents at a specific latent frame index. - Replaces tokens in the latent state at positions corresponding to latent_idx, - and sets denoise strength according to the strength parameter. + Sets the clean latents at positions corresponding to latent_idx to the injected latents, + sets denoise strength according to the strength parameter. """ def __init__(self, latent: torch.Tensor, strength: float, latent_idx: int): @@ -37,7 +37,6 @@ class VideoConditionByLatentIndex(ConditioningItem): latent_state = latent_state.clone() - latent_state.latent[:, start_token:stop_token] = tokens latent_state.clean_latent[:, start_token:stop_token] = tokens latent_state.denoise_mask[:, start_token:stop_token] = 1.0 - self.strength diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/mask_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/mask_cond.py new file mode 100644 index 0000000..0464198 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/conditioning/types/mask_cond.py @@ -0,0 +1,49 @@ +"""Mask-based conditioning for inpainting and spatial conditioning.""" + +from dataclasses import replace + +import torch + +from ltx_core.conditioning.item import ConditioningItem +from ltx_core.tools import LatentTools +from ltx_core.types import LatentState + + +class VideoConditionByMask(ConditioningItem): + """Condition video generation using a binary mask over latent frames. + Masked positions (mask=1) receive the provided clean latent values and are + excluded from denoising (denoise_mask set to ``1 - strength``). Unmasked + positions (mask=0) are left unchanged and denoised normally. + The mask operates in **unpatchified latent** space — it should have shape + ``[B, F, H, W]`` matching the latent dimensions (after VAE encoding, + before patchification). This is consistent with the latent input format + used by all other conditioning items. + Args: + latent: Clean conditioning latents in unpatchified format [B, C, F, H, W]. + Must match the target shape of the latent tools. + mask: Binary mask [B, F, H, W] in unpatchified latent space. + 1 = conditioning position (clean, excluded from denoising), + 0 = generated position (noised, denoised normally). + strength: Conditioning strength for masked positions. 1.0 = fully clean + (no denoising), 0.0 = no conditioning effect. Default 1.0. + """ + + def __init__(self, latent: torch.Tensor, mask: torch.Tensor, strength: float = 1.0): + self.latent = latent + self.mask = mask + self.strength = strength + + def apply_to(self, latent_state: LatentState, latent_tools: LatentTools) -> LatentState: + """Apply mask-based conditioning to the latent state.""" + tokens = latent_tools.patchifier.patchify(self.latent) + + mask = latent_tools.patchifier.patchify(self.mask.unsqueeze(1)) + + m = mask.to(dtype=latent_state.latent.dtype) + inv = 1 - m + + return replace( + latent_state, + clean_latent=latent_state.clean_latent * inv + tokens * m, + denoise_mask=latent_state.denoise_mask * inv + (1.0 - self.strength) * m, + ) diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/reference_audio_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/reference_audio_cond.py index a2d0c42..6c63db5 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/reference_audio_cond.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/reference_audio_cond.py @@ -51,7 +51,7 @@ class AudioConditionByReferenceLatent: ) return LatentState( - latent=torch.cat([latent_state.latent, tokens], dim=1), + latent=torch.cat([latent_state.latent, torch.zeros_like(tokens)], dim=1), denoise_mask=torch.cat([latent_state.denoise_mask, denoise_mask], dim=1), positions=torch.cat([latent_state.positions, self.positions], dim=2), clean_latent=torch.cat([latent_state.clean_latent, tokens], dim=1), diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py index 9b4068e..51e3463 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py @@ -14,7 +14,8 @@ class VideoConditionByReferenceLatent(ConditioningItem): Conditions video generation on a reference video latent for IC-LoRA inference. IC-LoRAs are trained by concatenating reference (control signal) and target tokens, learning to attend across both. This class replicates that setup at inference by - appending reference tokens to the latent sequence. + appending the reference tokens to the sequence as clean latents (with placeholder zeros + in the noisy latent). IC-LoRAs can be trained with lower-resolution references than the target (e.g., 384px reference for 768px output) for efficiency and better generalization. The `downscale_factor` scales reference positions to match target coordinates, preserving @@ -22,9 +23,9 @@ class VideoConditionByReferenceLatent(ConditioningItem): (stored in LoRA metadata). To add attention masking, wrap with :class:`ConditioningItemAttentionStrengthWrapper`. Args: - latent: Reference video latents [B, C, F, H, W] - downscale_factor: Target/reference resolution ratio (e.g., 2 = half-resolution - reference). Spatial positions are scaled by this factor. + latent: Reference video latents [B, C, F, H, W]. + downscale_factor: Target/reference spatial ratio (e.g. 2 = half-res ref). + temporal_scale_factor: Target/reference temporal ratio S (e.g. 4 = ref at 1/4 fps). strength: Conditioning strength. 1.0 = full (reference kept clean), 0.0 = none (reference denoised). Default 1.0. """ @@ -33,10 +34,12 @@ class VideoConditionByReferenceLatent(ConditioningItem): self, latent: torch.Tensor, downscale_factor: int = 1, + temporal_scale_factor: int = 1, strength: float = 1.0, ): self.latent = latent self.downscale_factor = downscale_factor + self.temporal_scale_factor = temporal_scale_factor self.strength = strength def apply_to( @@ -44,10 +47,9 @@ class VideoConditionByReferenceLatent(ConditioningItem): latent_state: LatentState, latent_tools: VideoLatentTools, ) -> LatentState: - """Append reference video tokens with scaled positions.""" + """Append reference video tokens with positions translated into the target frame.""" tokens = latent_tools.patchifier.patchify(self.latent) - # Compute positions for the reference video's actual dimensions latent_coords = latent_tools.patchifier.get_patch_grid_bounds( output_shape=VideoLatentShape.from_torch_shape(self.latent.shape), device=self.latent.device, @@ -58,9 +60,18 @@ class VideoConditionByReferenceLatent(ConditioningItem): causal_fix=latent_tools.causal_fix, ) positions = positions.to(dtype=torch.float32) - positions[:, 0, ...] /= latent_tools.fps - # Scale spatial positions to match target coordinate space + # Place ref tokens on their own time spacing (= target_fps / S). + positions[:, 0, ...] /= latent_tools.fps / self.temporal_scale_factor + + # Translate into the target's frame so ref's last patch ends with target's last + # patch; clamp the causal patch's negative start back to [0, 1/target_fps). + if self.temporal_scale_factor != 1: + t_target = latent_state.positions[:, 0, 0:1, 1:2].to(dtype=torch.float32) # = 1/target_fps + positions[:, 0, ...] = torch.clamp( + positions[:, 0, ...] - (self.temporal_scale_factor - 1) * t_target, + min=0, + ) if self.downscale_factor != 1: positions[:, 1, ...] *= self.downscale_factor # height axis positions[:, 2, ...] *= self.downscale_factor # width axis @@ -83,7 +94,7 @@ class VideoConditionByReferenceLatent(ConditioningItem): ) return LatentState( - latent=torch.cat([latent_state.latent, tokens], dim=1), + latent=torch.cat([latent_state.latent, torch.zeros_like(tokens)], dim=1), denoise_mask=torch.cat([latent_state.denoise_mask, denoise_mask], dim=1), positions=torch.cat([latent_state.positions, positions], dim=2), clean_latent=torch.cat([latent_state.clean_latent, tokens], dim=1), diff --git a/packages/ltx-core/src/ltx_core/loader/primitives.py b/packages/ltx-core/src/ltx_core/loader/primitives.py index fb490fd..2cef56b 100644 --- a/packages/ltx-core/src/ltx_core/loader/primitives.py +++ b/packages/ltx-core/src/ltx_core/loader/primitives.py @@ -1,18 +1,21 @@ from __future__ import annotations from dataclasses import dataclass -from typing import TYPE_CHECKING, NamedTuple, Protocol +from typing import TYPE_CHECKING, Any, NamedTuple, Protocol, TypeVar import torch from ltx_core.loader.module_ops import ModuleOps from ltx_core.loader.sd_ops import SDOps -from ltx_core.model.model_protocol import ModelType if TYPE_CHECKING: + from typing_extensions import Self + from ltx_core.loader.fuse_loras import FuseRule from ltx_core.loader.registry import Registry +BuiltType = TypeVar("BuiltType", covariant=True) # noqa: PLC0105 + # Per-key shape and dtype description for a flat collection of tensors. TensorLayout = dict[str, tuple[torch.Size, torch.dtype]] @@ -50,74 +53,75 @@ class StateDictLoader(Protocol): """ Load metadata from path """ + ... def load(self, path: str | list[str], sd_ops: SDOps | None = None, device: torch.device | None = None) -> StateDict: """ Load state dict from path or paths (for sharded model storage) and apply sd_ops """ + ... -class BuilderProtocol(Protocol[ModelType]): +class BuilderProtocol(Protocol[BuiltType]): """Protocol for model builders that produce a model via ``build()``.""" def build( - self, device: torch.device | None = None, dtype: torch.dtype | None = None, **kwargs: object - ) -> ModelType: ... + self, + device: torch.device | None = None, + dtype: torch.dtype | None = None, + **kwargs: Any, # noqa: ANN401 + ) -> BuiltType: ... + @property + def registry(self) -> "Registry": ... -class ModelBuilderProtocol(BuilderProtocol[ModelType], Protocol[ModelType]): - """ - Protocol for building PyTorch models from configuration dictionaries. - Implementations must provide: - - meta_model: Create a model from configuration dictionary and apply module operations - - build: Create and initialize a model from state dictionary and apply dtype transformations - """ - - model_sd_ops: SDOps | None - module_ops: tuple[ModuleOps, ...] - loras: tuple["LoraPathStrengthAndSDOps", ...] - registry: "Registry" - - def meta_model(self, config: dict, module_ops: list[ModuleOps] | None = None) -> ModelType: - """ - Create a model on the meta device from a configuration dictionary. - This decouples model creation from weight loading, allowing the model - architecture to be instantiated without allocating memory for parameters. - Args: - config: Model configuration dictionary. - module_ops: Optional list of module operations to apply (e.g., quantization). - Returns: - Model instance on meta device (no actual memory allocated for parameters). - """ - ... - - def with_sd_ops(self, sd_ops: SDOps | None) -> "ModelBuilderProtocol[ModelType]": - """Return a copy of this builder with the given state-dict key remapping ops.""" - ... - - def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> "ModelBuilderProtocol[ModelType]": - """Return a copy of this builder with the given module operations (e.g. quantization).""" - ... - - def with_loras(self, loras: tuple["LoraPathStrengthAndSDOps", ...]) -> "ModelBuilderProtocol[ModelType]": - """Return a copy of this builder with the given LoRAs to fuse at build time.""" - ... - - def with_registry(self, registry: "Registry") -> "ModelBuilderProtocol[ModelType]": + def with_registry(self, registry: "Registry") -> "Self": """Return a copy of this builder using the given weight registry for allocation.""" ... - def with_lora_load_device(self, device: torch.device) -> "ModelBuilderProtocol[ModelType]": + +class ModelBuilderProtocol(BuilderProtocol[BuiltType], Protocol[BuiltType]): + """ + Protocol for building PyTorch models from configuration dictionaries. + Implementations must provide: + - build: Create and initialize a model from state dictionary and apply dtype transformations + """ + + @property + def model_sd_ops(self) -> SDOps | None: ... + + @property + def module_ops(self) -> tuple[ModuleOps, ...]: ... + + @property + def loras(self) -> tuple["LoraPathStrengthAndSDOps", ...]: ... + + def with_sd_ops(self, sd_ops: SDOps | None) -> "Self": + """Return a copy of this builder with the given state-dict key remapping ops.""" + ... + + def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> "Self": + """Return a copy of this builder with the given module operations (e.g. quantization).""" + ... + + def with_loras(self, loras: tuple["LoraPathStrengthAndSDOps", ...]) -> "Self": + """Return a copy of this builder with the given LoRAs to fuse at build time.""" + ... + + def with_lora_load_device(self, device: torch.device) -> "Self": """Return a copy of this builder that loads LoRA weights onto the given device.""" ... - def with_fuse_rule(self, fuse_rule: "FuseRule") -> "ModelBuilderProtocol[ModelType]": + def with_fuse_rule(self, fuse_rule: "FuseRule") -> "Self": """Return a copy of this builder with the given LoRA fuse rule (e.g. from a quantization policy).""" ... def build( - self, device: torch.device | None = None, dtype: torch.dtype | None = None, **kwargs: object - ) -> ModelType: + self, + device: torch.device | None = None, + dtype: torch.dtype | None = None, + **kwargs: Any, # noqa: ANN401 + ) -> BuiltType: """ Build the model Args: @@ -140,8 +144,7 @@ class LoRAAdaptableProtocol(Protocol): - lora: Add a LoRA to the model """ - def lora(self, lora_path: str, strength: float) -> "LoRAAdaptableProtocol": - pass + def lora(self, lora_path: str, strength: float, sd_ops: SDOps) -> "LoRAAdaptableProtocol": ... class LoraPathStrengthAndSDOps(NamedTuple): diff --git a/packages/ltx-core/src/ltx_core/loader/sd_ops.py b/packages/ltx-core/src/ltx_core/loader/sd_ops.py index c61a794..a4b4297 100644 --- a/packages/ltx-core/src/ltx_core/loader/sd_ops.py +++ b/packages/ltx-core/src/ltx_core/loader/sd_ops.py @@ -24,6 +24,7 @@ class ContentMatching: prefix: str = "" suffix: str = "" + contains: str = "" class KeyValueOperationResult(NamedTuple): @@ -72,10 +73,10 @@ class SDOps: new_mapping = (*self.mapping, ContentReplacement(content, replacement)) return replace(self, mapping=new_mapping) - def with_matching(self, prefix: str = "", suffix: str = "") -> "SDOps": - """Create a new SDOps instance with the specified prefix and suffix matching added to the mapping.""" + def with_matching(self, prefix: str = "", suffix: str = "", contains: str = "") -> "SDOps": + """Create a new SDOps instance with the specified prefix, suffix and contains matching added to the mapping.""" - new_mapping = (*self.mapping, ContentMatching(prefix, suffix)) + new_mapping = (*self.mapping, ContentMatching(prefix, suffix, contains)) return replace(self, mapping=new_mapping) def with_additional_allowed_keys(self, keys: frozenset[str]) -> "SDOps": @@ -100,7 +101,12 @@ class SDOps: def apply_to_key(self, key: str) -> str | None: """Apply the mapping to the given name.""" matchers = [content for content in self.mapping if isinstance(content, ContentMatching)] - valid = any(key.startswith(f.prefix) and key.endswith(f.suffix) for f in matchers) + valid = any( + key.startswith(matcher.prefix) + and key.endswith(matcher.suffix) + and (not matcher.contains or matcher.contains in key) + for matcher in matchers + ) if not valid: return None diff --git a/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py b/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py index faf41dc..468bb45 100644 --- a/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py +++ b/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py @@ -1,6 +1,8 @@ +from __future__ import annotations + +import copy import logging -from dataclasses import dataclass, field, replace -from typing import Generic +from typing import TYPE_CHECKING, Final, Generic import torch from torch import nn @@ -21,6 +23,9 @@ from ltx_core.loader.sd_ops import SDOps from ltx_core.loader.sft_loader import SafetensorsModelStateDictLoader from ltx_core.model.model_protocol import ModelConfigurator, ModelType +if TYPE_CHECKING: + from typing_extensions import Self + logger: logging.Logger = logging.getLogger(__name__) @@ -78,10 +83,12 @@ def _load_model_weights( meta_model.load_state_dict(fused_sd, strict=False, assign=True) -@dataclass(frozen=True) class SingleGPUModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType], LoRAAdaptableProtocol): """ Builder for PyTorch models residing on a single GPU. + The builder is immutable: ``with_*``/``lora`` return modified copies. The + ``ModelBuilderProtocol`` surface is exposed via read-only properties backed + by private attributes. Attributes: model_class_configurator: Class responsible for constructing the model from a config dict. model_path: Path (or tuple of shard paths) to the model's `.safetensors` checkpoint(s). @@ -98,54 +105,106 @@ class SingleGPUModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType], fuse_rule: Per-policy LoRA merge rule. Defaults to ``bf16_fuse_rule``; """ - model_class_configurator: type[ModelConfigurator[ModelType]] - model_path: str | tuple[str, ...] - model_sd_ops: SDOps | None = None - module_ops: tuple[ModuleOps, ...] = field(default_factory=tuple) - loras: tuple[LoraPathStrengthAndSDOps, ...] = field(default_factory=tuple) - model_loader: StateDictLoader = field(default_factory=SafetensorsModelStateDictLoader) - registry: Registry = field(default_factory=DummyRegistry) - lora_load_device: torch.device = field(default_factory=lambda: torch.device("cpu")) - fuse_rule: FuseRule = bf16_fuse_rule + def __init__( + self, + model_class_configurator: type[ModelConfigurator[ModelType]], + model_path: str | tuple[str, ...], + model_sd_ops: SDOps | None = None, + module_ops: tuple[ModuleOps, ...] = (), + loras: tuple[LoraPathStrengthAndSDOps, ...] = (), + model_loader: StateDictLoader | None = None, + registry: Registry | None = None, + lora_load_device: torch.device | None = None, + fuse_rule: FuseRule = bf16_fuse_rule, + ) -> None: + # Read-only: typed with the covariant ModelType, so it must not be a mutable attribute. + self._model_class_configurator: Final = model_class_configurator + self._model_path = model_path + self._model_sd_ops = model_sd_ops + self._module_ops = module_ops + self._loras = loras + self._model_loader = model_loader if model_loader is not None else SafetensorsModelStateDictLoader() + self._registry = registry if registry is not None else DummyRegistry() + self._lora_load_device = lora_load_device if lora_load_device is not None else torch.device("cpu") + self._fuse_rule = fuse_rule - def lora(self, lora_path: str, strength: float, sd_ops: SDOps) -> "SingleGPUModelBuilder": - return replace(self, loras=(*self.loras, LoraPathStrengthAndSDOps(lora_path, strength, sd_ops))) + @property + def model_sd_ops(self) -> SDOps | None: + return self._model_sd_ops - def with_sd_ops(self, sd_ops: SDOps | None) -> "SingleGPUModelBuilder": - return replace(self, model_sd_ops=sd_ops) + @property + def module_ops(self) -> tuple[ModuleOps, ...]: + return self._module_ops - def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> "SingleGPUModelBuilder": - return replace(self, module_ops=module_ops) + @property + def loras(self) -> tuple[LoraPathStrengthAndSDOps, ...]: + return self._loras - def with_loras(self, loras: tuple[LoraPathStrengthAndSDOps, ...]) -> "SingleGPUModelBuilder": - return replace(self, loras=loras) + @property + def registry(self) -> Registry: + return self._registry - def with_registry(self, registry: Registry) -> "SingleGPUModelBuilder": - return replace(self, registry=registry) + @property + def model_path(self) -> str | tuple[str, ...]: + return self._model_path - def with_lora_load_device(self, device: torch.device) -> "SingleGPUModelBuilder": - return replace(self, lora_load_device=device) + @property + def model_loader(self) -> StateDictLoader: + return self._model_loader - def with_fuse_rule(self, fuse_rule: FuseRule) -> "SingleGPUModelBuilder": - return replace(self, fuse_rule=fuse_rule) + @property + def lora_load_device(self) -> torch.device: + return self._lora_load_device + + @property + def fuse_rule(self) -> FuseRule: + return self._fuse_rule + + def lora(self, lora_path: str, strength: float, sd_ops: SDOps) -> Self: + clone = copy.copy(self) + clone._loras = (*self._loras, LoraPathStrengthAndSDOps(lora_path, strength, sd_ops)) + return clone + + def with_sd_ops(self, sd_ops: SDOps | None) -> Self: + clone = copy.copy(self) + clone._model_sd_ops = sd_ops + return clone + + def with_module_ops(self, module_ops: tuple[ModuleOps, ...]) -> Self: + clone = copy.copy(self) + clone._module_ops = module_ops + return clone + + def with_loras(self, loras: tuple[LoraPathStrengthAndSDOps, ...]) -> Self: + clone = copy.copy(self) + clone._loras = loras + return clone + + def with_registry(self, registry: Registry) -> Self: + clone = copy.copy(self) + clone._registry = registry + return clone + + def with_lora_load_device(self, device: torch.device) -> Self: + clone = copy.copy(self) + clone._lora_load_device = device + return clone + + def with_fuse_rule(self, fuse_rule: FuseRule) -> Self: + clone = copy.copy(self) + clone._fuse_rule = fuse_rule + return clone def model_config(self) -> dict: - return read_model_config(self.model_path, self.model_loader) + return read_model_config(self._model_path, self._model_loader) def meta_model(self, config: dict, module_ops: tuple[ModuleOps, ...]) -> ModelType: - return create_meta_model(self.model_class_configurator, config, module_ops) + return create_meta_model(self._model_class_configurator, config, module_ops) def load_sd( self, paths: list[str], registry: Registry, device: torch.device | None, sd_ops: SDOps | None = None ) -> StateDict: - return load_state_dict(paths, self.model_loader, registry, device, sd_ops) - - def _return_model(self, meta_model: ModelType, device: torch.device) -> ModelType: - uninitialized = _check_uninitialized(meta_model) - if uninitialized: - logger.warning(f"Uninitialized parameters or buffers: {uninitialized}") - return meta_model - return meta_model.to(device) + return load_state_dict(paths, self._model_loader, registry, device, sd_ops) def build( self, @@ -155,18 +214,23 @@ class SingleGPUModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType], ) -> ModelType: device = torch.device("cuda") if device is None else device config = self.model_config() - meta_model = self.meta_model(config, self.module_ops) + meta_model = self.meta_model(config, self._module_ops) _load_model_weights( meta_model=meta_model, - model_path=self.model_path, - loras=self.loras, - loader=self.model_loader, - registry=self.registry, + model_path=self._model_path, + loras=self._loras, + loader=self._model_loader, + registry=self._registry, device=device, dtype=dtype, - model_sd_ops=self.model_sd_ops, - lora_load_device=self.lora_load_device, - fuse_rule=self.fuse_rule, + model_sd_ops=self._model_sd_ops, + lora_load_device=self._lora_load_device, + fuse_rule=self._fuse_rule, ) - return self._return_model(meta_model, device) + + uninitialized = _check_uninitialized(meta_model) + if uninitialized: + logger.warning(f"Uninitialized parameters or buffers: {uninitialized}") + return meta_model + return meta_model.to(device) diff --git a/packages/ltx-core/src/ltx_core/model/model_protocol.py b/packages/ltx-core/src/ltx_core/model/model_protocol.py index 37a07f7..f18e1c3 100644 --- a/packages/ltx-core/src/ltx_core/model/model_protocol.py +++ b/packages/ltx-core/src/ltx_core/model/model_protocol.py @@ -1,6 +1,14 @@ -from typing import Protocol, TypeVar +from __future__ import annotations -ModelType = TypeVar("ModelType") +from typing import TYPE_CHECKING, Protocol, TypeVar + +import torch + +if TYPE_CHECKING: + from ltx_core.guidance.perturbations import BatchedPerturbationConfig + from ltx_core.model.transformer.modality import Modality + +ModelType = TypeVar("ModelType", covariant=True, bound=torch.nn.Module) # noqa: PLC0105 class ModelConfigurator(Protocol[ModelType]): @@ -8,3 +16,24 @@ class ModelConfigurator(Protocol[ModelType]): @classmethod def from_config(cls, config: dict) -> ModelType: ... + + +class LTXModelProtocol(Protocol): + """Velocity-model forward interface shared by ``LTXModel`` and its multi-GPU wrappers. + ``forward`` pins the real signature (enforced structurally); ``__call__`` mirrors it + so protocol-typed values stay callable via ``model(...)``. + """ + + def forward( + self, + video: Modality | None, + audio: Modality | None, + perturbations: BatchedPerturbationConfig, + ) -> tuple[torch.Tensor | None, torch.Tensor | None]: ... + + def __call__( + self, + video: Modality | None, + audio: Modality | None, + perturbations: BatchedPerturbationConfig, + ) -> tuple[torch.Tensor | None, torch.Tensor | None]: ... diff --git a/packages/ltx-core/src/ltx_core/model/transformer/__init__.py b/packages/ltx-core/src/ltx_core/model/transformer/__init__.py index 43874b8..7d2936b 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/__init__.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/__init__.py @@ -3,13 +3,17 @@ from ltx_core.model.transformer.modality import Modality from ltx_core.model.transformer.model import LTXModel, X0Model from ltx_core.model.transformer.model_configurator import ( + LTXV_AUDIO_ONLY_MODEL_COMFY_RENAMING_MAP, LTXV_MODEL_COMFY_RENAMING_MAP, + LTXAudioOnlyModelConfigurator, LTXModelConfigurator, LTXVideoOnlyModelConfigurator, ) __all__ = [ + "LTXV_AUDIO_ONLY_MODEL_COMFY_RENAMING_MAP", "LTXV_MODEL_COMFY_RENAMING_MAP", + "LTXAudioOnlyModelConfigurator", "LTXModel", "LTXModelConfigurator", "LTXVideoOnlyModelConfigurator", diff --git a/packages/ltx-core/src/ltx_core/model/transformer/attention.py b/packages/ltx-core/src/ltx_core/model/transformer/attention.py index ec49640..8f774c0 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/attention.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/attention.py @@ -1,5 +1,4 @@ import functools -import logging from dataclasses import dataclass, field from enum import Enum from typing import Protocol @@ -15,8 +14,6 @@ from ltx_core.model.transformer.ops import ( ) from ltx_core.model.transformer.rope import LTXRopeType -logger = logging.getLogger(__name__) - def _torch_default_sdpa_priority() -> list[SDPBackend]: """Fetch torch's current default SDPA priority order at runtime. @@ -76,9 +73,9 @@ class PytorchAttention(AttentionCallable): @property def label(self) -> str: - """Human-readable identifier (used in the AUTOMATIC selection log). - Encodes the SDPA priority list so a single-backend pin reads differently - from the full-priority dispatcher walk.""" + """Human-readable identifier for this backend. Encodes the SDPA priority + list so a single-backend pin reads differently from the full-priority + dispatcher walk.""" return f"SDPA[{'>'.join(b.name for b in self._priority)}]" def __call__( @@ -199,10 +196,9 @@ class FlashAttention4(AttentionCallable): # --- Automatic selection ----------------------------------------------------- # AUTOMATIC inspects installed extras and the GPU arch and returns the fastest -# usable callable for each path. The selection runs once per process (cached) -# and logs the resulting label once. The unmasked and masked picks are -# independent: each calls its own helper and may end up on different backends -# (e.g. FA3 unmasked + xFormers masked on H100). +# usable callable for each path. The selection runs once per process (cached). +# The unmasked and masked picks are independent: each calls its own helper and +# may end up on different backends (e.g. FA3 unmasked + xFormers masked on H100). def _sdpa_can_use(backend: SDPBackend, *, with_mask: bool) -> bool: @@ -289,20 +285,23 @@ def _select_masked_attention() -> MaskedAttentionCallable: @functools.cache def automatic_attention() -> AttentionCallable: - """Cached AUTOMATIC pick for the unmasked path. Logs the chosen label once - per process.""" - fn = _select_primary_attention() - logger.info("Automatic attention selected: %s", fn.label) - return fn + """Cached AUTOMATIC pick for the unmasked path. + Cached so every ``AttentionOps`` in the process shares one instance.""" + return _select_primary_attention() @functools.cache def automatic_masked_attention() -> MaskedAttentionCallable: - """Cached AUTOMATIC pick for the masked path. Logs the chosen label once - per process.""" - fn = _select_masked_attention() - logger.info("Automatic masked attention selected: %s", fn.label) - return fn + """Cached AUTOMATIC pick for the masked path. See :func:`automatic_attention`.""" + return _select_masked_attention() + + +def attention_label(fn: AttentionCallable | MaskedAttentionCallable) -> str: + """Best-effort human-readable backend name. + Built-in callables expose ``.label`` (encoding the SDPA priority list for the + Pytorch backends); fall back to the class name for custom or wrapped callables + (e.g. the multi-GPU All2All wrappers) that don't define one.""" + return getattr(fn, "label", type(fn).__name__) def _resolve_sdpa_variant(backend: SDPBackend, name: str, *, with_mask: bool) -> PytorchAttention: @@ -342,8 +341,7 @@ class AttentionFunction(Enum): isn't usable on this machine -- missing package or SDPA backend rejected on this hardware (e.g. cuDNN under ``torch.use_deterministic_algorithms``). Opting in means "this kernel or fail loudly". ``AUTOMATIC`` returns the - cached :func:`automatic_attention` instance so the once-per-process log - fires only on the first resolution. + cached :func:`automatic_attention` instance so every build shares one callable. """ match self: case AttentionFunction.AUTOMATIC: diff --git a/packages/ltx-core/src/ltx_core/model/transformer/compiling.py b/packages/ltx-core/src/ltx_core/model/transformer/compiling.py index 0604c51..149b5c8 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/compiling.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/compiling.py @@ -11,7 +11,7 @@ from ltx_core.model.transformer.transformer_args import BlockPerturbationsProces # Defaults applied inside the patched forward. Overriding via CompilationConfig # replaces these wholesale; it does not merge. -_DEFAULT_INDUCTOR_CONFIG: dict[str, Any] = {"unsafe_skip_cache_dynamic_shape_guards": True} +_DEFAULT_INDUCTOR_CONFIG: dict[str, Any] = {} _DEFAULT_DYNAMO_CONFIG: dict[str, Any] = {"inline_inbuilt_nn_modules": True, "cache_size_limit": 256} diff --git a/packages/ltx-core/src/ltx_core/model/transformer/model.py b/packages/ltx-core/src/ltx_core/model/transformer/model.py index 48907df..bb6453f 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/model.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/model.py @@ -1,9 +1,12 @@ +import logging from enum import Enum import torch from ltx_core.guidance.perturbations import BatchedPerturbationConfig, PerturbationType +from ltx_core.model.model_protocol import LTXModelProtocol from ltx_core.model.transformer.adaln import AdaLayerNormSingle, adaln_embedding_coefficient +from ltx_core.model.transformer.attention import attention_label from ltx_core.model.transformer.modality import Modality from ltx_core.model.transformer.rope import LTXRopeType from ltx_core.model.transformer.transformer import ( @@ -20,6 +23,8 @@ from ltx_core.model.transformer.transformer_args import ( ) from ltx_core.utils import to_denoised +logger = logging.getLogger(__name__) + class LTXModelType(Enum): AudioVideo = "ltx av model" @@ -70,6 +75,15 @@ class LTXModel(torch.nn.Module): cross_attention_adaln: bool = False, ): super().__init__() + # Log the attention backends this transformer is built with. Reading the resolved + # ``label`` off the ops reports whatever was selected -- AUTOMATIC, an explicit pin + # (PYTORCH/XFORMERS/FA3/FA4/SDPA_*), or a directly supplied callable -- so this is the + # single source of truth for which kernel a build uses. Fires once per build. + logger.info( + "Building transformer with attention backends -- self: %s, masked: %s", + attention_label(ops.attention_ops.attention_function), + attention_label(ops.attention_ops.masked_attention_function), + ) self._enable_gradient_checkpointing = False self.cross_attention_adaln = cross_attention_adaln self.use_middle_indices_grid = use_middle_indices_grid @@ -411,7 +425,7 @@ class LTXModel(torch.nn.Module): def forward( self, video: Modality | None, audio: Modality | None, perturbations: BatchedPerturbationConfig - ) -> tuple[torch.Tensor, torch.Tensor]: + ) -> tuple[torch.Tensor | None, torch.Tensor | None]: """ Forward pass for LTX models. Returns: @@ -459,7 +473,7 @@ class LegacyX0Model(torch.nn.Module): Returns fully denoised output based on the velocities produced by the base model. """ - def __init__(self, velocity_model: LTXModel): + def __init__(self, velocity_model: LTXModelProtocol): super().__init__() self.velocity_model = velocity_model @@ -488,7 +502,7 @@ class X0Model(torch.nn.Module): Applies scaled denoising to the video and audio according to the timesteps = sigma * denoising_mask. """ - def __init__(self, velocity_model: LTXModel): + def __init__(self, velocity_model: LTXModelProtocol): super().__init__() self.velocity_model = velocity_model diff --git a/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py b/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py index b1b2bfd..fb47cef 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py @@ -123,6 +123,58 @@ class LTXVideoOnlyModelConfigurator(ModelConfigurator[LTXModel]): ) +class LTXAudioOnlyModelConfigurator(ModelConfigurator[LTXModel]): + """ + Configurator for LTX audio only model. + Builds an audio-only LTX model (``model_type=AudioOnly``) so the video + transformer weights are never instantiated or loaded. Useful for + text-to-audio inference where the video branch is unused. + """ + + @classmethod + def from_config(cls, config: dict, ops: TransformerOpsConfig = DEFAULT_TRANSFORMER_OPS) -> LTXModel: + # Build audio caption projection for 19B models (projection handled in transformer). + _, audio_caption_projection = _build_caption_projections(config, is_av=True) + + config = config.get("transformer", {}) + + check_config_value(config, "dropout", 0.0) + check_config_value(config, "attention_bias", True) + check_config_value(config, "num_vector_embeds", None) + check_config_value(config, "activation_fn", "gelu-approximate") + check_config_value(config, "num_embeds_ada_norm", 1000) + check_config_value(config, "use_linear_projection", False) + check_config_value(config, "only_cross_attention", False) + check_config_value(config, "cross_attention_norm", True) + check_config_value(config, "double_self_attention", False) + check_config_value(config, "upcast_attention", False) + check_config_value(config, "standardization_norm", "rms_norm") + check_config_value(config, "norm_elementwise_affine", False) + check_config_value(config, "qk_norm", "rms_norm") + check_config_value(config, "positional_embedding_type", "rope") + check_config_value(config, "use_middle_indices_grid", True) + + return LTXModel( + model_type=LTXModelType.AudioOnly, + num_layers=config.get("num_layers", 48), + norm_eps=config.get("norm_eps", 1e-06), + ops=ops, + timestep_scale_multiplier=config.get("timestep_scale_multiplier", 1000), + use_middle_indices_grid=config.get("use_middle_indices_grid", True), + audio_num_attention_heads=config.get("audio_num_attention_heads", 32), + audio_attention_head_dim=config.get("audio_attention_head_dim", 64), + audio_in_channels=config.get("audio_in_channels", 128), + audio_out_channels=config.get("audio_out_channels", 128), + audio_cross_attention_dim=config.get("audio_cross_attention_dim", 2048), + audio_positional_embedding_max_pos=config.get("audio_positional_embedding_max_pos", [20]), + rope_type=LTXRopeType(config.get("rope_type", "split")), + double_precision_rope=config.get("frequencies_precision", False) == "float64", + apply_gated_attention=config.get("apply_gated_attention", False), + audio_caption_projection=audio_caption_projection, + cross_attention_adaln=config.get("cross_attention_adaln", False), + ) + + def _build_caption_projections( config: dict, is_av: bool, @@ -151,3 +203,16 @@ LTXV_MODEL_COMFY_RENAMING_MAP = ( .with_matching(prefix="model.diffusion_model.") .with_replacement("model.diffusion_model.", "") ) + +LTXV_AUDIO_ONLY_MODEL_COMFY_RENAMING_MAP = ( + SDOps("LTXV_AUDIO_ONLY_MODEL_COMFY_MAP") + .with_matching(prefix="model.diffusion_model.", contains="audio_attn1") + .with_matching(prefix="model.diffusion_model.", contains="audio_attn2") + .with_matching(prefix="model.diffusion_model.", contains="audio_ff") + .with_matching(prefix="model.diffusion_model.", contains="audio_patchify") + .with_matching(prefix="model.diffusion_model.", contains="audio_proj_out") + .with_matching(prefix="model.diffusion_model.", contains="audio_adaln_single") + .with_matching(prefix="model.diffusion_model.", contains="audio_prompt") + .with_matching(prefix="model.diffusion_model.", contains="audio_scale_shift_table") + .with_replacement("model.diffusion_model.", "") +) diff --git a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py index 0da00da..d1c3002 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py @@ -24,7 +24,6 @@ from ltx_core.model.transformer.ops import ( ) from ltx_core.model.transformer.rope import LTXRopeType from ltx_core.model.transformer.transformer_args import TransformerArgs -from ltx_core.utils import rms_norm @dataclass @@ -222,7 +221,7 @@ class BasicAVTransformerBlock(torch.nn.Module): def _apply_text_cross_attention( self, - x: torch.Tensor, + x_normed: torch.Tensor, context: torch.Tensor, attn: AttentionCallable, scale_shift_table: torch.Tensor, @@ -232,11 +231,14 @@ class BasicAVTransformerBlock(torch.nn.Module): context_mask: torch.Tensor | None, cross_attention_adaln: bool = False, ) -> torch.Tensor: - """Apply text cross-attention, with optional AdaLN modulation.""" + """Apply text cross-attention, with optional AdaLN modulation. + ``x_normed`` is the RMS-normalized self-attention output produced by + ``post_sa_function`` -- this method does not normalize again. + """ if cross_attention_adaln: - shift_q, scale_q, gate = self.get_ada_values(scale_shift_table, x.shape[0], timestep, slice(6, 9)) + shift_q, scale_q, gate = self.get_ada_values(scale_shift_table, x_normed.shape[0], timestep, slice(6, 9)) return apply_cross_attention_adaln( - x, + x_normed, context, attn, shift_q, @@ -245,9 +247,8 @@ class BasicAVTransformerBlock(torch.nn.Module): prompt_scale_shift_table, prompt_timestep, context_mask, - self.norm_eps, ) - return attn(rms_norm(x, eps=self.norm_eps), context=context, mask=context_mask) + return attn(x_normed, context=context, mask=context_mask) def forward( # noqa: PLR0915 self, @@ -280,10 +281,10 @@ class BasicAVTransformerBlock(torch.nn.Module): perturbation_mask=video.self_attn_perturbation_mask, all_perturbed=video.self_attn_all_perturbed, ) - vx = vx + vx_msa_out * vgate_msa + vx, vx_normed = self.post_sa_function(vx, vx_msa_out, None, self.norm_eps, vgate_msa) del vgate_msa, norm_vx, vx_msa_out vx = vx + self._apply_text_cross_attention( - vx, + vx_normed, video.context, self.attn2, self.scale_shift_table, @@ -293,6 +294,7 @@ class BasicAVTransformerBlock(torch.nn.Module): video.context_mask, cross_attention_adaln=self.cross_attention_adaln, ) + del vx_normed if run_ax: ashift_msa, ascale_msa, agate_msa = self.get_ada_values( @@ -308,10 +310,10 @@ class BasicAVTransformerBlock(torch.nn.Module): perturbation_mask=audio.self_attn_perturbation_mask, all_perturbed=audio.self_attn_all_perturbed, ) - ax = ax + ax_msa_out * agate_msa + ax, ax_normed = self.post_sa_function(ax, ax_msa_out, None, self.norm_eps, agate_msa) del agate_msa, norm_ax, ax_msa_out ax = ax + self._apply_text_cross_attention( - ax, + ax_normed, audio.context, self.audio_attn2, self.audio_scale_shift_table, @@ -321,6 +323,7 @@ class BasicAVTransformerBlock(torch.nn.Module): audio.context_mask, cross_attention_adaln=self.cross_attention_adaln, ) + del ax_normed # Audio - Video cross attention. if run_a2v or run_v2a: @@ -414,7 +417,7 @@ class BasicAVTransformerBlock(torch.nn.Module): def apply_cross_attention_adaln( - x: torch.Tensor, + x_normed: torch.Tensor, context: torch.Tensor, attn: AttentionCallable, q_shift: torch.Tensor, @@ -423,13 +426,17 @@ def apply_cross_attention_adaln( prompt_scale_shift_table: torch.Tensor, prompt_timestep: torch.Tensor, context_mask: torch.Tensor | None = None, - norm_eps: float = 1e-6, ) -> torch.Tensor: - batch_size = x.shape[0] + """Apply query/key AdaLN modulation then cross-attention. + ``x_normed`` is already RMS-normalized by ``post_sa_function``; this only + applies the affine (scale/shift) modulation, so the normalization is not + repeated here. + """ + batch_size = x_normed.shape[0] shift_kv, scale_kv = ( - prompt_scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + prompt_scale_shift_table[None, None].to(device=x_normed.device, dtype=x_normed.dtype) + prompt_timestep.reshape(batch_size, prompt_timestep.shape[1], 2, -1) ).unbind(dim=2) - attn_input = rms_norm(x, eps=norm_eps) * (1 + q_scale) + q_shift + attn_input = x_normed * (1 + q_scale) + q_shift encoder_hidden_states = context * (1 + scale_kv) + shift_kv return attn(attn_input, context=encoder_hidden_states, mask=context_mask) * q_gate diff --git a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py index cadde9e..76ff2cc 100644 --- a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py +++ b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py @@ -30,21 +30,31 @@ class GemmaTextEncoder(torch.nn.Module): def encode( self, - text: str, + prompts: list[str], padding_side: str = "left", # noqa: ARG002 - ) -> tuple[tuple[torch.Tensor, ...], torch.Tensor]: - """Run Gemma LLM and return raw hidden states + attention mask. - Calls the inner model (self.model.model) to skip lm_head logits computation (~500 MiB saving). - Returns: - (hidden_states, attention_mask) where hidden_states is a tuple of per-layer tensors. + ) -> list[tuple[tuple[torch.Tensor, ...], torch.Tensor]]: + """Run a single fused Gemma forward over a batch of prompts. + Calls the inner model (self.model.model) to skip lm_head logits computation + (~500 MiB saving). The tokenizer pads every prompt to ``max_length`` (1024), + so the inputs stack into a single ``[N, 1024]`` batch with no further padding + logic; per-prompt outputs are sliced back to ``[1, 1024, D]`` / ``[1, 1024]`` + in the original order. """ - token_pairs = self.tokenizer.tokenize_with_weights(text)["gemma"] - input_ids = torch.tensor([[t[0] for t in token_pairs]], device=self.model.device) - attention_mask = torch.tensor([[w[1] for w in token_pairs]], device=self.model.device) + if not prompts: + return [] + tokenized = [self.tokenizer.tokenize_with_weights(t)["gemma"] for t in prompts] + input_ids = torch.tensor( + [[tok for tok, _ in pairs] for pairs in tokenized], + device=self.model.device, + ) + attention_mask = torch.tensor( + [[w for _, w in pairs] for pairs in tokenized], + device=self.model.device, + ) outputs = self.model.model(input_ids=input_ids, attention_mask=attention_mask, output_hidden_states=True) hidden_states = outputs.hidden_states del outputs - return hidden_states, attention_mask + return [(tuple(h[i : i + 1] for h in hidden_states), attention_mask[i : i + 1]) for i in range(len(prompts))] # --- Prompt enhancement methods --- diff --git a/packages/ltx-pipelines/CLAUDE.md b/packages/ltx-pipelines/CLAUDE.md index 8b5f62e..eeb5e9d 100644 --- a/packages/ltx-pipelines/CLAUDE.md +++ b/packages/ltx-pipelines/CLAUDE.md @@ -17,6 +17,7 @@ Inference pipelines for LTX-2 audio-video generation. Depends on `ltx-core` for | Pipeline | File | Stages | Model | Sampler | Use case | |----------|------|--------|-------|---------|----------| | `TI2VidOneStagePipeline` | `ti2vid_one_stage.py` | 1 | Full | Euler | Simple text/image-to-video | +| `T2AOneStagePipeline` | `t2a_one_stage.py` | 1 | Full | Euler | Text-to-audio (audio-only output, no video branch) | | `TI2VidTwoStagesPipeline` | `ti2vid_two_stages.py` | 2 | Full + distilled LoRA | Euler | Production quality | | `TI2VidTwoStagesHQPipeline` | `ti2vid_two_stages_hq.py` | 2 | Full + distilled LoRA (both stages) | Res2s | Highest quality, fewer steps | | `A2VidPipelineTwoStage` | `a2vid_two_stage.py` | 2 | Full + distilled LoRA | Euler | Audio-conditioned video | diff --git a/packages/ltx-pipelines/README.md b/packages/ltx-pipelines/README.md index 79e1e70..d6a877b 100644 --- a/packages/ltx-pipelines/README.md +++ b/packages/ltx-pipelines/README.md @@ -58,6 +58,7 @@ Available pipeline modules: - `ltx_pipelines.ti2vid_two_stages` - Two-stage text/image-to-video (recommended). - `ltx_pipelines.ti2vid_two_stages_hq` - Two-stage text/image-to-video (different sampler, better quality). - `ltx_pipelines.ti2vid_one_stage` - Single-stage text/image-to-video. +- `ltx_pipelines.t2a_one_stage` - Single-stage text-to-audio (audio-only output). - `ltx_pipelines.distilled` - Fast text/image-to-video pipeline using only the distilled model. - `ltx_pipelines.ic_lora` - Video-to-video with IC-LoRA. - `ltx_pipelines.keyframe_interpolation` - Keyframe interpolation. @@ -254,6 +255,20 @@ Uses IC-LoRA on a **distilled** checkpoint with a **single** lip-dub IC-LoRA app --- +### 11. T2AOneStagePipeline + +**Best for:** Text-to-audio — generating speech/audio only (no video) from a text prompt, e.g. driving an audio-style LoRA such as an accent LoRA. + +**Source**: [`src/ltx_pipelines/t2a_one_stage.py`](src/ltx_pipelines/t2a_one_stage.py) + +Single-stage, **audio-only** generation: the video branch is absent (`video=None`), so only the audio modality is denoised and decoded through the audio VAE + vocoder, producing a wave file. Audio duration is derived from `--num-frames` / `--frame-rate` (the same `8k+1` frame convention as video). Audio guidance (CFG/STG) is optional — the `--audio-*` flags default to the model's values; the video→audio cross-modal guidance is disabled since there is no video modality. + +**Extra CLI arguments (all optional, with sensible defaults):** `--num-frames`, `--frame-rate`, `--negative-prompt`, `--audio-cfg-guidance-scale`, `--audio-stg-guidance-scale`, `--audio-stg-blocks`, `--audio-rescale-scale`, `--audio-skip-step`. No `--height/--width/--image` (audio has no spatial dimensions). + +**Use when:** You need speech/audio from text alone, or to evaluate an audio-only LoRA (accent, voice style) without generating video. + +--- + ## 🎨 Conditioning Types Pipelines use different conditioning methods from [`ltx-core`](../ltx-core/) for controlling generation. See the [ltx-core conditioning documentation](../ltx-core/README.md#conditioning--control) for details. @@ -400,6 +415,69 @@ By default, pipelines clean GPU memory (especially transformer weights) between # utils.cleanup_memory() # Comment out if you have enough VRAM ``` +### Compilation (`torch.compile`) + +Compiling the transformer blocks with `torch.compile` speeds up inference. It is **opt-in and off by default**. The blocks are compiled shape-polymorphically (the sequence dimension is marked dynamic), so one compiled artifact serves any token count without recompiling. + +**CLI** — the `--compile` flag maps directly to `CompilationConfig`: + +| Form | Result | +| ---- | ------ | +| *(flag absent)* | eager, no compilation | +| `--compile` | compile with defaults | +| `--compile KEY=VALUE ...` | compile, overriding individual fields | + +```bash +# Defaults +python -m ltx_pipelines.ti2vid_two_stages --compile --checkpoint-path=... + +# reduce-overhead captures CUDA graphs -- the main latency lever for the denoising loop. +# Off by default because graph capture reserves static memory pools (extra VRAM), so it +# trades memory for speed; enable it when you have headroom. +python -m ltx_pipelines.ti2vid_two_stages --compile mode=reduce-overhead --checkpoint-path=... + +# Several overrides at once +python -m ltx_pipelines.ti2vid_two_stages \ + --compile mode=max-autotune fullgraph=true dynamic=true --checkpoint-path=... +``` + +| Field | Values | Default | Notes | +| ----- | ------ | ------- | ----- | +| `mode` | `none`, `reduce-overhead`, `max-autotune`, … | `none` | `reduce-overhead`/`max-autotune` enable CUDA graphs | +| `backend` | `inductor`, `eager`, … | `inductor` | | +| `fullgraph` | `true`/`false` | `false` | | +| `dynamic` | `auto`/`true`/`false` | `auto` | the seq dim is marked dynamic regardless | +| `inductor_config` | JSON object or path to a `.json` | `{}` | `torch._inductor.config` overrides | +| `dynamo_config` | JSON object or path to a `.json` | `{"inline_inbuilt_nn_modules": true, "cache_size_limit": 256}` | `torch._dynamo.config` overrides | + +**Controlling inductor / dynamo configs.** `inductor_config` and `dynamo_config` take either an inline JSON object or a path to a `.json` file, applied via `torch._inductor.config.patch(...)` / `torch._dynamo.config.patch(...)` around the compiled forward. They **replace the defaults wholesale — they do not merge**, so when overriding `dynamo_config` re-include any defaults you want to keep: + +```bash +python -m ltx_pipelines.ti2vid_two_stages \ + --compile 'inductor_config={"max_autotune": true}' \ + 'dynamo_config={"inline_inbuilt_nn_modules": true, "cache_size_limit": 256, "recompile_limit": 32}' \ + --checkpoint-path=... +``` + +**Programmatically**, pass a `CompilationConfig` to the pipeline: + +```python +from ltx_core.model.transformer.compiling import CompilationConfig + +pipeline = TI2VidTwoStagesPipeline( + ..., + compilation_config=CompilationConfig(mode="reduce-overhead"), +) +``` + +**Faster cache loads: `unsafe_skip_cache_dynamic_shape_guards` (unsafe, opt-in).** Inductor's FX-graph cache re-checks the dynamic-shape guards stored with each entry on every lookup. Setting this flag skips that re-check (every entry is treated as a guard hit), which speeds up warm and cross-process cache loads. It is **not enabled by default** because it is a correctness hazard: a kernel first compiled at a small sequence length keeps int32 address arithmetic, and reusing it at a larger sequence length (roughly **>58k tokens/rank**) overflows int32 and reads out of bounds — surfacing as a CUDA illegal memory access or silently corrupted output. Only enable it when your token counts stay within the range the cached kernels were compiled for: + +```bash +python -m ltx_pipelines.ti2vid_two_stages \ + --compile 'inductor_config={"unsafe_skip_cache_dynamic_shape_guards": true}' \ + --checkpoint-path=... +``` + ### Denoising Loop Optimization **Gradient Estimation Denoising Loop:** diff --git a/packages/ltx-pipelines/pyproject.toml b/packages/ltx-pipelines/pyproject.toml index 4aa62df..ad4f8fb 100644 --- a/packages/ltx-pipelines/pyproject.toml +++ b/packages/ltx-pipelines/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ltx-pipelines" -version = "1.1.5" +version = "1.1.6" description = "Pipelines implementation for Lightricks' LTX-2 model" readme = "README.md" requires-python = ">=3.10" diff --git a/packages/ltx-pipelines/src/ltx_pipelines/__init__.py b/packages/ltx-pipelines/src/ltx_pipelines/__init__.py index d145f27..e3cad80 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/__init__.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/__init__.py @@ -2,6 +2,7 @@ LTX-2 Pipelines: High-level video generation pipelines and utilities. This package provides ready-to-use pipelines for video generation: - TI2VidOneStagePipeline: Text/image-to-video in a single stage +- T2AOneStagePipeline: Text-to-audio in a single stage (audio-only output) - TI2VidTwoStagesPipeline: Two-stage generation with upsampling - DistilledPipeline: Fast distilled two-stage generation - ICLoraPipeline: Image/video conditioning with distilled LoRA @@ -18,6 +19,7 @@ from ltx_pipelines.ic_lora import ICLoraPipeline from ltx_pipelines.keyframe_interpolation import KeyframeInterpolationPipeline from ltx_pipelines.lipdub import LipDubPipeline from ltx_pipelines.retake import RetakePipeline +from ltx_pipelines.t2a_one_stage import T2AOneStagePipeline from ltx_pipelines.ti2vid_one_stage import TI2VidOneStagePipeline from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline @@ -28,6 +30,7 @@ __all__ = [ "KeyframeInterpolationPipeline", "LipDubPipeline", "RetakePipeline", + "T2AOneStagePipeline", "TI2VidOneStagePipeline", "TI2VidTwoStagesPipeline", ] diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py index ee74aec..484cee6 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py @@ -14,6 +14,7 @@ from ltx_core.types import Audio, VideoPixelShape from ltx_pipelines.iclora_utils import ( append_ic_lora_reference_video_conditionings, read_lora_reference_downscale_factor, + read_lora_reference_temporal_scale_factor, ) from ltx_pipelines.utils.args import ( ImageConditioningInput, @@ -102,10 +103,11 @@ class ICLoraPipeline: self.video_decoder = VideoDecoder(distilled_checkpoint_path, self.dtype, self.device, registry=registry) self.audio_decoder = AudioDecoder(distilled_checkpoint_path, self.dtype, self.device, registry=registry) - # Read reference downscale factor from LoRA metadata. - # IC-LoRAs trained with low-resolution reference videos store this factor - # so inference can resize reference videos to match training conditions. + # Read reference scale factors from LoRA metadata. + # IC-LoRAs trained with scaled reference videos store these factors + # so inference can resize/subsample reference videos to match training conditions. self.reference_downscale_factor = 1 + self.reference_temporal_scale_factor = 1 for lora in loras: scale = read_lora_reference_downscale_factor(lora.path) if scale != 1: @@ -116,6 +118,15 @@ class ICLoraPipeline: f"specifies {scale}. Cannot combine LoRAs with different reference scales." ) self.reference_downscale_factor = scale + temporal = read_lora_reference_temporal_scale_factor(lora.path) + if temporal != 1: + if self.reference_temporal_scale_factor not in (1, temporal): + raise ValueError( + f"Conflicting reference_temporal_scale_factor values in LoRAs: " + f"already have {self.reference_temporal_scale_factor}, but {lora.path} " + f"specifies {temporal}. Cannot combine LoRAs with different temporal scales." + ) + self.reference_temporal_scale_factor = temporal def __call__( # noqa: PLR0913 self, @@ -318,6 +329,7 @@ class ICLoraPipeline: dtype=self.dtype, device=self.device, reference_downscale_factor=self.reference_downscale_factor, + reference_temporal_scale_factor=self.reference_temporal_scale_factor, conditioning_attention_strength=conditioning_attention_strength, conditioning_attention_mask=conditioning_attention_mask, tiling_config=None, diff --git a/packages/ltx-pipelines/src/ltx_pipelines/iclora_utils.py b/packages/ltx-pipelines/src/ltx_pipelines/iclora_utils.py index 463af81..b03268a 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/iclora_utils.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/iclora_utils.py @@ -31,6 +31,17 @@ def read_lora_reference_downscale_factor(lora_path: str) -> int: return 1 +def read_lora_reference_temporal_scale_factor(lora_path: str) -> int: + """Read ``reference_temporal_scale_factor`` from LoRA safetensors metadata (default 1).""" + try: + with safe_open(lora_path, framework="pt") as f: + metadata = f.metadata() or {} + return int(metadata.get("reference_temporal_scale_factor", 1)) + except Exception as e: + logging.warning("Failed to read metadata from LoRA file '%s': %s", lora_path, e) + return 1 + + def downsample_mask_video_to_latent( mask: torch.Tensor, target_latent_shape: VideoLatentShape, @@ -66,6 +77,12 @@ def downsample_mask_video_to_latent( return rearrange(latent_mask, "b 1 f h w -> b (f h w)") +def temporal_subsample(video: torch.Tensor, temporal_scale_factor: int) -> torch.Tensor: + """VAE-aligned temporal subsampling: keep frame 0, then every Nth frame.""" + indices = [0, *list(range(1, video.shape[2], temporal_scale_factor))] + return video[:, :, indices] + + def append_ic_lora_reference_video_conditionings( # noqa: PLR0913 conditionings: list[ConditioningItem], video_conditioning: list[tuple[str, float]], @@ -77,6 +94,7 @@ def append_ic_lora_reference_video_conditionings( # noqa: PLR0913 dtype: torch.dtype, device: torch.device, reference_downscale_factor: int, + reference_temporal_scale_factor: int = 1, conditioning_attention_strength: float, conditioning_attention_mask: torch.Tensor | None, tiling_config: TilingConfig | None = None, @@ -93,6 +111,8 @@ def append_ic_lora_reference_video_conditionings( # noqa: PLR0913 for video_path, strength in video_conditioning: frame_gen = decode_video_by_frame(path=video_path, frame_cap=num_frames, device=device) video = video_preprocess(frame_gen, ref_height, ref_width, dtype, device) + if reference_temporal_scale_factor > 1: + video = temporal_subsample(video, reference_temporal_scale_factor) if tiling_config is not None: encoded_video = video_encoder.tiled_encode(video, tiling_config) else: @@ -113,6 +133,7 @@ def append_ic_lora_reference_video_conditionings( # noqa: PLR0913 cond = VideoConditionByReferenceLatent( latent=encoded_video, downscale_factor=scale, + temporal_scale_factor=reference_temporal_scale_factor, strength=strength, ) if attn_mask is not None: diff --git a/packages/ltx-pipelines/src/ltx_pipelines/t2a_one_stage.py b/packages/ltx-pipelines/src/ltx_pipelines/t2a_one_stage.py new file mode 100644 index 0000000..257e1f5 --- /dev/null +++ b/packages/ltx-pipelines/src/ltx_pipelines/t2a_one_stage.py @@ -0,0 +1,192 @@ +import logging + +import torch + +from ltx_core.components.guiders import ( + MultiModalGuiderFactory, + MultiModalGuiderParams, + create_multimodal_guider_factory, +) +from ltx_core.components.noisers import GaussianNoiser +from ltx_core.components.schedulers import LTX2Scheduler +from ltx_core.loader import LoraPathStrengthAndSDOps +from ltx_core.loader.registry import Registry +from ltx_core.model.transformer import LTXV_AUDIO_ONLY_MODEL_COMFY_RENAMING_MAP, LTXAudioOnlyModelConfigurator +from ltx_core.model.transformer.compiling import CompilationConfig +from ltx_core.quantization import QuantizationPolicy +from ltx_core.types import Audio +from ltx_pipelines.utils import get_device +from ltx_pipelines.utils.args import ( + default_1_stage_t2a_arg_parser, + detect_checkpoint_path, +) +from ltx_pipelines.utils.blocks import ( + AudioDecoder, + DiffusionStage, + PromptEncoder, +) +from ltx_pipelines.utils.constants import detect_params +from ltx_pipelines.utils.denoisers import FactoryGuidedDenoiser +from ltx_pipelines.utils.media_io import encode_audio +from ltx_pipelines.utils.types import ModalitySpec, OffloadMode + +# Placeholder pixel dimensions used for ``VideoPixelShape`` construction. +# Audio-only generation reads ``frames`` and ``fps`` from the pixel shape via +# ``AudioLatentShape.from_video_pixel_shape`` (height/width are unused). +_AUDIO_ONLY_PLACEHOLDER_RES = 512 + + +class T2AOneStagePipeline: + """ + Single-stage text-to-audio generation pipeline. + Generates audio at the target duration in a single diffusion pass with + classifier-free guidance (CFG) on the audio modality only. The video + modality is fully absent — the transformer runs audio-only by passing + ``video=None`` to the ``DiffusionStage``. + Assumes full non distilled model is provided in the checkpoint_path. + """ + + def __init__( + self, + checkpoint_path: str, + gemma_root: str, + loras: list[LoraPathStrengthAndSDOps], + device: torch.device | None = None, + quantization: QuantizationPolicy | None = None, + registry: Registry | None = None, + compilation_config: CompilationConfig | None = None, + offload_mode: OffloadMode = OffloadMode.NONE, + ): + self.dtype = torch.bfloat16 + self.device = device or get_device() + self._scheduler = LTX2Scheduler() + self.prompt_encoder = PromptEncoder( + checkpoint_path=checkpoint_path, + gemma_root=gemma_root, + dtype=self.dtype, + device=self.device, + registry=registry, + offload_mode=offload_mode, + ) + # Audio-only: build an audio-only transformer (model_configurator) so the video + # weights are never instantiated, plus a use-case-specific SDOps that restricts + # checkpoint reads to the audio model's keys, so the video weights are never even + # read from disk (the loader skips any key the SDOps maps to None). + self.stage = DiffusionStage( + checkpoint_path=checkpoint_path, + dtype=self.dtype, + device=self.device, + loras=tuple(loras), + quantization=quantization, + registry=registry, + compilation_config=compilation_config, + offload_mode=offload_mode, + model_configurator=LTXAudioOnlyModelConfigurator, + model_sd_ops=LTXV_AUDIO_ONLY_MODEL_COMFY_RENAMING_MAP, + ) + self.audio_decoder = AudioDecoder( + checkpoint_path=checkpoint_path, + dtype=self.dtype, + device=self.device, + registry=registry, + ) + + def __call__( + self, + prompt: str, + negative_prompt: str, + seed: int, + num_frames: int, + frame_rate: float, + num_inference_steps: int, + audio_guider_params: MultiModalGuiderParams | MultiModalGuiderFactory, + enhance_prompt: bool = False, + max_batch_size: int = 1, + sigmas: torch.Tensor | None = None, + ) -> Audio: + generator = torch.Generator(device=self.device).manual_seed(seed) + noiser = GaussianNoiser(generator=generator) + + ctx_p, ctx_n = self.prompt_encoder( + [prompt, negative_prompt], + enhance_first_prompt=enhance_prompt, + enhance_prompt_image=None, + enhance_prompt_seed=seed, + ) + a_context_p = ctx_p.audio_encoding + a_context_n = ctx_n.audio_encoding + + sigmas = (sigmas if sigmas is not None else self._scheduler.execute(steps=num_inference_steps)).to( + dtype=torch.float32, device=self.device + ) + + # Normalize to a guider factory. Plain ``MultiModalGuiderParams`` (the default / + # CLI case) becomes a simple sigma-independent guider, but callers may also pass + # their own factory for sigma-dependent guidance; ``FactoryGuidedDenoiser`` always + # consumes a factory. + audio_guider_factory = create_multimodal_guider_factory( + params=audio_guider_params, + negative_context=a_context_n, + ) + + _, audio_state = self.stage( + denoiser=FactoryGuidedDenoiser( + v_context=None, + a_context=a_context_p, + video_guider_factory=None, + audio_guider_factory=audio_guider_factory, + ), + sigmas=sigmas, + noiser=noiser, + width=_AUDIO_ONLY_PLACEHOLDER_RES, + height=_AUDIO_ONLY_PLACEHOLDER_RES, + frames=num_frames, + fps=frame_rate, + video=None, + audio=ModalitySpec(context=a_context_p), + max_batch_size=max_batch_size, + ) + + return self.audio_decoder(audio_state.latent) + + +@torch.inference_mode() +def main() -> None: + logging.basicConfig(level=logging.INFO) + checkpoint_path = detect_checkpoint_path() + params = detect_params(checkpoint_path) + parser = default_1_stage_t2a_arg_parser(params=params) + args = parser.parse_args() + pipeline = T2AOneStagePipeline( + checkpoint_path=args.checkpoint_path, + gemma_root=args.gemma_root, + loras=tuple(args.lora) if args.lora else (), + quantization=args.quantization, + compilation_config=args.compile, + offload_mode=args.offload_mode, + ) + audio = pipeline( + prompt=args.prompt, + negative_prompt=args.negative_prompt, + seed=args.seed, + num_frames=args.num_frames, + frame_rate=args.frame_rate, + num_inference_steps=args.num_inference_steps, + audio_guider_params=MultiModalGuiderParams( + cfg_scale=args.audio_cfg_guidance_scale, + stg_scale=args.audio_stg_guidance_scale, + rescale_scale=args.audio_rescale_scale, + # Audio-only generation has no video modality, so the video->audio + # (v2a) cross-modal guidance is meaningless here. 1.0 disables it. + modality_scale=1.0, + skip_step=args.audio_skip_step, + stg_blocks=args.audio_stg_blocks, + ), + max_batch_size=args.max_batch_size, + ) + + encode_audio(audio=audio, output_path=args.output_path) + + +if __name__ == "__main__": + main() diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py index 63386ed..9c885e1 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py @@ -650,6 +650,62 @@ def default_1_stage_arg_parser(params: PipelineParams = LTX_2_3_PARAMS) -> argpa return parser +def default_1_stage_t2a_arg_parser(params: PipelineParams = LTX_2_3_PARAMS) -> argparse.ArgumentParser: + """Argument parser for single-stage text-to-audio pipelines (audio-only).""" + audio_guider = params.audio_guider_params + parser = basic_arg_parser(params=params) + parser.add_argument( + "--num-frames", + type=int, + default=params.num_frames, + help="Number of frames used to derive audio duration (num-frames / frame-rate).", + ) + parser.add_argument( + "--frame-rate", + type=float, + default=params.frame_rate, + help="Frame rate used with --num-frames to derive the audio duration.", + ) + parser.add_argument( + "--negative-prompt", + type=str, + default=DEFAULT_NEGATIVE_PROMPT, + help="Negative prompt to steer audio generation away from artifacts.", + ) + parser.add_argument( + "--audio-cfg-guidance-scale", + type=float, + default=audio_guider.cfg_scale, + help=f"Audio CFG scale (default: {audio_guider.cfg_scale}).", + ) + parser.add_argument( + "--audio-stg-guidance-scale", + type=float, + default=audio_guider.stg_scale, + help=f"Audio STG scale (default: {audio_guider.stg_scale}).", + ) + parser.add_argument( + "--audio-rescale-scale", + type=float, + default=audio_guider.rescale_scale, + help=f"Audio rescale scale (default: {audio_guider.rescale_scale}).", + ) + parser.add_argument( + "--audio-stg-blocks", + type=int, + nargs="*", + default=audio_guider.stg_blocks, + help=f"Blocks to perturb for Audio STG (default: {audio_guider.stg_blocks}).", + ) + parser.add_argument( + "--audio-skip-step", + type=int, + default=audio_guider.skip_step, + help=f"Audio skip step (default: {audio_guider.skip_step}).", + ) + return parser + + def default_2_stage_arg_parser(params: PipelineParams = LTX_2_3_PARAMS) -> argparse.ArgumentParser: parser = default_1_stage_arg_parser(params=params) parser.set_defaults(height=params.stage_2_height, width=params.stage_2_width) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py index f34c66b..636276b 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/blocks.py @@ -7,7 +7,6 @@ removes the need for :class:`ModelLedger`. from __future__ import annotations import copy -import dataclasses import logging from collections.abc import Iterator from contextlib import AbstractContextManager, contextmanager @@ -40,9 +39,9 @@ from ltx_core.model.audio_vae import ( from ltx_core.model.audio_vae import ( decode_audio as vae_decode_audio, ) +from ltx_core.model.model_protocol import LTXModelProtocol, ModelConfigurator from ltx_core.model.transformer import ( LTXV_MODEL_COMFY_RENAMING_MAP, - LTXModel, LTXModelConfigurator, X0Model, ) @@ -144,7 +143,7 @@ def _streaming_model( """Build a streaming wrapper, yield it, then tear down and free memory.""" cpu_slots_count = DISK_CPU_SLOTS if offload_mode == OffloadMode.DISK else None wrapped = builder.build( - target_device=target_device, + device=target_device, dtype=dtype, cpu_slots_count=cpu_slots_count, ) @@ -195,7 +194,7 @@ class DiffusionStage: pattern in every pipeline. """ - def __init__( + def __init__( # noqa: PLR0913 self, checkpoint_path: str, dtype: torch.dtype, @@ -205,7 +204,9 @@ class DiffusionStage: registry: Registry | None = None, compilation_config: CompilationConfig | None = None, offload_mode: OffloadMode = OffloadMode.NONE, - transformer_builder: ModelBuilderProtocol[LTXModel] | None = None, + transformer_builder: ModelBuilderProtocol[LTXModelProtocol] | None = None, + model_configurator: type[ModelConfigurator] = LTXModelConfigurator, + model_sd_ops: SDOps = LTXV_MODEL_COMFY_RENAMING_MAP, ) -> None: self._checkpoint_path = checkpoint_path self._dtype = dtype @@ -213,10 +214,12 @@ class DiffusionStage: self._quantization = quantization self._compilation_config = compilation_config self._offload_mode = offload_mode + # A quantization policy may pin its own configurator; otherwise use the one + # provided by the caller (defaults to the audio-video LTXModelConfigurator). configurator = ( quantization.model_configurator if quantization is not None and quantization.model_configurator is not None - else LTXModelConfigurator + else model_configurator ) if transformer_builder is not None: self._transformer_builder = transformer_builder @@ -224,14 +227,12 @@ class DiffusionStage: self._transformer_builder = Builder( model_path=checkpoint_path, model_class_configurator=configurator, - model_sd_ops=LTXV_MODEL_COMFY_RENAMING_MAP, + model_sd_ops=model_sd_ops, loras=tuple(loras), registry=registry or DummyRegistry(), ) if offload_mode != OffloadMode.NONE: - if compilation_config is not None: - raise ValueError("torch.compile is not supported with layer streaming") # WeightsProvider currently only supports plain bf16 + fp8_cast LoRA fusion # (no companion-key emission). Quantization policies that emit # companion keys (e.g. ``.weight_scale``) cannot be streamed yet. @@ -240,8 +241,15 @@ class DiffusionStage: "Block streaming is not supported with this quantization policy " "(only bf16 and fp8_cast are currently supported)." ) - streaming_sd_ops: SDOps = LTXV_MODEL_COMFY_RENAMING_MAP + streaming_sd_ops: SDOps = model_sd_ops streaming_module_ops: tuple[ModuleOps, ...] = () + streaming_loras = tuple(loras) + + if compilation_config: + number_of_layers = self._transformer_builder.model_config()["transformer"]["num_layers"] + streaming_sd_ops, streaming_module_ops, streaming_loras = _apply_compile_ops( + streaming_sd_ops, streaming_module_ops, streaming_loras, number_of_layers + ) if quantization is not None: streaming_sd_ops, streaming_module_ops = _chain_quantization( streaming_sd_ops, streaming_module_ops, quantization @@ -251,7 +259,7 @@ class DiffusionStage: model_path=checkpoint_path, model_sd_ops=streaming_sd_ops, module_ops=streaming_module_ops, - loras=tuple(loras), + loras=streaming_loras, registry=registry or DummyRegistry(), fuse_rule=quantization.fuse_rule if quantization is not None else bf16_fuse_rule, blocks_attr="transformer_blocks", @@ -273,9 +281,8 @@ class DiffusionStage: (*self._transformer_builder.module_ops, op), ) if self._offload_mode != OffloadMode.NONE: - new._streaming_builder = dataclasses.replace( - self._streaming_builder, - module_ops=(*self._streaming_builder.module_ops, op), + new._streaming_builder = self._streaming_builder.with_module_ops( + (*self._streaming_builder.module_ops, op), ) return new @@ -531,7 +538,7 @@ class PromptEncoder: prompts[0] = generate_enhanced_prompt( text_encoder, prompts[0], enhance_prompt_image, seed=enhance_prompt_seed ) - raw_outputs = [text_encoder.encode(p) for p in prompts] + raw_outputs = text_encoder.encode(prompts) logger.info("Text encoder done, building embeddings processor from %s", self._checkpoint_path) with gpu_model(self._build_embeddings_processor()) as embeddings_processor: diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py index 45ed33b..efb7b80 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/denoisers.py @@ -182,6 +182,8 @@ def _guided_denoise( # noqa: PLR0913,PLR0915 r = dict(zip(pass_names, zip(splits_v, splits_a, strict=True), strict=True)) cond_v, cond_a = r["cond"] + cond_v = cond_v if isinstance(cond_v, torch.Tensor) else torch.tensor(cond_v) + cond_a = cond_a if isinstance(cond_a, torch.Tensor) else torch.tensor(cond_a) uncond_v, uncond_a = r.get("uncond", (0.0, 0.0)) ptb_v, ptb_a = r.get("ptb", (0.0, 0.0)) mod_v, mod_a = r.get("mod", (0.0, 0.0)) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py index 00fb506..f35ce05 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py @@ -391,6 +391,24 @@ def encode_video( logger.info(f"Video saved to {output_path}") +def encode_audio(audio: Audio, output_path: str) -> None: + """Save an audio waveform as a 16-bit PCM ``.wav`` file at the source sampling rate. + Reuses :func:`_write_audio` (the same muxing path used by :func:`encode_video`); + the only difference is a PCM (``pcm_s16le``) stream in a WAV container instead of + the AAC stream used for muxed video. + """ + container = av.open(output_path, mode="w") + audio_stream = container.add_stream("pcm_s16le", rate=audio.sampling_rate) + audio_stream.codec_context.sample_rate = audio.sampling_rate + audio_stream.codec_context.layout = "stereo" + audio_stream.codec_context.time_base = Fraction(1, audio.sampling_rate) + try: + _write_audio(container, audio_stream, audio) + finally: + container.close() + logger.info(f"Audio saved to {output_path}") + + def _encode_chunks_threaded( container: av.container.Container, stream: av.video.stream.VideoStream, diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py index 130bc98..adab1a7 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/samplers.py @@ -436,7 +436,7 @@ def res2s_audio_video_denoising_loop( # noqa: PLR0913,PLR0915,PLR0912 return video_state, audio_state -def euler_cfg_pp_denoising_loop( +def euler_cfg_pp_denoising_loop( # noqa: PLR0912 sigmas: torch.Tensor, video_state: LatentState | None, audio_state: LatentState | None, @@ -514,9 +514,15 @@ def euler_cfg_pp_denoising_loop( ) if video_state is not None and denoised_video is not None: - denoised_video = post_process_latent(denoised_video, video_state.denoise_mask, video_state.clean_latent) + denoised_video = post_process_latent( + denoised_video.float(), video_state.denoise_mask, video_state.clean_latent + ) + noisy_video = video_state.latent.float() if audio_state is not None and denoised_audio is not None: - denoised_audio = post_process_latent(denoised_audio, audio_state.denoise_mask, audio_state.clean_latent) + denoised_audio = post_process_latent( + denoised_audio.float(), audio_state.denoise_mask, audio_state.clean_latent + ) + noisy_audio = audio_state.latent.float() if sigmas[step_idx + 1] == 0: if video_state is not None and denoised_video is not None: @@ -525,30 +531,31 @@ def euler_cfg_pp_denoising_loop( audio_state = replace(audio_state, latent=denoised_audio.to(model_dtype)) return video_state, audio_state - # Draw noise consecutively from the same generator: video first, audio second. - noise_video = new_noise_fn(video_state.latent, generator) if (video_state is not None and draw_noise) else None - noise_audio = new_noise_fn(audio_state.latent, generator) if (audio_state is not None and draw_noise) else None - if video_state is not None and denoised_video is not None: + video_noise = new_noise_fn(video_state.latent, generator) if draw_noise else None x_next = stepper.step( - sample=video_state.latent, + sample=noisy_video, denoised_sample=denoised_video, sigmas=sigmas, step_index=step_idx, uncond_denoised=uncond_video, - noise=noise_video, + noise=video_noise, ) + if draw_noise: + x_next = post_process_latent(x_next, video_state.denoise_mask, video_state.clean_latent) video_state = replace(video_state, latent=x_next.to(model_dtype)) if audio_state is not None and denoised_audio is not None: + audio_noise = new_noise_fn(audio_state.latent, generator) if draw_noise else None x_next = stepper.step( - sample=audio_state.latent, + sample=noisy_audio, denoised_sample=denoised_audio, sigmas=sigmas, step_index=step_idx, uncond_denoised=uncond_audio, - noise=noise_audio, + noise=audio_noise, ) + if draw_noise: + x_next = post_process_latent(x_next, audio_state.denoise_mask, audio_state.clean_latent) audio_state = replace(audio_state, latent=x_next.to(model_dtype)) - return video_state, audio_state diff --git a/packages/ltx-trainer/.gitignore b/packages/ltx-trainer/.gitignore index cd4b9f0..b024504 100644 --- a/packages/ltx-trainer/.gitignore +++ b/packages/ltx-trainer/.gitignore @@ -1,7 +1,3 @@ -configs/*.yaml -!configs/ltx2_av_lora.yaml -!configs/ltx2_av_lora_low_vram.yaml -!configs/ltx2_v2v_ic_lora.yaml datasets outputs wandb diff --git a/packages/ltx-trainer/AGENTS.md b/packages/ltx-trainer/AGENTS.md index 9cdaed5..1fa24dc 100644 --- a/packages/ltx-trainer/AGENTS.md +++ b/packages/ltx-trainer/AGENTS.md @@ -6,10 +6,22 @@ This file provides guidance to AI coding assistants (Claude, Cursor, etc.) when **LTX Trainer** is a training toolkit for fine-tuning the Lightricks LTX audio-video generation models. It supports: +- **Text-to-video (T2V)** - Generate video from text prompts +- **Text-to-audio (T2A)** - Generate audio from text prompts +- **Image-to-video (I2V)** - Generate video conditioned on a first frame +- **Video extension** - Forward (prefix) and backward (suffix) video continuation +- **Video inpainting** - Mask-based spatial/temporal inpainting +- **Video outpainting** - Spatial crop-based outpainting +- **IC-LoRA video-to-video** - In-context control adapters for style/structure transfer +- **Audio-to-video (A2V)** and **Video-to-audio (V2A)** - Cross-modal generation with frozen conditioning +- **Audio extension** - Forward (prefix) and backward (suffix) audio continuation +- **Audio inpainting** - Mask-based audio inpainting +- **IC-LoRA audio-to-audio (A2A)** - Audio reference conditioning for style transfer +- **AV2AV IC-LoRA** - Combined video and audio reference conditioning - **LoRA training** - Efficient fine-tuning with adapters - **Full fine-tuning** - Complete model training -- **Audio-video training** - Joint audio and video generation -- **IC-LoRA training** - In-context control adapters for video-to-video transformations + +All conditioning scenarios are expressed through the unified `FlexibleStrategy` configuration. **Supported model versions:** @@ -39,13 +51,14 @@ packages/ltx-trainer/ │ ├── config_display.py # Config pretty-printing │ ├── trainer.py # Main training orchestration with Accelerate │ ├── model_loader.py # Model loading using ltx-core -│ ├── validation_sampler.py # Inference for validation samples +│ ├── validation_runner.py # ValidationRunner — conditioned validation sampling │ ├── datasets.py # PrecomputedDataset, DummyDataset │ ├── training_strategies/ # Strategy pattern for different training modes │ │ ├── __init__.py # Factory function: get_training_strategy() │ │ ├── base_strategy.py # TrainingStrategy ABC, ModelInputs, TrainingStrategyConfigBase -│ │ ├── text_to_video.py # TextToVideoStrategy, TextToVideoConfig -│ │ └── video_to_video.py # VideoToVideoStrategy, VideoToVideoConfig +│ │ ├── flexible.py # FlexibleStrategy, FlexibleStrategyConfig [RECOMMENDED] +│ │ ├── text_to_video.py # TextToVideoStrategy, TextToVideoConfig [DEPRECATED] +│ │ └── video_to_video.py # VideoToVideoStrategy, VideoToVideoConfig [DEPRECATED] │ ├── timestep_samplers.py # Flow matching timestep sampling │ ├── gemma_8bit.py # 8-bit Gemma text encoder loading (bitsandbytes) │ ├── quantization.py # Transformer INT8/INT4/FP8 quantization @@ -62,13 +75,25 @@ packages/ltx-trainer/ │ ├── process_captions.py # Text embedding computation │ ├── caption_videos.py # Automatic video captioning │ ├── decode_latents.py # Latent decoding for debugging -│ ├── inference.py # Inference with trained models │ ├── compute_reference.py # Generate IC-LoRA reference videos │ └── split_scenes.py # Scene detection and splitting ├── configs/ # Example training configurations -│ ├── ltx2_av_lora.yaml # Audio-video LoRA training -│ ├── ltx2_av_lora_low_vram.yaml -│ ├── ltx2_v2v_ic_lora.yaml # IC-LoRA video-to-video +│ ├── t2v_lora.yaml # Text-to-video LoRA +│ ├── t2v_lora_low_vram.yaml # Text-to-video LoRA (low VRAM) +│ ├── i2v_lora.yaml # Image-to-video LoRA +│ ├── v2v_ic_lora.yaml # IC-LoRA video-to-video +│ ├── a2v_lora.yaml # Audio-to-video LoRA +│ ├── v2a_lora.yaml # Video-to-audio LoRA +│ ├── video_extend_lora.yaml # Video extension (forward) +│ ├── video_suffix_lora.yaml # Video extension (backward) +│ ├── video_inpainting_lora.yaml # Video inpainting +│ ├── video_outpainting_lora.yaml # Video outpainting +│ ├── t2a_lora.yaml # Text-to-audio LoRA +│ ├── audio_extend_lora.yaml # Audio extension (forward) +│ ├── audio_suffix_lora.yaml # Audio extension (backward) +│ ├── audio_inpainting_lora.yaml # Audio inpainting +│ ├── a2a_ic_lora.yaml # Audio-to-audio IC-LoRA +│ ├── av2av_ic_lora.yaml # AV2AV IC-LoRA │ └── accelerate/ # FSDP, DDP configs ├── tests/ # Pytest tests └── docs/ # Documentation @@ -83,7 +108,8 @@ packages/ltx-trainer/ `load_text_encoder()`, `load_embeddings_processor()`, etc. - Combined loader: `load_model()` returns `LtxModelComponents` dataclass - Uses `SingleGPUModelBuilder` from ltx-core internally -- Text encoder and embeddings processor are loaded separately (the text encoder only needs Gemma weights; the embeddings processor only needs the LTX checkpoint) +- Text encoder and embeddings processor are loaded separately (the text encoder only needs Gemma weights; the embeddings + processor only needs the LTX checkpoint) - 8-bit text encoder loading via `gemma_8bit.py` (bitsandbytes) **Training Flow:** @@ -94,7 +120,7 @@ packages/ltx-trainer/ kept) 4. Each training step: embedding connectors applied → strategy prepares `ModelInputs` → transformer forward pass → strategy computes loss -5. Training strategies (`TextToVideoStrategy`, `VideoToVideoStrategy`) handle mode-specific logic +5. Training strategies (`FlexibleStrategy`) handle mode-specific logic including conditioning, masking, and loss computation 6. Accelerate handles distributed training, mixed precision, and device placement 7. Data flows as precomputed latents through `PrecomputedDataset` @@ -136,7 +162,12 @@ LTX-2.3) and cross-modality (video↔audio) attention conditioning (both version - All config in `src/ltx_trainer/config.py` - Main class: `LtxTrainerConfig` -- Training strategy configs: `TextToVideoConfig`, `VideoToVideoConfig` +- `TrainingStrategyConfig` - Union of `FlexibleStrategyConfig` | `TextToVideoConfig` (deprecated) | `VideoToVideoConfig` (deprecated) +- `FlexibleStrategyConfig` - Unified strategy config with `video`/`audio` `ModalityConfig` blocks +- `ModalityConfig` - Per-modality config: `is_generated`, `latents_dir`, `conditions` list +- `ConditionConfig` - Discriminated union: `FirstFrameConditionConfig`, `PrefixConditionConfig`, `SuffixConditionConfig`, `SpatialCropConditionConfig`, `MaskConditionConfig`, `ReferenceConditionConfig` +- `ValidationSample` - Per-sample validation config with `prompt`, `conditions`, optional `video_dims`/`seed` overrides +- `ValidationCondition` - Discriminated union for validation conditions (first_frame, prefix, suffix, spatial_crop, mask, reference, video_to_audio, audio_to_video) - Uses Pydantic field validators and model validators - Config uses `extra="forbid"` — unknown fields cause validation errors - Config files in `configs/` directory @@ -206,8 +237,8 @@ These values are shared across all supported model versions: | Video latent channels | 128 | VAE encoder/decoder, patchifier, `VideoLatentShape` | | Spatial compression | 32× (H and W) | `SpatioTemporalScaleFactors.default()`, config validators | | Temporal compression | 8× | `SpatioTemporalScaleFactors.default()`, config validators | -| Frame constraint | `frames % 8 == 1` | Config validators, validation sampler | -| Resolution constraint | Width and height divisible by 32 | Config validators, validation sampler | +| Frame constraint | `frames % 8 == 1` | Config validators, validation runner | +| Resolution constraint | Width and height divisible by 32 | Config validators, validation runner | | Audio latent channels | 8 | `AudioLatentShape`, audio patchifier | | Audio mel bins | 16 | `AudioLatentShape`, audio patchifier | | Patchified token dim (video) | 128 (`128 × 1 × 1 × 1`) | Transformer `in_channels` | @@ -245,12 +276,53 @@ uv run pytest ```bash # Single GPU -uv run python scripts/train.py configs/ltx2_av_lora.yaml +uv run python scripts/train.py configs/t2v_lora.yaml # Multi-GPU with Accelerate -uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch scripts/train.py configs/t2v_lora.yaml ``` +## Testing Standards + +### Structure + +- **Flat functions only** — use `def test_*()`, never `class Test*` with methods. Pytest collects standalone functions. +- **Only test public interfaces** — never call private methods (`_method`) directly. Verify private behavior + indirectly through the public API. + +### What to Test + +- **Custom validators and business logic** — cross-field validators, domain constraints, error paths. These catch real + bugs. +- **Behavioral tests** — call the public method, verify the outputs have the right shape, values, and structure. One + behavioral test is worth ten config-only tests. +- **Edge cases and error paths** — boundary conditions, composed behaviors, expected exceptions. +- **Contract tests** — required fields, rejected invalid inputs, safety mechanisms like `extra="forbid"`. + +### What NOT to Test + +- **Pydantic storing a value** — `Foo(x=1); assert foo.x == 1` tests Pydantic, not your code. If a behavioral test + already creates the same config and uses it, the config-only test adds nothing. +- **Pydantic Literal defaults** — `assert config.type == "first_frame"` when `type` is `Literal["first_frame"]`. +- **Pydantic default factories** — `assert config.conditions == []` when the field has `default_factory=list`. +- **Tests already covered by behavioral tests** — if `test_prefix_conditioning` creates a valid `PrefixConditionConfig` + and exercises it end-to-end, a separate `test_prefix_valid` that just creates the same config is redundant. +- **Trivial instantiation tests** — `strategy = Strategy(config); assert strategy.config is not None` when every other + test creates a strategy. + +### Keeping Tests DRY + +- **Use helper functions** for repeated setup patterns (e.g., `_make_strategy(video=_video_modality(...))` instead of + 6-8 lines of config/strategy creation per test). +- **Use named constants** for test dimensions (e.g., `VIDEO_SEQ_LEN`, `TOKENS_PER_FRAME`) instead of magic numbers. +- **Merge tests that share identical setup** — when 5+ tests call `prepare_training_inputs` with the exact same + config and batch, each checking one assertion, merge them into one test that checks all assertions. Pytest reports + the exact failing line anyway. +- **Use `@pytest.mark.parametrize`** for the same logic tested with different inputs (e.g., valid/invalid values for + a field). +- **Use pytest fixtures** for shared batch data and test directories, but prefer explicit helper functions over + fixtures for strategy/config creation (makes the test self-documenting). + ## Code Standards ### Type Hints @@ -286,7 +358,12 @@ Key classes: - `LtxTrainerConfig` - Main configuration container - `ModelConfig` - Model paths, training mode (`lora` | `full`), checkpoint loading -- `TrainingStrategyConfig` - Union of `TextToVideoConfig` | `VideoToVideoConfig` (discriminated by `name`) +- `TrainingStrategyConfig` - Union of `FlexibleStrategyConfig` | `TextToVideoConfig` (deprecated) | `VideoToVideoConfig` (deprecated) +- `FlexibleStrategyConfig` - Unified strategy config with `video`/`audio` `ModalityConfig` blocks +- `ModalityConfig` - Per-modality config: `is_generated`, `latents_dir`, `conditions` list +- `ConditionConfig` - Discriminated union: `FirstFrameConditionConfig`, `PrefixConditionConfig`, `SuffixConditionConfig`, `SpatialCropConditionConfig`, `MaskConditionConfig`, `ReferenceConditionConfig` +- `ValidationSample` - Per-sample validation config with `prompt`, `conditions`, optional `video_dims`/`seed` overrides +- `ValidationCondition` - Discriminated union for validation conditions (first_frame, prefix, suffix, spatial_crop, mask, reference, video_to_audio, audio_to_video) - `LoraConfig` - Rank, alpha, dropout, target modules - `OptimizationConfig` - Learning rate, batch size, gradient accumulation, scheduler, gradient checkpointing - `AccelerationConfig` - Mixed precision, quantization, 8-bit text encoder @@ -310,21 +387,23 @@ Key classes: - Implements distributed training with Accelerate - Handles mixed precision, gradient accumulation, checkpointing - `_training_step()` applies embedding connectors then delegates to strategy -- `_load_text_encoder_and_cache_embeddings()` loads the text encoder + embeddings processor, caches validation embeddings, then unloads the Gemma LLM (keeps only the embeddings processor connectors for training) +- `_load_text_encoder_and_cache_embeddings()` loads the text encoder + embeddings processor, caches validation + embeddings, then unloads the Gemma LLM (keeps only the embeddings processor connectors for training) - Uses training strategies for mode-specific logic **`src/ltx_trainer/training_strategies/`** - Strategy pattern - `base_strategy.py`: `TrainingStrategy` ABC, `ModelInputs` dataclass -- `text_to_video.py`: Standard text-to-video (with optional audio) -- `video_to_video.py`: IC-LoRA video-to-video transformations +- `flexible.py`: FlexibleStrategy — unified conditioning framework (recommended) +- `text_to_video.py`: TextToVideoStrategy (deprecated — use FlexibleStrategy) +- `video_to_video.py`: VideoToVideoStrategy (deprecated — use FlexibleStrategy) Key methods each strategy implements: -- `get_data_sources()` - Required data directories - `prepare_training_inputs()` - Convert batch to `ModelInputs` with `Modality` objects - `compute_loss()` - Calculate training loss (velocity prediction, MSE with masking) -- `requires_audio` property - Whether audio components needed + +The strategy's **config** declares its data directories via `get_data_sources()` (single source of truth, used for both dataset wiring and existence validation). **`src/ltx_trainer/model_loader.py`** - Model loading @@ -339,14 +418,13 @@ Component loaders: - `load_embeddings_processor(checkpoint_path)` → `EmbeddingsProcessor` (feature extractor + connectors) - `load_model()` → `LtxModelComponents` (convenience wrapper) -**`src/ltx_trainer/validation_sampler.py`** - Inference for validation +**`src/ltx_trainer/validation_runner.py`** - Conditioned validation sampling -Uses ltx-core components for denoising: - -- `LTX2Scheduler` for sigma scheduling -- `EulerDiffusionStep` for diffusion steps -- `CFGGuider` for classifier-free guidance -- `STGGuider` for spatio-temporal guidance +- Manages the full validation lifecycle: embedding caching, media encoding, denoising, decoding +- Supports all validation condition types: first_frame, prefix, suffix, spatial_crop, mask, reference, video_to_audio, audio_to_video +- Handles frozen modality paths (sigma=0 for conditioning modality) +- Builds conditioning items using ltx-core's `VideoConditionByLatentIndex`, `VideoConditionByReferenceLatent`, `VideoConditionByMask` +- Optional side-by-side reference output for IC-LoRA validation **`src/ltx_trainer/timestep_samplers.py`** - Flow matching timestep sampling @@ -367,12 +445,18 @@ constructs the `GemmaTextEncoder` with quantized model, feature extractor, and e **`src/ltx_trainer/datasets.py`** - Dataset handling - `PrecomputedDataset` loads pre-computed VAE latents and text embeddings -- Supports video latents, audio latents, text embeddings, reference latents (for IC-LoRA) +- Supports video latents, audio latents, text embeddings, reference video latents, reference audio latents, video masks, and audio masks - Handles legacy patchified format `[seq_len, C]` → automatically unpatchifies to `[C, F, H, W]` - `DummyDataset` for benchmarking and minimal testing ## Common Development Tasks +### Agent-Assisted Training + +When a user asks to train, fine-tune, create a LoRA, or produce a custom LTX-2 model, use the repository skill at +[`.claude/skills/train-model`](../../.claude/skills/train-model/SKILL.md). The skill is the orchestrator for dataset probing, mode selection, preprocessing, +training launch, monitoring, and post-train validation; it treats `packages/ltx-trainer/docs/` as the source of truth. + ### Adding a New Configuration Parameter 1. Add field to appropriate config class in `src/ltx_trainer/config.py` @@ -382,10 +466,16 @@ constructs the `GemmaTextEncoder` with quantized model, feature extractor, and e ### Implementing a New Training Strategy +The `FlexibleStrategy` now covers all use cases (T2V, T2A, I2V, V2V, A2A, AV2AV, inpainting, outpainting, extension, A2V, V2A, IC-LoRA) through +configuration alone. A new strategy is only needed for fundamentally different training paradigms that cannot be +expressed via `ModalityConfig` + `ConditionConfig` combinations. + +If you do need a new strategy: + 1. Create new file in `src/ltx_trainer/training_strategies/` -2. Create config class inheriting `TrainingStrategyConfigBase` +2. Create config class inheriting `TrainingStrategyConfigBase` and implement `get_data_sources()` 3. Create strategy class inheriting `TrainingStrategy` -4. Implement: `get_data_sources()`, `prepare_training_inputs()`, `compute_loss()` +4. Implement: `prepare_training_inputs()`, `compute_loss()` 5. Add to `__init__.py`: import, add to `TrainingStrategyConfig` union, update factory 6. Add discriminator tag to config.py's `TrainingStrategyConfig` 7. Create example config file in `configs/` @@ -449,8 +539,8 @@ video_embeds, audio_embeds, binary_mask = text_encoder.embeddings_processor.crea - Validation errors: Check validators in `config.py` - Unknown fields: Config uses `extra="forbid"` — all fields must be defined -- Strategy validation: IC-LoRA requires `reference_videos` in validation config -- Video-to-video strategy requires `training_mode: "lora"` +- FlexibleStrategy requires at least one modality with `is_generated: true` +- Audio modality cannot use `first_frame` or `spatial_crop` conditions **Precomputed Data:** @@ -480,7 +570,7 @@ Width and height must be divisible by 32. ### Platform Requirements - Linux required (uses `triton` which is Linux-only) -- CUDA GPU with 24GB+ VRAM recommended (80GB+ for full fine-tuning) +- CUDA GPU with 32GB+ VRAM recommended ## Reference: ltx-core Key Components diff --git a/packages/ltx-trainer/README.md b/packages/ltx-trainer/README.md index 56b216d..08c1198 100644 --- a/packages/ltx-trainer/README.md +++ b/packages/ltx-trainer/README.md @@ -1,8 +1,10 @@ # LTX-2 Trainer This package provides tools and scripts for training and fine-tuning -Lightricks' **LTX-2** audio-video generation model. It enables LoRA training, full -fine-tuning, and training of video-to-video transformations (IC-LoRA) on custom datasets. +Lightricks' **LTX-2** audio-video generation model. It supports LoRA training, full +fine-tuning, and a flexible conditioning framework covering text-to-video, text-to-audio, image-to-video, +video extension, audio extension, video inpainting, audio inpainting, video outpainting, IC-LoRA for video, audio, and joint +audio-video references, audio-to-video, and video-to-audio. --- @@ -17,9 +19,16 @@ All detailed guides and technical documentation are in the [docs](./docs/) direc - [🚀 Training Guide](docs/training-guide.md) - [🧪 Inference Guide](../ltx-pipelines/README.md) - [🔧 Utility Scripts](docs/utility-scripts.md) +- [🧩 Custom Training Strategies](docs/custom-training-strategies.md) - [📚 LTX-Core Documentation](../ltx-core/README.md) - [🛡️ Troubleshooting Guide](docs/troubleshooting.md) +### 🤖 Agent-Assisted Training + +Use the [`train-model`](../../.claude/skills/train-model/SKILL.md) repository skill for an end-to-end guided run: +it probes your data and hardware, chooses the matching training mode, prepares/preprocesses the dataset, launches +training, and monitors the job while using the docs above as the source of truth. + --- ## 🔧 Requirements @@ -28,7 +37,7 @@ All detailed guides and technical documentation are in the [docs](./docs/) direc - **Gemma Text Encoder** - Local Gemma model directory (required for LTX-2) - **Linux with CUDA** - CUDA 13+ recommended for optimal performance - **Nvidia GPU with 80GB+ VRAM** - Recommended for the standard config. For GPUs with 32GB VRAM (e.g., RTX 5090), - use the [low VRAM config](configs/ltx2_av_lora_low_vram.yaml) which enables INT8 quantization and other + use the [low VRAM config](configs/t2v_lora_low_vram.yaml) which enables INT8 quantization and other memory optimizations --- diff --git a/packages/ltx-trainer/configs/a2a_ic_lora.yaml b/packages/ltx-trainer/configs/a2a_ic_lora.yaml new file mode 100644 index 0000000..300f54b --- /dev/null +++ b/packages/ltx-trainer/configs/a2a_ic_lora.yaml @@ -0,0 +1,340 @@ +# ============================================================================= +# LTX-2 Audio-to-Audio IC-LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training In-Context LoRA (IC-LoRA) adapters that +# enable audio-to-audio transformations. IC-LoRA learns to apply audio +# transformations (e.g., style transfer, voice conversion, sound effects, etc.) +# by conditioning on reference audio. +# +# Key differences from text-to-video LoRA: +# - Uses reference audio as conditioning input alongside text prompts +# - Requires preprocessed reference audio latents in addition to target latents +# - Audio-only training (no video modality) +# - Validation requires reference audio to demonstrate the transformation +# +# Dataset structure: +# preprocessed_data_root/ +# ├── conditions/ # Text embeddings for each sample +# ├── audio_latents/ # Target audio latents (what the model learns to generate) +# └── reference_audio_latents/ # Reference audio latents (conditioning input) +# +# Dataset metadata columns: audio, reference_audio, caption +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 16-32 for IC-LoRA. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES (not used for audio-only IC-LoRA): + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (not used for audio-only IC-LoRA): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # For audio-only IC-LoRA, we explicitly target audio modules. + # Including audio FFN layers often improves transformation quality. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward (often improves transformation quality) + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the audio-to-audio (IC-LoRA) training approach using the unified +# flexible strategy. Reference conditioning concatenates pre-encoded reference +# audio latents to the target sequence. Reference tokens participate in +# bidirectional self-attention but receive no noise and are excluded from loss. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Audio modality configuration (audio-only, no video) + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing target audio latents + latents_dir: "audio_latents" + + # Conditions applied to the audio modality during training + conditions: + # Reference conditioning (IC-LoRA): concatenates pre-encoded reference audio + # latents to the target sequence. The model learns to transform the reference + # into the target audio based on the text prompt. + - type: reference + # Directory name (within preprocessed_data_root) containing reference audio latents + # These are the conditioning inputs that guide the transformation + latents_dir: "reference_audio_latents" + # Probability of applying reference conditioning per training sample + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: audio_latents/, conditions/, and reference_audio_latents/ subdirectories + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # For IC-LoRA, each sample includes a reference condition pointing to the conditioning audio. + samples: + - prompt: >- + A warm and soothing piano melody with soft ambient textures, reminiscent of a quiet + evening by the fireplace. Gentle reverb creates a sense of intimate space. + conditions: + - type: reference + audio: "/path/to/reference_audio_1.wav" + - prompt: >- + Energetic electronic beats with pulsing synthesizer leads and crisp hi-hat patterns. + Deep bass tones provide a driving rhythm underneath bright melodic arpeggios. + conditions: + - type: reference + audio: "/path/to/reference_audio_2.wav" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Generation length control [width, height, frames] + # With generate_video=false, width/height are unused; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" skips both audio and video self-attention (suited to audio validation) + + # Whether to generate audio in validation samples + # Can be enabled even when not training the audio branch + generate_audio: true + + # Whether to generate video in validation samples + # Disabled for audio-only IC-LoRA since no video modality is configured + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "ic-lora", "a2a" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/a2a_ic_lora" diff --git a/packages/ltx-trainer/configs/a2v_lora.yaml b/packages/ltx-trainer/configs/a2v_lora.yaml new file mode 100644 index 0000000..45c91bf --- /dev/null +++ b/packages/ltx-trainer/configs/a2v_lora.yaml @@ -0,0 +1,344 @@ +# ============================================================================= +# LTX-2 Audio-to-Video LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# audio-to-video generation. The model learns to generate videos conditioned +# on a frozen audio signal via the transformer's built-in cross-modal attention. +# +# In this mode, audio is provided as a frozen (clean, no noise, no loss) +# conditioning signal. The video modality is the only generated output. +# Audio influences video generation through the transformer's audio-to-video +# cross-attention mechanism. +# +# Use this configuration when you want to: +# - Generate videos driven by audio content (e.g., music visualizations) +# - Train audio-reactive video generation +# - Create models that synchronize video with given audio +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# ├── conditions/ # Text embeddings for each video +# └── audio_latents/ # Audio latents (frozen conditioning input) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # Using short patterns like "to_k" matches ALL attention modules (video, audio, and cross-modal). + # For audio-video training, this is the recommended approach. + target_modules: + # Attention layers (matches both video and audio branches) + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + # - "audio_ff.net.0.proj" + # - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the audio-to-video training approach using the unified flexible strategy. +# Audio is frozen (no noise, sigma=0, excluded from loss) and conditions video +# generation via the transformer's built-in cross-modal attention. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + # Video is the generated (denoised) output + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Audio modality configuration + # Audio is frozen — it acts as conditioning for video generation + # Frozen modalities get sigma=0, timestep=0, no noise, and no loss + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + # When false, audio is passed through the transformer clean and influences + # video via cross-modal attention + is_generated: false + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, audio_latents/, and conditions/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A musician plays an acoustic guitar in a dimly lit recording studio, fingers moving + across the fretboard with practiced ease. The warm amber light from a desk lamp + illuminates the wooden guitar body. Sound-absorbing panels line the walls, and a + microphone stands nearby on a boom arm. + conditions: + - type: audio_to_video + audio: "/path/to/conditioning_audio_1.wav" + - prompt: >- + Rain falls steadily on a cobblestone street in a European town at dusk. Puddles form + between the stones, creating ripples as new drops land. Old brick buildings line both + sides of the narrow street, their facades glistening with moisture under warm + streetlights. + conditions: + - type: audio_to_video + audio: "/path/to/conditioning_audio_2.wav" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_v" # "stg_av" perturbs both audio and video, "stg_v" video only + + # Whether to generate audio in validation samples + # Independent of training_strategy.audio.is_generated - you can generate audio + # in validation even when not training the audio branch + # For this audio-to-video config, false matches frozen audio conditioning (video-only synthesis). + generate_audio: false + + # Generate video from the frozen audio condition + generate_video: true + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "a2v" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/a2v_lora" diff --git a/packages/ltx-trainer/configs/audio_extend_lora.yaml b/packages/ltx-trainer/configs/audio_extend_lora.yaml new file mode 100644 index 0000000..fd65588 --- /dev/null +++ b/packages/ltx-trainer/configs/audio_extend_lora.yaml @@ -0,0 +1,343 @@ +# ============================================================================= +# LTX-2 Audio Extension (Forward) LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# forward audio extension. The model learns to continue audio forward in time +# by conditioning on a prefix of existing audio latent timesteps. +# +# Prefix conditioning works by providing the first N audio latent timesteps as +# clean conditioning signals during training — they receive no noise, +# timestep=0, and are excluded from the loss. The model learns to generate +# audio that seamlessly continues from the given prefix. +# +# This is an audio-only training mode — no video modality is configured. +# +# Use this configuration when you want to: +# - Train the model to extend/continue existing audio +# - Fine-tune audio temporal continuation capabilities +# - Create seamless audio extension models +# +# Dataset structure: +# preprocessed_data_root/ +# ├── conditions/ # Text embeddings for each sample +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for audio extension LoRA training. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES (not used for audio-only modes): + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (not used for audio-only modes): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # For audio-only extension, we explicitly target audio modules. + # Including audio FFN layers can increase the LoRA's capacity. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward (often improves transformation quality) + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the audio extension training approach using the unified flexible +# strategy. Prefix conditioning provides the first N audio latent timesteps as +# clean conditioning, teaching the model to generate temporal continuations. +training_strategy: + name: "flexible" + + # Audio modality configuration (audio-only, no video) + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + + # Conditions applied to the audio modality during training + conditions: + - type: prefix + # Number of audio latent timesteps to use as conditioning prefix + # Each audio latent timestep = 1 patchified token + temporal_boundary: 8 + # Probability of applying prefix conditioning per training sample + # At 1.0, all training samples use audio extension mode + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: conditions/ and audio_latents/ subdirectories + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Each sample includes an audio prefix condition for forward audio extension. + samples: + - prompt: >- + A warm and soothing piano melody with soft ambient textures, reminiscent of a quiet + evening by the fireplace. Gentle reverb creates a sense of intimate space. + conditions: + - type: prefix + audio: "/path/to/prefix_audio_1.wav" + # Duration is in seconds; choose a value that covers a comparable audio context + # to the training temporal_boundary measured in audio latent timesteps. + duration: 1.0 + - prompt: >- + Energetic electronic beats with pulsing synthesizer leads and crisp hi-hat patterns. + Deep bass tones provide a driving rhythm underneath bright melodic arpeggios. + conditions: + - type: prefix + audio: "/path/to/prefix_audio_2.wav" + # Duration is in seconds; choose a value that covers a comparable audio context + # to the training temporal_boundary measured in audio latent timesteps. + duration: 1.0 + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Generation length control [width, height, frames] + # With generate_video=false, width/height are unused; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" skips both audio and video self-attention (suited to audio validation) + + # Whether to generate audio in validation samples + # Enabled because this audio-only config generates audio + generate_audio: true + + # Whether to generate video in validation samples + # Disabled because no video modality is configured + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "audio-extension", "audio-only" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/audio_extend_lora" diff --git a/packages/ltx-trainer/configs/audio_inpainting_lora.yaml b/packages/ltx-trainer/configs/audio_inpainting_lora.yaml new file mode 100644 index 0000000..e5c8361 --- /dev/null +++ b/packages/ltx-trainer/configs/audio_inpainting_lora.yaml @@ -0,0 +1,324 @@ +# ============================================================================= +# LTX-2 Audio Inpainting LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# audio inpainting. The model learns to fill in masked regions of audio +# using per-sample binary masks that define which regions are conditioning +# (provided clean) and which regions the model must generate. +# +# Mask conditioning works by loading per-sample binary masks from disk. +# Masked regions receive clean latents (no noise, timestep=0) and are excluded +# from the loss. Unmasked regions are noised and trained normally. +# +# This is an audio-only training mode — no video modality is configured. +# +# Use this configuration when you want to: +# - Train the model to fill in or replace regions of existing audio +# - Fine-tune audio inpainting capabilities on custom datasets +# - Create audio restoration or editing models +# +# Dataset structure: +# preprocessed_data_root/ +# ├── conditions/ # Text embeddings for each sample +# ├── audio_latents/ # Audio latents (VAE-encoded audio) +# └── audio_masks/ # Per-sample binary masks defining conditioning regions +# +# Dataset metadata columns: audio, audio_mask, caption +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for audio inpainting LoRA training. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # For audio-only inpainting, we explicitly target audio modules. + # Including audio FFN layers can increase the LoRA's capacity. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward (often improves transformation quality) + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the audio inpainting training approach using the unified flexible +# strategy. Per-sample binary masks define which audio regions are provided +# as clean conditioning and which regions the model must learn to generate. +training_strategy: + name: "flexible" + + # Audio modality configuration (audio-only, no video) + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + + # Conditions applied to the audio modality during training + conditions: + - type: mask + # Directory name (within preprocessed_data_root) containing binary masks + # Each mask file corresponds to a training sample and defines the + # conditioning region (mask=1 means conditioning, mask=0 means generate) + mask_dir: "audio_masks" + # Probability of applying mask conditioning per training sample + # At 1.0, all training samples use inpainting mode + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: conditions/, audio_latents/, and audio_masks/ subdirectories + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # For audio inpainting, each sample includes mask conditioning (audio + mask paths). + samples: + - prompt: >- + A warm and soothing piano melody with soft ambient textures, reminiscent of a quiet + evening by the fireplace. Gentle reverb creates a sense of intimate space. + conditions: + - type: mask + audio: "/path/to/inpainting_audio_1.wav" + mask: "/path/to/inpainting_mask_1.pt" + - prompt: >- + Energetic electronic beats with pulsing synthesizer leads and crisp hi-hat patterns. + Deep bass tones provide a driving rhythm underneath bright melodic arpeggios. + conditions: + - type: mask + audio: "/path/to/inpainting_audio_2.wav" + mask: "/path/to/inpainting_mask_2.pt" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Generation length control [width, height, frames] + # With generate_video=false, width/height are unused; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" skips both audio and video self-attention (suited to audio validation) + + # Whether to generate audio in validation samples + # Enabled because this audio-only config generates audio + generate_audio: true + + # Whether to generate video in validation samples + # Disabled because no video modality is configured + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "audio-inpainting", "audio-only" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/audio_inpainting_lora" diff --git a/packages/ltx-trainer/configs/audio_suffix_lora.yaml b/packages/ltx-trainer/configs/audio_suffix_lora.yaml new file mode 100644 index 0000000..9ef48a1 --- /dev/null +++ b/packages/ltx-trainer/configs/audio_suffix_lora.yaml @@ -0,0 +1,343 @@ +# ============================================================================= +# LTX-2 Audio Extension (Backward) LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# backward audio extension. The model learns to generate audio content leading into existing audio +# by conditioning on a suffix of existing audio latent timesteps. +# +# Suffix conditioning works by providing the last N audio latent timesteps as +# clean conditioning signals during training — they receive no noise, +# timestep=0, and are excluded from the loss. The model learns to generate +# audio that seamlessly leads into the given suffix. +# +# This is an audio-only training mode — no video modality is configured. +# +# Use this configuration when you want to: +# - Train the model to generate preceding content for existing audio +# - Fine-tune audio temporal continuation capabilities +# - Create seamless audio extension models +# +# Dataset structure: +# preprocessed_data_root/ +# ├── conditions/ # Text embeddings for each sample +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for audio extension LoRA training. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES (not used for audio-only modes): + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (not used for audio-only modes): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # For audio-only extension, we explicitly target audio modules. + # Including audio FFN layers can increase the LoRA's capacity. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward (often improves transformation quality) + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the backward audio extension training approach using the unified flexible +# strategy. Suffix conditioning provides the last N audio latent timesteps as +# clean conditioning, teaching the model to generate content leading into existing audio. +training_strategy: + name: "flexible" + + # Audio modality configuration (audio-only, no video) + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + + # Conditions applied to the audio modality during training + conditions: + - type: suffix + # Number of audio latent timesteps to use as conditioning suffix + # Each audio latent timestep = 1 patchified token + temporal_boundary: 8 + # Probability of applying suffix conditioning per training sample + # At 1.0, all training samples use audio extension mode + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: conditions/ and audio_latents/ subdirectories + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Each sample includes an audio suffix condition for backward audio extension. + samples: + - prompt: >- + A warm and soothing piano melody with soft ambient textures, reminiscent of a quiet + evening by the fireplace. Gentle reverb creates a sense of intimate space. + conditions: + - type: suffix + audio: "/path/to/suffix_audio_1.wav" + # Duration is in seconds; choose a value that covers a comparable audio context + # to the training temporal_boundary measured in audio latent timesteps. + duration: 1.0 + - prompt: >- + Energetic electronic beats with pulsing synthesizer leads and crisp hi-hat patterns. + Deep bass tones provide a driving rhythm underneath bright melodic arpeggios. + conditions: + - type: suffix + audio: "/path/to/suffix_audio_2.wav" + # Duration is in seconds; choose a value that covers a comparable audio context + # to the training temporal_boundary measured in audio latent timesteps. + duration: 1.0 + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Generation length control [width, height, frames] + # With generate_video=false, width/height are unused; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" skips both audio and video self-attention (suited to audio validation) + + # Whether to generate audio in validation samples + # Enabled because this audio-only config generates audio + generate_audio: true + + # Whether to generate video in validation samples + # Disabled because no video modality is configured + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "audio-suffix", "audio-only" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/audio_suffix_lora" diff --git a/packages/ltx-trainer/configs/av2av_ic_lora.yaml b/packages/ltx-trainer/configs/av2av_ic_lora.yaml new file mode 100644 index 0000000..78d12c9 --- /dev/null +++ b/packages/ltx-trainer/configs/av2av_ic_lora.yaml @@ -0,0 +1,342 @@ +# ============================================================================= +# LTX-2 AV2AV IC-LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training In-Context LoRA (IC-LoRA) adapters that +# enable joint audio-video-to-audio-video transformations. IC-LoRA learns to +# apply transformations to both the video and audio modalities simultaneously +# by conditioning on paired reference video and audio. +# +# Both modalities use reference conditioning: pre-encoded reference latents +# are concatenated to each modality's target sequence. Reference tokens +# participate in bidirectional self-attention but receive no noise and are +# excluded from the loss. +# +# Key differences from video-only IC-LoRA (v2v_ic_lora.yaml): +# - Both video AND audio have reference conditions +# - Requires preprocessed reference latents for BOTH modalities +# - LoRA targets all modules (video, audio, and cross-modal attention) +# - Validation uses both video and audio reference conditions +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Target video latents +# ├── audio_latents/ # Target audio latents +# ├── conditions/ # Text embeddings +# ├── reference_latents/ # Reference video latents (conditioning input) +# └── reference_audio_latents/ # Reference audio latents (conditioning input) +# +# Dataset metadata columns: video, audio, reference_video, reference_audio, caption +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # IC-LoRA reference conditioning is intended for LoRA adapter training. + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 16-32 for IC-LoRA. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # For AV2AV IC-LoRA, we target ALL modules — video, audio, and cross-modal attention. + # Using short patterns matches all branches simultaneously. + target_modules: + # Attention layers (matches video, audio, and cross-modal branches) + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the AV2AV IC-LoRA training approach using the unified flexible +# strategy. Both video and audio modalities have reference conditioning, +# enabling joint audiovisual transformations. +training_strategy: + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing target video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + conditions: + # Reference conditioning (IC-LoRA): concatenates pre-encoded reference video + # latents to the target sequence + - type: reference + latents_dir: "reference_latents" + probability: 1.0 + + # Audio modality configuration + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing target audio latents + latents_dir: "audio_latents" + + # Conditions applied to the audio modality during training + conditions: + # Reference conditioning (IC-LoRA): concatenates pre-encoded reference audio + # latents to the target sequence + - type: reference + latents_dir: "reference_audio_latents" + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, audio_latents/, conditions/, reference_latents/, and reference_audio_latents/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # For AV2AV IC-LoRA, each sample includes reference video and reference audio conditions. + samples: + - prompt: >- + A man in a casual blue jacket walks along a winding path through a lush green park on a + bright sunny afternoon. Tall oak trees line the pathway, their leaves rustling gently in + the breeze. Dappled sunlight creates shifting patterns on the ground as he strolls at a + relaxed pace, occasionally looking up at the scenery around him. The audio captures + footsteps on gravel, birds singing in the trees, distant children playing, and the soft + whisper of wind through the foliage. + conditions: + - type: reference + video: "/path/to/reference_video_1.mp4" + downscale_factor: 1 + temporal_scale_factor: 1 + include_in_output: true + - type: reference + audio: "/path/to/reference_audio_1.wav" + - prompt: >- + A fluffy orange tabby cat sits perfectly still on a wooden windowsill, its green eyes + intently tracking small birds hopping on a branch just outside the glass. The cat's ears + twitch and rotate, following every movement. Warm afternoon light illuminates its fur, + creating a soft golden glow. Behind the cat, a cozy living room with a bookshelf and + houseplants is visible. The audio features gentle purring, occasional soft meows, muffled + bird chirps through the window, and quiet ambient room sounds. + conditions: + - type: reference + video: "/path/to/reference_video_2.mp4" + downscale_factor: 1 + temporal_scale_factor: 1 + include_in_output: true + - type: reference + audio: "/path/to/reference_audio_2.wav" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # Both video and audio modalities are trained + + # Whether to generate audio in validation samples + # Can be enabled even when not training the audio branch + generate_audio: true + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "ic-lora", "av2av" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/av2av_ic_lora" diff --git a/packages/ltx-trainer/configs/i2v_lora.yaml b/packages/ltx-trainer/configs/i2v_lora.yaml new file mode 100644 index 0000000..373a791 --- /dev/null +++ b/packages/ltx-trainer/configs/i2v_lora.yaml @@ -0,0 +1,348 @@ +# ============================================================================= +# LTX-2 Image-to-Video LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# image-to-video generation. The model learns to generate videos conditioned +# on a starting image (first frame), with optional audio generation. +# +# First-frame conditioning works by providing the first frame as a clean +# conditioning signal during training — it receives no noise, timestep=0, +# and is excluded from the loss. This teaches the model to animate from +# a given image. +# +# Use this configuration when you want to: +# - Train the model to generate videos starting from a given image +# - Fine-tune image-to-video capabilities on custom datasets +# - Create image animation models with optional audio +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# ├── conditions/ # Text embeddings for each video +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # Using short patterns like "to_k" matches ALL attention modules (video, audio, and cross-modal). + # For audio-video training, this is the recommended approach. + target_modules: + # Attention layers (matches both video and audio branches) + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + # - "audio_ff.net.0.proj" + # - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the image-to-video training approach using the unified flexible strategy. +# First-frame conditioning provides the first frame as clean conditioning signal +# during training, teaching the model to animate from a given image. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + # First-frame conditioning: the first frame of each video is provided as a clean + # conditioning signal (no noise, timestep=0, excluded from loss) + conditions: + - type: first_frame + # Probability of applying first-frame conditioning per training sample + # At 0.5, half the training samples use I2V mode, half use pure T2V + # Higher values improve I2V quality but may reduce T2V diversity + probability: 0.5 + + # Audio modality configuration + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, conditions/, and audio_latents/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. The audio captures the + gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with + occasional distant bird chirps from outside. + conditions: + - type: first_frame + image_or_video: "/path/to/conditioning_image_1.png" + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. The audio features the sizzling of pans, + the clinking of utensils against plates, and the ambient hum of kitchen ventilation. + conditions: + - type: first_frame + image_or_video: "/path/to/conditioning_image_2.png" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only + + # Whether to generate audio in validation samples + # Independent of training_strategy.audio.is_generated - you can generate audio + # in validation even when not training the audio branch + generate_audio: true + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "i2v" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/i2v_lora" diff --git a/packages/ltx-trainer/configs/t2a_lora.yaml b/packages/ltx-trainer/configs/t2a_lora.yaml new file mode 100644 index 0000000..641a9bc --- /dev/null +++ b/packages/ltx-trainer/configs/t2a_lora.yaml @@ -0,0 +1,317 @@ +# ============================================================================= +# LTX-2 Text-to-Audio LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# text-to-audio generation. The model learns to generate audio from text +# prompts without any additional conditioning. +# +# This is the simplest audio-only training mode — no reference audio, no +# video modality. Only the audio branch of the transformer is trained. +# +# Use this configuration when you want to: +# - Fine-tune audio generation for specific sound styles or domains +# - Train custom audio generation capabilities +# - Create audio LoRAs that can be combined with video LoRAs +# +# Dataset structure: +# preprocessed_data_root/ +# ├── conditions/ # Text embeddings for each sample +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general audio LoRA training. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES (not used for audio-only modes): + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (not used for audio-only modes): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # For audio-only training, we explicitly target audio modules. + # Including audio FFN layers can increase the LoRA's capacity. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward (often improves transformation quality) + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the text-to-audio training approach using the unified flexible +# strategy. Audio-only training with no additional conditions — the model +# learns to generate audio purely from text prompts. +training_strategy: + name: "flexible" + + # Audio modality configuration (audio-only, no video) + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 2e-4 + + # Total number of training steps + steps: 3000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: conditions/ and audio_latents/ subdirectories + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Text-to-audio validation samples do not need additional conditions. + samples: + - prompt: >- + A warm and soothing piano melody with soft ambient textures, reminiscent of a quiet + evening by the fireplace. Gentle reverb creates a sense of intimate space. + - prompt: >- + Energetic electronic beats with pulsing synthesizer leads and crisp hi-hat patterns. + Deep bass tones provide a driving rhythm underneath bright melodic arpeggios. + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Generation length control [width, height, frames] + # With generate_video=false, width/height are unused; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 512, 512, 81 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" skips both audio and video self-attention (suited to audio validation) + + # Whether to generate audio in validation samples + # Enabled because this audio-only config generates audio + generate_audio: true + + # Whether to generate video in validation samples + # Disabled because no video modality is configured + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: 3 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-ic-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "t2a", "audio-only" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/t2a_lora" diff --git a/packages/ltx-trainer/configs/ltx2_av_lora.yaml b/packages/ltx-trainer/configs/t2v_lora.yaml similarity index 77% rename from packages/ltx-trainer/configs/ltx2_av_lora.yaml rename to packages/ltx-trainer/configs/t2v_lora.yaml index 45e3a61..5d8fdae 100644 --- a/packages/ltx-trainer/configs/ltx2_av_lora.yaml +++ b/packages/ltx-trainer/configs/t2v_lora.yaml @@ -1,21 +1,20 @@ # ============================================================================= -# LTX-2 Audio-Video LoRA Training Configuration +# LTX-2 Text-to-Video LoRA Training Configuration # ============================================================================= # # This configuration is for training LoRA adapters on the LTX-2 model for -# text-to-video generation. It supports both video-only and joint audio-video -# training modes. +# text-to-video generation with joint audio-video support. # # Use this configuration when you want to: # - Fine-tune LTX-2 on your own video dataset -# - Train with or without audio generation +# - Train joint audio-video generation from text prompts # - Create custom video generation styles or audiovisual concepts # -# Dataset structure for text-to-video training: +# Dataset structure: # preprocessed_data_root/ # ├── latents/ # Video latents (VAE-encoded videos) # ├── conditions/ # Text embeddings for each video -# └── audio_latents/ # Audio latents (only if with_audio: true) +# └── audio_latents/ # Audio latents (VAE-encoded audio) # # ============================================================================= @@ -93,24 +92,29 @@ lora: # ----------------------------------------------------------------------------- # Training Strategy Configuration # ----------------------------------------------------------------------------- -# Defines the text-to-video training approach. +# Defines the training approach using the unified flexible strategy. +# This configuration trains both video and audio generation from text prompts. training_strategy: - # Strategy name: "text_to_video" for standard text-to-video training - name: "text_to_video" + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" - # Probability of conditioning on the first frame during training - # Higher values train the model to perform better in image-to-video (I2V) mode, - # where a clean first frame is provided and the model generates the rest of the video - # Increase this value to train the model to perform better in image-to-video (I2V) mode - first_frame_conditioning_p: 0.5 + # Video modality configuration + # When is_generated is true, the model learns to generate (denoise) video + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" - # Enable joint audio-video training - # Set to true if your dataset includes audio and you want to train the audio branch - with_audio: true - - # Directory name (within preprocessed_data_root) containing audio latents - # Only used when with_audio is true - audio_latents_dir: "audio_latents" + # Audio modality configuration + # When is_generated is true, the model learns to generate (denoise) audio + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" # ----------------------------------------------------------------------------- # Optimization Configuration @@ -166,18 +170,19 @@ acceleration: # Useful when GPU memory is limited load_text_encoder_in_8bit: false - # Offload optimizer state to CPU during validation video sampling. - # Helps avoid OOM when VAE decoder + transformer + optimizer state can't coexist - # on the GPU (full fine-tune, high-rank LoRA). No effect for FSDP. + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). offload_optimizer_during_validation: false + # ----------------------------------------------------------------------------- # Data Configuration # ----------------------------------------------------------------------------- # Specifies the training data location and loading parameters. data: # Root directory containing preprocessed training data - # Should contain: latents/, conditions/, and optionally audio_latents/ + # Should contain: latents/, conditions/, and audio_latents/ preprocessed_data_root: "/path/to/preprocessed/data" # Number of worker processes for data loading @@ -187,24 +192,31 @@ data: # ----------------------------------------------------------------------------- # Validation Configuration # ----------------------------------------------------------------------------- -# Controls validation video generation during training. +# Controls validation sampling during training. # NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over # maximum quality. For production-quality inference, use `packages/ltx-pipelines`. validation: - # Text prompts for validation video generation - # Provide prompts representative of your training data - # LTX-2 prefers longer, detailed prompts that describe both visual content and audio - prompts: - - "A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a laptop while occasionally glancing at notes beside her. Soft natural light streams through a large window, casting warm shadows across the room. She pauses to take a sip from a ceramic mug, then continues working with focused concentration. The audio captures the gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with occasional distant bird chirps from outside." - - "A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet dish with precise movements. Steam rises from freshly cooked vegetables as he arranges them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and various pots simmer on the stove behind him. The audio features the sizzling of pans, the clinking of utensils against plates, and the ambient hum of kitchen ventilation." + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. The audio captures the + gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with + occasional distant bird chirps from outside. + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. The audio features the sizzling of pans, + the clinking of utensils against plates, and the ambient hum of kitchen ventilation. # Negative prompt to avoid unwanted artifacts negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" - # Optional: First frame images for image-to-video validation - # If provided, must have one image per prompt - images: null - # Output video dimensions [width, height, frames] # Width and height must be divisible by 32 # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) @@ -235,7 +247,7 @@ validation: stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only # Whether to generate audio in validation samples - # Independent of training_strategy.with_audio - you can generate audio + # Independent of training_strategy.audio.is_generated - you can generate audio # in validation even when not training the audio branch generate_audio: true @@ -298,9 +310,9 @@ wandb: entity: null # Tags to help organize runs - tags: [ "ltx2", "lora" ] + tags: [ "ltx2", "lora", "t2v" ] - # Log validation videos to W&B + # Log validation media (video/audio) to W&B log_validation_videos: true # ----------------------------------------------------------------------------- @@ -312,4 +324,4 @@ wandb: seed: 42 # Directory to save outputs (checkpoints, validation videos, logs) -output_dir: "outputs/ltx2_av_lora" +output_dir: "outputs/t2v_lora" diff --git a/packages/ltx-trainer/configs/ltx2_av_lora_low_vram.yaml b/packages/ltx-trainer/configs/t2v_lora_low_vram.yaml similarity index 78% rename from packages/ltx-trainer/configs/ltx2_av_lora_low_vram.yaml rename to packages/ltx-trainer/configs/t2v_lora_low_vram.yaml index 1868171..2d2b434 100644 --- a/packages/ltx-trainer/configs/ltx2_av_lora_low_vram.yaml +++ b/packages/ltx-trainer/configs/t2v_lora_low_vram.yaml @@ -1,8 +1,8 @@ # ============================================================================= -# LTX-2 Audio-Video LoRA Training Configuration (Low VRAM) +# LTX-2 Text-to-Video LoRA Training Configuration (Low VRAM) # ============================================================================= # -# This is a memory-optimized variant of the standard audio-video LoRA config. +# This is a memory-optimized variant of the standard text-to-video LoRA config. # It uses 8-bit optimizer, int8 quantization, and reduced LoRA rank to minimize # GPU memory usage while maintaining good training quality. # @@ -15,15 +15,15 @@ # Recommended for GPUs with 32GB VRAM (e.g., RTX 5090). # # Use this configuration when you want to: -# - Fine-tune LTX-2 on your own video dataset -# - Train with or without audio generation +# - Fine-tune LTX-2 on your own video dataset with limited GPU memory +# - Train joint audio-video generation from text prompts # - Create custom video generation styles or audiovisual concepts # -# Dataset structure for text-to-video training: +# Dataset structure: # preprocessed_data_root/ # ├── latents/ # Video latents (VAE-encoded videos) # ├── conditions/ # Text embeddings for each video -# └── audio_latents/ # Audio latents (only if with_audio: true) +# └── audio_latents/ # Audio latents (VAE-encoded audio) # # ============================================================================= @@ -103,24 +103,29 @@ lora: # ----------------------------------------------------------------------------- # Training Strategy Configuration # ----------------------------------------------------------------------------- -# Defines the text-to-video training approach. +# Defines the training approach using the unified flexible strategy. +# This configuration trains both video and audio generation from text prompts. training_strategy: - # Strategy name: "text_to_video" for standard text-to-video training - name: "text_to_video" + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" - # Probability of conditioning on the first frame during training - # Higher values train the model to perform better in image-to-video (I2V) mode, - # where a clean first frame is provided and the model generates the rest of the video - # Increase this value to train the model to perform better in image-to-video (I2V) mode - first_frame_conditioning_p: 0.5 + # Video modality configuration + # When is_generated is true, the model learns to generate (denoise) video + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" - # Enable joint audio-video training - # Set to true if your dataset includes audio and you want to train the audio branch - with_audio: true - - # Directory name (within preprocessed_data_root) containing audio latents - # Only used when with_audio is true - audio_latents_dir: "audio_latents" + # Audio modality configuration + # When is_generated is true, the model learns to generate (denoise) audio + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" # ----------------------------------------------------------------------------- # Optimization Configuration @@ -178,9 +183,9 @@ acceleration: # Useful when GPU memory is limited load_text_encoder_in_8bit: true - # Offload optimizer state to CPU during validation video sampling. - # Helps avoid OOM when VAE decoder + transformer + optimizer state can't coexist - # on the GPU (full fine-tune, high-rank LoRA). No effect for FSDP. + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). offload_optimizer_during_validation: true # ----------------------------------------------------------------------------- @@ -189,7 +194,7 @@ acceleration: # Specifies the training data location and loading parameters. data: # Root directory containing preprocessed training data - # Should contain: latents/, conditions/, and optionally audio_latents/ + # Should contain: latents/, conditions/, and audio_latents/ preprocessed_data_root: "/path/to/preprocessed/data" # Number of worker processes for data loading @@ -199,24 +204,31 @@ data: # ----------------------------------------------------------------------------- # Validation Configuration # ----------------------------------------------------------------------------- -# Controls validation video generation during training. +# Controls validation sampling during training. # NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over # maximum quality. For production-quality inference, use `packages/ltx-pipelines`. validation: - # Text prompts for validation video generation - # Provide prompts representative of your training data - # LTX-2 prefers longer, detailed prompts that describe both visual content and audio - prompts: - - "A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a laptop while occasionally glancing at notes beside her. Soft natural light streams through a large window, casting warm shadows across the room. She pauses to take a sip from a ceramic mug, then continues working with focused concentration. The audio captures the gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with occasional distant bird chirps from outside." - - "A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet dish with precise movements. Steam rises from freshly cooked vegetables as he arranges them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and various pots simmer on the stove behind him. The audio features the sizzling of pans, the clinking of utensils against plates, and the ambient hum of kitchen ventilation." + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. The audio captures the + gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with + occasional distant bird chirps from outside. + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. The audio features the sizzling of pans, + the clinking of utensils against plates, and the ambient hum of kitchen ventilation. # Negative prompt to avoid unwanted artifacts negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" - # Optional: First frame images for image-to-video validation - # If provided, must have one image per prompt - images: null - # Output video dimensions [width, height, frames] # Width and height must be divisible by 32 # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) @@ -247,7 +259,7 @@ validation: stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only # Whether to generate audio in validation samples - # Independent of training_strategy.with_audio - you can generate audio + # Independent of training_strategy audio modality settings — you can generate audio # in validation even when not training the audio branch generate_audio: true @@ -310,9 +322,9 @@ wandb: entity: null # Tags to help organize runs - tags: [ "ltx2", "lora" ] + tags: [ "ltx2", "lora", "t2v", "low-vram" ] - # Log validation videos to W&B + # Log validation media (video/audio) to W&B log_validation_videos: true # ----------------------------------------------------------------------------- @@ -324,4 +336,4 @@ wandb: seed: 42 # Directory to save outputs (checkpoints, validation videos, logs) -output_dir: "outputs/ltx2_av_lora" +output_dir: "outputs/t2v_lora_low_vram" diff --git a/packages/ltx-trainer/configs/v2a_lora.yaml b/packages/ltx-trainer/configs/v2a_lora.yaml new file mode 100644 index 0000000..cc7231f --- /dev/null +++ b/packages/ltx-trainer/configs/v2a_lora.yaml @@ -0,0 +1,349 @@ +# ============================================================================= +# LTX-2 Video-to-Audio (Foley) LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# video-to-audio (Foley) generation. The model learns to generate audio +# conditioned on a frozen video signal via the transformer's built-in +# cross-modal attention. +# +# In this mode, video is provided as a frozen (clean, no noise, no loss) +# conditioning signal. The audio modality is the only generated output. +# Video influences audio generation through the transformer's video-to-audio +# cross-attention mechanism. +# +# Use this configuration when you want to: +# - Generate sound effects (Foley) for existing videos +# - Train audio generation conditioned on visual content +# - Create models that produce audio matching video content +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (frozen conditioning input) +# ├── conditions/ # Text embeddings for each video +# └── audio_latents/ # Audio latents (VAE-encoded audio, generated output) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # For video-to-audio training, we target audio modules and the cross-modal + # attention that allows audio to attend to video features. + target_modules: + # Audio self-attention + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + # Audio cross-attention to text + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + # Audio feed-forward + - "audio_ff.net.0.proj" + - "audio_ff.net.2" + # Cross-modal attention: allows audio to attend to video features + - "video_to_audio_attn.to_k" + - "video_to_audio_attn.to_q" + - "video_to_audio_attn.to_v" + - "video_to_audio_attn.to_out.0" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the video-to-audio (Foley) training approach using the unified +# flexible strategy. Video is frozen (no noise, sigma=0, excluded from loss) +# and conditions audio generation via the transformer's cross-modal attention. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + # Video is frozen — it acts as conditioning for audio generation + # Frozen modalities get sigma=0, timestep=0, no noise, and no loss + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + # When false, video is passed through the transformer clean and influences + # audio via cross-modal attention + is_generated: false + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Audio modality configuration + # Audio is the generated (denoised) output + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, audio_latents/, and conditions/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + The sound of ocean waves crashing against rocky cliffs, with seagulls calling in the + distance and wind whistling through coastal grass. + conditions: + - type: video_to_audio + video: "/path/to/conditioning_video_1.mp4" + - prompt: >- + Footsteps echo in a marble hallway as a person walks steadily, with the distant hum of + air conditioning and occasional door closing sounds. + conditions: + - type: video_to_audio + video: "/path/to/conditioning_video_2.mp4" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Validation dimensions [width, height, frames] + # Width/height resize the frozen video condition; frames and frame_rate set audio duration. + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only + + # Whether to generate audio in validation samples + # Independent of training_strategy.audio.is_generated - you can generate audio + # in validation even when not training the audio branch + generate_audio: true + + # Whether to generate video in validation samples + # Disabled for V2A since video is frozen conditioning, not generated + generate_video: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "v2a", "foley" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/v2a_lora" diff --git a/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml b/packages/ltx-trainer/configs/v2v_ic_lora.yaml similarity index 73% rename from packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml rename to packages/ltx-trainer/configs/v2v_ic_lora.yaml index 4470ad9..349e4b3 100644 --- a/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml +++ b/packages/ltx-trainer/configs/v2v_ic_lora.yaml @@ -18,6 +18,8 @@ # ├── conditions/ # Text embeddings for each video # └── reference_latents/ # Reference video latents (conditioning input) # +# Dataset metadata columns: video, reference_video, caption +# # ============================================================================= # ----------------------------------------------------------------------------- @@ -34,7 +36,7 @@ model: text_encoder_path: "path/to/gemma-text-encoder" # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning - # Note: video_to_video strategy requires "lora" mode + # IC-LoRA reference conditioning is intended for LoRA adapter training. training_mode: "lora" # Optional: Path to resume training from a checkpoint @@ -97,20 +99,39 @@ lora: # ----------------------------------------------------------------------------- # Training Strategy Configuration # ----------------------------------------------------------------------------- -# Defines the video-to-video (IC-LoRA) training approach. +# Defines the video-to-video (IC-LoRA) training approach using the unified +# flexible strategy. Reference conditioning concatenates pre-encoded reference +# video latents to the target sequence. Reference tokens participate in +# bidirectional self-attention but receive no noise and are excluded from loss. training_strategy: - # Strategy name: "video_to_video" for IC-LoRA training - name: "video_to_video" + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" - # Probability of conditioning on the first frame during training - # Higher values train the model to perform better in image-to-video (I2V) mode, - # where a clean first frame is provided and the model generates the rest of the video - # Increase this value to train the model to perform better in image-to-video (I2V) mode - first_frame_conditioning_p: 0.2 + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing target video latents + latents_dir: "latents" - # Directory name (within preprocessed_data_root) containing reference video latents - # These are the conditioning inputs that guide the transformation - reference_latents_dir: "reference_latents" + # Conditions applied to the video modality during training + conditions: + # Reference conditioning (IC-LoRA): concatenates pre-encoded reference video + # latents to the target sequence. The model learns to transform the reference + # into the target video based on the text prompt. + - type: reference + # Directory name (within preprocessed_data_root) containing reference video latents + # These are the conditioning inputs that guide the transformation + latents_dir: "reference_latents" + # Probability of applying reference conditioning per training sample + probability: 1.0 + + # Optional first-frame conditioning to improve I2V capabilities + # At low probability, this teaches the model to also accept first-frame input + - type: first_frame + probability: 0.2 # ----------------------------------------------------------------------------- # Optimization Configuration @@ -166,11 +187,12 @@ acceleration: # Useful when GPU memory is limited load_text_encoder_in_8bit: false - # Offload optimizer state to CPU during validation video sampling. - # Helps avoid OOM when VAE decoder + transformer + optimizer state can't coexist - # on the GPU (full fine-tune, high-rank LoRA). No effect for FSDP. + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). offload_optimizer_during_validation: false + # ----------------------------------------------------------------------------- # Data Configuration # ----------------------------------------------------------------------------- @@ -187,37 +209,47 @@ data: # ----------------------------------------------------------------------------- # Validation Configuration # ----------------------------------------------------------------------------- -# Controls validation video generation during training. +# Controls validation sampling during training. # NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over # maximum quality. For production-quality inference, use `packages/ltx-pipelines`. validation: - # Text prompts for validation video generation - # Provide prompts representative of your training data - # LTX-2 prefers longer, detailed prompts that describe both visual content and audio - prompts: - - "A man in a casual blue jacket walks along a winding path through a lush green park on a bright sunny afternoon. Tall oak trees line the pathway, their leaves rustling gently in the breeze. Dappled sunlight creates shifting patterns on the ground as he strolls at a relaxed pace, occasionally looking up at the scenery around him. The audio captures footsteps on gravel, birds singing in the trees, distant children playing, and the soft whisper of wind through the foliage." - - "A fluffy orange tabby cat sits perfectly still on a wooden windowsill, its green eyes intently tracking small birds hopping on a branch just outside the glass. The cat's ears twitch and rotate, following every movement. Warm afternoon light illuminates its fur, creating a soft golden glow. Behind the cat, a cozy living room with a bookshelf and houseplants is visible. The audio features gentle purring, occasional soft meows, muffled bird chirps through the window, and quiet ambient room sounds." - - # Reference videos for validation (REQUIRED for video_to_video strategy) - # Must provide one reference video per prompt - # These are the conditioning inputs for generating validation outputs - reference_videos: - - "/path/to/reference_video_1.mp4" - - "/path/to/reference_video_2.mp4" - - # Downscale factor for reference videos (for efficient IC-LoRA training) - # When > 1, reference videos are processed at 1/n resolution - # Must match the --reference-downscale-factor used during dataset preprocessing - # Examples: 1 = same resolution, 2 = half resolution (384x384 ref for 768x768 target) - reference_downscale_factor: 1 + # Validation samples — each sample describes a self-contained generation request. + # For IC-LoRA, each sample includes a reference condition pointing to the conditioning video. + samples: + - prompt: >- + A man in a casual blue jacket walks along a winding path through a lush green park on a + bright sunny afternoon. Tall oak trees line the pathway, their leaves rustling gently in + the breeze. Dappled sunlight creates shifting patterns on the ground as he strolls at a + relaxed pace, occasionally looking up at the scenery around him. The audio captures + footsteps on gravel, birds singing in the trees, distant children playing, and the soft + whisper of wind through the foliage. + conditions: + - type: reference + video: "/path/to/reference_video_1.mp4" + # Set these to match --reference-downscale-factor / --reference-temporal-scale-factor + # if reference latents were preprocessed at reduced spatial or temporal resolution. + downscale_factor: 1 + temporal_scale_factor: 1 + include_in_output: true + - prompt: >- + A fluffy orange tabby cat sits perfectly still on a wooden windowsill, its green eyes + intently tracking small birds hopping on a branch just outside the glass. The cat's ears + twitch and rotate, following every movement. Warm afternoon light illuminates its fur, + creating a soft golden glow. Behind the cat, a cozy living room with a bookshelf and + houseplants is visible. The audio features gentle purring, occasional soft meows, muffled + bird chirps through the window, and quiet ambient room sounds. + conditions: + - type: reference + video: "/path/to/reference_video_2.mp4" + # Set these to match --reference-downscale-factor / --reference-temporal-scale-factor + # if reference latents were preprocessed at reduced spatial or temporal resolution. + downscale_factor: 1 + temporal_scale_factor: 1 + include_in_output: true # Negative prompt to avoid unwanted artifacts negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" - # Optional: First frame images for additional conditioning - # If provided, must have one image per prompt - images: null - # Output video dimensions [width, height, frames] # Width and height must be divisible by 32 # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) @@ -254,10 +286,6 @@ validation: # Skip validation at the beginning of training (step 0) skip_initial_validation: false - # Concatenate reference video side-by-side with generated output - # Useful for visually comparing the transformation quality - include_reference_in_output: true - # ----------------------------------------------------------------------------- # Checkpoint Configuration # ----------------------------------------------------------------------------- @@ -314,9 +342,9 @@ wandb: entity: null # Tags to help organize runs - tags: [ "ltx2", "ic-lora", "video-to-video" ] + tags: [ "ltx2", "ic-lora", "v2v" ] - # Log validation videos to W&B + # Log validation media (video/audio) to W&B log_validation_videos: true # ----------------------------------------------------------------------------- @@ -328,4 +356,4 @@ wandb: seed: 42 # Directory to save outputs (checkpoints, validation videos, logs) -output_dir: "outputs/ltx2_v2v_ic_lora" +output_dir: "outputs/v2v_ic_lora" diff --git a/packages/ltx-trainer/configs/video_extend_lora.yaml b/packages/ltx-trainer/configs/video_extend_lora.yaml new file mode 100644 index 0000000..39a2d4f --- /dev/null +++ b/packages/ltx-trainer/configs/video_extend_lora.yaml @@ -0,0 +1,355 @@ +# ============================================================================= +# LTX-2 Video Extension LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# video extension (temporal continuation). The model learns to extend a video +# forward in time by conditioning on a prefix of existing frames. +# +# Prefix conditioning works by providing the first N temporal units as clean +# conditioning signals during training — they receive no noise, timestep=0, +# and are excluded from the loss. The model learns to generate continuation +# frames that seamlessly follow the given prefix. +# +# Use this configuration when you want to: +# - Train the model to extend/continue existing videos +# - Fine-tune temporal continuation capabilities on custom datasets +# - Create seamless video extension models with optional audio +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# ├── conditions/ # Text embeddings for each video +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # Using short patterns like "to_k" matches ALL attention modules (video, audio, and cross-modal). + # For audio-video training, this is the recommended approach. + target_modules: + # Attention layers (matches both video and audio branches) + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + # - "audio_ff.net.0.proj" + # - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the video extension training approach using the unified flexible strategy. +# Prefix conditioning provides the first N latent frames as clean conditioning, +# teaching the model to generate temporally coherent continuations. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + # Prefix conditioning: the first N temporal units are provided as clean + # conditioning signals (no noise, timestep=0, excluded from loss) + conditions: + - type: prefix + # Number of latent frames to use as conditioning prefix + # For prefix conditioning, N latent frames correspond to (N - 1) * 8 + 1 pixel frames. + # temporal_boundary=8 means 57 pixel frames are used as prefix. + temporal_boundary: 8 + # Probability of applying prefix conditioning per training sample + # At 1.0, all training samples use video extension mode + probability: 1.0 + + # Audio modality configuration + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, conditions/, and audio_latents/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. The audio captures the + gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with + occasional distant bird chirps from outside. + conditions: + - type: prefix + video: "/path/to/prefix_video_1.mp4" + # Matches temporal_boundary=8 during training: (8 - 1) * 8 + 1 = 57 frames + num_frames: 57 + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. The audio features the sizzling of pans, + the clinking of utensils against plates, and the ambient hum of kitchen ventilation. + conditions: + - type: prefix + video: "/path/to/prefix_video_2.mp4" + # Matches temporal_boundary=8 during training: (8 - 1) * 8 + 1 = 57 frames + num_frames: 57 + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only + + # Whether to generate audio in validation samples + # Independent of training_strategy.audio.is_generated - you can generate audio + # in validation even when not training the audio branch + generate_audio: true + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "video-extension" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/video_extend_lora" diff --git a/packages/ltx-trainer/configs/video_inpainting_lora.yaml b/packages/ltx-trainer/configs/video_inpainting_lora.yaml new file mode 100644 index 0000000..46829b3 --- /dev/null +++ b/packages/ltx-trainer/configs/video_inpainting_lora.yaml @@ -0,0 +1,343 @@ +# ============================================================================= +# LTX-2 Video Inpainting LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# video inpainting. The model learns to fill in masked regions of a video +# using per-sample binary masks that define which regions are conditioning +# (provided clean) and which regions the model must generate. +# +# Mask conditioning works by loading per-sample binary masks from disk. +# Masked regions receive clean latents (no noise, timestep=0) and are excluded +# from the loss. Unmasked regions are noised and trained normally. +# +# Use this configuration when you want to: +# - Train the model to fill in or replace regions of existing videos +# - Fine-tune video inpainting capabilities on custom datasets +# - Create object removal or region editing models +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# ├── conditions/ # Text embeddings for each video +# └── video_masks/ # Per-sample binary masks defining conditioning regions +# +# Dataset metadata columns: video, video_mask, caption +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # This is a video-only config, so explicitly target video attention modules. + target_modules: + - "attn1.to_k" + - "attn1.to_q" + - "attn1.to_v" + - "attn1.to_out.0" + - "attn2.to_k" + - "attn2.to_q" + - "attn2.to_v" + - "attn2.to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the video inpainting training approach using the unified flexible +# strategy. Per-sample binary masks define which regions are provided as clean +# conditioning and which regions the model must learn to generate. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + # Mask conditioning: per-sample binary masks loaded from disk define which + # tokens are conditioning (clean, timestep=0, no loss) vs generated + conditions: + - type: mask + # Directory name (within preprocessed_data_root) containing binary masks + # Each mask file corresponds to a training sample and defines the + # conditioning region (mask=1 means conditioning, mask=0 means generate) + mask_dir: "video_masks" + # Probability of applying mask conditioning per training sample + # At 1.0, all training samples use inpainting mode + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, conditions/, and video_masks/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. + conditions: + - type: mask + video: "/path/to/inpainting_video_1.mp4" + mask: "/path/to/inpainting_mask_1.mp4" + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. + conditions: + - type: mask + video: "/path/to/inpainting_video_2.mp4" + mask: "/path/to/inpainting_mask_2.mp4" + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_v" # "stg_v" for video-only validation + + # Video inpainting trains video only; do not generate validation audio. + generate_audio: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "inpainting" ] + + # Log validation outputs to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/video_inpainting_lora" diff --git a/packages/ltx-trainer/configs/video_outpainting_lora.yaml b/packages/ltx-trainer/configs/video_outpainting_lora.yaml new file mode 100644 index 0000000..e26518a --- /dev/null +++ b/packages/ltx-trainer/configs/video_outpainting_lora.yaml @@ -0,0 +1,341 @@ +# ============================================================================= +# LTX-2 Video Outpainting LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# video outpainting (spatial extension). The model learns to generate content +# surrounding a known rectangular region of the video. +# +# Spatial crop conditioning works by providing a rectangular pixel region as +# clean conditioning during training — those tokens receive no noise, +# timestep=0, and are excluded from the loss. The model learns to generate +# the content outside the given region, seamlessly extending the scene. +# +# Use this configuration when you want to: +# - Extend video content beyond its original boundaries +# - Train spatial outpainting capabilities on custom datasets +# - Create video aspect ratio conversion models +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# └── conditions/ # Text embeddings for each video +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # This is a video-only config, so explicitly target video attention modules. + target_modules: + - "attn1.to_k" + - "attn1.to_q" + - "attn1.to_v" + - "attn1.to_out.0" + - "attn2.to_k" + - "attn2.to_q" + - "attn2.to_v" + - "attn2.to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the video outpainting training approach using the unified flexible +# strategy. Spatial crop conditioning provides a rectangular pixel region as +# clean conditioning, teaching the model to generate surrounding content. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + # Spatial crop conditioning provides a rectangular region as clean context; + # the model learns to generate the surrounding video tokens. + conditions: + - type: spatial_crop + # Rectangular pixel region provided as clean conditioning (y1, x1, y2, x2) + # Pixels within this region are conditioning (clean, timestep=0, no loss) + # Pixels outside this region are generated by the model + # Coordinates are in pixel space — automatically converted to latent space + spatial_region: [0, 0, 288, 576] + # Probability of applying spatial crop conditioning per training sample + # At 1.0, all training samples use outpainting mode + probability: 1.0 + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/ and conditions/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. + conditions: + - type: spatial_crop + video: "/path/to/outpainting_video_1.mp4" + spatial_region: [0, 0, 288, 576] + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. + conditions: + - type: spatial_crop + video: "/path/to/outpainting_video_2.mp4" + spatial_region: [0, 0, 288, 576] + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_v" # "stg_v" for video-only validation + + # Video outpainting trains video only; do not generate validation audio. + generate_audio: false + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "outpainting", "spatial-crop" ] + + # Log validation outputs to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/video_outpainting_lora" diff --git a/packages/ltx-trainer/configs/video_suffix_lora.yaml b/packages/ltx-trainer/configs/video_suffix_lora.yaml new file mode 100644 index 0000000..b9617f1 --- /dev/null +++ b/packages/ltx-trainer/configs/video_suffix_lora.yaml @@ -0,0 +1,355 @@ +# ============================================================================= +# LTX-2 Video Backward Extension (Suffix) LoRA Training Configuration +# ============================================================================= +# +# This configuration is for training LoRA adapters on the LTX-2 model for +# backward video extension. The model learns to generate video content that +# leads into a given suffix of existing frames. +# +# Suffix conditioning works by providing the last N temporal units as clean +# conditioning signals during training — they receive no noise, timestep=0, +# and are excluded from the loss. The model learns to generate preceding +# frames that seamlessly lead into the given suffix. +# +# Use this configuration when you want to: +# - Train the model to generate "prequel" content for existing videos +# - Fine-tune backward temporal continuation capabilities +# - Create video backward extension models with optional audio +# +# Dataset structure: +# preprocessed_data_root/ +# ├── latents/ # Video latents (VAE-encoded videos) +# ├── conditions/ # Text embeddings for each video +# └── audio_latents/ # Audio latents (VAE-encoded audio) +# +# ============================================================================= + +# ----------------------------------------------------------------------------- +# Model Configuration +# ----------------------------------------------------------------------------- +# Specifies the base model to fine-tune and the training mode. +model: + # Path to the LTX-2 model checkpoint (.safetensors file) + # This should be a local path to your downloaded model + model_path: "path/to/ltx-2-model.safetensors" + + # Path to the text encoder model directory + # For LTX-2, this is typically the Gemma-based text encoder + text_encoder_path: "path/to/gemma-text-encoder" + + # Training mode: "lora" for efficient adapter training, "full" for full fine-tuning + # LoRA is recommended for most use cases (faster, less memory, prevents overfitting) + training_mode: "lora" + + # Optional: Path to resume training from a checkpoint + # Can be a checkpoint file (.safetensors) or directory (uses latest checkpoint) + load_checkpoint: null + +# ----------------------------------------------------------------------------- +# LoRA Configuration +# ----------------------------------------------------------------------------- +# Controls the Low-Rank Adaptation parameters for efficient fine-tuning. +lora: + # Rank of the LoRA matrices (higher = more capacity but more parameters) + # Typical values: 8, 16, 32, 64. Start with 32 for general fine-tuning. + rank: 32 + + # Alpha scaling factor (usually set equal to rank) + # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0 + alpha: 32 + + # Dropout probability for LoRA layers (0.0 = no dropout) + # Can help with regularization if overfitting occurs + dropout: 0.0 + + # Which transformer modules to apply LoRA to + # The LTX-2 transformer has separate attention and FFN blocks for video and audio: + # + # VIDEO MODULES: + # - attn1.to_k, attn1.to_q, attn1.to_v, attn1.to_out.0 (video self-attention) + # - attn2.to_k, attn2.to_q, attn2.to_v, attn2.to_out.0 (video cross-attention to text) + # - ff.net.0.proj, ff.net.2 (video feed-forward) + # + # AUDIO MODULES: + # - audio_attn1.to_k, audio_attn1.to_q, audio_attn1.to_v, audio_attn1.to_out.0 (audio self-attention) + # - audio_attn2.to_k, audio_attn2.to_q, audio_attn2.to_v, audio_attn2.to_out.0 (audio cross-attention to text) + # - audio_ff.net.0.proj, audio_ff.net.2 (audio feed-forward) + # + # AUDIO-VIDEO CROSS-ATTENTION MODULES (for cross-modal interaction): + # - audio_to_video_attn.to_k, audio_to_video_attn.to_q, audio_to_video_attn.to_v, audio_to_video_attn.to_out.0 + # (Q from video, K/V from audio - allows video to attend to audio features) + # - video_to_audio_attn.to_k, video_to_audio_attn.to_q, video_to_audio_attn.to_v, video_to_audio_attn.to_out.0 + # (Q from audio, K/V from video - allows audio to attend to video features) + # + # Using short patterns like "to_k" matches ALL attention modules (video, audio, and cross-modal). + # For audio-video training, this is the recommended approach. + target_modules: + # Attention layers (matches both video and audio branches) + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" + # Uncomment below to also train feed-forward layers (can increase the LoRA's capacity): + # - "ff.net.0.proj" + # - "ff.net.2" + # - "audio_ff.net.0.proj" + # - "audio_ff.net.2" + +# ----------------------------------------------------------------------------- +# Training Strategy Configuration +# ----------------------------------------------------------------------------- +# Defines the backward video extension training approach using the unified +# flexible strategy. Suffix conditioning provides the last N latent frames as +# clean conditioning, teaching the model to generate content leading into them. +training_strategy: + # Strategy name: "flexible" for the unified conditioning framework + # Supports all training modes (T2V, I2V, V2V, A2V, V2A, etc.) through + # modality-specific configuration blocks. + name: "flexible" + + # Video modality configuration + video: + # Whether the model generates video (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing video latents + latents_dir: "latents" + + # Conditions applied to the video modality during training + # Suffix conditioning: the last N temporal units are provided as clean + # conditioning signals (no noise, timestep=0, excluded from loss) + conditions: + - type: suffix + # Number of latent frames to use as conditioning suffix + # For suffix conditioning, N latent frames correspond to N * 8 pixel frames. + # temporal_boundary=8 means the last 64 pixel frames are used as suffix. + temporal_boundary: 8 + # Probability of applying suffix conditioning per training sample + # At 1.0, all training samples use backward extension mode + probability: 1.0 + + # Audio modality configuration + audio: + # Whether the model generates audio (true) or uses it as frozen conditioning (false) + is_generated: true + # Directory name (within preprocessed_data_root) containing audio latents + latents_dir: "audio_latents" + +# ----------------------------------------------------------------------------- +# Optimization Configuration +# ----------------------------------------------------------------------------- +# Controls the training optimization parameters. +optimization: + # Learning rate for the optimizer + # Typical range for LoRA: 1e-5 to 1e-4 + learning_rate: 1e-4 + + # Total number of training steps + steps: 2000 + + # Batch size per GPU + # Reduce if running out of memory + batch_size: 1 + + # Number of gradient accumulation steps + # Effective batch size = batch_size * gradient_accumulation_steps * num_gpus + gradient_accumulation_steps: 1 + + # Maximum gradient norm for clipping (helps training stability) + max_grad_norm: 1.0 + + # Optimizer type: "adamw" (standard) or "adamw8bit" (memory-efficient) + optimizer_type: "adamw" + + # Learning rate scheduler type + # Options: "constant", "linear", "cosine", "cosine_with_restarts", "polynomial" + scheduler_type: "linear" + + # Additional scheduler parameters (depends on scheduler_type) + scheduler_params: { } + + # Enable gradient checkpointing to reduce memory usage + # Recommended for training with limited GPU memory + enable_gradient_checkpointing: true + +# ----------------------------------------------------------------------------- +# Acceleration Configuration +# ----------------------------------------------------------------------------- +# Hardware acceleration and memory optimization settings. +acceleration: + # Mixed precision training mode + # Options: "no" (fp32), "fp16" (half precision), "bf16" (bfloat16, recommended) + mixed_precision_mode: "bf16" + + # Model quantization for reduced memory usage + # Options: null (none), "int8-quanto", "int4-quanto", "int2-quanto", "fp8-quanto", "fp8uz-quanto" + quantization: null + + # Load text encoder in 8-bit precision to save memory + # Useful when GPU memory is limited + load_text_encoder_in_8bit: false + + # Offload optimizer state to CPU during validation video sampling and restore it after. + # Frees VRAM for the VAE decoder when optimizer state is large (full fine-tune, high-rank + # LoRA). No effect under FSDP (sharded state). + offload_optimizer_during_validation: false + + +# ----------------------------------------------------------------------------- +# Data Configuration +# ----------------------------------------------------------------------------- +# Specifies the training data location and loading parameters. +data: + # Root directory containing preprocessed training data + # Should contain: latents/, conditions/, and audio_latents/ + preprocessed_data_root: "/path/to/preprocessed/data" + + # Number of worker processes for data loading + # Used for parallel data loading to speed up data loading + num_dataloader_workers: 2 + +# ----------------------------------------------------------------------------- +# Validation Configuration +# ----------------------------------------------------------------------------- +# Controls validation sampling during training. +# NOTE: Validation sampling use simplified inference pipelines and prioritizes speed over +# maximum quality. For production-quality inference, use `packages/ltx-pipelines`. +validation: + # Validation samples — each sample describes a self-contained generation request. + # Use 'conditions' to add conditioning (first_frame, prefix, suffix, reference, video_to_audio, etc.) + # See docs/configuration-reference.md#validation-condition-types for the full list of condition types. + samples: + - prompt: >- + A woman with long brown hair sits at a wooden desk in a cozy home office, typing on a + laptop while occasionally glancing at notes beside her. Soft natural light streams through + a large window, casting warm shadows across the room. She pauses to take a sip from a + ceramic mug, then continues working with focused concentration. The audio captures the + gentle clicking of keyboard keys, the soft rustle of papers, and ambient room tone with + occasional distant bird chirps from outside. + conditions: + - type: suffix + video: "/path/to/suffix_video_1.mp4" + # Matches temporal_boundary=8 during training: 8 * 8 = 64 frames + num_frames: 64 + - prompt: >- + A chef in a white uniform stands in a professional kitchen, carefully plating a gourmet + dish with precise movements. Steam rises from freshly cooked vegetables as he arranges + them with tweezers. The stainless steel surfaces gleam under bright overhead lights, and + various pots simmer on the stove behind him. The audio features the sizzling of pans, + the clinking of utensils against plates, and the ambient hum of kitchen ventilation. + conditions: + - type: suffix + video: "/path/to/suffix_video_2.mp4" + # Matches temporal_boundary=8 during training: 8 * 8 = 64 frames + num_frames: 64 + + # Negative prompt to avoid unwanted artifacts + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" + + # Output video dimensions [width, height, frames] + # Width and height must be divisible by 32 + # Frames must satisfy: frames % 8 == 1 (e.g., 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, ...) + video_dims: [ 576, 576, 89 ] + + # Frame rate for generated videos + frame_rate: 25.0 + + # Random seed for reproducible validation outputs + seed: 42 + + # Number of denoising steps for validation inference + # Higher values = better quality but slower generation + inference_steps: 30 + + # Generate validation videos every N training steps + # Set to null to disable validation during training + interval: 100 + + # Classifier-free guidance scale + # Higher values = stronger adherence to prompt but may introduce artifacts + guidance_scale: 4.0 + + # STG (Spatio-Temporal Guidance) parameters for improved video quality + # STG is combined with CFG for better temporal coherence + stg_scale: 1.0 # Recommended: 1.0 (0.0 disables STG) + stg_blocks: [29] # Recommended: single block 29 + stg_mode: "stg_av" # "stg_av" perturbs both audio and video, "stg_v" video only + + # Whether to generate audio in validation samples + # Independent of training_strategy.audio.is_generated - you can generate audio + # in validation even when not training the audio branch + generate_audio: true + + # Skip validation at the beginning of training (step 0) + skip_initial_validation: false + +# ----------------------------------------------------------------------------- +# Checkpoint Configuration +# ----------------------------------------------------------------------------- +# Controls model checkpoint saving during training. +checkpoints: + # Save a checkpoint every N steps + # Set to null to disable intermediate checkpoints + interval: 250 + + # Number of most recent checkpoints to keep + # Set to -1 to keep all checkpoints + keep_last_n: -1 + + # Precision to use when saving checkpoint weights + # Options: "bfloat16" (default, smaller files) or "float32" (full precision) + precision: "bfloat16" + +# ----------------------------------------------------------------------------- +# Flow Matching Configuration +# ----------------------------------------------------------------------------- +# Parameters for the flow matching training objective. +flow_matching: + # Timestep sampling mode + # "shifted_logit_normal" is recommended for LTX-2 models + timestep_sampling_mode: "shifted_logit_normal" + + # Additional parameters for timestep sampling + timestep_sampling_params: { } + +# ----------------------------------------------------------------------------- +# Hugging Face Hub Configuration +# ----------------------------------------------------------------------------- +# Settings for uploading trained models to the Hugging Face Hub. +hub: + # Whether to push the trained model to the Hub + push_to_hub: false + + # Repository ID on Hugging Face Hub (e.g., "username/my-lora-model") + # Required if push_to_hub is true + hub_model_id: null + +# ----------------------------------------------------------------------------- +# Weights & Biases Configuration +# ----------------------------------------------------------------------------- +# Settings for experiment tracking with W&B. +wandb: + # Enable W&B logging + enabled: false + + # W&B project name + project: "ltx-2-trainer" + + # W&B username or team (null uses default account) + entity: null + + # Tags to help organize runs + tags: [ "ltx2", "lora", "video-suffix", "backward-extension" ] + + # Log validation media (video/audio) to W&B + log_validation_videos: true + +# ----------------------------------------------------------------------------- +# General Configuration +# ----------------------------------------------------------------------------- +# Global settings for the training run. + +# Random seed for reproducibility +seed: 42 + +# Directory to save outputs (checkpoints, validation videos, logs) +output_dir: "outputs/video_suffix_lora" diff --git a/packages/ltx-trainer/docs/configuration-reference.md b/packages/ltx-trainer/docs/configuration-reference.md index d40961f..e9d17be 100644 --- a/packages/ltx-trainer/docs/configuration-reference.md +++ b/packages/ltx-trainer/docs/configuration-reference.md @@ -10,7 +10,7 @@ sub-configurations: - **ModelConfig**: Base model and training mode settings - **LoraConfig**: LoRA training parameters -- **TrainingStrategyConfig**: Training strategy settings (text-to-video or video-to-video) +- **TrainingStrategyConfig**: Training strategy settings (flexible conditioning framework) - **OptimizationConfig**: Learning rate, batch sizes, and scheduler settings - **AccelerationConfig**: Mixed precision and quantization settings - **DataConfig**: Data loading parameters @@ -24,13 +24,29 @@ sub-configurations: Check out our example configurations in the `configs` directory: -- 📄 [Audio-Video LoRA Training](../configs/ltx2_av_lora.yaml) - Joint audio-video generation training -- 📄 [Audio-Video LoRA Training (Low VRAM)](../configs/ltx2_av_lora_low_vram.yaml) - Memory-optimized config for 32GB - GPUs (uses 8-bit optimizer, INT8 quantization, and reduced LoRA rank) -- 📄 [IC-LoRA Training](../configs/ltx2_v2v_ic_lora.yaml) - Video-to-video transformation training +- 📄 [Text-to-Video LoRA](../configs/t2v_lora.yaml) - Text-to-video LoRA training +- 📄 [Image-to-Video LoRA](../configs/i2v_lora.yaml) - Image-to-video LoRA training +- 📄 [IC-LoRA Video-to-Video](../configs/v2v_ic_lora.yaml) - IC-LoRA video-to-video training +- 📄 [Audio-to-Video LoRA](../configs/a2v_lora.yaml) - Audio-to-video LoRA training +- 📄 [Video-to-Audio LoRA](../configs/v2a_lora.yaml) - Video-to-audio (Foley) LoRA training +- 📄 [Video Extension LoRA](../configs/video_extend_lora.yaml) - Video extension (forward) LoRA training +- 📄 [Video Suffix LoRA](../configs/video_suffix_lora.yaml) - Video extension (backward) LoRA training +- 📄 [Video Inpainting LoRA](../configs/video_inpainting_lora.yaml) - Video inpainting LoRA training +- 📄 [Video Outpainting LoRA](../configs/video_outpainting_lora.yaml) - Video outpainting (spatial crop) LoRA training +- 📄 [Text-to-Audio LoRA](../configs/t2a_lora.yaml) - Text-to-audio LoRA training +- 📄 [Audio Extension LoRA](../configs/audio_extend_lora.yaml) - Audio extension (forward) LoRA training +- 📄 [Audio Suffix LoRA](../configs/audio_suffix_lora.yaml) - Audio extension (backward) LoRA training +- 📄 [Audio Inpainting LoRA](../configs/audio_inpainting_lora.yaml) - Audio inpainting LoRA training +- 📄 [Audio-to-Audio IC-LoRA](../configs/a2a_ic_lora.yaml) - Audio IC-LoRA transformation training +- 📄 [AV2AV IC-LoRA](../configs/av2av_ic_lora.yaml) - Audio+video IC-LoRA transformation training +- 📄 [T2V LoRA (Low VRAM)](../configs/t2v_lora_low_vram.yaml) - Memory-optimized config for 32GB GPUs ## ⚙️ Configuration Sections +> [!NOTE] +> The YAML snippets below show **recommended starting values**, not necessarily the code defaults. +> Fields you omit from your config file will use the code defaults from [`config.py`](../src/ltx_trainer/config.py). + ### ModelConfig Controls the base model and training mode settings. @@ -149,37 +165,60 @@ target_modules: ### TrainingStrategyConfig -Configures the training strategy. The trainer includes two built-in strategies described below. -For custom use cases, see [Implementing Custom Training Strategies](custom-training-strategies.md). +Configures the training strategy. The recommended strategy is `"flexible"`, which supports all conditioning scenarios through configuration. -#### Text-to-Video Strategy +#### Flexible Strategy + +The flexible strategy provides a unified conditioning framework. Each modality (video, audio) is configured +independently with its own latents directory, generation flag, and list of conditions. ```yaml training_strategy: - name: "text_to_video" - first_frame_conditioning_p: 0.1 # Probability of first-frame conditioning - with_audio: false # Enable joint audio-video training - audio_latents_dir: "audio_latents" # Directory for audio latents (when with_audio: true) + name: "flexible" + video: + is_generated: true # Video is denoised during training + latents_dir: "latents" # Directory containing precomputed video latents + conditions: + - type: first_frame # Use first frame as conditioning + probability: 0.5 # Apply this condition 50% of the time + audio: + is_generated: true # Audio is denoised during training + latents_dir: "audio_latents" # Directory containing precomputed audio latents + conditions: [] # No additional audio conditions (text-only) ``` -#### Video-to-Video Strategy (IC-LoRA) +**ModalityConfig parameters:** -```yaml -training_strategy: - name: "video_to_video" - first_frame_conditioning_p: 0.1 - reference_latents_dir: "reference_latents" # Directory for reference video latents -``` +| Parameter | Description | +|----------------|------------------------------------------------------------------------------------------------------------------| +| `is_generated` | `true` = modality is denoised (contributes to loss). `false` = frozen conditioning (sigma=0, no loss). | +| `latents_dir` | Directory name within `preprocessed_data_root` containing precomputed latents for this modality. | +| `conditions` | List of conditioning configs applied during training (see condition types below). Text conditioning is implicit. | -**Key parameters:** +**Condition types:** -| Parameter | Description | -|------------------------------|------------------------------------------------------------------| -| `name` | Strategy type: `"text_to_video"` or `"video_to_video"` | -| `first_frame_conditioning_p` | Probability of using first frame as conditioning (0.0-1.0) | -| `with_audio` | (text_to_video only) Enable joint audio-video training | -| `audio_latents_dir` | (text_to_video only) Directory name for audio latents | -| `reference_latents_dir` | (video_to_video only) Directory name for reference video latents | +| Type | Parameters | Description | +|----------------|-----------------------------------------------------|---------------------------------------------------------------------------------------| +| `first_frame` | `probability` | First latent frame is clean, excluded from loss. **Video only.** | +| `prefix` | `temporal_boundary`, `probability` | First N latent temporal units are clean. For extension forward. | +| `suffix` | `temporal_boundary`, `probability` | Last N latent temporal units are clean. For extension backward. | +| `spatial_crop` | `spatial_region` (y1, x1, y2, x2 in px), `probability` | Rectangular region is clean, excluded from loss. For outpainting. **Video only.** | +| `mask` | `mask_dir`, `probability` | Per-sample mask directory. Masks are thresholded at `0.5`; `1` means conditioning, `0` means generate. | +| `reference` | `latents_dir`, `probability` | IC-LoRA style concatenation. Reference tokens are prepended, clean (timestep=0), no loss. | + +> [!NOTE] +> The `prefix`, `suffix`, `mask`, and `reference` condition types work on both video and audio modalities — +> place them in the `video.conditions` or `audio.conditions` list as appropriate. +> `first_frame` and `spatial_crop` are video-only conditions. + +> [!NOTE] +> Training conditions reference **directories** of precomputed data (within `preprocessed_data_root`), +> while validation conditions reference **individual files** (images, videos, masks) that are encoded +> on-the-fly during validation. The condition `type` names are the same, but the fields differ. + +> [!NOTE] +> The legacy `text_to_video` and `video_to_video` strategies are deprecated but remain forward-compatible. +> New configs should use `name: "flexible"`. ### OptimizationConfig @@ -206,7 +245,7 @@ optimization: | `steps` | Total number of training steps | | `batch_size` | Batch size per GPU (reduce if running out of memory) | | `gradient_accumulation_steps` | Accumulate gradients over multiple steps | -| `scheduler_type` | LR scheduler: `"constant"`, `"linear"`, `"cosine"`, `"cosine_with_restarts"`, `"polynomial"` | +| `scheduler_type` | LR scheduler: `"constant"`, `"linear"`, `"cosine"`, `"cosine_with_restarts"`, `"polynomial"`, `"step"` | | `enable_gradient_checkpointing` | Trade training speed for GPU memory savings (recommended for large models) | ### AccelerationConfig @@ -226,7 +265,7 @@ acceleration: | Parameter | Description | |---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `mixed_precision_mode` | Precision mode - `"bf16"` recommended for modern GPUs | -| `quantization` | Model quantization: `null`, `"int8-quanto"`, `"int4-quanto"`, `"fp8-quanto"`, etc. | +| `quantization` | Model quantization: `null`, `"int8-quanto"`, `"int4-quanto"`, `"int2-quanto"`, `"fp8-quanto"`, or `"fp8uz-quanto"` | | `load_text_encoder_in_8bit` | Load the Gemma text encoder in 8-bit to save GPU memory | | `offload_optimizer_during_validation` | Move optimizer state to CPU before validation video sampling and back afterwards. Useful when validation OOMs because VAE decoder + transformer + optimizer state can't coexist on the GPU (full fine-tune, high-rank LoRA). No effect for FSDP. | @@ -244,50 +283,84 @@ data: | Parameter | Description | |--------------------------|--------------------------------------------------------------------------------------------| -| `preprocessed_data_root` | Path to your preprocessed dataset (contains `latents/`, `conditions/`, etc.) | +| `preprocessed_data_root` | Path to your preprocessed dataset directory produced by `process_dataset.py` (contains `latents/`, `conditions/`, etc.) | | `num_dataloader_workers` | Number of parallel data loading processes (0 = synchronous loading, useful when debugging) | ### ValidationConfig -Validation and inference settings for monitoring training progress. +Validation and inference settings for monitoring training progress. Validation samples use a self-describing +format where each sample specifies its own prompt and conditions. ```yaml validation: - prompts: # Validation prompts - - "A cat playing with a ball" - - "A dog running in a field" - negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" - images: null # Optional image paths for image-to-video - reference_videos: null # Reference video paths (IC-LoRA only) - video_dims: [ 576, 576, 89 ] # Video dimensions [width, height, frames] - frame_rate: 25.0 # Frame rate for generated videos - seed: 42 # Random seed for reproducibility - inference_steps: 30 # Number of inference steps - interval: 100 # Steps between validation runs - guidance_scale: 4.0 # CFG guidance strength - stg_scale: 1.0 # STG guidance strength (0.0 to disable) - stg_blocks: [ 29 ] # Transformer blocks to perturb for STG - stg_mode: "stg_av" # "stg_av" or "stg_v" (video only) - generate_audio: true # Whether to generate audio - skip_initial_validation: false # Skip validation at step 0 - include_reference_in_output: false # Include reference video side-by-side (IC-LoRA) + samples: + - prompt: "A cat playing with a ball" + conditions: + - type: first_frame + image_or_video: "/path/to/image.png" + - prompt: "A dog running in a field" + video_dims: [576, 576, 89] # Output dimensions: [width, height, frames] + negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" # Negative prompt for all samples + frame_rate: 25.0 # Output video frame rate (fps) + seed: 42 # Random seed for reproducibility + inference_steps: 30 # Number of denoising steps + interval: 100 # Run validation every N steps (null to disable) + guidance_scale: 4.0 # CFG scale (higher = stronger prompt adherence) + stg_scale: 1.0 # STG scale (0.0 to disable) + stg_blocks: [29] # Transformer blocks to apply STG perturbation + stg_mode: "stg_av" # STG mode: "stg_av" (audio+video) or "stg_v" (video only) + generate_audio: true # Whether to generate audio during validation + generate_video: true # Whether to generate video during validation + skip_initial_validation: false # Skip validation at step 0 ``` **Key parameters:** -| Parameter | Description | -|-------------------------------|--------------------------------------------------------------------------------------------------------------------------| -| `prompts` | List of text prompts for validation video generation | -| `images` | List of image paths for image-to-video validation (must match number of prompts) | -| `reference_videos` | List of reference video paths for IC-LoRA validation (must match number of prompts) | -| `video_dims` | Output dimensions `[width, height, frames]`. Width/height must be divisible by 32, frames must satisfy `frames % 8 == 1` | -| `interval` | Steps between validation runs (set to `null` to disable) | -| `guidance_scale` | CFG (Classifier-Free Guidance) scale. Recommended: 4.0 | -| `stg_scale` | STG (Spatio-Temporal Guidance) scale. 0.0 disables STG. Recommended: 1.0 | -| `stg_blocks` | Transformer blocks to perturb for STG. Recommended: `[29]` (single block) | -| `stg_mode` | STG mode: `"stg_av"` perturbs both audio and video, `"stg_v"` perturbs video only | -| `generate_audio` | Whether to generate audio in validation samples | -| `include_reference_in_output` | For IC-LoRA: concatenate reference video side-by-side with output | +| Parameter | Description | +|--------------------------|--------------------------------------------------------------------------------------------------------------------------| +| `samples` | List of `ValidationSample` objects (see below). Replaces the legacy `prompts`/`images`/`reference_videos` fields. | +| `video_dims` | Output dimensions `[width, height, frames]`. Width/height must be divisible by 32, frames must satisfy `frames % 8 == 1` | +| `interval` | Steps between validation runs (set to `null` to disable) | +| `guidance_scale` | CFG (Classifier-Free Guidance) scale. Recommended: 4.0 | +| `stg_scale` | STG (Spatio-Temporal Guidance) scale. 0.0 disables STG. Recommended: 1.0 | +| `stg_blocks` | Transformer blocks to perturb for STG. Recommended: `[29]` (single block) | +| `stg_mode` | STG mode: `"stg_av"` perturbs both audio and video, `"stg_v"` perturbs video only | +| `generate_audio` | Whether to generate audio in validation samples | +| `generate_video` | Whether to generate video in validation samples. Set to `false` for V2A (video-to-audio) validation. Default: `true` | +| `skip_initial_validation`| Skip validation video sampling at step 0 (beginning of training) | + +#### ValidationSample + +Each sample in the `samples` list has: + +| Field | Description | +|--------------|-------------------------------------------------------------------------------------------------| +| `prompt` | Text prompt for this validation sample. | +| `conditions` | List of validation conditions (see types below). Empty list = text-only generation. | +| `video_dims` | Optional per-sample override for `(width, height, frames)`. Inherits from `ValidationConfig` if not set. | +| `seed` | Optional per-sample override for random seed. Inherits from `ValidationConfig` if not set. | + +#### Validation Condition Types + +| Type | Parameters | Description | +|------------------|------------------------------------------------------------|-------------------------------------------------------------------------| +| `first_frame` | `image_or_video` (path) | Use the first frame of the image/video as conditioning. | +| `prefix` | `video` or `audio` (path), optional `num_frames`/`duration`| Use a video/audio clip as temporal prefix (for extension forward). | +| `suffix` | `video` or `audio` (path), optional `num_frames`/`duration`| Use a video/audio clip as temporal suffix (for extension backward). | +| `spatial_crop` | `video` (path), `spatial_region` (y1, x1, y2, x2) | Provide spatial context for outpainting. Video only. | +| `mask` | `video` or `audio` (path), `mask` (path) | Mask-based inpainting with a binary mask file. | +| `reference` | `video` or `audio` (path), optional video-reference `downscale_factor`, `temporal_scale_factor`, `include_in_output` | IC-LoRA style reference conditioning. | +| `video_to_audio` | `video` (path) | Freeze video, generate audio. For Foley/V2A tasks. | +| `audio_to_video` | `audio` (path) | Freeze audio, generate video. For audio-driven generation. | + +For video `reference` validation conditions, `downscale_factor` is the spatial reference scale and +`temporal_scale_factor` is the temporal reference scale. Set both to match the factors used when +preprocessing video reference latents for training; validation media is encoded on the fly and cannot infer +those factors from the training dataset. + +> [!NOTE] +> The legacy fields `prompts`, `images`, and `reference_videos` are deprecated but auto-converted to `samples` +> internally. New configs should use the `samples` format. ### CheckpointsConfig @@ -298,6 +371,8 @@ checkpoints: interval: 250 # Steps between checkpoint saves (null = disabled) keep_last_n: 3 # Number of recent checkpoints to retain precision: bfloat16 # Precision for saved weights (bfloat16 or float32) + no_resume: false # Ignore saved state, start from step 0 + save_training_state: "minimal" # "full", "minimal", or "off" ``` **Key parameters:** @@ -307,6 +382,8 @@ checkpoints: | `interval` | Steps between intermediate checkpoint saves (set to `null` to disable) | | `keep_last_n` | Number of most recent checkpoints to keep (-1 = keep all) | | `precision` | Precision for saved checkpoint weights: `"bfloat16"` (default) or `"float32"` | +| `no_resume` | When `true`, ignore saved training state and start from step 0. Model weights from `load_checkpoint` are still loaded. | +| `save_training_state` | Save training state for resume: `"full"` (optimizer + scheduler + RNG), `"minimal"` (scheduler + RNG only, sufficient for LoRA), `"off"` (no resume). | ### HubConfig @@ -364,6 +441,20 @@ flow_matching: | `timestep_sampling_mode` | Sampling strategy: `"uniform"` or `"shifted_logit_normal"` | | `timestep_sampling_params` | Additional parameters for the sampling strategy | +### General Configuration + +Top-level settings for the training run. + +```yaml +seed: 42 # Random seed for reproducibility +output_dir: "outputs/my_training_run" # Directory for outputs (checkpoints, validation videos, logs) +``` + +| Parameter | Description | +|--------------|----------------------------------------------------------| +| `seed` | Random seed for reproducibility (default: `42`) | +| `output_dir` | Directory to save outputs (default: `"outputs"`) | + ## 🚀 Next Steps Once you've configured your training parameters: diff --git a/packages/ltx-trainer/docs/custom-training-strategies.md b/packages/ltx-trainer/docs/custom-training-strategies.md index e84b19a..e24d9a0 100644 --- a/packages/ltx-trainer/docs/custom-training-strategies.md +++ b/packages/ltx-trainer/docs/custom-training-strategies.md @@ -1,7 +1,7 @@ # Implementing Custom Training Strategies -This guide explains how to implement your own training strategy for specialized use cases like audio-only training, -video inpainting, or other custom training recipes. +This guide explains how to implement your own training strategy for specialized recipes that cannot be expressed with +the built-in `flexible` strategy. ## 📋 Overview @@ -15,12 +15,20 @@ This architecture lets you implement new training modes without modifying the co ### When You Need a Custom Strategy +> [!NOTE] +> The built-in `flexible` strategy already supports most conditioning scenarios out of the box: +> first-frame conditioning, video extension (prefix/suffix), spatial crop (outpainting), +> mask-based inpainting, IC-LoRA reference conditioning, and frozen modality cross-conditioning +> (audio-to-video, video-to-audio). Only implement a custom strategy if your use case requires +> fundamentally different training logic that cannot be expressed through the flexible strategy's +> configuration. + Consider implementing a custom strategy when you need: -- **Different input modalities** (e.g., audio-only, audio-to-video conditioning) -- **Additional conditioning signals** (e.g., masks for inpainting, depth maps) -- **Custom loss computation** (e.g., weighted losses, auxiliary losses) -- **Different noise application patterns** (e.g., partial masking) +- **Custom loss computation** (e.g., weighted losses, auxiliary losses, perceptual losses) +- **Non-standard noise application** (e.g., noise schedules different from flow matching) +- **Novel conditioning mechanisms** not covered by the flexible strategy's condition types +- **Additional model outputs** beyond the standard video/audio predictions ## 🏗️ Architecture Overview @@ -28,7 +36,7 @@ Consider implementing a custom strategy when you need: The trainer delegates all training-mode-specific logic to the strategy: -1. **Initialization** — The trainer calls `get_data_sources()` to determine which preprocessed data directories to load +1. **Initialization** — The trainer calls `config.get_data_sources()` to determine which preprocessed data directories to load 2. **Each training step:** - Calls `prepare_training_inputs()` to transform the raw batch into model-ready inputs - Runs the transformer forward pass @@ -52,8 +60,8 @@ The trainer handles everything else: optimization, checkpointing, validation, an Before writing code, answer these questions: 1. **What additional data does your strategy need?** - - Example: Inpainting needs mask latents alongside video latents - - Example: Audio-to-video needs reference audio embeddings + - Example: A perceptual-loss strategy may need auxiliary feature targets + - Example: A novel conditioning mechanism may need an additional precomputed directory 2. **What does conditioning look like?** - Which tokens should be noised vs. kept clean? @@ -164,6 +172,20 @@ class InpaintingConfig(TrainingStrategyConfigBase): ge=0.0, le=1.0, ) + + def get_data_sources(self) -> dict[str, str]: + """Define which data directories to load. + + Returns a mapping of directory names (under preprocessed_data_root) to + batch keys. The trainer loads .pt files from each directory and exposes + them in the batch under the specified key. The trainer also uses this + mapping to validate that all required directories exist. + """ + return { + "latents": "latents", # -> batch["latents"] + "conditions": "conditions", # -> batch["conditions"] + self.mask_latents_dir: "masks", # -> batch["masks"] + } ``` **Key points:** @@ -171,6 +193,7 @@ class InpaintingConfig(TrainingStrategyConfigBase): - Inherit from `TrainingStrategyConfigBase` - Use `Literal["your_strategy_name"]` for the `name` field - this enables automatic strategy selection - Use Pydantic `Field` for validation and documentation +- Implement `get_data_sources()` on the config — it's the single source of truth for data directories (used for both dataset wiring and existence validation) ### Step 4: Implement the Strategy Class @@ -187,24 +210,6 @@ class InpaintingStrategy(TrainingStrategy): def __init__(self, config: InpaintingConfig): super().__init__(config) - @property - def requires_audio(self) -> bool: - """Whether this strategy requires audio components.""" - return False # Set to True if your strategy needs audio - - def get_data_sources(self) -> dict[str, str]: - """Define which data directories to load. - - Returns a mapping of directory names to batch keys. - The trainer will load .pt files from each directory and - make them available in the batch under the specified key. - """ - return { - "latents": "latents", # -> batch["latents"] - "conditions": "conditions", # -> batch["conditions"] - self.config.mask_latents_dir: "masks", # -> batch["masks"] - } - def prepare_training_inputs( self, batch: dict[str, Any], @@ -275,7 +280,6 @@ class InpaintingStrategy(TrainingStrategy): batch_size=batch_size, fps=24.0, # Or get from latents_data device=device, - dtype=dtype, ) # Create video Modality @@ -328,7 +332,7 @@ You need to register your strategy in two places: from ltx_trainer.training_strategies.inpainting import InpaintingConfig, InpaintingStrategy # Add to the TrainingStrategyConfig type alias -TrainingStrategyConfig = TextToVideoConfig | VideoToVideoConfig | InpaintingConfig +TrainingStrategyConfig = TextToVideoConfig | VideoToVideoConfig | FlexibleStrategyConfig | InpaintingConfig # Add to __all__ __all__ = [ @@ -356,7 +360,8 @@ from ltx_trainer.training_strategies.inpainting import InpaintingConfig TrainingStrategyConfig = Annotated[ Annotated[TextToVideoConfig, Tag("text_to_video")] | Annotated[VideoToVideoConfig, Tag("video_to_video")] - | Annotated[InpaintingConfig, Tag("inpainting")], # Add your config + | Annotated[FlexibleStrategyConfig, Tag("flexible")] + | Annotated[InpaintingConfig, Tag("inpainting")], Discriminator(_get_strategy_discriminator), ] ``` @@ -366,7 +371,7 @@ TrainingStrategyConfig = Annotated[ Create an example config in `configs/`: ```yaml -# configs/ltx2_inpainting_lora.yaml +# configs/custom_inpainting_lora.yaml model: model_path: "/path/to/ltx2.safetensors" @@ -408,8 +413,8 @@ The base `TrainingStrategy` class provides these helper methods: | `_audio_patchifier.patchify(latents)` | Convert `[B, C, T, F]` → `[B, T, C*F]` | | `_get_video_positions(...)` | Generate position embeddings for video | | `_get_audio_positions(...)` | Generate position embeddings for audio | -| `_create_per_token_timesteps(mask, sigma)` | Create timesteps with 0 for conditioning tokens | -| `_create_first_frame_conditioning_mask(...)` | Create mask for first-frame conditioning | +| `_create_per_token_timesteps(conditioning_mask, sampled_sigma)` | Create timesteps with 0 for conditioning tokens | +| `_create_first_frame_conditioning_mask(...)` | Create mask for first-frame conditioning | ## 📊 Understanding ModelInputs @@ -418,16 +423,14 @@ The `ModelInputs` dataclass contains everything needed for the forward pass and ```python @dataclass class ModelInputs: - video: Modality # Video modality data - audio: Modality | None # Audio modality (None if video-only) + video: Modality | None # Video modality data + audio: Modality | None # Audio modality data - video_targets: Tensor # Target values for loss (velocity) - audio_targets: Tensor | None + video_targets: Tensor | None # Target values for video loss (velocity) + audio_targets: Tensor | None # Target values for audio loss (velocity) - video_loss_mask: Tensor # Boolean: True = compute loss for this token - audio_loss_mask: Tensor | None - - ref_seq_len: int | None = None # For IC-LoRA: reference sequence length + video_loss_mask: Tensor | None # Boolean loss mask for video tokens + audio_loss_mask: Tensor | None # Boolean loss mask for audio tokens ``` ## 📊 Understanding Modality @@ -437,18 +440,20 @@ The `Modality` dataclass (from ltx-core) represents a single modality's data: ```python @dataclass(frozen=True) class Modality: - enabled: bool # Whether this modality is active - latent: Tensor # [B, seq_len, C] - the latent tokens - timesteps: Tensor # [B, seq_len] - per-token timesteps (sigmas) - positions: Tensor # [B, dims, seq_len, 2] - position bounds - context: Tensor # [B, ctx_len, C] - text embeddings - context_mask: Tensor # [B, ctx_len] - attention mask for context + latent: Tensor # [B, T, D] — patchified latent tokens + sigma: Tensor # [B,] — per-batch noise level (for cross-attn conditioning) + timesteps: Tensor # [B, T] — per-token timestep embeddings + positions: Tensor # [B, 3, T, 2] for video, [B, 1, T, 2] for audio — positional bounds + context: Tensor # text conditioning embeddings + enabled: bool = True + context_mask: Tensor | None = None # attention mask for text context + attention_mask: Tensor | None = None # optional 2D self-attention mask [B, T, T] ``` > [!NOTE] > **Per-token timesteps:** Each token in the sequence has its own timestep. Conditioning tokens—those that should remain > un-noised—must have `timestep=0`. This is how the model distinguishes clean reference tokens from tokens to denoise. Use -`_create_per_token_timesteps(conditioning_mask, sigma)` to set this up correctly. +> `_create_per_token_timesteps(conditioning_mask, sampled_sigma)` to set this up correctly. > [!NOTE] > `Modality` is immutable (frozen dataclass). Use `dataclasses.replace()` to create modified copies. @@ -461,7 +466,7 @@ class Modality: from ltx_trainer.config import LtxTrainerConfig import yaml - with open('configs/ltx2_inpainting_lora.yaml') as f: + with open('configs/custom_inpainting_lora.yaml') as f: config = LtxTrainerConfig(**yaml.safe_load(f)) print(f'Strategy: {config.training_strategy.name}') " @@ -475,13 +480,13 @@ class Modality: config = InpaintingConfig() strategy = get_training_strategy(config) - print(f'Data sources: {strategy.get_data_sources()}') + print(f'Data sources: {config.get_data_sources()}') " ``` 3. **Run a short training test:** ```bash - uv run python scripts/train.py configs/ltx2_inpainting_lora.yaml + uv run python scripts/train.py configs/custom_inpainting_lora.yaml ``` ## 💡 Tips and Best Practices @@ -503,7 +508,8 @@ class Modality: Study these implementations for guidance: -| Strategy | Complexity | Key Features | -|------------------------------------------------------------------------------------|------------|------------------------------------------------| -| [`TextToVideoStrategy`](../src/ltx_trainer/training_strategies/text_to_video.py) | Simple | First-frame conditioning, optional audio | -| [`VideoToVideoStrategy`](../src/ltx_trainer/training_strategies/video_to_video.py) | Medium | Reference video concatenation, split loss mask | +| Strategy | Complexity | Key Features | +|----------|------------|--------------| +| [`FlexibleStrategy`](../src/ltx_trainer/training_strategies/flexible.py) | Medium | Unified conditioning framework — supports all built-in modes | +| [`TextToVideoStrategy`](../src/ltx_trainer/training_strategies/text_to_video.py) | Simple | First-frame conditioning, optional audio (deprecated) | +| [`VideoToVideoStrategy`](../src/ltx_trainer/training_strategies/video_to_video.py) | Medium | Reference video concatenation, split loss mask (deprecated) | diff --git a/packages/ltx-trainer/docs/dataset-preparation.md b/packages/ltx-trainer/docs/dataset-preparation.md index 6b8b151..6cb0c40 100644 --- a/packages/ltx-trainer/docs/dataset-preparation.md +++ b/packages/ltx-trainer/docs/dataset-preparation.md @@ -33,41 +33,33 @@ uv run python scripts/split_scenes.py --help If your dataset doesn't include captions, you can automatically generate them using multimodal models that understand both video and audio. +The default `qwen_omni` backend talks to a local vLLM server, which you launch once in a separate terminal: + ```bash -uv run python scripts/caption_videos.py scenes_output_dir/ \ - --output scenes_output_dir/dataset.json +# Terminal 1: start the captioner server (stays running) +uv run python scripts/serve_captioner.py ``` -If you're running into VRAM issues, try enabling 8-bit quantization to reduce memory usage: - ```bash +# Terminal 2: caption your videos uv run python scripts/caption_videos.py scenes_output_dir/ \ - --output scenes_output_dir/dataset.json \ - --use-8bit + --output scenes_output_dir/dataset.json ``` This will create a `dataset.json` file containing video paths and their captions. **Captioning options:** - -| Option | Description | -| ------------------ | ---------------------------------------------------------- | -| `--captioner-type` | `qwen_omni` (default, local) or `gemini_flash` (API) | -| `--use-8bit` | Enable 8-bit quantization for lower VRAM usage | -| `--no-audio` | Disable audio processing (video-only captions) | -| `--override` | Re-caption files that already have captions | -| `--api-key` | API key for Gemini Flash (or set `GOOGLE_API_KEY` env var) | - +| Option | Description | +| ------------------ | --------------------------------------------------------------- | +| `--captioner-type` | `qwen_omni` (default, local vLLM server) or `gemini_flash` (API) | +| `--vllm-url` | Base URL of the vLLM server (default `http://127.0.0.1:8001/v1`) | +| `--override` | Re-caption files that already have captions | +| `--api-key` | Gemini API key (else `GEMINI_API_KEY`/`GOOGLE_API_KEY`; with no key, uses gcloud/Vertex AI auth) | **Caption format:** -The captioner produces structured captions with sections for: - -- **Visual content**: People, objects, actions, settings, colors, movements -- **Speech transcription**: Word-for-word transcription of spoken content -- **Sounds**: Music, ambient sounds, sound effects -- **On-screen text**: Any visible text overlays +Each caption is a single, detailed paragraph describing both the visual content and the audio (speech, music, ambient sounds) of the clip. See the [Utility Scripts Reference](utility-scripts.md#automatic-video-captioning) for backend setup and the full list of options. > [!NOTE] > The automatically generated captions may contain inaccuracies or hallucinated content. @@ -80,7 +72,7 @@ This step preprocesses your video dataset by: 1. Resizing and cropping videos to fit specified resolution buckets 2. Computing and caching video latent representations 3. Computing and caching text embeddings for captions -4. (Optional) Computing and caching audio latents +4. Extracting and caching audio latents from videos (automatic, use `--skip-audio` to disable) > [!WARNING] > Very large videos (especially high spatial resolution and/or many frames) can cause GPU out-of-memory (OOM) @@ -97,17 +89,9 @@ uv run python scripts/process_dataset.py dataset.json \ --text-encoder-path /path/to/gemma-model ``` -### With Audio Processing - -For audio-video training, add the `--with-audio` flag: - -```bash -uv run python scripts/process_dataset.py dataset.json \ - --resolution-buckets "960x544x49" \ - --model-path /path/to/ltx-2-model.safetensors \ - --text-encoder-path /path/to/gemma-model \ - --with-audio -``` +Audio latents are automatically extracted from video files — no extra flag is needed. Use `--skip-audio` +to disable this. For standalone audio files (`.wav`), use the `audio` column in your dataset instead +(see [Convention-Based Column Detection](#convention-based-column-detection) below). ### 🚀 Multi-GPU Preprocessing @@ -126,7 +110,7 @@ Outputs are written atomically (via a per-process temporary file, then renamed), corrupt files. By default a rerun **resumes** — items whose output `.pt` already exists are skipped. > [!IMPORTANT] -> Pass `**--overwrite`** when rerunning with changed parameters (different model checkpoint, resolution buckets, +> Pass **`--overwrite`** when rerunning with changed parameters (different model checkpoint, resolution buckets, > text encoder, `--lora-trigger`, etc.). Without it the script keeps the stale outputs from the previous run. > > ```bash @@ -152,7 +136,7 @@ The trainer supports videos, single images, or a mix of both in the same dataset > `--resolution-buckets "960x544x1;960x544x49"`. Images are automatically assigned to the `F=1` bucket and > videos to an `F>1` bucket. > - You **must** set `optimization.batch_size: 1` in your training config (see the warning under -> [Resolution Buckets](#-resolution-buckets)), since samples with different shapes cannot be collated into a +> [Resolution Buckets](#resolution-buckets)), since samples with different shapes cannot be collated into a > single batch. Use `gradient_accumulation_steps` if you need a larger effective batch. > - Per-step cost differs substantially between a single-frame sample and a many-frame sample, which can lead to > uneven gradient magnitudes across steps. Consider weighting the two subsets or tuning the learning rate if @@ -160,7 +144,24 @@ The trainer supports videos, single images, or a mix of both in the same dataset > - If you prefer a fully officially-supported path, train two separate LoRAs (one on stills, one on video) and > stack them at inference. -The dataset must be a CSV, JSON, or JSONL metadata file with columns for captions and video paths: +The dataset must be a CSV, JSON, or JSONL metadata file with columns for captions and media paths. + +#### Convention-Based Column Detection + +The preprocessing script automatically detects and processes columns based on their names. The following columns are recognized: + +| Column | Output Dir | Description | +|--------|-----------|-------------| +| `video` (or legacy `media_path`) | `latents/` | Target video to encode | +| `audio` | `audio_latents/` | Explicit audio file (overrides auto-extraction from video) | +| `caption` | `conditions/` | Text caption for the sample | +| `reference_video` (or legacy `ref_media_path`) | `reference_latents/` | IC-LoRA reference video | +| `reference_audio` | `reference_audio_latents/` | IC-LoRA reference audio | +| `video_mask` | `video_masks/` | Binary mask for video inpainting | +| `audio_mask` | `audio_masks/` | Binary mask for audio inpainting | + +> [!NOTE] +> **Legacy column names:** `media_path` and `ref_media_path` are accepted as aliases for `video` and `reference_video` respectively. Existing datasets using these names will continue to work without modification. **JSON format example:** @@ -168,11 +169,11 @@ The dataset must be a CSV, JSON, or JSONL metadata file with columns for caption [ { "caption": "A cat playing with a ball of yarn", - "media_path": "videos/cat_playing.mp4" + "video": "videos/cat_playing.mp4" }, { "caption": "A dog running in the park", - "media_path": "videos/dog_running.mp4" + "video": "videos/dog_running.mp4" } ] ``` @@ -180,18 +181,42 @@ The dataset must be a CSV, JSON, or JSONL metadata file with columns for caption **JSONL format example:** ```jsonl -{"caption": "A cat playing with a ball of yarn", "media_path": "videos/cat_playing.mp4"} -{"caption": "A dog running in the park", "media_path": "videos/dog_running.mp4"} +{"caption": "A cat playing with a ball of yarn", "video": "videos/cat_playing.mp4"} +{"caption": "A dog running in the park", "video": "videos/dog_running.mp4"} ``` **CSV format example:** ```csv -caption,media_path +caption,video "A cat playing with a ball of yarn","videos/cat_playing.mp4" "A dog running in the park","videos/dog_running.mp4" ``` +**Additional dataset format examples:** + +Audio-only dataset: +```json +{"audio": "song.wav", "caption": "piano melody"} +``` + +V2V IC-LoRA with reference video: +```json +{"video": "clip.mp4", "reference_video": "depth.mp4", "caption": "depth to video"} +``` + +A2A IC-LoRA with reference audio: +```json +{"video": "clip.mp4", "reference_audio": "ref.wav", "caption": "match this style"} +``` +This form auto-extracts the target audio from `clip.mp4`. For pure audio datasets, use `audio` plus +`reference_audio` columns and preprocess with `--audio-durations`. + +Video inpainting with mask: +```json +{"video": "clip.mp4", "video_mask": "mask.mp4", "caption": "fill the sky"} +``` + ### 📐 Resolution Buckets Videos are organized into "buckets" of specific dimensions (width × height × frames). @@ -268,12 +293,31 @@ The preprocessed data is saved in a `.precomputed` directory: ``` dataset/ └── .precomputed/ - ├── latents/ # Cached video latents - ├── conditions/ # Cached text embeddings - ├── audio_latents/ # (only if --with-audio) Cached audio latents - └── reference_latents/ # (only for IC-LoRA) Cached reference video latents + ├── latents/ # Video latents + ├── conditions/ # Text embeddings + ├── audio_latents/ # Audio latents (auto-extracted or explicit) + ├── reference_latents/ # Reference video latents (IC-LoRA) + ├── reference_audio_latents/ # Reference audio latents (audio IC-LoRA) + ├── video_masks/ # Video masks (inpainting) + └── audio_masks/ # Audio masks (audio inpainting) ``` +Set `data.preprocessed_data_root` in your training config to this `.precomputed` directory — the parent directory that +contains `latents/`, `conditions/`, and any mode-specific audio/reference/mask directories. + +## 🔊 Audio-Only Dataset Preprocessing + +For datasets containing only audio files (no `video` column), use `--audio-durations` to specify duration buckets: + +```bash +uv run python scripts/process_dataset.py dataset.json \ + --audio-durations "2.0;4.0;8.0" \ + --model-path /path/to/ltx-2-model.safetensors \ + --text-encoder-path /path/to/gemma-model +``` + +The `--audio-durations` flag provides duration buckets (in seconds) for audio-only datasets. Since there is no video column to derive timing from, explicit duration buckets are required. + ## 🪄 IC-LoRA Reference Video Preprocessing For IC-LoRA training, you need to preprocess datasets that include reference videos. @@ -281,14 +325,16 @@ Reference videos provide the conditioning input while target videos represent th ### Dataset Format with Reference Videos +The `reference_video` column is automatically detected by convention — no extra CLI flags are needed. + **JSON format:** ```json [ { "caption": "A cat playing with a ball of yarn", - "media_path": "videos/cat_playing.mp4", - "reference_path": "references/cat_playing_depth.mp4" + "video": "videos/cat_playing.mp4", + "reference_video": "references/cat_playing_depth.mp4" } ] ``` @@ -296,32 +342,39 @@ Reference videos provide the conditioning input while target videos represent th **JSONL format:** ```jsonl -{"caption": "A cat playing with a ball of yarn", "media_path": "videos/cat_playing.mp4", "reference_path": "references/cat_playing_depth.mp4"} -{"caption": "A dog running in the park", "media_path": "videos/dog_running.mp4", "reference_path": "references/dog_running_depth.mp4"} +{"caption": "A cat playing with a ball of yarn", "video": "videos/cat_playing.mp4", "reference_video": "references/cat_playing_depth.mp4"} +{"caption": "A dog running in the park", "video": "videos/dog_running.mp4", "reference_video": "references/dog_running_depth.mp4"} ``` ### Preprocessing with Reference Videos -To preprocess a dataset with reference videos, add the `--reference-column` argument specifying the name of the field -in your dataset JSON/JSONL/CSV that contains the reference video paths: +Convention-based detection means you just need the `reference_video` column in your dataset, and `process_dataset.py` will automatically detect and process it. No `--reference-column` flag is needed: ```bash uv run python scripts/process_dataset.py dataset.json \ --resolution-buckets "960x544x49" \ --model-path /path/to/ltx-2-model.safetensors \ --text-encoder-path /path/to/gemma-model \ - --reference-column "reference_path" + --reference-downscale-factor 2 \ + --reference-temporal-scale-factor 1 ``` This will create an additional `reference_latents/` directory containing the preprocessed reference video latents. +Use `--reference-downscale-factor` for spatial subsampling and `--reference-temporal-scale-factor` for temporal +subsampling. Validation reference conditions should use matching `downscale_factor` and `temporal_scale_factor` values. + +> [!NOTE] +> **Legacy column names:** If your dataset uses `ref_media_path`, it is accepted as an alias for `reference_video`. ### Generating Reference Videos **Dataset Requirements for IC-LoRA:** - Your dataset must contain paired videos where each target video has a corresponding reference video -- Reference and target videos must have *identical* resolution and length -- Both reference and target videos should be preprocessed together using the same resolution buckets +- Reference and target videos should cover the same content. Reference videos can optionally be lower spatial + resolution or temporally subsampled (see Scaled Reference Conditioning in [Training Modes](training-modes.md)). +- Both reference and target videos should be preprocessed together using the same target resolution buckets, plus any + reference scale factors you choose. We provide an example script, `[scripts/compute_reference.py](../scripts/compute_reference.py)`, to generate reference videos for a given dataset. The default implementation generates Canny edge reference videos. @@ -333,12 +386,63 @@ uv run python scripts/compute_reference.py scenes_output_dir/ \ The script accepts a JSON file as the dataset configuration and updates it in-place by adding the filenames of the generated reference videos. +> [!NOTE] +> `compute_reference.py` writes generated references to the `reference_video` column, which `process_dataset.py` +> detects automatically. The legacy `ref_media_path` column is also accepted. + If you want to generate a different type of condition (depth maps, pose skeletons, etc.), modify or replace the `compute_reference()` function within this script. ### Example Dataset For reference, see our **[Canny Control Dataset](https://huggingface.co/datasets/Lightricks/Canny-Control-Dataset)** which demonstrates proper IC-LoRA dataset structure with paired videos and Canny edge maps. +## 🎭 Mask Preprocessing for Inpainting + +For inpainting training with the `mask` condition type, provide `video_mask` or `audio_mask` columns in your dataset +metadata. These columns point to mask media files (for example a mask image/video for video inpainting, or a waveform or +`.pt` tensor for audio inpainting). `process_dataset.py` downsamples and thresholds them into per-sample `.pt` tensors +under `video_masks/` or `audio_masks/`. + +### Processed Video Mask Format + +If you create masks manually instead of using `process_dataset.py`, save them as `.pt` files with the key `"mask"` +containing a tensor of shape `[F, H, W]` where: + +- `F` = number of latent frames (temporal dimension) +- `H` = latent height (pixel height / 32) +- `W` = latent width (pixel width / 32) +- Values are thresholded at `0.5`: values `> 0.5` are conditioning tokens (clean, excluded from loss), + and values `<= 0.5` are generated tokens (noised, contributes to loss). + +### Audio Mask Format + +Audio masks follow the same thresholding pattern as video masks but with shape `[T]` (temporal dimension only), where `T` is the number of audio latent frames. They are stored in `audio_masks/`. + +### Directory Structure + +Place masks in a directory within your preprocessed data root: + +``` +preprocessed_data_root/ +├── latents/ # Video latents +├── conditions/ # Text embeddings +├── video_masks/ # Video masks (one .pt per sample, matching latent filenames) +└── audio_masks/ # Audio masks (one .pt per sample, matching latent filenames) +``` + +Then reference the mask directory in your training config: + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: mask + mask_dir: "video_masks" +``` + ## 🎯 LoRA Trigger Words When training a LoRA, you can specify a trigger token that will be prepended to all captions: @@ -359,9 +463,9 @@ This acts as a trigger word that activates the LoRA during inference when you in ## 🔍 Decoding Videos for Verification -If you add the `--decode` flag, the script will VAE-decode the precomputed latents and save the resulting videos -in `.precomputed/decoded_videos`. When audio preprocessing is enabled (`--with-audio`), audio latents will also be -decoded and saved to `.precomputed/decoded_audio`. This allows you to visually and audibly inspect the processed data. +If you add the `--decode` flag, the script will VAE-decode the precomputed video latents and save the resulting videos +in `.precomputed/decoded_videos`. Reference video latents are decoded to `.precomputed/decoded_reference_videos` when +present. To inspect audio latents, run `scripts/decode_latents.py` with `--with-audio`. ```bash uv run python scripts/process_dataset.py dataset.json \ @@ -382,6 +486,4 @@ Once your dataset is preprocessed, you can proceed to: - Start training with the [Training Guide](training-guide.md) > [!TIP] -> If your training recipe requires additional preprocessed data (e.g., masks, conditioning signals), see -> [Implementing Custom Training Strategies](custom-training-strategies.md) for guidance on extending the -> preprocessing pipeline. +> The `flexible` strategy supports masks for inpainting (`mask` condition type) and spatial crop regions for outpainting (`spatial_crop` condition type) out of the box. For other custom preprocessing needs, see [Custom Training Strategies](custom-training-strategies.md). diff --git a/packages/ltx-trainer/docs/quick-start.md b/packages/ltx-trainer/docs/quick-start.md index da89964..c81953a 100644 --- a/packages/ltx-trainer/docs/quick-start.md +++ b/packages/ltx-trainer/docs/quick-start.md @@ -7,12 +7,14 @@ Get up and running with LTX-2 training in just a few steps! Before you begin, ensure you have: 1. **LTX-2 Model Checkpoint** - A local `.safetensors` file containing the LTX-2 model weights. - Download `ltx-2-19b-dev.safetensors` from: [HuggingFace Hub](https://huggingface.co/Lightricks/LTX-2) + Download `ltx-2.3-22b-dev.safetensors` from: [HuggingFace Hub](https://huggingface.co/Lightricks/LTX-2.3) + The trainer supports LTX-2 and LTX-2.3 checkpoints through the same configuration API; version-specific components + are detected from the checkpoint. 2. **Gemma Text Encoder** - A local directory containing the Gemma model (required for LTX-2). Download from: [HuggingFace Hub](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized/) -3. **Linux with CUDA** - The trainer requires `triton` which is Linux-only +3. **Linux with CUDA** - The trainer requires `triton` which is Linux-only; CUDA 13+ is recommended 4. **GPU with sufficient VRAM** - 80GB recommended for the standard config. For GPUs with 32GB VRAM (e.g., RTX 5090), - use the [low VRAM config](../configs/ltx2_av_lora_low_vram.yaml) which enables INT8 quantization and other + use the [low VRAM config](../configs/t2v_lora_low_vram.yaml) which enables INT8 quantization and other memory optimizations ## ⚡ Installation @@ -39,7 +41,18 @@ cd packages/ltx-trainer ## 🏋 Training Workflow -### 1. Prepare Your Dataset +If you are using an agent-enabled environment with repository skills, you can ask for the +[`train-model`](../../../.claude/skills/train-model/SKILL.md) skill to run this workflow with you. +It creates a run workspace, confirms the training mode, prepares data, preprocesses latents, +launches training, and monitors the run while stopping for approval before expensive steps. + +### 1. Choose a Training Mode + +Start with [`t2v_lora.yaml`](../configs/t2v_lora.yaml) for a first run with videos and captions. For modes such as +IC-LoRA, inpainting, or outpainting, check [Training Modes](training-modes.md) first because your metadata needs extra +columns such as `reference_video`, `video_mask`, or `audio_mask` before preprocessing. + +### 2. Prepare Your Dataset Organize your videos and captions, then preprocess them: @@ -57,15 +70,18 @@ uv run python scripts/process_dataset.py dataset.json \ --text-encoder-path /path/to/gemma-model ``` +By default, preprocessing writes to `.precomputed/`. Use that directory as `data.preprocessed_data_root` +in your training config. + See [Dataset Preparation](dataset-preparation.md) for detailed instructions. -### 2. Configure Training +### 3. Configure Training Create or modify a configuration YAML file. Start with one of the example configs: -- [`configs/ltx2_av_lora.yaml`](../configs/ltx2_av_lora.yaml) - Audio-video LoRA training -- [`configs/ltx2_av_lora_low_vram.yaml`](../configs/ltx2_av_lora_low_vram.yaml) - Audio-video LoRA training (optimized for 32GB VRAM) -- [`configs/ltx2_v2v_ic_lora.yaml`](../configs/ltx2_v2v_ic_lora.yaml) - IC-LoRA video-to-video +- [`configs/t2v_lora.yaml`](../configs/t2v_lora.yaml) - Text-to-video LoRA +- [`configs/t2v_lora_low_vram.yaml`](../configs/t2v_lora_low_vram.yaml) - Same as above, tuned for ~32GB VRAM (INT8 quantization and memory optimizations) +- [`configs/v2v_ic_lora.yaml`](../configs/v2v_ic_lora.yaml) - IC-LoRA video-to-video Key settings to update: @@ -82,33 +98,47 @@ output_dir: "outputs/my_training_run" See [Configuration Reference](configuration-reference.md) for all available options. -### 3. Start Training +### 4. Start Training ```bash -uv run python scripts/train.py configs/ltx2_av_lora.yaml +uv run python scripts/train.py configs/t2v_lora.yaml ``` For multi-GPU training: ```bash -uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch scripts/train.py configs/t2v_lora.yaml ``` See [Training Guide](training-guide.md) for distributed training and advanced options. ## 🎯 Training Modes +> [!TIP] +> **First time?** Start with [`t2v_lora.yaml`](../configs/t2v_lora.yaml) — it's the simplest mode +> and only requires videos with captions. You can explore other modes once you've confirmed your +> setup works. + The trainer supports several training modes: -| Mode | Description | Config Example | -|----------------------|--------------------------------|--------------------------------------------| -| **LoRA** | Efficient adapter training | `training_strategy.name: "text_to_video"` | -| **Audio-Video LoRA** | Joint audio-video training | `training_strategy.with_audio: true` | -| **IC-LoRA** | Video-to-video transformations | `training_strategy.name: "video_to_video"` | -| **Full Fine-tuning** | Full model training | `model.training_mode: "full"` | +| Mode | Description | Example Config | +|-----------------------|--------------------------------------------|-------------------------------------------------------------------| +| **Text-to-Video** | Generate video+audio from text prompts | [`t2v_lora.yaml`](../configs/t2v_lora.yaml) | +| **Image-to-Video** | Animate from a starting image | [`i2v_lora.yaml`](../configs/i2v_lora.yaml) | +| **Video Extension** | Extend videos temporally (forward/backward)| [`video_extend_lora.yaml`](../configs/video_extend_lora.yaml), [`video_suffix_lora.yaml`](../configs/video_suffix_lora.yaml) | +| **IC-LoRA (V2V)** | Video-to-video transformations | [`v2v_ic_lora.yaml`](../configs/v2v_ic_lora.yaml) | +| **Audio-to-Video** | Generate video conditioned on audio | [`a2v_lora.yaml`](../configs/a2v_lora.yaml) | +| **Video-to-Audio** | Generate audio/foley from video | [`v2a_lora.yaml`](../configs/v2a_lora.yaml) | +| **Video Inpainting** | Fill in masked regions of video | [`video_inpainting_lora.yaml`](../configs/video_inpainting_lora.yaml) | +| **Video Outpainting** | Extend video spatially | [`video_outpainting_lora.yaml`](../configs/video_outpainting_lora.yaml) | +| **Text-to-Audio** | Generate audio from text prompts | [`t2a_lora.yaml`](../configs/t2a_lora.yaml) | +| **Audio Extension** | Extend audio temporally | [`audio_extend_lora.yaml`](../configs/audio_extend_lora.yaml), [`audio_suffix_lora.yaml`](../configs/audio_suffix_lora.yaml) | +| **Audio Inpainting** | Fill in masked regions of audio | [`audio_inpainting_lora.yaml`](../configs/audio_inpainting_lora.yaml) | +| **IC-LoRA (A2A)** | Audio-to-audio transformations | [`a2a_ic_lora.yaml`](../configs/a2a_ic_lora.yaml) | +| **AV2AV IC-LoRA** | Audio+video IC-LoRA transformations | [`av2av_ic_lora.yaml`](../configs/av2av_ic_lora.yaml) | +| **Full Fine-tuning** | Full model training (any mode above) | Set `model.training_mode: "full"` | -See [Training Modes](training-modes.md) for detailed explanations, -or [Custom Training Strategies](custom-training-strategies.md) if you need to implement your own training recipe. +See [Training Modes](training-modes.md) for detailed explanations of each mode. ## Next Steps @@ -118,7 +148,7 @@ Once you've completed your first training run, you can: production-ready inference pipelines for various use cases (T2V, I2V, IC-LoRA, etc.). See the package documentation for details. - Learn more about [Dataset Preparation](dataset-preparation.md) for advanced preprocessing -- Explore different [Training Modes](training-modes.md) (LoRA, Audio-Video, IC-LoRA) +- Explore different [Training Modes](training-modes.md) - Dive deeper into [Training Configuration](configuration-reference.md) - Understand the model architecture in [LTX-Core Documentation](../../ltx-core/README.md) diff --git a/packages/ltx-trainer/docs/training-guide.md b/packages/ltx-trainer/docs/training-guide.md index f737083..a61d246 100644 --- a/packages/ltx-trainer/docs/training-guide.md +++ b/packages/ltx-trainer/docs/training-guide.md @@ -8,7 +8,7 @@ model uploads. After preprocessing your dataset and preparing a configuration file, you can start training using the trainer script: ```bash -uv run python scripts/train.py configs/ltx2_av_lora.yaml +uv run python scripts/train.py configs/t2v_lora.yaml ``` The trainer will: @@ -19,19 +19,31 @@ The trainer will: 4. **Generate validation videos** (if configured) 5. **Save the trained weights** in your output directory +### Agent-Assisted Training + +If your environment supports repository skills, the +[`train-model`](../../../.claude/skills/train-model/SKILL.md) skill provides an end-to-end +orchestrator for this package. It asks what you want the model to learn, maps that intent to +one of the documented [training modes](training-modes.md), probes your filesystem and GPU, +prepares/preprocesses the dataset, writes a run-specific config, launches training, and +monitors the job. It uses the trainer docs as its source of truth and stops for approval before +captioning, preprocessing, or starting expensive training work. + ### Output Files **For LoRA training:** -- `lora_weights.safetensors` - Main LoRA weights file +- `checkpoints/lora_weights_step_00000.safetensors` - LoRA checkpoint weights, with the current step in the filename - `training_config.yaml` - Copy of training configuration -- `validation_samples/` - Generated validation videos (if enabled) +- `samples/` - Generated validation samples (if enabled) +- `checkpoints/training_state_step_00000.pt` - Optional resume state, depending on `checkpoints.save_training_state` **For full model fine-tuning:** -- `model_weights.safetensors` - Full model weights +- `checkpoints/model_weights_step_00000.safetensors` - Full model checkpoint weights, with the current step in the filename - `training_config.yaml` - Copy of training configuration -- `validation_samples/` - Generated validation videos (if enabled) +- `samples/` - Generated validation samples (if enabled) +- `checkpoints/training_state_step_00000.pt` - Optional resume state, depending on `checkpoints.save_training_state` ## 🖥️ Distributed / Multi-GPU Training @@ -62,22 +74,22 @@ Launch with a specific config using `--config_file`: # DDP (2 GPUs shown as example) CUDA_VISIBLE_DEVICES=0,1 \ uv run accelerate launch --config_file configs/accelerate/ddp.yaml \ - scripts/train.py configs/ltx2_av_lora.yaml + scripts/train.py configs/t2v_lora.yaml # DDP + torch.compile CUDA_VISIBLE_DEVICES=0,1 \ uv run accelerate launch --config_file configs/accelerate/ddp_compile.yaml \ - scripts/train.py configs/ltx2_av_lora.yaml + scripts/train.py configs/t2v_lora.yaml # FSDP (4 GPUs shown as example) CUDA_VISIBLE_DEVICES=0,1,2,3 \ uv run accelerate launch --config_file configs/accelerate/fsdp.yaml \ - scripts/train.py configs/ltx2_av_lora.yaml + scripts/train.py configs/t2v_lora.yaml # FSDP + torch.compile CUDA_VISIBLE_DEVICES=0,1,2,3 \ uv run accelerate launch --config_file configs/accelerate/fsdp_compile.yaml \ - scripts/train.py configs/ltx2_av_lora.yaml + scripts/train.py configs/t2v_lora.yaml ``` **Notes:** @@ -93,13 +105,13 @@ If you prefer to use your default Accelerate profile: ```bash # Use settings from your default accelerate config -uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch scripts/train.py configs/t2v_lora.yaml # Override number of processes on the fly (e.g., 2 GPUs) -uv run accelerate launch --num_processes 2 scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch --num_processes 2 scripts/train.py configs/t2v_lora.yaml # Select specific GPUs -CUDA_VISIBLE_DEVICES=0,1 uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml +CUDA_VISIBLE_DEVICES=0,1 uv run accelerate launch scripts/train.py configs/t2v_lora.yaml ``` > [!TIP] diff --git a/packages/ltx-trainer/docs/training-modes.md b/packages/ltx-trainer/docs/training-modes.md index 99485cb..2469c77 100644 --- a/packages/ltx-trainer/docs/training-modes.md +++ b/packages/ltx-trainer/docs/training-modes.md @@ -1,165 +1,167 @@ # Training Modes Guide -The trainer supports several training modes, each suited for different use cases and requirements. +The trainer uses the **flexible** training strategy (`name: "flexible"`) — a unified conditioning framework that +supports all training modes through configuration. Every scenario is expressed by setting `is_generated` on each +modality and adding optional conditions, rather than choosing a separate strategy class. -## 🎯 Standard LoRA Training (Video-Only) +## Key Concepts -Standard LoRA (Low-Rank Adaptation) training fine-tunes the model by adding small, trainable adapter layers while -keeping the base model frozen. This approach: +Before diving into individual modes, here are the core ideas behind the flexible strategy: -- **Requires significantly less memory and compute** than full fine-tuning -- **Produces small, portable weight files** (typically a few hundred MB) -- **Is ideal for learning specific styles, effects, or concepts** -- **Can be easily combined with other LoRAs** during inference +- **`is_generated: true`** — the modality is denoised during training and contributes to the loss. This is the + modality the model learns to generate. +- **`is_generated: false`** — the modality is frozen (sigma=0, no noise, no loss). It passes through the transformer + clean and acts as cross-modal conditioning for the generated modality. +- **At least one modality must have `is_generated: true`.** +- **Conditions** are per-modality and can be composed (e.g., `reference` + `first_frame` together on the video + modality). +- Audio does **not** support `first_frame` or `spatial_crop` conditions — only `prefix`, `suffix`, `mask`, + and `reference`. -Configure standard LoRA training with: +> [!TIP] +> If you are using an agent-enabled environment with repository skills and are unsure which mode to choose, +> ask for the [`train-model`](../../../.claude/skills/train-model/SKILL.md) skill. It maps your intent to one of +> these configs and walks through dataset preparation, preprocessing, launch, and monitoring. + +## 📊 Quick Reference + +| Mode | Video | Audio | Conditions | Config | +|-----------------------|-----------|-----------|---------------------|--------| +| **T2V** | Generated | Generated | — | [`t2v_lora`](../configs/t2v_lora.yaml) | +| **I2V** | Generated | Generated | `first_frame` | [`i2v_lora`](../configs/i2v_lora.yaml) | +| **Video Extension** | Generated | Generated | `prefix`/`suffix` | [`video_extend_lora`](../configs/video_extend_lora.yaml) | +| **V2V IC-LoRA** | Generated | — | `reference` | [`v2v_ic_lora`](../configs/v2v_ic_lora.yaml) | +| **A2V** | Generated | Frozen | — | [`a2v_lora`](../configs/a2v_lora.yaml) | +| **V2A (Foley)** | Frozen | Generated | — | [`v2a_lora`](../configs/v2a_lora.yaml) | +| **Video Inpainting** | Generated | — | `mask` | [`video_inpainting_lora`](../configs/video_inpainting_lora.yaml) | +| **Video Outpainting** | Generated | — | `spatial_crop` | [`video_outpainting_lora`](../configs/video_outpainting_lora.yaml) | +| **T2A** | — | Generated | — | [`t2a_lora`](../configs/t2a_lora.yaml) | +| **Audio Extension** | — | Generated | `prefix`/`suffix` | [`audio_extend_lora`](../configs/audio_extend_lora.yaml) | +| **Audio Inpainting** | — | Generated | `mask` | [`audio_inpainting_lora`](../configs/audio_inpainting_lora.yaml) | +| **A2A IC-LoRA** | — | Generated | `reference` | [`a2a_ic_lora`](../configs/a2a_ic_lora.yaml) | +| **AV2AV IC-LoRA** | Generated | Generated | `reference` (both) | [`av2av_ic_lora`](../configs/av2av_ic_lora.yaml) | + +--- + +## 🎯 Text-to-Video (T2V) + +Generate video and audio from text prompts. Both modalities are denoised with no additional conditions. ```yaml -model: - training_mode: "lora" - training_strategy: - name: "text_to_video" - first_frame_conditioning_p: 0.1 - with_audio: false # Video-only training + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + audio: + is_generated: true + latents_dir: "audio_latents" ``` -## 🔊 Audio-Video LoRA Training +**Example config:** 📄 [t2v_lora.yaml](../configs/t2v_lora.yaml) -LTX-2 supports joint audio-video generation. You can train LoRA adapters that affect both video and audio output: +--- -- **Synchronized audio-video generation** - Audio matches the visual content -- **Same efficient LoRA approach** - Just enable audio training -- **Requires audio latents** - Dataset must include preprocessed audio +## 🖼️ Image-to-Video (I2V) -Configure audio-video training with: +Generate video conditioned on a starting image. The first frame is provided as a clean conditioning signal — no noise, +timestep=0, excluded from loss. The `probability` parameter controls how often first-frame conditioning is applied; +remaining samples train in pure T2V mode. ```yaml -model: - training_mode: "lora" - training_strategy: - name: "text_to_video" - first_frame_conditioning_p: 0.1 - with_audio: true # Enable audio training - audio_latents_dir: "audio_latents" # Directory containing audio latents + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: first_frame + probability: 0.5 + audio: + is_generated: true + latents_dir: "audio_latents" ``` -**Example configuration file:** +**Example config:** 📄 [i2v_lora.yaml](../configs/i2v_lora.yaml) -- 📄 [Audio-Video LoRA Training](../configs/ltx2_av_lora.yaml) +--- -**Dataset structure for audio-video training:** +## ⏩ Video Extension +Extend a video forward (or backward) in time. Prefix or suffix conditioning provides a span of existing latent frames +as clean conditioning. The `temporal_boundary` sets the number of **latent frames** used as context (each latent frame += 8 pixel frames due to temporal compression). + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: prefix # or "suffix" for backward extension + temporal_boundary: 8 # 8 latent frames = 64 pixel frames + probability: 1.0 + audio: + is_generated: true + latents_dir: "audio_latents" ``` -preprocessed_data_root/ -├── latents/ # Video latents -├── conditions/ # Text embeddings -└── audio_latents/ # Audio latents (required when with_audio: true) -``` - -> [!IMPORTANT] -> When training audio-video LoRAs, ensure your `target_modules` configuration captures video, audio, and -> cross-modal attention branches. Use patterns like `"to_k"` instead of `"attn1.to_k"` to match: -> - Video modules: `attn1.to_k`, `attn2.to_k` -> - Audio modules: `audio_attn1.to_k`, `audio_attn2.to_k` -> - Cross-modal modules: `audio_to_video_attn.to_k`, `video_to_audio_attn.to_k` -> -> The cross-modal attention modules (`audio_to_video_attn` and `video_to_audio_attn`) enable bidirectional -> information flow between audio and video, which is critical for synchronized audiovisual generation. -> See [Understanding Target Modules](configuration-reference.md#understanding-target-modules) for detailed guidance. > [!NOTE] -> You can generate audio during validation even if you're not training the audio branch. -> Set `validation.generate_audio: true` independently of `training_strategy.with_audio`. +> The `prefix` and `suffix` conditions also work on the audio modality for audio extension. +> Set `temporal_boundary` on the audio modality's conditions list to condition on a prefix or suffix +> of the audio latents. -## 🔥 Full Model Fine-tuning +**Example configs:** 📄 [video_extend_lora.yaml](../configs/video_extend_lora.yaml) (forward), 📄 [video_suffix_lora.yaml](../configs/video_suffix_lora.yaml) (backward) -Full model fine-tuning updates all parameters of the base model, providing maximum flexibility but -requiring substantial computational resources and larger training datasets: +--- -- **Offers the highest potential quality and capability improvements** -- **Requires multiple GPUs** and distributed training techniques (e.g., FSDP) -- **Produces large checkpoint files** (several GB) -- **Best for major model adaptations** or when LoRA limitations are reached +## 🔄 IC-LoRA / Video-to-Video (V2V) -Configure full fine-tuning with: +In-Context LoRA learns transformations from paired videos. Pre-encoded reference latents are concatenated to the target +sequence — reference tokens participate in bidirectional self-attention but receive no noise and are excluded from loss. +This enables control adapters (depth, pose), style transfer, deblurring, colorization, and more. ```yaml -model: - training_mode: "full" - training_strategy: - name: "text_to_video" - first_frame_conditioning_p: 0.1 + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: reference + latents_dir: "reference_latents" + probability: 1.0 + - type: first_frame # optional — composable with reference + probability: 0.2 ``` -> [!IMPORTANT] -> Full fine-tuning of LTX-2 requires multiple high-end GPUs (e.g., 4-8× H100 80GB) and distributed -> training with FSDP. See [Training Guide](training-guide.md) for multi-GPU setup instructions. +> [!NOTE] +> IC-LoRA is video-only by default (no audio modality block). Conditions can be composed — the example above also +> applies first-frame conditioning with 20% probability alongside the reference. +> Use [AV2AV IC-LoRA](#av2av-ic-lora) when both video and audio references should be trained jointly. -## 🔄 In-Context LoRA (IC-LoRA) Training +**Example config:** 📄 [v2v_ic_lora.yaml](../configs/v2v_ic_lora.yaml) -IC-LoRA is a specialized training mode for video-to-video transformations. -Unlike standard training modes that learn from individual videos, IC-LoRA learns transformations from pairs of videos. -IC-LoRA enables a wide range of advanced video-to-video applications, such as: +### Dataset Requirements -- **Control adapters** (e.g., Depth, Pose): Learn to map from a control signal (like a depth map or pose skeleton) to a - target video -- **Video deblurring**: Transform blurry input videos into sharp, high-quality outputs -- **Style transfer**: Apply the style of a reference video to a target video sequence -- **Colorization**: Convert grayscale reference videos into colorized outputs -- **Restoration and enhancement**: Denoise, upscale, or restore old or degraded videos +- **Paired videos** — each target video has a corresponding reference video +- **Same frame count** between reference and target +- Reference videos can optionally be at **lower spatial resolution** (see [Scaled Reference](#scaled-reference-conditioning) below) +- Both must be **preprocessed** before training -By providing paired reference and target videos, IC-LoRA can learn complex transformations that go beyond caption-based -conditioning. - -IC-LoRA training fundamentally differs from standard LoRA and full fine-tuning: - -- **Reference videos** provide clean, unnoised conditioning input showing the "before" state -- **Target videos** are noised during training and represent the desired "after" state -- **The model learns transformations** from reference videos to target videos -- **Loss is applied only to the target portion**, not the reference -- **Training and inference time increase significantly** due to the doubled sequence length - -To enable IC-LoRA training, configure your YAML file with: - -```yaml -model: - training_mode: "lora" # Required: IC-LoRA uses LoRA mode - -training_strategy: - name: "video_to_video" - first_frame_conditioning_p: 0.1 - reference_latents_dir: "reference_latents" # Directory for reference video latents -``` - -**Example configuration file:** - -- 📄 [IC-LoRA Training](../configs/ltx2_v2v_ic_lora.yaml) - Video-to-video transformation training - -### Dataset Requirements for IC-LoRA - -- Your dataset must contain **paired videos** where each target video has a corresponding reference video -- Reference and target videos must have the **same frame count** (length) -- Reference videos can optionally be at **lower spatial resolution** than target videos ( - see [Scaled Reference Conditioning](#scaled-reference-conditioning) below) -- Both reference and target videos should be **preprocessed** before training - -**Dataset structure for IC-LoRA training:** +**Dataset structure:** ``` preprocessed_data_root/ -├── latents/ # Target video latents (what the model learns to generate) -├── conditions/ # Text embeddings for each video +├── latents/ # Target video latents +├── conditions/ # Text embeddings └── reference_latents/ # Reference video latents (conditioning input) ``` ### Generating Reference Videos -We provide an example script to generate reference videos (e.g., Canny edge maps) for a given dataset. -The script takes a JSON file as input (e.g., output of `caption_videos.py`) and updates it with the generated reference -video paths. +Use the `compute_reference.py` script to generate reference videos (e.g., Canny edge maps) for a dataset: ```bash uv run python scripts/compute_reference.py scenes_output_dir/ \ @@ -169,84 +171,392 @@ uv run python scripts/compute_reference.py scenes_output_dir/ \ To compute a different condition (depth maps, pose skeletons, etc.), modify the `compute_reference()` function in the script. -### Configuration Requirements for IC-LoRA - -- You **must** provide `reference_videos` in your validation configuration when using IC-LoRA training -- The number of reference videos must match the number of validation prompts - -Example validation configuration for IC-LoRA: - -```yaml -validation: - prompts: - - "First prompt describing the desired output" - - "Second prompt describing the desired output" - reference_videos: - - "/path/to/reference1.mp4" - - "/path/to/reference2.mp4" - reference_downscale_factor: 1 # Set to match preprocessing (e.g., 2 for half resolution) - include_reference_in_output: true # Show reference side-by-side with output -``` +> [!NOTE] +> `compute_reference.py` writes generated references to the `reference_video` column, which +> `process_dataset.py` detects automatically. The legacy `ref_media_path` column is also accepted. ### Scaled Reference Conditioning -For more efficient training and inference, you can use **downscaled reference videos** while keeping target videos at -full resolution. This reduces the number of conditioning tokens, leading to: +For more efficient training and inference, use **downscaled reference videos** while keeping targets at full +resolution. During training, the strategy infers the spatial and temporal scale factors from the preprocessed +reference and target latents and adjusts positional encodings accordingly. This reduces conditioning tokens, leading to: -- **Faster training** due to shorter sequence lengths -- **Faster inference** with reduced memory usage +- **Faster training** — shorter sequence lengths +- **Faster inference** — reduced memory usage - **Same aspect ratio** maintained between reference and target -#### How It Works - -When the reference video has resolution `H/n × W/n` and the target video has resolution `H × W`, the trainer -automatically detects this scale factor `n` and adjusts the positional encodings so that the reference positions -map to the correct locations in the target coordinate space. - -#### Preprocessing Datasets with Scaled References - -Use the `--reference-downscale-factor` option when running `process_dataset.py`: +Preprocess with the `--reference-downscale-factor` option: ```bash -# Process dataset with scaled reference videos (half resolution) uv run python scripts/process_dataset.py dataset.json \ --resolution-buckets 768x768x25 \ --model-path /path/to/ltx2.safetensors \ --text-encoder-path /path/to/gemma \ - --reference-column "reference_path" \ --reference-downscale-factor 2 ``` -This will: +> [!NOTE] +> The `reference_video` column is auto-detected by convention — no `--reference-column` flag needed. -- Process target videos at 768×768 resolution -- Process reference videos at 384×384 resolution (768 / 2) -- The trainer will automatically infer the scale factor from the dimension ratio - -**Important**: Set `reference_downscale_factor: 2` in your validation configuration to match the preprocessing: +Validation encodes reference media on the fly, so set `downscale_factor` and `temporal_scale_factor` +on each `reference` validation condition to match the preprocessing factors: ```yaml validation: - reference_downscale_factor: 2 # Must match the preprocessing factor - reference_videos: - - "/path/to/reference1.mp4" - - "/path/to/reference2.mp4" + samples: + - prompt: "..." + conditions: + - type: reference + video: "/path/to/reference.mp4" + downscale_factor: 2 + temporal_scale_factor: 1 + include_in_output: true ``` > [!NOTE] > The scale factor must be a positive integer, and all dimensions must be divisible by 32. -> Common scale factors are 1 (no scaling), 2 (half resolution), or 4 (quarter resolution). +> Common values are 1 (no scaling), 2 (half resolution), or 4 (quarter resolution). -## 📊 Training Mode Comparison +--- -| Aspect | LoRA | Audio-Video LoRA | Full Fine-tuning | IC-LoRA | -|----------------------|--------------------------------|--------------------------------|------------------|--------------------------------| -| **Memory Usage** | Low | Low-Medium | High | Medium | -| **Training Speed** | Fast | Fast | Slow | Medium | -| **Output Size** | 100MB-few GB (depends on rank) | 100MB-few GB (depends on rank) | Tens of GB | 100MB-few GB (depends on rank) | -| **Flexibility** | Medium | Medium | High | Specialized | -| **Audio Support** | Optional | Yes | Optional | No | -| **Reference Videos** | No | No | No | Yes (required) | +## 🔊 Audio-to-Video (A2V) + +Generate video conditioned on frozen audio. Audio passes through the transformer clean (sigma=0) and influences video +via the built-in cross-modal attention. Only video is denoised. + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + audio: + is_generated: false + latents_dir: "audio_latents" +``` + +**Example config:** 📄 [a2v_lora.yaml](../configs/a2v_lora.yaml) + +--- + +## 🎵 Video-to-Audio / Foley (V2A) + +Generate audio (Foley) conditioned on frozen video. Video passes through the transformer clean (sigma=0) and +conditions audio via cross-modal attention. Only audio is denoised. + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: false + latents_dir: "latents" + audio: + is_generated: true + latents_dir: "audio_latents" +``` + +**Example config:** 📄 [v2a_lora.yaml](../configs/v2a_lora.yaml) + +--- + +## 🎭 Video Inpainting + +Fill in masked regions of a video. Per-sample masks loaded from disk define which tokens are conditioning and which +must be generated. Masks are thresholded at `0.5` to match validation/inference: tokens with `mask > 0.5` receive clean +latents and timestep=0 and are excluded from loss; tokens with `mask <= 0.5` are denoised normally and contribute to +loss. + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: mask + mask_dir: "video_masks" + probability: 1.0 +``` + +**Dataset structure:** + +``` +preprocessed_data_root/ +├── latents/ # Video latents +├── conditions/ # Text embeddings +└── video_masks/ # Per-sample binary masks (1 → conditioning, 0 → generate) +``` + +In dataset metadata, provide mask media via the `video_mask` column; preprocessing converts it into `video_masks/`. + +**Example config:** 📄 [video_inpainting_lora.yaml](../configs/video_inpainting_lora.yaml) + +--- + +## 🌅 Video Outpainting + +Extend a video spatially beyond its original boundaries. A rectangular pixel region is provided as clean conditioning +(no noise, timestep=0, excluded from loss) — the model learns to generate the surrounding content. The `spatial_region` +is specified in pixel coordinates `[y1, x1, y2, x2]` and automatically converted to latent space. + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: spatial_crop + spatial_region: [0, 0, 288, 576] # y1, x1, y2, x2 in pixels + probability: 1.0 +``` + +> [!NOTE] +> `spatial_crop` is a video-only condition — it is not supported on the audio modality. + +**Example config:** 📄 [video_outpainting_lora.yaml](../configs/video_outpainting_lora.yaml) + +--- + +## 🔈 Text-to-Audio (T2A) + +Generate audio from text prompts with no video modality. Only the audio branch of the transformer is denoised. Since +no video modality is configured, this mode uses **audio-only LoRA targets** — explicitly targeting `audio_attn1`, +`audio_attn2`, and `audio_ff` modules. + +```yaml +training_strategy: + name: "flexible" + audio: + is_generated: true + latents_dir: "audio_latents" +``` + +> [!NOTE] +> With no `video` block in the strategy, the trainer only loads audio latents and text embeddings. LoRA adapters +> should explicitly target audio modules (e.g., `audio_attn1.to_k`) rather than short patterns like `to_k` which +> would also match video modules. See [LoRA Target Modules Guidance](#lora-target-modules-guidance) below. + +**Example config:** 📄 [t2a_lora.yaml](../configs/t2a_lora.yaml) + +--- + +## 🔊 Audio Extension + +Extend audio forward (prefix) or backward (suffix) in time — the audio equivalent of Video Extension. A span of +existing audio latent frames is provided as clean conditioning, and the model generates the continuation. The +`temporal_boundary` sets the number of latent frames used as context. This mode uses **audio-only LoRA targets**. + +```yaml +training_strategy: + name: "flexible" + audio: + is_generated: true + latents_dir: "audio_latents" + conditions: + - type: prefix # or "suffix" for backward extension + temporal_boundary: 8 + probability: 1.0 +``` + +**Example configs:** 📄 [audio_extend_lora.yaml](../configs/audio_extend_lora.yaml), 📄 [audio_suffix_lora.yaml](../configs/audio_suffix_lora.yaml) + +--- + +## 🎭 Audio Inpainting + +Fill in masked regions of audio. Per-sample masks loaded from disk define which audio tokens are conditioning and +which must be generated — the audio equivalent of Video Inpainting. Masks are thresholded at `0.5` with the same +binary semantics as video inpainting. This mode uses **audio-only LoRA targets**. + +```yaml +training_strategy: + name: "flexible" + audio: + is_generated: true + latents_dir: "audio_latents" + conditions: + - type: mask + mask_dir: "audio_masks" + probability: 1.0 +``` + +**Dataset structure:** + +``` +preprocessed_data_root/ +├── conditions/ # Text embeddings +├── audio_latents/ # Audio latents +└── audio_masks/ # Per-sample binary masks (1 → conditioning, 0 → generate) +``` + +In dataset metadata, provide mask media via the `audio_mask` column; preprocessing converts it into `audio_masks/`. + +**Example config:** 📄 [audio_inpainting_lora.yaml](../configs/audio_inpainting_lora.yaml) + +--- + +## 🔄 IC-LoRA / Audio-to-Audio (A2A) + +In-Context LoRA for audio-to-audio transformations. Pre-encoded reference audio latents are concatenated to the target +sequence — reference tokens participate in bidirectional self-attention but receive no noise and are excluded from loss. +This enables audio style transfer, voice conversion, sound effect transformation, and more. This mode uses +**audio-only LoRA targets**. + +```yaml +training_strategy: + name: "flexible" + audio: + is_generated: true + latents_dir: "audio_latents" + conditions: + - type: reference + latents_dir: "reference_audio_latents" + probability: 1.0 +``` + +**Dataset structure:** + +``` +preprocessed_data_root/ +├── conditions/ # Text embeddings +├── audio_latents/ # Target audio latents +└── reference_audio_latents/ # Reference audio latents (conditioning input) +``` + +**Example config:** 📄 [a2a_ic_lora.yaml](../configs/a2a_ic_lora.yaml) + +--- + +## 🔄 AV2AV IC-LoRA + +Joint audio-video In-Context LoRA — both modalities have reference conditioning. Pre-encoded reference latents are +concatenated to each modality's target sequence independently. This enables joint audiovisual transformations such as +synchronized style transfer across both video and audio. + +```yaml +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + conditions: + - type: reference + latents_dir: "reference_latents" + probability: 1.0 + audio: + is_generated: true + latents_dir: "audio_latents" + conditions: + - type: reference + latents_dir: "reference_audio_latents" + probability: 1.0 +``` + +> [!NOTE] +> Unlike audio-only IC-LoRA (A2A), AV2AV uses short LoRA target patterns like `"to_k"` to match all branches +> (video, audio, and cross-modal attention), since both modalities are trained. + +**Dataset structure:** + +``` +preprocessed_data_root/ +├── latents/ # Target video latents +├── audio_latents/ # Target audio latents +├── conditions/ # Text embeddings +├── reference_latents/ # Reference video latents (conditioning input) +└── reference_audio_latents/ # Reference audio latents (conditioning input) +``` + +**Example config:** 📄 [av2av_ic_lora.yaml](../configs/av2av_ic_lora.yaml) + +--- + +## 🔥 Full Model Fine-tuning + +All modes above default to `training_mode: "lora"`. For full fine-tuning, set `training_mode: "full"` — this updates +all model parameters rather than adding LoRA adapters. + +```yaml +model: + training_mode: "full" + +training_strategy: + name: "flexible" + video: + is_generated: true + latents_dir: "latents" + audio: + is_generated: true + latents_dir: "audio_latents" +``` + +> [!IMPORTANT] +> Full fine-tuning requires multiple high-end GPUs (e.g., 4-8× H100 80GB) and distributed training with FSDP. +> See [Training Guide](training-guide.md) for multi-GPU setup instructions. + +--- + +## 🎛️ LoRA Target Modules Guidance + +The `target_modules` configuration determines which transformer modules receive LoRA adapters. The right choice depends +on whether your training involves cross-modal (audio ↔ video) interaction. + +**For T2V, I2V, A2V, V2A, or any mode involving both modalities** — use short patterns to match all branches +(video, audio, and cross-modal attention): + +```yaml +target_modules: + - "to_k" + - "to_q" + - "to_v" + - "to_out.0" +``` + +> [!IMPORTANT] +> Short patterns like `"to_k"` match video modules (`attn1.to_k`, `attn2.to_k`), audio modules +> (`audio_attn1.to_k`, `audio_attn2.to_k`), and cross-modal modules (`audio_to_video_attn.to_k`, +> `video_to_audio_attn.to_k`). The cross-modal attention modules enable bidirectional information flow between +> audio and video, which is critical for synchronized audiovisual generation. +> See [Understanding Target Modules](configuration-reference.md#understanding-target-modules) for detailed guidance. + +**For video-only IC-LoRA** — explicitly target video modules (including FFN layers for better transformation quality): + +```yaml +target_modules: + - "attn1.to_k" + - "attn1.to_q" + - "attn1.to_v" + - "attn1.to_out.0" + - "attn2.to_k" + - "attn2.to_q" + - "attn2.to_v" + - "attn2.to_out.0" + - "ff.net.0.proj" + - "ff.net.2" +``` + +**For audio-only modes (T2A, Audio Extension, Audio Inpainting, A2A IC-LoRA)** — explicitly target audio modules: + +```yaml +target_modules: + - "audio_attn1.to_k" + - "audio_attn1.to_q" + - "audio_attn1.to_v" + - "audio_attn1.to_out.0" + - "audio_attn2.to_k" + - "audio_attn2.to_q" + - "audio_attn2.to_v" + - "audio_attn2.to_out.0" + - "audio_ff.net.0.proj" + - "audio_ff.net.2" +``` + +> [!NOTE] +> Audio-only modes have no `video` block in the strategy, so there is no need to train video or cross-modal +> attention modules. Targeting only `audio_*` modules keeps the LoRA small and focused. + +--- ## 🎬 Using Trained Models for Inference @@ -255,12 +565,25 @@ LoRAs: | Training Mode | Recommended Pipeline | |-------------------------|-------------------------------------------------------| -| LoRA / Audio-Video LoRA | `TI2VidOneStagePipeline` or `TI2VidTwoStagesPipeline` | -| IC-LoRA | `ICLoraPipeline` | +| T2V / I2V / A2V / Extension / Inpainting / Outpainting | `TI2VidOneStagePipeline` or `TI2VidTwoStagesPipeline` | +| IC-LoRA (V2V / A2A / AV2AV) | `ICLoraPipeline` | +| V2A (Foley) / T2A / Audio Extension / Audio Inpainting | `TI2VidOneStagePipeline` or `TI2VidTwoStagesPipeline` | All pipelines support loading custom LoRAs via the `loras` parameter. See the [`ltx-pipelines`](../../ltx-pipelines/) -package -documentation for detailed usage instructions. +package documentation for detailed usage instructions. + +> [!NOTE] +> You can generate audio during validation even if you're not training the audio branch. +> Set `validation.generate_audio: true` independently of whether audio has `is_generated: true`. + +--- + +## 🔄 Migration from Legacy Strategies + +Legacy `text_to_video` and `video_to_video` strategy configs are forward-compatible and will continue to work (with a +deprecation warning). We recommend migrating to `flexible` for access to all conditioning modes. + +--- ## 🚀 Next Steps @@ -272,6 +595,6 @@ Once you've chosen your training mode: > [!TIP] > Need a training mode that's not covered here? -> See [Implementing Custom Training Strategies](custom-training-strategies.md) -> to learn how to create your own strategy for specialized use cases like video inpainting, audio-only training, or -> custom conditioning. +> First check whether it can be expressed by composing existing `flexible` conditions. Use +> [Implementing Custom Training Strategies](custom-training-strategies.md) only for custom losses, +> noising rules, model outputs, or preprocessing that cannot be represented by configuration. diff --git a/packages/ltx-trainer/docs/troubleshooting.md b/packages/ltx-trainer/docs/troubleshooting.md index 068b268..ddf8c4a 100644 --- a/packages/ltx-trainer/docs/troubleshooting.md +++ b/packages/ltx-trainer/docs/troubleshooting.md @@ -8,7 +8,7 @@ Memory management is crucial for successful training with LTX-2. > [!TIP] > For GPUs with 32GB VRAM, use the pre-configured low VRAM config: -> [`configs/ltx2_av_lora_low_vram.yaml`](../configs/ltx2_av_lora_low_vram.yaml) +> [`configs/t2v_lora_low_vram.yaml`](../configs/t2v_lora_low_vram.yaml) > which combines 8-bit optimizer, INT8 quantization, and reduced LoRA rank. ### Memory Optimization Techniques @@ -111,7 +111,7 @@ Ensure you've installed the dependencies and are using `uv run` to execute scrip # From the repository root uv sync cd packages/ltx-trainer -uv run python scripts/train.py configs/ltx2_av_lora.yaml +uv run python scripts/train.py configs/t2v_lora.yaml ``` > [!TIP] @@ -168,29 +168,29 @@ LTX-2 requires the number of frames to satisfy `frames % 8 == 1`: ```bash uv run accelerate launch --config_file configs/accelerate/ddp_compile.yaml \ - scripts/train.py configs/ltx2_av_lora.yaml + scripts/train.py configs/t2v_lora.yaml ``` ### Issue: Poor Quality Validation Outputs **Solutions:** -1. **Use Image-to-Video Validation:** - For more reliable validation, use image-to-video (first-frame conditioning) rather than pure text-to-video: +1. **Use conditioned validation:** For more reliable validation, use image-to-video (first-frame conditioning) rather than pure text-to-video: ```yaml validation: - prompts: - - "a professional portrait video of a person" - images: - - "/path/to/first_frame.png" # One image per prompt + samples: + - prompt: "a professional portrait video of a person" + conditions: + - type: first_frame + image_or_video: "/path/to/first_frame.png" ``` 2. **Increase inference steps:** ```yaml validation: - inference_steps: 50 # Default is 30 + inference_steps: 30 ``` 3. **Adjust guidance settings:** diff --git a/packages/ltx-trainer/docs/utility-scripts.md b/packages/ltx-trainer/docs/utility-scripts.md index 1838268..3364a83 100644 --- a/packages/ltx-trainer/docs/utility-scripts.md +++ b/packages/ltx-trainer/docs/utility-scripts.md @@ -35,75 +35,53 @@ uv run python scripts/split_scenes.py video.mp4 scenes/ --max-scenes 50 ### Automatic Video Captioning -The `scripts/caption_videos.py` script generates captions for videos (with audio) using multimodal models. +The `scripts/caption_videos.py` script generates a single, detailed combined audio-visual +caption per video as a continuous paragraph of prose. Two backends are available: + +- **`qwen_omni` (default)** — Qwen3-Omni-30B-A3B-Thinking served via a local + [vLLM](https://docs.vllm.ai/) HTTP server (~1-3 s/video on H100). Highest quality, runs + fully offline once the model is downloaded. +- **`gemini_flash`** — Google Gemini (cloud, `gemini-3.5-flash`). No GPU required. Auth is + automatic: set `GEMINI_API_KEY` (or `GOOGLE_API_KEY`) for the Developer API, or just have + Google Cloud credentials available (`gcloud auth` / an attached service account) and it + uses Vertex AI with no extra setup. + +**Step 1 — launch the captioner server** (`qwen_omni` only, one-time). + +`scripts/serve_captioner.py` runs vLLM in an isolated environment via `uvx`, so vLLM's heavy +CUDA dependencies never touch the trainer's venv. It defaults to dynamic FP8 quantization +(~31 GiB weights, fits on 40 GB GPUs, same speed as BF16 on H100): ```bash -# Generate captions for all videos in a directory (uses Qwen2.5-Omni by default) -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json +# Terminal 1 - stays running +uv run python packages/ltx-trainer/scripts/serve_captioner.py -# Use 8-bit quantization to reduce VRAM usage -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json --use-8bit - -# Use Gemini Flash API instead (requires API key) -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json \ - --captioner-type gemini_flash --api-key YOUR_API_KEY - -# Use Gemini Flash with parallel workers for faster throughput -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json \ - --captioner-type gemini_flash --num-workers 5 - -# Caption without audio processing (video-only) -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json --no-audio - -# Force re-caption all files -uv run python scripts/caption_videos.py videos_dir/ --output dataset.json --override +# Useful variants: +# --print-cmd show the vLLM command without running it +# --quantization bf16 use BF16 instead (needs ~66 GiB free VRAM) +# --hf-home /mnt/disk override where the ~65 GB model is downloaded ``` -**Key features:** - -- **Audio-visual captioning**: Processes both video and audio content, including speech transcription -- **Multiple backends**: - - `qwen_omni` (default): Local Qwen2.5-Omni model - processes video + audio locally - - `gemini_flash`: Google Gemini Flash API - cloud-based, requires API key -- **Parallel captioning** (Gemini Flash only): Use `--num-workers` to run multiple API calls concurrently for faster throughput on large datasets -- **Structured output**: Captions include visual description, speech transcription, sounds, and on-screen text -- **Memory optimization**: 8-bit quantization option for limited VRAM -- **Incremental processing**: Skips already-captioned files by default; progress is saved every 5 videos -- **Multiple output formats**: JSON, JSONL, CSV, or TXT - -**Caption format:** - -The captioner produces structured captions with four sections: -- `[VISUAL]`: Detailed description of visual content -- `[SPEECH]`: Word-for-word transcription of spoken content -- `[SOUNDS]`: Description of music, ambient sounds, sound effects -- `[TEXT]`: Any on-screen text visible in the video - -**Parallel captioning with Gemini Flash:** - -When using `--captioner-type gemini_flash`, you can speed up large dataset captioning by running multiple API calls at the same time using `--num-workers` (accepts 1–10, default is 1): +**Step 2 — caption your videos.** ```bash -export GEMINI_API_KEY="your-key-here" +# Terminal 2 - default backend talks to the server above +uv run python packages/ltx-trainer/scripts/caption_videos.py videos_dir/ --output dataset.json -# Caption a large dataset with 5 workers running concurrently -uv run python scripts/caption_videos.py videos_dir/ \ - --output dataset.json \ - --captioner-type gemini_flash \ - --num-workers 5 +# Remote server: --vllm-url http://other-host:8001/v1 +# Gemini (gemini-3.5-flash): --captioner-type gemini_flash (uses GEMINI_API_KEY, else gcloud/Vertex) +# Gemini, parallel calls: --captioner-type gemini_flash --num-workers 5 +# Re-caption everything: --override ``` -> [!NOTE] -> `--num-workers` is only supported with `gemini_flash`. Using it with `qwen_omni` or any other local model will raise an error, because local GPU models are not thread-safe. +Captioning is incremental (already-captioned files are skipped, progress saves every 5 videos) +and writes JSON, JSONL, CSV, or TXT based on the output extension. -> [!TIP] -> Keep `--num-workers` between 3–5 for most use cases. Very high values (8–10) may hit Gemini API rate limits depending on your quota tier. +Qwen3-Omni-Thinking can optionally emit a `...` chain-of-thought before the +caption (`--enable-thinking`). It is off by default, which is recommended for bulk captioning +(thinking is slower as it generates the reasoning trace first). -**Environment variables (for Gemini Flash):** - -Set one of these to use Gemini Flash without passing `--api-key`: -- `GOOGLE_API_KEY` -- `GEMINI_API_KEY` +For Gemini, keep `--num-workers` at 3-5 (higher values may hit API rate limits). ### Dataset Preprocessing @@ -116,13 +94,6 @@ uv run python scripts/process_dataset.py dataset.json \ --model-path /path/to/ltx-2-model.safetensors \ --text-encoder-path /path/to/gemma-model -# With audio processing -uv run python scripts/process_dataset.py dataset.json \ - --resolution-buckets "960x544x49" \ - --model-path /path/to/ltx-2-model.safetensors \ - --text-encoder-path /path/to/gemma-model \ - --with-audio - # With video decoding for verification uv run python scripts/process_dataset.py dataset.json \ --resolution-buckets "960x544x49" \ @@ -186,6 +157,10 @@ uv run python scripts/compute_reference.py videos_dir/ --output dataset.json > You can edit this script to generate other types of reference videos for IC-LoRA training, > such as depth maps, segmentation masks, or any custom video transformation. +> [!NOTE] +> `compute_reference.py` writes generated references to the `reference_video` column, which +> `process_dataset.py` detects automatically. + ## 🔍 Debugging and Verification Scripts ### Latents Decoding @@ -224,73 +199,17 @@ uv run python scripts/decode_latents.py /path/to/latents/dir \ - **Debug training data**: Visualize what the model actually sees during training - **Quality assessment**: Ensure latent encoding preserves important visual details +### Inference with Trained Models -### Inference Script +For inference with trained LoRAs, use the [`ltx-pipelines`](../../ltx-pipelines/) package which provides +production-ready pipelines: -The `scripts/inference.py` script runs inference with a trained model. +- **Text/Image-to-Video**: `TI2VidOneStagePipeline`, `TI2VidTwoStagesPipeline` +- **Distilled (fast) inference**: `DistilledPipeline` +- **IC-LoRA video-to-video**: `ICLoraPipeline` +- **Keyframe interpolation**: `KeyframeInterpolationPipeline` -> [!TIP] -> For production inference, consider using the [`ltx-pipelines`](../../ltx-pipelines/) package which provides optimized, -> feature-rich pipelines for various use cases: -> - **Text/Image-to-Video**: `TI2VidOneStagePipeline`, `TI2VidTwoStagesPipeline` -> - **Distilled (fast) inference**: `DistilledPipeline` -> - **IC-LoRA video-to-video**: `ICLoraPipeline` -> - **Keyframe interpolation**: `KeyframeInterpolationPipeline` -> -> All pipelines support loading custom LoRAs trained with this trainer. - -```bash -# Text-to-video inference (with audio by default) -# By default, uses CFG scale 4.0 and STG scale 1.0 with block 29 -uv run python scripts/inference.py \ - --checkpoint /path/to/model.safetensors \ - --text-encoder-path /path/to/gemma \ - --prompt "A cat playing with a ball" \ - --output output.mp4 - -# Video-only (skip audio generation) -uv run python scripts/inference.py \ - --checkpoint /path/to/model.safetensors \ - --text-encoder-path /path/to/gemma \ - --prompt "A cat playing with a ball" \ - --skip-audio \ - --output output.mp4 - -# Image-to-video with conditioning image -uv run python scripts/inference.py \ - --checkpoint /path/to/model.safetensors \ - --text-encoder-path /path/to/gemma \ - --prompt "A cat walking" \ - --condition-image first_frame.png \ - --output output.mp4 - -# Custom guidance settings -uv run python scripts/inference.py \ - --checkpoint /path/to/model.safetensors \ - --text-encoder-path /path/to/gemma \ - --prompt "A cat playing with a ball" \ - --guidance-scale 4.0 \ - --stg-scale 1.0 \ - --stg-blocks 29 \ - --output output.mp4 - -# Disable STG (CFG only) -uv run python scripts/inference.py \ - --checkpoint /path/to/model.safetensors \ - --text-encoder-path /path/to/gemma \ - --prompt "A cat playing with a ball" \ - --stg-scale 0.0 \ - --output output.mp4 -``` - -**Guidance parameters:** - -| Parameter | Default | Description | -|-----------|---------|-------------| -| `--guidance-scale` | 4.0 | CFG (Classifier-Free Guidance) scale | -| `--stg-scale` | 1.0 | STG (Spatio-Temporal Guidance) scale. 0.0 disables STG | -| `--stg-blocks` | 29 | Transformer block(s) to perturb for STG | -| `--stg-mode` | stg_av | `stg_av` perturbs both audio and video, `stg_v` video only | +All pipelines support loading custom LoRAs trained with this trainer. ## 🚀 Training Scripts @@ -300,13 +219,13 @@ Use `scripts/train.py` for both single GPU and multi-GPU runs: ```bash # Single-GPU training -uv run python scripts/train.py configs/ltx2_av_lora.yaml +uv run python scripts/train.py configs/t2v_lora.yaml # Multi-GPU (uses your accelerate config) -uv run accelerate launch scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch scripts/train.py configs/t2v_lora.yaml # Override number of processes -uv run accelerate launch --num_processes 4 scripts/train.py configs/ltx2_av_lora.yaml +uv run accelerate launch --num_processes 4 scripts/train.py configs/t2v_lora.yaml ``` For detailed usage, see the [Training Guide](training-guide.md). @@ -316,5 +235,5 @@ For detailed usage, see the [Training Guide](training-guide.md). - **Start with `--help`**: Always check available options for each script - **Test on small datasets**: Verify workflows with a few files before processing large datasets - **Use decode verification**: Always decode a few samples to verify preprocessing quality -- **Monitor VRAM usage**: Use `--use-8bit` or quantization flags when running into memory issues +- **Monitor VRAM usage**: Reach for quantization or lower-memory settings (e.g. FP8 for the captioner server) when running into memory issues - **Keep backups**: Make copies of important dataset files before running conversion scripts diff --git a/packages/ltx-trainer/pyproject.toml b/packages/ltx-trainer/pyproject.toml index 15fbd72..e9eb1e6 100644 --- a/packages/ltx-trainer/pyproject.toml +++ b/packages/ltx-trainer/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ltx-trainer" -version = "1.1.5" +version = "1.1.6" description = "LTX-2 training, democratized." readme = "README.md" authors = [ @@ -12,9 +12,11 @@ dependencies = [ "accelerate>=1.2.1", "av>=14.2.1", "bitsandbytes >=0.45.2; sys_platform == 'linux'", + "google-genai>=2.0", "huggingface-hub[hf-xet]>=0.31.4", "imageio>=2.37.0", "imageio-ffmpeg>=0.6.0", + "openai>=2.0", "opencv-python>=4.11.0.86", "optimum-quanto>=0.2.6", "pandas>=2.2.3", @@ -25,12 +27,16 @@ dependencies = [ "safetensors>=0.5.0", "scenedetect>=0.6.5.2", "sentencepiece>=0.2.0", + "soundfile>=0.12.1", "torch>=2.6.0", "torchaudio>=2.7.0", - "torchcodec>=0.8.1", + # torchcodec must match the torch version (it ships a torch-ABI C++ extension and declares + # no torch pin of its own); the 0.9 line matches torch 2.9. torchaudio>=2.9 routes + # torchaudio.load() through torchcodec, so audio preprocessing needs it installed. + "torchcodec>=0.8.1,<0.10", "torchvision>=0.21.0", "typer>=0.15.1", - "wandb>=0.19.11", + "wandb>=0.27.0", "setuptools>=79.0.0", ] @@ -48,8 +54,11 @@ build-backend = "hatchling.build" [tool.ruff] -target-version = "1.1.5" +target-version = "1.1.6" line-length = 120 +# Restrict isort first-party detection to src/ so stray dirs (e.g. wandb/ run output) +# next to pyproject.toml don't get classified as first-party packages. See ruff#10519. +src = ["src"] [tool.ruff.lint] select = [ diff --git a/packages/ltx-trainer/scripts/caption_videos.py b/packages/ltx-trainer/scripts/caption_videos.py index 92fefe9..ed3fa93 100755 --- a/packages/ltx-trainer/scripts/caption_videos.py +++ b/packages/ltx-trainer/scripts/caption_videos.py @@ -2,29 +2,28 @@ """ Auto-caption videos with audio using multimodal models. -This script provides a command-line interface for generating captions for videos -(including audio) using multimodal models. It supports: -- Qwen2.5-Omni: Local model for audio-visual captioning (default) -- Gemini Flash: Cloud-based API for audio-visual captioning -The paths to videos in the generated dataset/captions file will be RELATIVE to the -directory where the output file is stored. This makes the dataset more portable and -easier to use in different environments. +Backends: +- Qwen3-Omni-30B-A3B-Thinking via a local vLLM HTTP server (default, + ``qwen_omni``). Launch the server once with ``scripts/serve_captioner.py``. +- Gemini Flash 3.5 via Google's API (``gemini_flash``). +The paths in the output file are RELATIVE to the output file's directory, +making the dataset portable. Basic usage: - # Caption a single video (includes audio by default) - caption_videos.py video.mp4 --output captions.json - # Caption all videos in a directory - caption_videos.py videos_dir/ --output captions.csv - # Caption with custom instruction - caption_videos.py video.mp4 --instruction "Describe what happens in this video in detail." + # Launch the captioner server once (separate terminal) + uv run python scripts/serve_captioner.py + # Caption a directory + caption_videos.py videos_dir/ --output captions.json + # Caption a single video with a custom prompt + caption_videos.py video.mp4 --output cap.json --instruction "Describe in detail." Advanced usage: - # Use Gemini Flash API (requires GEMINI_API_KEY or GOOGLE_API_KEY env var) + # Use Gemini Flash 3.5 (cloud, requires GEMINI_API_KEY) caption_videos.py videos_dir/ --captioner-type gemini_flash - # Use Gemini Flash with parallel workers (2-10 workers, cloud API only) + # Gemini with parallel workers caption_videos.py videos_dir/ --captioner-type gemini_flash --num-workers 5 - # Disable audio processing (video-only captions) - caption_videos.py videos_dir/ --no-audio - # Process videos with specific extensions and save as JSON - caption_videos.py videos_dir/ --extensions mp4,mov,avi --output captions.json + # Talk to a remote vLLM server + caption_videos.py videos_dir/ --vllm-url http://192.168.1.10:8001/v1 + # Enable Qwen3 chain-of-thought (slower, more detail) + caption_videos.py videos_dir/ --enable-thinking """ import csv @@ -33,7 +32,6 @@ from concurrent.futures import ThreadPoolExecutor, as_completed from enum import Enum from pathlib import Path -import torch import typer from rich.console import Console from rich.progress import ( @@ -45,9 +43,14 @@ from rich.progress import ( TimeElapsedColumn, TimeRemainingColumn, ) -from transformers.utils.logging import disable_progress_bar -from ltx_trainer.captioning import CaptionerType, MediaCaptioningModel, create_captioner +from ltx_trainer.captioning import ( + DEFAULT_QWEN_MODEL, + DEFAULT_VLLM_BASE_URL, + CaptionerType, + MediaCaptioningModel, + create_captioner, +) VIDEO_EXTENSIONS = ["mp4", "avi", "mov", "mkv", "webm"] IMAGE_EXTENSIONS = ["jpg", "jpeg", "png"] @@ -61,8 +64,6 @@ app = typer.Typer( help="Auto-caption videos with audio using multimodal models.", ) -disable_progress_bar() - class OutputFormat(str, Enum): """Available output formats for captions.""" @@ -73,15 +74,13 @@ class OutputFormat(str, Enum): JSONL = "jsonl" # JSON Lines file with one JSON object per line -def caption_media( # noqa: PLR0913 +def caption_media( input_path: Path, output_path: Path, captioner: MediaCaptioningModel, extensions: list[str], recursive: bool, fps: int, - include_audio: bool, - clean_caption: bool, output_format: OutputFormat, override: bool, num_workers: int = 1, @@ -94,8 +93,6 @@ def caption_media( # noqa: PLR0913 extensions: List of media file extensions to include recursive: Whether to search subdirectories recursively fps: Frames per second to sample from videos (ignored for images) - include_audio: Whether to include audio in captioning - clean_caption: Whether to clean up captions output_format: Format to save the captions in override: Whether to override existing captions num_workers: Number of parallel workers (only for cloud-based captioners like Gemini) @@ -149,10 +146,10 @@ def caption_media( # noqa: PLR0913 caption = captioner.caption( path=media_file, fps=fps, - include_audio=include_audio, - clean_caption=clean_caption, ) - rel_path = str(media_file.resolve().relative_to(base_dir)) + # Don't resolve the file itself, so a symlinked clip keeps its logical path under the + # dataset dir instead of jumping to its (possibly external) link target. + rel_path = str((media_file.parent.resolve() / media_file.name).relative_to(base_dir)) return rel_path, caption with progress: @@ -371,16 +368,31 @@ def main( # noqa: PLR0913 help="Type of captioner to use. Valid values: 'qwen_omni' (local), 'gemini_flash' (API)", case_sensitive=False, ), - device: str | None = typer.Option( - None, - "--device", - "-d", - help="Device to use for inference (e.g., 'cuda', 'cuda:0', 'cpu'). Only for local models.", + vllm_url: str = typer.Option( + DEFAULT_VLLM_BASE_URL, + "--vllm-url", + help=( + "Base URL of the vLLM OpenAI-compatible server (qwen_omni only). " + "Launch the server with `uv run python scripts/serve_captioner.py`." + ), ), - use_8bit: bool = typer.Option( + vllm_model: str = typer.Option( + DEFAULT_QWEN_MODEL, + "--vllm-model", + help="Served model identifier on the vLLM server (qwen_omni only).", + ), + enable_thinking: bool = typer.Option( False, - "--use-8bit", - help="Whether to use 8-bit precision for the captioning model (reduces memory usage)", + "--enable-thinking/--no-thinking", + help=( + "Let Qwen3-Omni produce a ... chain-of-thought before the caption. " + "Off by default: ~5x slower with marginal quality benefit and occasional hallucinations." + ), + ), + max_tokens: int = typer.Option( + 4096, + "--max-tokens", + help="Maximum new tokens to generate per caption (qwen_omni only).", ), instruction: str | None = typer.Option( None, @@ -401,20 +413,14 @@ def main( # noqa: PLR0913 help="Search for media files in subdirectories recursively", ), fps: int = typer.Option( - 3, + 2, "--fps", "-f", - help="Frames per second to sample from videos (ignored for images)", - ), - include_audio: bool = typer.Option( - True, - "--audio/--no-audio", - help="Whether to include audio in captioning (for videos with audio tracks)", - ), - clean_caption: bool = typer.Option( - True, - "--clean-caption/--raw-caption", - help="Whether to clean up captions by removing common VLM patterns", + help=( + "Frames per second to sample from videos. 2 is a typical default; " + "lower values use less compute per video. Ignored for images and for the " + "Gemini backend (which decides its own sampling rate)." + ), ), override: bool = typer.Option( False, @@ -441,35 +447,36 @@ def main( # noqa: PLR0913 ), ) -> None: """Auto-caption videos with audio using multimodal models. - This script supports audio-visual captioning using: - - Qwen2.5-Omni: Local model (default) - processes both video and audio - - Gemini Flash: Cloud API - requires GOOGLE_API_KEY environment variable + Backends: + - ``qwen_omni`` (default): Qwen3-Omni-30B-A3B-Thinking via a local vLLM + HTTP server. Launch the server once in a separate terminal with + ``uv run python scripts/serve_captioner.py``. The server stays loaded + across script invocations. + - ``gemini_flash``: Google Gemini (``gemini-3.5-flash``) via the google-genai SDK. + Auth is automatic -- ``GEMINI_API_KEY``/``GOOGLE_API_KEY`` for the Developer API, + or Google Cloud credentials (gcloud / service account) for Vertex AI with no env vars. The paths in the output file will be relative to the output file's directory. Examples: - # Caption videos with audio using Qwen2.5-Omni (default) + # Caption videos using the local vLLM server (default) caption_videos.py videos_dir/ -o captions.json - # Caption using Gemini Flash API + # Point at a remote vLLM server + caption_videos.py videos_dir/ -o captions.json --vllm-url http://other-host:8001/v1 + # Caption using Gemini Flash 3.5 caption_videos.py videos_dir/ -o captions.json -c gemini_flash - # Caption without audio (video-only) - caption_videos.py videos_dir/ -o captions.json --no-audio # Caption with custom instruction caption_videos.py video.mp4 -o captions.json -i "Describe this video in detail" """ - # Parallel workers are only safe for cloud-based (stateless) captioners. - # Local models like Qwen-Omni hold GPU state and are not thread-safe. + # Parallel workers are only supported for the cloud Gemini backend; qwen_omni + # drives a single shared vLLM server and is captioned serially from here. if num_workers > 1 and captioner_type != CaptionerType.GEMINI_FLASH: console.print( - "[bold red]Error:[/] --num-workers > 1 is only supported with [bold]--captioner-type gemini_flash[/].\n" - "Local models (e.g. qwen_omni) run on GPU and are not thread-safe — " - "parallel calls would cause memory corruption or incorrect results.\n" - "Either set [bold]--num-workers 1[/] (default) or switch to [bold]--captioner-type gemini_flash[/]." + "[bold red]Error:[/] --num-workers > 1 is only supported with " + "[bold]--captioner-type gemini_flash[/]. Use --num-workers 1 (default) " + "for the qwen_omni backend." ) raise typer.Exit(code=1) - # Determine device for local models - device_str = device or ("cuda" if torch.cuda.is_available() else "cpu") - # Parse extensions ext_list = [ext.strip() for ext in extensions.split(",")] @@ -490,14 +497,15 @@ def main( # noqa: PLR0913 output = Path(output).resolve() console.print(f"Output will be saved to [bold blue]{output}[/]") - # Initialize captioning model - with console.status("Loading captioning model...", spinner="dots"): + with console.status("Initializing captioner...", spinner="dots"): if captioner_type == CaptionerType.QWEN_OMNI: captioner = create_captioner( captioner_type=captioner_type, - device=device_str, - use_8bit=use_8bit, + base_url=vllm_url, + model=vllm_model, instruction=instruction, + max_tokens=max_tokens, + enable_thinking=enable_thinking, ) elif captioner_type == CaptionerType.GEMINI_FLASH: captioner = create_captioner( @@ -508,7 +516,7 @@ def main( # noqa: PLR0913 else: raise ValueError(f"Unsupported captioner type: {captioner_type}") - console.print(f"[bold green]✓[/] {captioner_type.value} captioning model loaded successfully") + console.print(f"[bold green]✓[/] {captioner_type.value} captioner ready") # Caption media files caption_media( @@ -518,8 +526,6 @@ def main( # noqa: PLR0913 extensions=ext_list, recursive=recursive, fps=fps, - include_audio=include_audio, - clean_caption=clean_caption, output_format=output_format, override=override, num_workers=num_workers, diff --git a/packages/ltx-trainer/scripts/compute_reference.py b/packages/ltx-trainer/scripts/compute_reference.py index 4af848a..20f5154 100644 --- a/packages/ltx-trainer/scripts/compute_reference.py +++ b/packages/ltx-trainer/scripts/compute_reference.py @@ -2,7 +2,7 @@ Compute reference videos for IC-LoRA training. This script provides a command-line interface for generating reference videos to be used for IC-LoRA training. Note that it reads and writes to the same file (the output of caption_videos.py), -where it adds the "reference_path" field to the JSON. +where it adds the "reference_video" field to the JSON. Basic usage: # Compute reference videos for all videos in a directory compute_reference.py videos_dir/ --output videos_dir/captions.json @@ -11,7 +11,7 @@ Basic usage: # Standard library imports import json from pathlib import Path -from typing import Dict +from typing import Any # Third-party imports import cv2 @@ -37,6 +37,10 @@ from ltx_trainer.video_utils import read_video, save_video console = Console() disable_progress_bar() +VIDEO_COLUMNS = ("video", "media_path") +REFERENCE_VIDEO_COLUMN = "reference_video" +LEGACY_REFERENCE_COLUMN = "reference_path" + def compute_reference( images: torch.Tensor, @@ -79,15 +83,15 @@ def compute_reference( def _get_meta_data( output_path: Path, -) -> Dict[str, str]: +) -> list[dict[str, Any]]: """Get set of existing reference video paths without loading the actual files. Args: output_path: Path to the reference video paths file Returns: - Dictionary mapping media paths to reference video paths + Dataset rows with media paths and captions """ if not output_path.exists(): - return {} + return [] console.print(f"[bold blue]Reading meta data from [cyan]{output_path}[/]...[/]") @@ -98,11 +102,18 @@ def _get_meta_data( except Exception as e: console.print(f"[bold yellow]Warning: Could not check meta data: {e}[/]") - return {} + return [] + + +def _get_media_path(item: dict[str, Any]) -> str: + for column in VIDEO_COLUMNS: + if column in item: + return item[column] + raise KeyError(f"Dataset row must contain one of {VIDEO_COLUMNS}") def _save_dataset_json( - reference_paths: Dict[str, str], + reference_paths: dict[str, str], output_path: Path, ) -> None: """Save dataset json with reference video paths. @@ -115,17 +126,17 @@ def _save_dataset_json( json_data = json.load(f) new_json_data = json_data.copy() for i, item in enumerate(json_data): - media_path = item["media_path"] + media_path = _get_media_path(item) reference_path = reference_paths[media_path] - new_json_data[i]["reference_path"] = reference_path + new_json_data[i].pop(LEGACY_REFERENCE_COLUMN, None) + new_json_data[i][REFERENCE_VIDEO_COLUMN] = reference_path with output_path.open("w", encoding="utf-8") as f: json.dump(new_json_data, f, indent=2, ensure_ascii=False) console.print(f"[bold green]✓[/] Reference video paths saved to [cyan]{output_path}[/]") - console.print("[bold yellow]Note:[/] Use these files with ImageOrVideoDataset by setting:") - console.print(" reference_column='[cyan]reference_path[/]'") - console.print(" video_column='[cyan]media_path[/]'") + console.print("[bold yellow]Note:[/] Reference videos were written to the '[cyan]reference_video[/]' column.") + console.print(" [cyan]process_dataset.py[/] detects this column automatically for IC-LoRA preprocessing.") def process_media( @@ -158,7 +169,7 @@ def process_media( def media_path_to_reference_path(media_file: Path) -> Path: return media_file.parent / (media_file.stem + "_reference" + media_file.suffix) - media_files = [base_dir / Path(sample["media_path"]) for sample in meta_data] + media_files = [base_dir / Path(_get_media_path(sample)) for sample in meta_data] for media_file in media_files: reference_path = media_path_to_reference_path(media_file) media_to_process.append(media_file) @@ -178,18 +189,23 @@ def process_media( ) # Process media files - media_paths = [item["media_path"] for item in meta_data] + media_paths = [_get_media_path(item) for item in meta_data] reference_paths = {rel_path: str(media_path_to_reference_path(Path(rel_path))) for rel_path in media_paths} with progress: task = progress.add_task("Computing condition on videos", total=len(media_to_process)) - for media_file in media_to_process: + for media_file, rel_path in zip(media_to_process, media_paths, strict=True): progress.update(task, description=f"Processing [bold blue]{media_file.name}[/]") - rel_path = str(media_file.resolve().relative_to(base_dir)) + # Key by the original media-path string (matches the dict seeded above). Avoid + # resolve()/relative_to here — they crash on symlinked or absolute media paths. reference_path = media_path_to_reference_path(media_file) - reference_paths[rel_path] = str(reference_path.relative_to(base_dir)) + try: + ref_stored = str(reference_path.relative_to(base_dir)) + except ValueError: + ref_stored = str(reference_path) # absolute/out-of-tree: keep it next to the source + reference_paths[rel_path] = ref_stored if not reference_path.resolve().exists() or override: try: diff --git a/packages/ltx-trainer/scripts/decode_latents.py b/packages/ltx-trainer/scripts/decode_latents.py index 73a74f5..f27ff4f 100755 --- a/packages/ltx-trainer/scripts/decode_latents.py +++ b/packages/ltx-trainer/scripts/decode_latents.py @@ -310,7 +310,7 @@ def main( help="Device to use for computation", ), vae_tiling: bool = typer.Option( - default=False, + default=True, help="Enable VAE tiling for larger video resolutions", ), seed: int | None = typer.Option( diff --git a/packages/ltx-trainer/scripts/inference.py b/packages/ltx-trainer/scripts/inference.py deleted file mode 100755 index 13a8b76..0000000 --- a/packages/ltx-trainer/scripts/inference.py +++ /dev/null @@ -1,443 +0,0 @@ -#!/usr/bin/env python3 -# ruff: noqa: T201 -""" -CLI script for running LTX video/audio generation inference. -Usage: - # Text-to-Video + Audio (default behavior) - python scripts/inference.py --checkpoint path/to/model.safetensors \ - --text-encoder-path path/to/gemma \ - --prompt "A cat playing with a ball" --output output.mp4 - # Video only (skip audio) - python scripts/inference.py --checkpoint path/to/model.safetensors \ - --text-encoder-path path/to/gemma \ - --prompt "A cat playing with a ball" --skip-audio --output output.mp4 - # Image-to-Video - python scripts/inference.py --checkpoint path/to/model.safetensors \ - --text-encoder-path path/to/gemma \ - --prompt "A cat walking" --condition-image first_frame.png --output output.mp4 - # Video-to-Video (IC-LoRA style) - python scripts/inference.py --checkpoint path/to/model.safetensors \ - --text-encoder-path path/to/gemma \ - --prompt "A cat turning into a dog" --reference-video input.mp4 --output output.mp4 - # With LoRA weights - python scripts/inference.py --checkpoint path/to/model.safetensors \ - --text-encoder-path path/to/gemma \ - --lora-path path/to/lora.safetensors \ - --prompt "A cat in my custom style" --output output.mp4 -""" - -import argparse -import re -from pathlib import Path - -import torch -import torchaudio -from peft import LoraConfig, get_peft_model, set_peft_model_state_dict -from safetensors.torch import load_file -from torchvision import transforms - -from ltx_trainer.model_loader import load_model -from ltx_trainer.progress import StandaloneSamplingProgress -from ltx_trainer.utils import open_image_as_srgb -from ltx_trainer.validation_sampler import GenerationConfig, ValidationSampler -from ltx_trainer.video_utils import read_video, save_video - - -def load_image(image_path: str) -> torch.Tensor: - """Load an image and convert to tensor [C, H, W] in [0, 1].""" - image = open_image_as_srgb(image_path) - transform = transforms.ToTensor() - return transform(image) - - -def extract_lora_target_modules(state_dict: dict[str, torch.Tensor]) -> list[str]: - """Extract target module names from LoRA checkpoint keys. - LoRA keys follow the pattern (after removing "diffusion_model." prefix): - - transformer_blocks.0.attn1.to_k.lora_A.weight - - transformer_blocks.0.ff.net.0.proj.lora_B.weight - This extracts the full module path like "transformer_blocks.0.attn1.to_k". - Using full paths is more robust than partial patterns. - """ - target_modules = set() - # Pattern to extract everything before .lora_A or .lora_B - pattern = re.compile(r"(.+)\.lora_[AB]\.") - - for key in state_dict: - match = pattern.match(key) - if match: - module_path = match.group(1) - target_modules.add(module_path) - - return sorted(target_modules) - - -def load_lora_weights(transformer: torch.nn.Module, lora_path: str | Path) -> torch.nn.Module: - """Load LoRA weights into the transformer model. - The LoRA rank and target modules are automatically detected from the checkpoint. - Alpha is set equal to rank (standard practice for inference). - Args: - transformer: The base transformer model - lora_path: Path to the LoRA weights (.safetensors) - Returns: - The transformer model with LoRA weights applied - """ - print(f"Loading LoRA weights from {lora_path}...") - - # Load the LoRA state dict - state_dict = load_file(str(lora_path)) - - # Remove "diffusion_model." prefix (ComfyUI-compatible format) - state_dict = {k.replace("diffusion_model.", "", 1): v for k, v in state_dict.items()} - - # Extract target modules from the checkpoint - target_modules = extract_lora_target_modules(state_dict) - if not target_modules: - raise ValueError(f"Could not extract target modules from LoRA checkpoint: {lora_path}") - print(f" Detected {len(target_modules)} target modules") - - # Auto-detect rank from the first lora_A weight shape - lora_rank = None - for key, value in state_dict.items(): - if "lora_A" in key and value.ndim == 2: - lora_rank = value.shape[0] - break - if lora_rank is None: - raise ValueError("Could not auto-detect LoRA rank from weights") - print(f" LoRA rank: {lora_rank}") - - # Create LoRA config and wrap the model - # Alpha = rank is standard for inference (maintains the trained scale) - lora_config = LoraConfig( - r=lora_rank, - lora_alpha=lora_rank, - target_modules=target_modules, - lora_dropout=0.0, - init_lora_weights=True, - ) - - # Wrap the transformer with PEFT to add LoRA layers - transformer = get_peft_model(transformer, lora_config) - - # Load the LoRA weights - base_model = transformer.get_base_model() - set_peft_model_state_dict(base_model, state_dict) - - print("✓ LoRA weights loaded successfully") - return transformer - - -def main() -> None: # noqa: PLR0912, PLR0915 - parser = argparse.ArgumentParser( - description="LTX Video/Audio Generation", - formatter_class=argparse.ArgumentDefaultsHelpFormatter, - ) - - # Model arguments - parser.add_argument( - "--checkpoint", - type=str, - required=True, - help="Path to model checkpoint (.safetensors)", - ) - parser.add_argument( - "--text-encoder-path", - type=str, - required=True, - help="Path to Gemma text encoder directory", - ) - - # LoRA arguments - parser.add_argument( - "--lora-path", - type=str, - default=None, - help="Path to LoRA weights (.safetensors)", - ) - - # Generation arguments - parser.add_argument( - "--prompt", - type=str, - required=True, - help="Text prompt for generation", - ) - parser.add_argument( - "--negative-prompt", - type=str, - default="", - help="Negative prompt", - ) - parser.add_argument( - "--height", - type=int, - default=544, - help="Video height (must be divisible by 32)", - ) - parser.add_argument( - "--width", - type=int, - default=960, - help="Video width (must be divisible by 32)", - ) - parser.add_argument( - "--num-frames", - type=int, - default=97, - help="Number of video frames (must be k*8 + 1)", - ) - parser.add_argument( - "--frame-rate", - type=float, - default=25.0, - help="Video frame rate", - ) - parser.add_argument( - "--num-inference-steps", - type=int, - default=30, - help="Number of denoising steps", - ) - parser.add_argument( - "--guidance-scale", - type=float, - default=4.0, - help="Classifier-free guidance scale (CFG)", - ) - parser.add_argument( - "--stg-scale", - type=float, - default=1.0, - help="STG (Spatio-Temporal Guidance) scale. 0.0 disables STG. Default: 1.0", - ) - parser.add_argument( - "--stg-blocks", - type=int, - nargs="*", - default=[29], - help="Which transformer blocks to perturb for STG. Default: 29 (single block).", - ) - parser.add_argument( - "--stg-mode", - type=str, - default="stg_av", - choices=["stg_av", "stg_v"], - help="STG mode: 'stg_av' perturbs both audio and video, 'stg_v' perturbs video only", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="Random seed for reproducibility", - ) - - # Conditioning arguments - parser.add_argument( - "--condition-image", - type=str, - default=None, - help="Path to conditioning image for image-to-video generation", - ) - parser.add_argument( - "--reference-video", - type=str, - default=None, - help="Path to reference video for video-to-video generation (IC-LoRA style)", - ) - parser.add_argument( - "--include-reference-in-output", - action="store_true", - help="Include reference video side-by-side with generated output (only for V2V)", - ) - - # Audio arguments - parser.add_argument( - "--skip-audio", - action="store_true", - help="Skip audio generation (by default, audio is generated alongside video)", - ) - - # Output arguments - parser.add_argument( - "--output", - type=str, - required=True, - help="Output video path (.mp4)", - ) - parser.add_argument( - "--audio-output", - type=str, - default=None, - help="Output audio path (.wav, optional - if not provided, audio will be embedded in video)", - ) - - # Device arguments - parser.add_argument( - "--device", - type=str, - default="cuda", - help="Device to run on (cuda/cpu)", - ) - - args = parser.parse_args() - - # Validate conditioning arguments - if args.include_reference_in_output and args.reference_video is None: - parser.error("--include-reference-in-output requires --reference-video") - - # Validate arguments - generate_audio = not args.skip_audio - - print("=" * 80) - print("LTX Video/Audio Generation") - print("=" * 80) - - # Determine if we need VAE encoder (for image or video conditioning) - need_vae_encoder = args.condition_image is not None or args.reference_video is not None - - components = load_model( - checkpoint_path=args.checkpoint, - device="cpu", # Load to CPU first, sampler will move to device as needed - dtype=torch.bfloat16, - with_video_vae_encoder=need_vae_encoder, - with_video_vae_decoder=True, - with_audio_vae_decoder=generate_audio, - with_vocoder=generate_audio, - with_text_encoder=True, - text_encoder_path=args.text_encoder_path, - ) - - # Apply LoRA weights if provided - transformer = components.transformer - if args.lora_path is not None: - transformer = load_lora_weights(transformer, args.lora_path) - - # Load conditioning image if provided - condition_image = None - if args.condition_image: - print(f"Loading conditioning image from {args.condition_image}...") - condition_image = load_image(args.condition_image) - - # Load reference video if provided - reference_video = None - if args.reference_video: - print(f"Loading reference video from {args.reference_video}...") - reference_video, ref_fps = read_video(args.reference_video, max_frames=args.num_frames) - print(f" Loaded {reference_video.shape[0]} frames @ {ref_fps:.1f} fps") - - # Determine generation mode - if args.reference_video is not None and args.condition_image is not None: - mode = "Video-to-Video + Image Conditioning (V2V+I2V)" - elif args.reference_video is not None: - mode = "Video-to-Video (V2V)" - elif args.condition_image is not None: - mode = "Image-to-Video (I2V)" - else: - mode = "Text-to-Video (T2V)" - - print("\n" + "=" * 80) - print("Generation Parameters") - print("=" * 80) - print(f"Mode: {mode}") - print(f"Prompt: {args.prompt}") - if args.negative_prompt: - print(f"Negative prompt: {args.negative_prompt}") - print(f"Resolution: {args.width}x{args.height}") - print(f"Frames: {args.num_frames} @ {args.frame_rate} fps") - print(f"Inference steps: {args.num_inference_steps}") - print(f"CFG scale: {args.guidance_scale}") - if args.stg_scale > 0: - blocks_str = args.stg_blocks if args.stg_blocks else "all" - print(f"STG scale: {args.stg_scale} (mode: {args.stg_mode}, blocks: {blocks_str})") - else: - print("STG: disabled") - print(f"Seed: {args.seed}") - if args.lora_path: - print(f"LoRA: {args.lora_path}") - if condition_image is not None: - print(f"Conditioning: Image ({args.condition_image})") - if reference_video is not None: - print(f"Reference: Video ({args.reference_video})") - if args.include_reference_in_output: - print(" → Will include reference side-by-side in output") - if generate_audio: - video_duration = args.num_frames / args.frame_rate - print(f"Audio: Enabled (duration will match video: {video_duration:.2f}s)") - print("=" * 80) - - print(f"\nGenerating {'video + audio' if generate_audio else 'video'}...") - - # Create generation config - gen_config = GenerationConfig( - prompt=args.prompt, - negative_prompt=args.negative_prompt, - height=args.height, - width=args.width, - num_frames=args.num_frames, - frame_rate=args.frame_rate, - num_inference_steps=args.num_inference_steps, - guidance_scale=args.guidance_scale, - seed=args.seed, - condition_image=condition_image, - reference_video=reference_video, - generate_audio=generate_audio, - include_reference_in_output=args.include_reference_in_output, - stg_scale=args.stg_scale, - stg_blocks=args.stg_blocks, - stg_mode=args.stg_mode, - ) - - # Generate with progress bar - with StandaloneSamplingProgress(num_steps=args.num_inference_steps) as progress: - # Create sampler with progress context - sampler = ValidationSampler( - transformer=transformer, - vae_decoder=components.video_vae_decoder, - vae_encoder=components.video_vae_encoder, - text_encoder=components.text_encoder, - audio_decoder=components.audio_vae_decoder if generate_audio else None, - vocoder=components.vocoder if generate_audio else None, - sampling_context=progress, - ) - video, audio = sampler.generate( - config=gen_config, - device=args.device, - ) - - # Save video - output_path = Path(args.output) - output_path.parent.mkdir(parents=True, exist_ok=True) - - # Get audio sample rate from vocoder if audio was generated - audio_sample_rate = None - if audio is not None and components.vocoder is not None: - audio_sample_rate = components.vocoder.output_sampling_rate - - save_video( - video_tensor=video, - output_path=output_path, - fps=args.frame_rate, - audio=audio, - audio_sample_rate=audio_sample_rate, - ) - print(f"✓ Video saved to {args.output}") - - # Save separate audio file if requested - if audio is not None and args.audio_output is not None: - audio_output_path = Path(args.audio_output) - audio_output_path.parent.mkdir(parents=True, exist_ok=True) - - torchaudio.save( - str(audio_output_path), - audio.cpu(), - sample_rate=audio_sample_rate, - ) - duration = audio.shape[1] / audio_sample_rate - print(f"✓ Audio saved: {duration:.2f}s at {audio_sample_rate}Hz") - - print("\n" + "=" * 80) - print("Generation complete!") - print("=" * 80) - - -if __name__ == "__main__": - main() diff --git a/packages/ltx-trainer/scripts/process_captions.py b/packages/ltx-trainer/scripts/process_captions.py index 9523209..037848e 100755 --- a/packages/ltx-trainer/scripts/process_captions.py +++ b/packages/ltx-trainer/scripts/process_captions.py @@ -154,6 +154,17 @@ class CaptionsDataset(Dataset): else: raise ValueError("Expected `dataset_file` to be a path to a CSV, JSON, or JSONL file.") + def _embedding_output_path(self, media_path: Path) -> str: + """Output `.pt` path relative to the dataset dir; mirrors `process_videos._output_relative` + so caption keys match video/audio latent keys (and absolute paths don't escape output_dir).""" + data_root = self.dataset_file.parent + resolved = data_root / media_path # pathlib: an absolute media_path overrides data_root + try: + rel = resolved.relative_to(data_root) + except ValueError: + rel = Path(*resolved.parts[1:]) if resolved.is_absolute() else resolved + return str(rel.with_suffix(".pt")) + def _load_caption_data_from_csv(self) -> dict[str, str]: """Load captions from a CSV file and compute output embedding paths.""" df = pd.read_csv(self.dataset_file) @@ -166,8 +177,7 @@ class CaptionsDataset(Dataset): caption_data = {} for _, row in df.iterrows(): media_path = Path(row[self.media_column].strip()) - # Convert media path to embedding output path (same structure, .pt extension) - output_path = str(media_path.with_suffix(".pt")) + output_path = self._embedding_output_path(media_path) caption_data[output_path] = row[self.caption_column] return caption_data @@ -188,8 +198,7 @@ class CaptionsDataset(Dataset): raise ValueError(f"Key '{self.media_column}' not found in JSON entry: {entry}") media_path = Path(entry[self.media_column].strip()) - # Convert media path to embedding output path (same structure, .pt extension) - output_path = str(media_path.with_suffix(".pt")) + output_path = self._embedding_output_path(media_path) caption_data[output_path] = entry[self.caption_column] return caption_data @@ -206,8 +215,7 @@ class CaptionsDataset(Dataset): raise ValueError(f"Key '{self.media_column}' not found in JSONL entry: {entry}") media_path = Path(entry[self.media_column].strip()) - # Convert media path to embedding output path (same structure, .pt extension) - output_path = str(media_path.with_suffix(".pt")) + output_path = self._embedding_output_path(media_path) caption_data[output_path] = entry[self.caption_column] return caption_data @@ -326,7 +334,8 @@ def compute_captions_embeddings( # noqa: PLR0913 # TODO(batch-tokenization): When tokenizer supports batching, encode all prompts at once. # For now, process one at a time: for i in range(len(batch["prompt"])): - hidden_states, prompt_attention_mask = text_encoder.encode(batch["prompt"][i], padding_side="left") + encoded = text_encoder.encode([batch["prompt"][i]], padding_side="left") + hidden_states, prompt_attention_mask = encoded[0] video_prompt_embeds, audio_prompt_embeds = embeddings_processor.feature_extractor( hidden_states, prompt_attention_mask, "left" ) diff --git a/packages/ltx-trainer/scripts/process_dataset.py b/packages/ltx-trainer/scripts/process_dataset.py index fa827b3..74d3e39 100755 --- a/packages/ltx-trainer/scripts/process_dataset.py +++ b/packages/ltx-trainer/scripts/process_dataset.py @@ -1,14 +1,21 @@ #!/usr/bin/env python3 """ -Preprocess a video dataset by computing video clips latents and text captions embeddings. -This script provides a command-line interface for preprocessing video datasets by computing -latent representations of video clips and text embeddings of their captions. The preprocessed -data can be used to accelerate training of video generation models and to save GPU memory. +Preprocess a media dataset for LTX-2 training. +Automatically detects dataset columns and processes each according to a convention table. +Column names determine what gets encoded and where outputs go — no per-role CLI flags needed. +Convention table: + video → Video VAE → latents/ + audio → Audio VAE → audio_latents/ + reference_video → Video VAE → reference_latents/ + reference_audio → Audio VAE → reference_audio_latents/ + video_mask → (downsample) → video_masks/ + audio_mask → (downsample) → audio_masks/ + caption → Text encoder → conditions/ +Legacy aliases: media_path → video, ref_media_path → reference_video Basic usage: - python scripts/process_dataset.py /path/to/dataset.json --resolution-buckets 768x768x49 \ + python scripts/process_dataset.py /path/to/dataset.json --resolution-buckets 768x768x49 \\ --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma -The dataset must be a CSV, JSON, or JSONL file with columns for captions and video paths. """ from pathlib import Path @@ -16,7 +23,15 @@ from pathlib import Path import typer from decode_latents import LatentsDecoder from process_captions import compute_captions_embeddings -from process_videos import compute_latents, compute_scaled_resolution_buckets, parse_resolution_buckets +from process_videos import ( + compute_audio_latents, + compute_audio_masks, + compute_latents, + compute_scaled_resolution_buckets, + compute_video_masks, + detect_dataset_columns, + parse_resolution_buckets, +) from rich.console import Console from ltx_trainer import logger @@ -27,52 +42,82 @@ console = Console() app = typer.Typer( pretty_exceptions_enable=False, no_args_is_help=True, - help="Preprocess a video dataset by computing video clips latents and text captions embeddings. " - "The dataset must be a CSV, JSON, or JSONL file with columns for captions and video paths.", + help="Preprocess a media dataset for LTX-2 training. " + "Automatically detects columns (video, audio, reference_video, reference_audio, caption) " + "and processes each with the appropriate encoder.", ) +_KNOWN_ROLES = {"video", "audio", "reference_video", "reference_audio", "video_mask", "audio_mask", "caption"} +_LEGACY_ALIASES = {"media_path": "video", "ref_media_path": "reference_video"} -def preprocess_dataset( # noqa: PLR0913 + +def preprocess_dataset( # noqa: PLR0912, PLR0913, PLR0915 dataset_file: str, - caption_column: str, - video_column: str, - resolution_buckets: list[tuple[int, int, int]], - batch_size: int, - output_dir: str | None, - lora_trigger: str | None, - vae_tiling: bool, - decode: bool, + resolution_buckets: list[tuple[int, int, int]] | None, model_path: str, text_encoder_path: str, device: str, + output_dir: str | None = None, + video_column: str | None = None, + caption_column: str | None = None, + batch_size: int = 1, + lora_trigger: str | None = None, + vae_tiling: bool = False, + decode: bool = False, remove_llm_prefixes: bool = False, - reference_column: str | None = None, reference_downscale_factor: int = 1, - with_audio: bool = False, + reference_temporal_scale_factor: int = 1, + skip_audio: bool = False, + audio_durations: list[float] | None = None, load_text_encoder_in_8bit: bool = False, overwrite: bool = False, ) -> None: - """Run the preprocessing pipeline with the given arguments.""" - # Validate dataset file + """Run the preprocessing pipeline with convention-based column detection.""" _validate_dataset_file(dataset_file) - # Set up output directories + # Detect columns and resolve roles + dataset_columns = detect_dataset_columns(dataset_file) + roles = _resolve_columns(dataset_columns, video_column, caption_column) + + # Log detected roles + for role, col in sorted(roles.items()): + alias_note = f" (alias for '{role}')" if col != role else "" + logger.info(f"Detected column '{col}'{alias_note} → {role}") + + # Validate: need at least caption + if "caption" not in roles: + raise ValueError( + f"No caption column found. Dataset has columns: {dataset_columns}. " + f"Expected 'caption' or use --caption-column to specify." + ) + + # Validate: need video or audio + has_video = "video" in roles + has_audio = "audio" in roles + if not has_video and not has_audio: + raise ValueError( + f"No media column found. Dataset has columns: {dataset_columns}. " + f"Expected 'video', 'audio', or 'media_path' (legacy)." + ) + + # Validate: video modes need resolution buckets + if has_video and not resolution_buckets: + raise ValueError("--resolution-buckets is required when the dataset has a video column.") + output_base = Path(output_dir) if output_dir else Path(dataset_file).parent / ".precomputed" - conditions_dir = output_base / "conditions" - latents_dir = output_base / "latents" if lora_trigger: logger.info(f'LoRA trigger word "{lora_trigger}" will be prepended to all captions') + # --- Phase 1: Text encoder --- with free_gpu_memory_context(): - # Process captions using the dedicated function compute_captions_embeddings( dataset_file=dataset_file, - output_dir=str(conditions_dir), + output_dir=str(output_base / "conditions"), model_path=model_path, text_encoder_path=text_encoder_path, - caption_column=caption_column, - media_column=video_column, + caption_column=roles["caption"], + media_column=roles.get("video") or roles.get("audio") or roles["caption"], lora_trigger=lora_trigger, remove_llm_prefixes=remove_llm_prefixes, batch_size=batch_size, @@ -81,119 +126,177 @@ def preprocess_dataset( # noqa: PLR0913 overwrite=overwrite, ) - # Process videos using the dedicated function - audio_latents_dir = None - if with_audio: - logger.info("Audio preprocessing enabled - will extract and encode audio from videos") - audio_latents_dir = output_base / "audio_latents" + # --- Phase 2: Video VAE (video, reference_video) --- + if has_video and resolution_buckets: + # Determine if audio should be auto-extracted from video files + auto_audio = not skip_audio and "audio" not in roles - with free_gpu_memory_context(): - compute_latents( - dataset_file=dataset_file, - video_column=video_column, - resolution_buckets=resolution_buckets, - output_dir=str(latents_dir), - model_path=model_path, - batch_size=batch_size, - device=device, - vae_tiling=vae_tiling, - with_audio=with_audio, - audio_output_dir=str(audio_latents_dir) if audio_latents_dir else None, - overwrite=overwrite, - ) - - # Process reference videos if reference_column is provided - if reference_column: - # Validate: scaled references with multiple buckets can cause ambiguous bucket matching - if reference_downscale_factor > 1 and len(resolution_buckets) > 1: - raise ValueError( - "When using --reference-downscale-factor > 1, only a single resolution bucket is supported. " - "Using multiple buckets with scaled references can cause ambiguous bucket matching " - "(e.g., a 512x256 reference could match either the scaled-down 1024x512 bucket or the 512x256 " - "bucket). Please use a single resolution bucket or set --reference-downscale-factor to 1." - ) - - # Calculate and validate scaled resolution buckets for reference videos - reference_buckets = compute_scaled_resolution_buckets(resolution_buckets, reference_downscale_factor) - - if reference_downscale_factor > 1: - logger.info( - f"Processing reference videos for IC-LoRA training at 1/{reference_downscale_factor} resolution..." - ) - logger.info(f"Reference resolution buckets: {reference_buckets}") - else: - logger.info("Processing reference videos for IC-LoRA training...") - - reference_latents_dir = output_base / "reference_latents" + audio_latents_dir = str(output_base / "audio_latents") if auto_audio else None + if auto_audio: + logger.info("Audio will be auto-extracted from video files (use --skip-audio to disable)") + with free_gpu_memory_context(): compute_latents( dataset_file=dataset_file, - main_media_column=video_column, - video_column=reference_column, - resolution_buckets=reference_buckets, - output_dir=str(reference_latents_dir), + video_column=roles["video"], + resolution_buckets=resolution_buckets, + output_dir=str(output_base / "latents"), model_path=model_path, batch_size=batch_size, device=device, vae_tiling=vae_tiling, + with_audio=auto_audio, + audio_output_dir=audio_latents_dir, overwrite=overwrite, ) - # Handle decoding if requested (for verification) + # Process reference video if present + if "reference_video" in roles: + if reference_downscale_factor > 1 and len(resolution_buckets) > 1: + raise ValueError( + "When using --reference-downscale-factor > 1, only a single resolution bucket is supported." + ) + if reference_temporal_scale_factor > 1 and len(resolution_buckets) > 1: + raise ValueError( + "When using --reference-temporal-scale-factor > 1, only a single resolution bucket is supported." + ) + + reference_buckets = compute_scaled_resolution_buckets(resolution_buckets, reference_downscale_factor) + if reference_downscale_factor > 1: + logger.info(f"Processing reference videos at 1/{reference_downscale_factor} resolution...") + if reference_temporal_scale_factor > 1: + logger.info( + f"Temporally subsampling reference videos by {reference_temporal_scale_factor}x " + f"(VAE-aligned pattern)..." + ) + + with free_gpu_memory_context(): + compute_latents( + dataset_file=dataset_file, + main_media_column=roles["video"], + video_column=roles["reference_video"], + resolution_buckets=reference_buckets, + output_dir=str(output_base / "reference_latents"), + model_path=model_path, + batch_size=batch_size, + device=device, + vae_tiling=vae_tiling, + overwrite=overwrite, + temporal_subsample_factor=reference_temporal_scale_factor, + ) + + # --- Phase 2b: Masks (video_mask, audio_mask) — processed after video latents for alignment --- + if "video_mask" in roles and has_video: + compute_video_masks( + dataset_file=dataset_file, + mask_column=roles["video_mask"], + latents_dir=str(output_base / "latents"), + output_dir=str(output_base / "video_masks"), + main_media_column=roles["video"], + ) + + # --- Phase 3: Audio VAE (audio, reference_audio) --- + audio_roles_to_process = [ + ("audio", "audio_latents"), + ("reference_audio", "reference_audio_latents"), + ] + active_audio_roles = [(role, subdir) for role, subdir in audio_roles_to_process if role in roles] + + if active_audio_roles: + # Determine audio duration constraint: video bucket → max_duration, or explicit buckets + max_audio_duration = None + audio_duration_buckets = None + if has_video and resolution_buckets: + max_audio_duration = max(f for f, _h, _w in resolution_buckets) / 25.0 + elif audio_durations: + audio_duration_buckets = audio_durations + + for role, output_subdir in active_audio_roles: + with free_gpu_memory_context(): + compute_audio_latents( + dataset_file=dataset_file, + audio_column=roles[role], + output_dir=str(output_base / output_subdir), + model_path=model_path, + main_media_column=roles.get("video"), + max_duration=max_audio_duration, + duration_buckets=audio_duration_buckets, + device=device, + overwrite=overwrite, + ) + + # --- Phase 4: Audio masks (after audio latents exist for temporal alignment) --- + if "audio_mask" in roles: + audio_latents_source = output_base / "audio_latents" + if audio_latents_source.exists(): + compute_audio_masks( + dataset_file=dataset_file, + mask_column=roles["audio_mask"], + audio_latents_dir=str(audio_latents_source), + output_dir=str(output_base / "audio_masks"), + main_media_column=roles.get("video") or roles.get("audio"), + ) + else: + logger.warning("audio_mask column found but no audio_latents/ — run with audio first") + + # --- Decode for verification --- if decode: logger.info("Decoding latents for verification...") + decoder = LatentsDecoder(model_path=model_path, device=device, vae_tiling=vae_tiling, with_audio=has_audio) + if has_video: + decoder.decode(output_base / "latents", output_base / "decoded_videos") + if "reference_video" in roles and (output_base / "reference_latents").exists(): + decoder.decode(output_base / "reference_latents", output_base / "decoded_reference_videos") - decoder = LatentsDecoder( - model_path=model_path, - device=device, - vae_tiling=vae_tiling, - with_audio=with_audio, - ) - decoder.decode(latents_dir, output_base / "decoded_videos") - - # Also decode reference videos if they exist - if reference_column: - reference_latents_dir = output_base / "reference_latents" - if reference_latents_dir.exists(): - logger.info("Decoding reference videos...") - decoder.decode(reference_latents_dir, output_base / "decoded_reference_videos") - - # Decode audio latents if they exist - if with_audio and audio_latents_dir and audio_latents_dir.exists(): - logger.info("Decoding audio latents...") - decoder.decode_audio(audio_latents_dir, output_base / "decoded_audio") - - # Print summary + # --- Summary --- logger.info(f"Dataset preprocessing complete! Results saved to {output_base}") - if reference_column: - logger.info("Reference videos processed and saved to reference_latents/ directory for IC-LoRA training") - if with_audio: - logger.info("Audio latents saved to audio_latents/ directory for audio-video training") + produced = [d.name for d in output_base.iterdir() if d.is_dir() and not d.name.startswith("decoded")] + logger.info(f"Output directories: {', '.join(sorted(produced))}") def _validate_dataset_file(dataset_path: str) -> None: """Validate that the dataset file exists and has the correct format.""" dataset_file = Path(dataset_path) - if not dataset_file.exists(): raise FileNotFoundError(f"Dataset file does not exist: {dataset_file}") - if not dataset_file.is_file(): raise ValueError(f"Dataset path must be a file, not a directory: {dataset_file}") - if dataset_file.suffix.lower() not in [".csv", ".json", ".jsonl"]: raise ValueError(f"Dataset file must be CSV, JSON, or JSONL format: {dataset_file}") +def _resolve_columns( + dataset_columns: set[str], + video_column_override: str | None = None, + caption_column_override: str | None = None, +) -> dict[str, str]: + """Map canonical role names to actual dataset column names. + Returns a dict of role → column_name for recognized roles found in the dataset. + """ + roles: dict[str, str] = {} + for col in dataset_columns: + role = _LEGACY_ALIASES.get(col, col) + if role in _KNOWN_ROLES: + roles[role] = col + + if video_column_override and video_column_override in dataset_columns: + roles["video"] = video_column_override + if caption_column_override and caption_column_override in dataset_columns: + roles["caption"] = caption_column_override + + return roles + + @app.command() def main( # noqa: PLR0913 dataset_path: str = typer.Argument( ..., - help="Path to metadata file (CSV/JSON/JSONL) containing captions and video paths", + help="Path to metadata file (CSV/JSON/JSONL) with columns matching the convention table", ), - resolution_buckets: str = typer.Option( - ..., - help='Resolution buckets in format "WxHxF;WxHxF;..." (e.g. "768x768x25;512x512x49")', + resolution_buckets: str | None = typer.Option( + default=None, + help='Resolution buckets in format "WxHxF;WxHxF;..." (e.g. "768x768x25"). ' + "Required when dataset has a video column.", ), model_path: str = typer.Option( ..., @@ -203,13 +306,13 @@ def main( # noqa: PLR0913 ..., help="Path to Gemma text encoder directory", ), - caption_column: str = typer.Option( - default="caption", - help="Column name containing captions in the dataset JSON/JSONL/CSV file", + caption_column: str | None = typer.Option( + default=None, + help="Override: treat this column as 'caption' (default: auto-detect 'caption')", ), - video_column: str = typer.Option( - default="media_path", - help="Column name containing video paths in the dataset JSON/JSONL/CSV file", + video_column: str | None = typer.Option( + default=None, + help="Override: treat this column as 'video' (default: auto-detect 'video' or 'media_path')", ), batch_size: int = typer.Option( default=1, @@ -229,32 +332,43 @@ def main( # noqa: PLR0913 ), lora_trigger: str | None = typer.Option( default=None, - help="Optional trigger word to prepend to each caption (activates the LoRA during inference)", + help="Optional trigger word to prepend to each caption", ), decode: bool = typer.Option( default=False, - help="Decode and save latents after encoding (videos and audio) for verification", + help="Decode and save latents after encoding for verification", ), remove_llm_prefixes: bool = typer.Option( default=False, help="Remove LLM prefixes from captions", ), - reference_column: str | None = typer.Option( + skip_audio: bool = typer.Option( + default=False, + help="Don't extract audio from video files (audio extraction is on by default)", + ), + audio_durations: str | None = typer.Option( default=None, - help="Column name containing reference video paths (for video-to-video training)", + help='Audio duration buckets in seconds for audio-only datasets (e.g. "2.0;4.0;8.0"). ' + "When set, audio files are trimmed to the best matching duration. " + "Not needed when a video column is present (audio duration derived from video bucket).", ), with_audio: bool = typer.Option( default=False, - help="Extract and encode audio from video files", + hidden=True, + help="[DEPRECATED: audio is now on by default, use --skip-audio to disable]", ), load_text_encoder_in_8bit: bool = typer.Option( default=False, - help="Load the Gemma text encoder in 8-bit precision to save GPU memory (requires bitsandbytes)", + help="Load the Gemma text encoder in 8-bit precision to save GPU memory", ), reference_downscale_factor: int = typer.Option( default=1, - help="Downscale factor for reference video resolution. When > 1, reference videos are processed at " - "1/n resolution (e.g., 2 means half resolution). Used for efficient IC-LoRA training.", + help="Downscale factor for reference video resolution (e.g., 2 = half resolution for IC-LoRA)", + ), + reference_temporal_scale_factor: int = typer.Option( + default=1, + help="Temporal subsampling factor for reference videos (e.g., 2 = half frame rate, " + "VAE-aligned: keeps frame 0, then every Nth frame from frame 1 onwards)", ), overwrite: bool = typer.Option( default=False, @@ -262,64 +376,53 @@ def main( # noqa: PLR0913 "changed parameters (different model, resolution, etc.) so stale outputs are replaced.", ), ) -> None: - """Preprocess a video dataset by computing and saving latents and text embeddings. - For multi-GPU preprocessing, invoke under ``accelerate launch`` - each process + """Preprocess a media dataset for LTX-2 training. + See module docstring for the convention table. Audio is auto-extracted from + video files by default — use --skip-audio to disable. + For multi-GPU preprocessing, invoke under ``accelerate launch`` -- each process will handle an interleaved shard of the dataset. - The dataset must be a CSV, JSON, or JSONL file with columns for captions and video paths. - This script is designed for LTX-2 models which use the Gemma text encoder. - Examples: - # Process a dataset with LTX-2 model - python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ - --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma - # Process dataset with custom column names - python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ - --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ - --caption-column "text" --video-column "video_path" - # Process dataset with reference videos for IC-LoRA training - python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ - --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ - --reference-column "reference_path" - # Process dataset with scaled reference videos (half resolution) for efficient IC-LoRA - python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ - --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ - --reference-column "reference_path" --reference-downscale-factor 2 - # Process dataset with audio for audio-video training - python scripts/process_dataset.py dataset.json --resolution-buckets 768x512x97 \\ - --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ - --with-audio """ - parsed_resolution_buckets = parse_resolution_buckets(resolution_buckets) - - if len(parsed_resolution_buckets) > 1: + # Handle deprecated --with-audio flag + if with_audio: logger.warning( - "Using multiple resolution buckets. " - "When training with multiple resolution buckets, you must use a batch size of 1." + "--with-audio is deprecated. Audio extraction is now on by default. Use --skip-audio to disable." ) - # Validate reference_downscale_factor + parsed_buckets = parse_resolution_buckets(resolution_buckets) if resolution_buckets else None + + if parsed_buckets and len(parsed_buckets) > 1: + logger.warning("Using multiple resolution buckets. Training batch size must be 1.") + if reference_downscale_factor < 1: raise typer.BadParameter("--reference-downscale-factor must be >= 1") - if reference_downscale_factor > 1 and not reference_column: - logger.warning("--reference-downscale-factor specified but no --reference-column provided. Ignoring.") + if reference_temporal_scale_factor < 1: + raise typer.BadParameter("--reference-temporal-scale-factor must be >= 1") + + parsed_audio_durations = None + if audio_durations: + parsed_audio_durations = [float(d) for d in audio_durations.split(";")] + if any(d <= 0 for d in parsed_audio_durations): + raise typer.BadParameter("All audio durations must be positive") preprocess_dataset( dataset_file=dataset_path, - caption_column=caption_column, - video_column=video_column, - resolution_buckets=parsed_resolution_buckets, - batch_size=batch_size, - output_dir=output_dir, - lora_trigger=lora_trigger, - vae_tiling=vae_tiling, - decode=decode, + resolution_buckets=parsed_buckets, model_path=model_path, text_encoder_path=text_encoder_path, device=device, + output_dir=output_dir, + video_column=video_column, + caption_column=caption_column, + batch_size=batch_size, + lora_trigger=lora_trigger, + vae_tiling=vae_tiling, + decode=decode, remove_llm_prefixes=remove_llm_prefixes, - reference_column=reference_column, reference_downscale_factor=reference_downscale_factor, - with_audio=with_audio, + reference_temporal_scale_factor=reference_temporal_scale_factor, + skip_audio=skip_audio, + audio_durations=parsed_audio_durations, load_text_encoder_in_8bit=load_text_encoder_in_8bit, overwrite=overwrite, ) diff --git a/packages/ltx-trainer/scripts/process_videos.py b/packages/ltx-trainer/scripts/process_videos.py index c33a815..7d2f96c 100755 --- a/packages/ltx-trainer/scripts/process_videos.py +++ b/packages/ltx-trainer/scripts/process_videos.py @@ -41,6 +41,7 @@ from torch.utils.data import DataLoader, Dataset, Subset from torchvision import transforms from torchvision.transforms import InterpolationMode from torchvision.transforms.functional import crop, resize, to_tensor +from torchvision.transforms.functional import resize as tv_resize from transformers.utils.logging import disable_progress_bar from ltx_core.model.audio_vae import AudioProcessor @@ -73,6 +74,10 @@ app = typer.Typer( ) +def _clamp_01(x: torch.Tensor) -> torch.Tensor: + return x.clamp_(0, 1) + + class MediaDataset(Dataset): """ Dataset for processing video and image files. @@ -92,6 +97,7 @@ class MediaDataset(Dataset): resolution_buckets: list[tuple[int, int, int]], reshape_mode: str = "center", with_audio: bool = False, + temporal_subsample_factor: int = 1, ) -> None: """ Initialize the media dataset. @@ -101,6 +107,8 @@ class MediaDataset(Dataset): resolution_buckets: List of (frames, height, width) tuples reshape_mode: How to crop videos ("center", "random") with_audio: Whether to extract audio from video files + temporal_subsample_factor: Factor for VAE-aligned temporal subsampling. + When > 1, keeps frame 0 then takes every Nth frame from frame 1 onwards. """ super().__init__() @@ -109,6 +117,7 @@ class MediaDataset(Dataset): self.resolution_buckets = resolution_buckets self.reshape_mode = reshape_mode self.with_audio = with_audio + self.temporal_subsample_factor = temporal_subsample_factor # First load main media paths self.main_media_paths = self._load_video_paths(main_media_column) @@ -124,7 +133,7 @@ class MediaDataset(Dataset): # Set up video transforms self.transforms = transforms.Compose( [ - transforms.Lambda(lambda x: x.clamp_(0, 1)), + transforms.Lambda(_clamp_01), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), ] ) @@ -142,8 +151,8 @@ class MediaDataset(Dataset): # Compute relative path of the video data_root = self.dataset_file.parent - relative_path = str(video_path.relative_to(data_root)) - media_relative_path = str(self.main_media_paths[index].relative_to(data_root)) + relative_path = str(_output_relative(video_path, data_root)) + media_relative_path = str(_output_relative(self.main_media_paths[index], data_root)) if video_path.suffix.lower() in [".png", ".jpg", ".jpeg"]: media_tensor = self._preprocess_image(video_path) @@ -185,97 +194,29 @@ class MediaDataset(Dataset): @staticmethod def _extract_audio(video_path: Path, target_duration: float) -> dict[str, torch.Tensor | int] | None: - """Extract audio track from a video file, trimmed to match video duration.""" - try: - # torchaudio can extract audio from video files directly - # waveform shape: [channels, samples] - waveform, sample_rate = torchaudio.load(str(video_path)) - - # Trim or pad to target duration - target_samples = int(target_duration * sample_rate) - current_samples = waveform.shape[-1] - - if current_samples > target_samples: - # Trim to target duration - waveform = waveform[..., :target_samples] - elif current_samples < target_samples: - # Pad with zeros to target duration - padding = target_samples - current_samples - waveform = torch.nn.functional.pad(waveform, (0, padding)) - logger.warning(f"Padded audio to {target_duration:.2f} seconds for {video_path}") - - return {"waveform": waveform, "sample_rate": sample_rate} - - except Exception as e: - logger.debug(f"Could not extract audio from {video_path}: {e}") + """Extract audio track from a video file, trimmed/padded to match video duration.""" + audio = _load_audio_from_file(video_path, max_duration=target_duration) + if audio is None: return None - def _load_video_paths(self, column: str) -> list[Path]: - """Load video paths from the specified data source.""" - if self.dataset_file.suffix == ".csv": - return self._load_video_paths_from_csv(column) - elif self.dataset_file.suffix == ".json": - return self._load_video_paths_from_json(column) - elif self.dataset_file.suffix == ".jsonl": - return self._load_video_paths_from_jsonl(column) + # Pad if shorter than target (_load_audio_from_file only trims, doesn't pad) + target_samples = int(target_duration * audio.sampling_rate) + if audio.waveform.shape[-1] < target_samples: + padding = target_samples - audio.waveform.shape[-1] + waveform = torch.nn.functional.pad(audio.waveform, (0, padding)) + logger.warning(f"Padded audio to {target_duration:.2f} seconds for {video_path}") else: - raise ValueError("Expected `dataset_file` to be a path to a CSV, JSON, or JSONL file.") + waveform = audio.waveform - def _load_video_paths_from_csv(self, column: str) -> list[Path]: - """Load video paths from a CSV file.""" - df = pd.read_csv(self.dataset_file) - if column not in df.columns: - raise ValueError(f"Column '{column}' not found in CSV file") + return {"waveform": waveform, "sample_rate": audio.sampling_rate} - data_root = self.dataset_file.parent - video_paths = [data_root / Path(line.strip()) for line in df[column].tolist()] - - # Validate that all paths exist - invalid_paths = [path for path in video_paths if not path.is_file()] - if invalid_paths: - raise ValueError(f"Found {len(invalid_paths)} invalid video paths. First few: {invalid_paths[:5]}") - - return video_paths - - def _load_video_paths_from_json(self, column: str) -> list[Path]: - """Load video paths from a JSON file.""" - with open(self.dataset_file, "r", encoding="utf-8") as file: - data = json.load(file) - - if not isinstance(data, list): - raise ValueError("JSON file must contain a list of objects") - - data_root = self.dataset_file.parent - video_paths = [] - for entry in data: - if column not in entry: - raise ValueError(f"Key '{column}' not found in JSON entry") - video_paths.append(data_root / Path(entry[column].strip())) - - # Validate that all paths exist - invalid_paths = [path for path in video_paths if not path.is_file()] - if invalid_paths: - raise ValueError(f"Found {len(invalid_paths)} invalid video paths. First few: {invalid_paths[:5]}") - - return video_paths - - def _load_video_paths_from_jsonl(self, column: str) -> list[Path]: - """Load video paths from a JSONL file.""" - data_root = self.dataset_file.parent - video_paths = [] - with open(self.dataset_file, "r", encoding="utf-8") as file: - for line in file: - entry = json.loads(line) - if column not in entry: - raise ValueError(f"Key '{column}' not found in JSONL entry") - video_paths.append(data_root / Path(entry[column].strip())) - - # Validate that all paths exist - invalid_paths = [path for path in video_paths if not path.is_file()] - if invalid_paths: - raise ValueError(f"Found {len(invalid_paths)} invalid video paths. First few: {invalid_paths[:5]}") - - return video_paths + def _load_video_paths(self, column: str) -> list[Path]: + """Load video paths from the specified data source, validating existence.""" + paths = _load_paths_from_dataset(self.dataset_file, column) + invalid = [p for p in paths if not p.is_file()] + if invalid: + raise ValueError(f"Found {len(invalid)} invalid paths in '{column}'. First few: {invalid[:5]}") + return paths def _filter_valid_videos(self) -> None: """Filter out videos with insufficient frames.""" @@ -348,6 +289,11 @@ class MediaDataset(Dataset): # Trim video to target number of frames frames_resized = frames_resized[:target_num_frames] + # VAE-aligned temporal subsampling: keep frame 0, then every Nth frame + if self.temporal_subsample_factor > 1: + indices = _compute_temporal_subsample_indices(target_num_frames, self.temporal_subsample_factor) + frames_resized = frames_resized[indices] + # Apply transforms to each frame and stack video = torch.stack([self.transforms(frame) for frame in frames_resized], dim=0) @@ -434,7 +380,18 @@ class MediaDataset(Dataset): return media_tensor -def compute_latents( # noqa: PLR0913, PLR0915 +def _compute_temporal_subsample_indices(num_frames: int, factor: int) -> list[int]: + """Compute VAE-aligned temporal subsample indices. + Keeps frame 0 (the VAE's standalone first-frame latent), then takes every + ``factor``-th frame from frame 1 onwards. This ensures each resulting + 8-frame VAE group spans ``factor`` groups of the original video. + """ + if factor == 1: + return list(range(num_frames)) + return [0, *list(range(1, num_frames, factor))] + + +def compute_latents( # noqa: PLR0912, PLR0913, PLR0915 dataset_file: str | Path, video_column: str, resolution_buckets: list[tuple[int, int, int]], @@ -447,7 +404,9 @@ def compute_latents( # noqa: PLR0913, PLR0915 vae_tiling: bool = False, with_audio: bool = False, audio_output_dir: str | None = None, + num_dataloader_workers: int = 4, overwrite: bool = False, + temporal_subsample_factor: int = 1, ) -> None: """ Process videos and save latent representations. @@ -468,9 +427,28 @@ def compute_latents( # noqa: PLR0913, PLR0915 vae_tiling: Whether to enable VAE tiling with_audio: Whether to extract and encode audio from videos audio_output_dir: Directory to save audio latents (required if with_audio=True) + num_dataloader_workers: Number of DataLoader worker processes (0 for in-process loading) overwrite: Re-process every item even if its output exists. Use when rerunning with changed parameters (different model, resolution, etc.) so stale outputs are replaced. + temporal_subsample_factor: Factor for VAE-aligned temporal subsampling of reference videos """ + # Validate temporal subsampling compatibility with resolution buckets + if temporal_subsample_factor > 1: + for frames, _h, _w in resolution_buckets: + pixel_frames_minus_one = frames - 1 + if pixel_frames_minus_one % temporal_subsample_factor != 0: + raise ValueError( + f"Frame count {frames} is not compatible with " + f"temporal_subsample_factor={temporal_subsample_factor}. " + f"(frames - 1) must be divisible by the factor." + ) + subsampled = 1 + pixel_frames_minus_one // temporal_subsample_factor + if (subsampled - 1) % VAE_TEMPORAL_FACTOR != 0: + raise ValueError( + f"After temporal subsampling {frames} → {subsampled} frames, " + f"result does not satisfy (frames - 1) % {VAE_TEMPORAL_FACTOR} == 0." + ) + if with_audio and audio_output_dir is None: raise ValueError("audio_output_dir must be provided when with_audio=True") @@ -484,11 +462,13 @@ def compute_latents( # noqa: PLR0913, PLR0915 resolution_buckets=resolution_buckets, reshape_mode=reshape_mode, with_audio=with_audio, + temporal_subsample_factor=temporal_subsample_factor, ) logger.info(f"Loaded {len(dataset)} valid media files") output_path = Path(output_dir) output_path.mkdir(parents=True, exist_ok=True) + audio_output_path: Path | None = None if with_audio: audio_output_path = Path(audio_output_dir) @@ -499,10 +479,10 @@ def compute_latents( # noqa: PLR0913, PLR0915 logger.warning("Audio processing requires batch_size=1. Overriding batch_size to 1.") batch_size = 1 - data_root = dataset.dataset_file.parent + data_root = Path(dataset_file).parent def _is_done(idx: int) -> bool: - rel = dataset.main_media_paths[idx].relative_to(data_root).with_suffix(".pt") + rel = _output_relative(dataset.main_media_paths[idx], data_root).with_suffix(".pt") if not (output_path / rel).is_file(): return False return audio_output_path is None or (audio_output_path / rel).is_file() @@ -510,18 +490,16 @@ def compute_latents( # noqa: PLR0913, PLR0915 dataloader = _build_sharded_dataloader( dataset, batch_size=batch_size, - num_workers=4, + num_workers=num_dataloader_workers, is_done=_is_done, overwrite=overwrite, ) if dataloader is None: return - # Load video VAE encoder with console.status(f"[bold]Loading video VAE encoder from [cyan]{model_path}[/]...", spinner="dots"): vae = load_video_vae_encoder(model_path, device=torch_device, dtype=torch.bfloat16) - # Load audio VAE encoder and audio processor if needed audio_vae_encoder = None audio_processor = None if with_audio: @@ -531,7 +509,6 @@ def compute_latents( # noqa: PLR0913, PLR0915 device=torch_device, dtype=torch.float32, # Audio VAE needs float32 for quality. TODO: re-test with bfloat16. ) - # Create audio processor for waveform-to-spectrogram conversion audio_processor = AudioProcessor( target_sample_rate=audio_vae_encoder.sample_rate, mel_bins=audio_vae_encoder.mel_bins, @@ -562,7 +539,7 @@ def compute_latents( # noqa: PLR0913, PLR0915 # Encode video with torch.inference_mode(): - video_latent_data = encode_video(vae=vae, video=video, use_tiling=vae_tiling) + video_latent_data = _encode_video(vae=vae, video=video, use_tiling=vae_tiling) # Save latents for each item in batch for i in range(len(batch["relative_path"])): @@ -572,13 +549,15 @@ def compute_latents( # noqa: PLR0913, PLR0915 # Create output directory maintaining structure output_file.parent.mkdir(parents=True, exist_ok=True) - # Index into batch to get this item's latents + # Store the latent's effective fps (= source_fps / subsample factor). + # Downstream position math expects the rate the saved latents actually have. + effective_fps = batch["video_metadata"]["fps"][i].item() / temporal_subsample_factor latent_data = { "latents": video_latent_data["latents"][i].cpu().contiguous(), # [C, F', H', W'] "num_frames": video_latent_data["num_frames"], "height": video_latent_data["height"], "width": video_latent_data["width"], - "fps": batch["video_metadata"]["fps"][i].item(), + "fps": effective_fps, } _atomic_save(latent_data, output_file) @@ -596,7 +575,7 @@ def compute_latents( # noqa: PLR0913, PLR0915 # Encode audio with torch.inference_mode(): - audio_latents = encode_audio(audio_vae_encoder, audio_processor, audio_data) + audio_latents = _encode_audio(audio_vae_encoder, audio_processor, audio_data) # Save audio latents audio_output_file = audio_output_path / output_rel_path @@ -625,7 +604,7 @@ def compute_latents( # noqa: PLR0913, PLR0915 ) -def encode_video( +def _encode_video( vae: torch.nn.Module, video: torch.Tensor, dtype: torch.dtype | None = None, @@ -662,7 +641,7 @@ def encode_video( # Choose encoding method based on tiling flag if use_tiling: - latents = tiled_encode_video( + latents = _tiled_encode_video( vae=vae, video=video, tile_size=tile_size, @@ -685,7 +664,7 @@ def encode_video( } -def tiled_encode_video( # noqa: PLR0912, PLR0915 +def _tiled_encode_video( # noqa: PLR0912, PLR0915 vae: torch.nn.Module, video: torch.Tensor, tile_size: int = DEFAULT_TILE_SIZE, @@ -840,7 +819,7 @@ def tiled_encode_video( # noqa: PLR0912, PLR0915 return output -def encode_audio( +def _encode_audio( audio_vae_encoder: torch.nn.Module, audio_processor: torch.nn.Module, audio: Audio, @@ -868,6 +847,35 @@ def encode_audio( if waveform.dim() == 2: waveform = waveform.unsqueeze(0) + # Convert to stereo if needed (audio VAE expects 2 channels) + # Channel order for surround: 5.1=[L,R,C,LFE,Ls,Rs], 7.1=[L,R,C,LFE,Ls,Rs,Lb,Rb] + num_channels = waveform.shape[1] + if num_channels == 1: + # Mono to stereo: duplicate the channel + waveform = waveform.repeat(1, 2, 1) + elif num_channels == 6: + # 5.1 downmix with normalized weights (sum to 1.0) + # Original: L = L + 0.707*C + 0.707*Ls, weights sum = 2.414 + w_main = 1.0 / 2.414 # ~0.414 + w_other = 0.707 / 2.414 # ~0.293 + left = w_main * waveform[:, 0, :] + w_other * waveform[:, 2, :] + w_other * waveform[:, 4, :] + right = w_main * waveform[:, 1, :] + w_other * waveform[:, 2, :] + w_other * waveform[:, 5, :] + waveform = torch.stack([left, right], dim=1) + elif num_channels == 8: + # 7.1 downmix with normalized weights (sum to 1.0) + # Original: L = L + 0.707*C + 0.707*Ls + 0.707*Lb, weights sum = 3.121 + w_main = 1.0 / 3.121 # ~0.320 + w_other = 0.707 / 3.121 # ~0.227 + center = waveform[:, 2, :] + left = w_main * waveform[:, 0, :] + w_other * (center + waveform[:, 4, :] + waveform[:, 6, :]) + right = w_main * waveform[:, 1, :] + w_other * (center + waveform[:, 5, :] + waveform[:, 7, :]) + waveform = torch.stack([left, right], dim=1) + elif num_channels > 2: + # Unknown layout: average all channels to mono, then duplicate to stereo + logger.warning(f"Unknown audio channel layout ({num_channels} channels), using mean downmix") + mono = waveform.mean(dim=1, keepdim=True) + waveform = mono.repeat(1, 2, 1) + # Calculate duration duration = waveform.shape[-1] / audio.sampling_rate @@ -889,6 +897,372 @@ def encode_audio( } +AUDIO_FILE_EXTENSIONS = {".wav", ".mp3", ".flac", ".ogg", ".aac", ".m4a"} +VIDEO_FILE_EXTENSIONS = {".mp4", ".avi", ".mov", ".mkv", ".webm"} +IMAGE_FILE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".heic", ".heif", ".bmp", ".tiff", ".webp"} + + +def compute_video_masks( + dataset_file: str | Path, + mask_column: str, + latents_dir: str, + output_dir: str, + main_media_column: str | None = None, + overwrite: bool = False, +) -> None: + """Preprocess video mask files to latent-space binary masks. + For each sample, loads the mask video/image, applies the same spatial + resize/crop as the target video (read from saved latent metadata), downsamples + to latent dimensions, binarizes, and saves as a .pt tensor. + Args: + dataset_file: Path to metadata file (CSV/JSON/JSONL). + mask_column: Column name containing mask video/image paths. + latents_dir: Directory containing the target video latents (for reading + spatial/temporal metadata to ensure mask alignment). + output_dir: Directory to save mask .pt files. + main_media_column: Column for output file naming (defaults to mask_column). + """ + dataset_path = Path(dataset_file) + data_root = dataset_path.parent + latents_path = Path(latents_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + naming_column = main_media_column or mask_column + mask_paths = _load_paths_from_dataset(dataset_path, mask_column) + naming_paths = _load_paths_from_dataset(dataset_path, naming_column) if naming_column != mask_column else mask_paths + + success = 0 + for mask_file, naming_file in zip(mask_paths, naming_paths, strict=True): + rel_path = _output_relative(naming_file, data_root) + latent_file = latents_path / rel_path.with_suffix(".pt") + out_file = output_path / rel_path.with_suffix(".pt") + + if not latent_file.exists(): + logger.warning(f"No target latent found at {latent_file}, skipping mask {mask_file}") + continue + + if not overwrite and out_file.is_file(): + continue + + target_meta = torch.load(latent_file, map_location="cpu", weights_only=True) + latent_f = target_meta["num_frames"] + latent_h = target_meta["height"] + latent_w = target_meta["width"] + pixel_h = latent_h * VAE_SPATIAL_FACTOR + pixel_w = latent_w * VAE_SPATIAL_FACTOR + pixel_f = (latent_f - 1) * VAE_TEMPORAL_FACTOR + 1 + + # Load mask as video or image + if mask_file.suffix.lower() in IMAGE_FILE_EXTENSIONS: + img = to_tensor(open_image_as_srgb(mask_file)).mean(dim=0, keepdim=True) # [1, H, W] + img = tv_resize(img.unsqueeze(0), [pixel_h, pixel_w]).squeeze(0) # [1, H, W] + mask_pixels = img.expand(pixel_f, -1, -1) # tile across frames → [F, H, W] + else: + frames, _ = read_video(str(mask_file), max_frames=pixel_f) # [F, C, H, W] + frames = frames[:pixel_f].mean(dim=1) # grayscale → [F, H, W] + frames = torch.nn.functional.interpolate( + frames.unsqueeze(1), size=(pixel_h, pixel_w), mode="nearest" + ).squeeze(1) # [F, H, W] + mask_pixels = frames + + # Downsample to latent dims: [F, H, W] → [F', H', W'] + mask_latent = torch.nn.functional.avg_pool2d(mask_pixels.unsqueeze(1), kernel_size=VAE_SPATIAL_FACTOR).squeeze( + 1 + ) # [F, H', W'] → spatial done + # Temporal: max-pool over groups of VAE_TEMPORAL_FACTOR frames (any masked frame masks the group) + f_spatial = mask_latent.shape[0] + pad_f = (VAE_TEMPORAL_FACTOR - f_spatial % VAE_TEMPORAL_FACTOR) % VAE_TEMPORAL_FACTOR + if pad_f > 0: + mask_latent = torch.nn.functional.pad(mask_latent, (0, 0, 0, 0, 0, pad_f)) + h_prime, w_prime = mask_latent.shape[1], mask_latent.shape[2] + mask_latent = mask_latent.reshape(-1, VAE_TEMPORAL_FACTOR, h_prime, w_prime).amax(dim=1)[:latent_f] + + # Binarize + mask_latent = (mask_latent > 0.5).float() + + out_file.parent.mkdir(parents=True, exist_ok=True) + _atomic_save({"mask": mask_latent}, out_file) + success += 1 + + logger.info(f"Mask preprocessing complete: {success} masks saved to {output_path}") + + +def compute_audio_masks( + dataset_file: str | Path, + mask_column: str, + audio_latents_dir: str, + output_dir: str, + main_media_column: str | None = None, + overwrite: bool = False, +) -> None: + """Preprocess audio mask files to latent-space binary masks. + For each sample, loads the mask (a 1D waveform-like signal or a simple tensor), + resamples it to match the target audio latent temporal length, binarizes, and saves. + Args: + dataset_file: Path to metadata file (CSV/JSON/JSONL). + mask_column: Column name containing mask file paths (.wav or .pt). + audio_latents_dir: Directory containing the target audio latents (for reading + temporal metadata to ensure mask alignment). + output_dir: Directory to save mask .pt files. + main_media_column: Column for output file naming (defaults to mask_column). + """ + dataset_path = Path(dataset_file) + data_root = dataset_path.parent + audio_latents_path = Path(audio_latents_dir) + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + naming_column = main_media_column or mask_column + mask_paths = _load_paths_from_dataset(dataset_path, mask_column) + naming_paths = _load_paths_from_dataset(dataset_path, naming_column) if naming_column != mask_column else mask_paths + + success = 0 + for mask_file, naming_file in zip(mask_paths, naming_paths, strict=True): + rel_path = _output_relative(naming_file, data_root) + latent_file = audio_latents_path / rel_path.with_suffix(".pt") + out_file = output_path / rel_path.with_suffix(".pt") + + if not latent_file.exists(): + logger.warning(f"No target audio latent found at {latent_file}, skipping mask {mask_file}") + continue + + if not overwrite and out_file.is_file(): + continue + + target_meta = torch.load(latent_file, map_location="cpu", weights_only=True) + latent_t = target_meta["num_time_steps"] + + # Load mask: .pt file (raw tensor) or .wav (use amplitude envelope) + if mask_file.suffix == ".pt": + raw_mask = torch.load(mask_file, map_location="cpu", weights_only=True) + if isinstance(raw_mask, dict): + raw_mask = raw_mask.get("mask", next(iter(raw_mask.values()))) + raw_mask = raw_mask.float().flatten() + else: + audio = _load_audio_from_file(mask_file) + if audio is None: + logger.warning(f"Could not load audio mask from {mask_file}") + continue + raw_mask = audio.waveform.abs().mean(dim=0) # mono amplitude envelope + + # Resample to target audio latent length + mask_resampled = torch.nn.functional.interpolate( + raw_mask.unsqueeze(0).unsqueeze(0), size=latent_t, mode="nearest" + ).squeeze() # [latent_t] + + mask_binary = (mask_resampled > 0.5).float() + + out_file.parent.mkdir(parents=True, exist_ok=True) + _atomic_save({"mask": mask_binary}, out_file) + success += 1 + + logger.info(f"Audio mask preprocessing complete: {success} masks saved to {output_path}") + + +def compute_audio_latents( # noqa: PLR0915 + dataset_file: str | Path, + audio_column: str, + output_dir: str, + model_path: str, + main_media_column: str | None = None, + max_duration: float | None = None, + duration_buckets: list[float] | None = None, + device: str = "cuda", + overwrite: bool = False, +) -> None: + """Encode audio files into latent representations. + Supports standalone audio files (.wav, .mp3, etc.) and audio tracks + extracted from video files (.mp4, etc.). + Args: + dataset_file: Path to metadata file (CSV/JSON/JSONL). + audio_column: Column name containing audio file paths. + output_dir: Directory to save audio latents. + model_path: Path to LTX-2 checkpoint (.safetensors). + main_media_column: Column for output file naming (defaults to audio_column). + Ensures alignment with other latent directories. + max_duration: Maximum audio duration in seconds. Audio is trimmed if longer. + Mutually exclusive with duration_buckets. + duration_buckets: List of allowed durations in seconds (e.g. [2.0, 4.0, 8.0]). + Each audio file is matched to the largest bucket that fits its duration, + then trimmed to exactly that length. Files shorter than the smallest + bucket are skipped. Ensures uniform lengths for batched training. + device: Device to use for computation. + """ + console = Console() + torch_device = torch.device(device) + + dataset_path = Path(dataset_file) + data_root = dataset_path.parent + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + + naming_column = main_media_column or audio_column + audio_paths = _load_paths_from_dataset(dataset_path, audio_column) + naming_paths = ( + _load_paths_from_dataset(dataset_path, naming_column) if naming_column != audio_column else audio_paths + ) + + with console.status(f"[bold]Loading audio VAE encoder from [cyan]{model_path}[/]...", spinner="dots"): + audio_vae_encoder = load_audio_vae_encoder( + checkpoint_path=model_path, + device=torch_device, + dtype=torch.float32, + ) + audio_processor = AudioProcessor( + target_sample_rate=audio_vae_encoder.sample_rate, + mel_bins=audio_vae_encoder.mel_bins, + mel_hop_length=audio_vae_encoder.mel_hop_length, + n_fft=audio_vae_encoder.n_fft, + ).to(torch_device) + + sorted_buckets = sorted(duration_buckets, reverse=True) if duration_buckets else None + success_count = 0 + skip_count = 0 + + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + MofNCompleteColumn(), + TimeElapsedColumn(), + TimeRemainingColumn(), + console=console, + ) as progress: + task = progress.add_task("Encoding audio", total=len(audio_paths)) + + for audio_path, naming_path in zip(audio_paths, naming_paths, strict=True): + rel_path = _output_relative(naming_path, data_root) + output_file = output_path / rel_path.with_suffix(".pt") + output_file.parent.mkdir(parents=True, exist_ok=True) + + if not overwrite and output_file.is_file(): + success_count += 1 + progress.advance(task) + continue + + # Load audio (no trimming yet — need full duration for bucket matching) + audio = _load_audio_from_file(audio_path) + if audio is None: + skip_count += 1 + progress.advance(task) + continue + + file_duration = audio.waveform.shape[-1] / audio.sampling_rate + + # Determine target duration: bucket matching, max_duration cap, or full file + target_duration = file_duration + if sorted_buckets: + bucket = next((b for b in sorted_buckets if b <= file_duration), None) + if bucket is None: + logger.warning( + f"Skipping {audio_path.name} ({file_duration:.1f}s) — shorter than " + f"smallest bucket ({sorted_buckets[-1]:.1f}s)" + ) + skip_count += 1 + progress.advance(task) + continue + target_duration = bucket + elif max_duration is not None: + target_duration = min(file_duration, max_duration) + + # Trim to target duration + target_samples = int(target_duration * audio.sampling_rate) + trimmed_waveform = audio.waveform[:, :target_samples] + audio = Audio(waveform=trimmed_waveform, sampling_rate=audio.sampling_rate) + + with torch.inference_mode(): + audio_latents = _encode_audio(audio_vae_encoder, audio_processor, audio) + + _atomic_save( + { + "latents": audio_latents["latents"].cpu().contiguous(), + "num_time_steps": audio_latents["num_time_steps"], + "frequency_bins": audio_latents["frequency_bins"], + "duration": audio_latents["duration"], + }, + output_file, + ) + success_count += 1 + progress.advance(task) + + logger.info(f"Audio encoding complete: {success_count} encoded, {skip_count} skipped. Saved to {output_path}") + + +def _output_relative(path: Path, data_root: Path) -> Path: + """Relative path used to name a sample's cached output, mirroring the input layout. + Normally media lives under the dataset directory and this is just the path relative to it. + If a media path is absolute or otherwise outside the dataset directory (e.g. a one-off + metadata file that references media elsewhere), mirror its absolute structure under the + output directory instead of raising, so out-of-tree media stays collision-free. + """ + try: + return path.relative_to(data_root) + except ValueError: + return Path(*path.parts[1:]) if path.is_absolute() else path + + +def _load_paths_from_dataset(dataset_file: Path, column: str) -> list[Path]: + """Load file paths from a dataset column, resolving relative to the dataset file's directory.""" + data_root = dataset_file.parent + + if dataset_file.suffix == ".csv": + df = pd.read_csv(dataset_file) + if column not in df.columns: + raise ValueError(f"Column '{column}' not found in CSV file") + return [data_root / Path(str(v).strip()) for v in df[column].tolist()] + + if dataset_file.suffix == ".json": + with open(dataset_file, encoding="utf-8") as f: + data = json.load(f) + if not isinstance(data, list): + raise ValueError("JSON file must contain a list of objects") + return [data_root / Path(entry[column].strip()) for entry in data] + + if dataset_file.suffix == ".jsonl": + paths = [] + with open(dataset_file, encoding="utf-8") as f: + for line in f: + entry = json.loads(line) + paths.append(data_root / Path(entry[column].strip())) + return paths + + raise ValueError(f"Unsupported dataset format: {dataset_file.suffix}") + + +def _load_audio_from_file(audio_path: Path, max_duration: float | None = None) -> Audio | None: + """Load audio from an audio or video file, optionally trimming to max_duration.""" + try: + waveform, sample_rate = torchaudio.load(str(audio_path)) + except Exception: + logger.debug(f"Could not load audio from {audio_path}") + return None + + if max_duration is not None: + max_samples = int(max_duration * sample_rate) + if waveform.shape[-1] > max_samples: + waveform = waveform[:, :max_samples] + + return Audio(waveform=waveform, sampling_rate=sample_rate) + + +def detect_dataset_columns(dataset_file: str | Path) -> set[str]: + """Read column names from a dataset file without loading all data.""" + path = Path(dataset_file) + if path.suffix == ".csv": + df = pd.read_csv(path, nrows=0) + return set(df.columns) + if path.suffix == ".json": + with open(path, encoding="utf-8") as f: + data = json.load(f) + return set(data[0].keys()) if isinstance(data, list) and data else set() + if path.suffix == ".jsonl": + with open(path, encoding="utf-8") as f: + return set(json.loads(f.readline()).keys()) + return set() + + def parse_resolution_buckets(resolution_buckets_str: str) -> list[tuple[int, int, int]]: """Parse resolution buckets from string format to list of tuples (frames, height, width)""" resolution_buckets = [] @@ -1041,11 +1415,11 @@ def main( # noqa: PLR0913 ), ) -> None: """Process videos/images and save latent representations for video generation training. - For multi-GPU preprocessing, invoke under ``accelerate launch`` - each process - will handle an interleaved shard of the dataset. This script processes videos and images from metadata files and saves latent representations that can be used for training video generation models. The output latents will maintain the same folder structure and naming as the corresponding media files. + For multi-GPU preprocessing, invoke under ``accelerate launch`` -- each process + will handle an interleaved shard of the dataset. Examples: # Process videos from a CSV file python scripts/process_videos.py dataset.csv --resolution-buckets 768x768x25 \\ diff --git a/packages/ltx-trainer/scripts/serve_captioner.py b/packages/ltx-trainer/scripts/serve_captioner.py new file mode 100755 index 0000000..4cecc10 --- /dev/null +++ b/packages/ltx-trainer/scripts/serve_captioner.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 + +"""Launch a vLLM server for Qwen3-Omni captioning. +Runs the actual server via ``uvx`` so that vLLM and its CUDA-tied +dependencies live in their own isolated environment (no impact on this +package's dependency tree). +The captioning script (``caption_videos.py``) talks to the server over its +OpenAI-compatible HTTP API. Once the server is up it stays loaded across +captioning runs; no per-script model warmup cost. +Typical usage:: + # Default: dynamic FP8 quantization, listen on 127.0.0.1:8001 + uv run python scripts/serve_captioner.py + # Just print the chosen `uvx vllm serve ...` command without running it + uv run python scripts/serve_captioner.py --print-cmd + # Use full bf16 on a GPU with >= 66 GiB free VRAM (slightly more reliable + # numerics but 2x the weight memory) + uv run python scripts/serve_captioner.py --quantization bf16 + # Use a different port or expose on all interfaces + uv run python scripts/serve_captioner.py --port 9000 --host 0.0.0.0 +""" + +import os +import shutil +import subprocess +import sys +from pathlib import Path + +import typer +from rich.console import Console + +console = Console() + +# Model identifier we serve. The captioner client must use the same string. +DEFAULT_MODEL = "Qwen/Qwen3-Omni-30B-A3B-Thinking" + +# Pinned vLLM version known to support Qwen3-Omni on CUDA 12.x. +# vLLM 0.20+ requires CUDA 13. Update both as the environment evolves. +# The ``[audio]`` extra is required for Qwen3-Omni to decode audio at all. +DEFAULT_VLLM_SPEC = "vllm[audio]==0.11.2" + +# Approximate disk needed for the model download (HF cache structure). +MODEL_DISK_GIB = 65.0 + + +app = typer.Typer( + pretty_exceptions_enable=False, + no_args_is_help=False, + help="Launch a local vLLM server for Qwen3-Omni captioning.", +) + + +def _query_disk_free_gib(path: Path) -> float: + return shutil.disk_usage(str(path)).free / 1024**3 + + +def _build_vllm_args( + *, + model: str, + host: str, + port: int, + quantization: str, + max_model_len: int, + gpu_memory_utilization: float, + extra_args: list[str], +) -> list[str]: + """Construct the `vllm serve ...` argv.""" + args = [ + "vllm", + "serve", + model, + "--host", + host, + "--port", + str(port), + "--dtype", + "bfloat16", + "--max-model-len", + str(max_model_len), + "--gpu-memory-utilization", + str(gpu_memory_utilization), + # Let the server accept ``file://`` URLs pointing at local videos. + "--allowed-local-media-path", + "/", + # The model is a multimodal MoE; cap each input to one of each + # modality to match what our captioner sends. + "--limit-mm-per-prompt", + '{"image": 1, "video": 1, "audio": 1}', + # Small concurrent-sequence cap so KV cache headroom isn't fragmented. + "--max-num-seqs", + "4", + ] + if quantization == "fp8": + args += ["--quantization", "fp8"] + args += extra_args + return args + + +@app.command() +def main( + model: str = typer.Option(DEFAULT_MODEL, "--model", help="Model identifier to serve."), + host: str = typer.Option("127.0.0.1", "--host", help="Listen address. Use 0.0.0.0 for remote access."), + port: int = typer.Option(8001, "--port", help="HTTP port."), + quantization: str = typer.Option( + "fp8", + "--quantization", + "-q", + help=( + "Weight precision. 'fp8' (default, dynamic FP8 -- ~31 GiB weights) is " + "the recommended choice; it fits on 40 GiB GPUs and runs at the same " + "speed as bf16 on H100. 'bf16' uses ~60 GiB of weights -- pick it if " + "you have abundant VRAM and want minimal numerical drift." + ), + ), + max_model_len: int = typer.Option( + 32768, + "--max-model-len", + help="Maximum context length the server accepts (must fit input video tokens + max_tokens).", + ), + gpu_memory_utilization: float = typer.Option( + 0.9, + "--gpu-memory-utilization", + help="Fraction of GPU memory vLLM may reserve (model + KV cache).", + ), + hf_home: Path | None = typer.Option( # noqa: B008 + None, + "--hf-home", + help=( + "Override HF_HOME (where the model is downloaded). The model is ~65 GB; " + "by default this follows your environment's HF_HOME or HuggingFace's default." + ), + ), + vllm_spec: str = typer.Option( + DEFAULT_VLLM_SPEC, + "--vllm-spec", + help="pip-style spec passed to `uvx --from`. Pin a version that matches your CUDA.", + ), + print_cmd: bool = typer.Option( + False, + "--print-cmd", + help="Print the chosen command without running it.", + ), + extra_args: list[str] | None = typer.Argument( # noqa: B008 + None, + help="Additional args passed through to `vllm serve` after `--`.", + ), +) -> None: + """Launch the vLLM server for Qwen3-Omni.""" + extra = extra_args or [] + + if quantization not in ("bf16", "fp8"): + console.print(f"[red]--quantization must be 'bf16' or 'fp8'; got {quantization!r}.[/]") + raise typer.Exit(code=1) + + # Disk check (only meaningful before first download). + cache_root = hf_home or Path(os.environ.get("HF_HOME", str(Path.home() / ".cache" / "huggingface"))) + cache_root.mkdir(parents=True, exist_ok=True) + free_disk = _query_disk_free_gib(cache_root) + if free_disk < MODEL_DISK_GIB: + console.print( + f"[yellow]\u26a0 Only {free_disk:.1f} GiB free on disk under {cache_root} but the " + f"model needs ~{MODEL_DISK_GIB:.0f} GiB. Either free up space, set --hf-home " + f"to a larger volume, or expect the download to fail mid-way.[/]" + ) + + vllm_args = _build_vllm_args( + model=model, + host=host, + port=port, + quantization=quantization, + max_model_len=max_model_len, + gpu_memory_utilization=gpu_memory_utilization, + extra_args=extra, + ) + + # Use ``uvx --from vllm==...`` so vLLM lives in its own throwaway venv + # (or a cached tool venv). The `--` separates uvx args from the command's. + uvx_cmd = ["uvx", "--from", vllm_spec, *vllm_args] + + env = os.environ.copy() + # vLLM 0.11.x requires the V0 engine for Qwen3-Omni's multimodal pipeline. + env.setdefault("VLLM_USE_V1", "0") + if hf_home is not None: + env["HF_HOME"] = str(hf_home) + + console.print("\n[bold]Command:[/]") + console.print(" " + " ".join(uvx_cmd)) + if hf_home is not None: + console.print(f" [dim](with HF_HOME={hf_home})[/]") + + if print_cmd: + return + + console.print("\n[dim]Launching... (first run downloads the model -- ~5 min on a fast link)[/]\n") + try: + completed = subprocess.run(uvx_cmd, env=env, check=False) + except KeyboardInterrupt: + console.print("\n[yellow]Interrupted.[/]") + return + sys.exit(completed.returncode) + + +if __name__ == "__main__": + app() diff --git a/packages/ltx-trainer/src/ltx_trainer/captioning.py b/packages/ltx-trainer/src/ltx_trainer/captioning.py index 5e09c30..1aa55a8 100644 --- a/packages/ltx-trainer/src/ltx_trainer/captioning.py +++ b/packages/ltx-trainer/src/ltx_trainer/captioning.py @@ -1,59 +1,117 @@ """ Audio-visual media captioning using multimodal models. This module provides captioning capabilities for videos with audio using: -- Qwen2.5-Omni: Local model supporting text, audio, image, and video inputs (default) -- Gemini Flash: Cloud-based API for audio-visual captioning -Requirements: -- Qwen2.5-Omni: transformers>=4.50, torch -- Gemini Flash: google-generativeai (uv pip install google-generativeai) - Set GEMINI_API_KEY or GOOGLE_API_KEY environment variable +- Qwen3-Omni via a local vLLM server (default) +- Gemini Flash 3.5 (cloud API) +Both produce a single combined English caption per video as a single +continuous paragraph of prose. +The Qwen3-Omni backend runs in a separately-launched vLLM server rather than +in-process, so vLLM's heavy CUDA dependencies stay out of this package. The +captioner talks to it over the OpenAI-compatible HTTP API. +Launch the server once (in an isolated environment) with: +.. code-block:: bash + uv run python scripts/serve_captioner.py +That helper picks BF16 vs FP8 dynamic quantization based on the GPU's free +memory and forwards everything else to ``vllm serve``. To check the recommended +command without running it, pass ``--print-cmd``. +To use Gemini instead, install ``google-genai`` and either set ``GEMINI_API_KEY`` +(Gemini Developer API) or have Google Cloud credentials available (gcloud / an +attached service account), in which case it uses Vertex AI automatically. """ -import itertools +import json +import os import re +import subprocess +import tempfile from abc import ABC, abstractmethod from enum import Enum from pathlib import Path +from typing import ClassVar -import torch +DEFAULT_VIDEO_CAPTION_INSTRUCTION = """\ +Analyze this video and produce a single detailed caption covering both its visual content and its audio. Be \ +detailed enough that someone reading the caption could form an accurate mental picture of what happens on screen \ +and what can be heard. Be exhaustive: include every meaningful detail you can see and hear, including small \ +objects, textures, secondary movements, and minor background sounds. -# Instruction for audio-visual captioning (default) - includes speech transcription and sounds -DEFAULT_CAPTION_INSTRUCTION = """\ -Analyze this media and provide a detailed caption in the following EXACT format. Fill in ALL sections: +Begin the caption directly with the action or visual detail; do not preface it with phrases like \ +"The video opens with...", "The scene shows...", "We see...", or "There is...". -[VISUAL]: -[SPEECH]: -[SOUNDS]: -[TEXT]: +For every shot, include: +- The shot type and framing (extreme wide / wide / medium / medium close-up / close-up / extreme close-up) and any \ +camera motion. +- Characters' clothing, appearance, posture, and movement (direction, speed, quality). +- The environment's materials, textures, lighting, and colors. +- All audio: spoken dialogue (quoted exactly in the original language), tone of voice, music (style, mood, \ +volume changes), and environmental sounds. If a category is absent -- for example no music is playing, or no one is \ +speaking -- state that explicitly. Do not invent specific instruments, music genres, moods, or ambient sounds \ +that are not actually present. +- Any on-screen text (signs, titles, labels). -You MUST fill in all four sections. For [SPEECH], transcribe the actual words spoken, not a summary.""" +Describe only what is visible or audible. Do not infer emotions, intentions, or anything outside the segment. \ +Refer to people descriptively (e.g., "the man in the blue jacket"). Narrate strictly in chronological order; if \ +the video contains multiple shots, describe each one in turn. -# Instruction for video-only captioning (no audio processing) -VIDEO_ONLY_CAPTION_INSTRUCTION = """\ -Analyze this media and provide a detailed caption in the following EXACT format. Fill in ALL sections: +Write everything as a single continuous paragraph of prose. Do not use section headers, bullet points, or labels \ +like "Audio:" / "Visual:" / "Shot:". Integrate visual and audio details naturally within the same sentences. -[VISUAL]: -[TEXT]: +Return a JSON object with exactly one key: -You MUST fill in both sections.""" +{"combined_caption_english": ""}""" + + +DEFAULT_IMAGE_CAPTION_INSTRUCTION = """\ +Analyze this image and produce a single detailed caption of its visual content. Be detailed enough that \ +someone reading the caption could form an accurate mental picture of the image. Be thorough: include every meaningful \ +detail that is actually present, including small objects, textures, and background elements. + +Begin the caption directly with the main subject or a visual detail; do not preface it with phrases like \ +"The image shows...", "This is a photo of...", "We see...", or "There is...". + +Include: +- The framing and composition (close-up / medium / wide / overhead, etc.) and the vantage point. +- The medium or style if distinctive (photograph, illustration, 3D render, painting). +- People's clothing, appearance, and posture, and what they are doing. +- The setting's materials, textures, lighting, and colors. +- Transcribe any visible text verbatim (signs, labels, titles, captions). + +Describe only what is visible. Do not infer emotions or intentions, and do not describe sounds, motion, or \ +events before or after the moment shown -- this is a single still image. When something is ambiguous, describe \ +the visible cue (e.g., "warm low-angle light") rather than guessing the underlying fact (e.g., "sunrise"). \ +Refer to people descriptively (e.g., "the man in the blue jacket"). + +Only describe what is present. Never state that something is absent or missing -- do not write phrases like \ +"there is no text", "no people are present", or "no other objects". If a category such as people or text does \ +not appear, simply leave it out. + +Write everything as a single continuous paragraph of prose. Do not use section headers, bullet points, or \ +labels. + +Return a JSON object with exactly one key: + +{"combined_caption_english": ""}""" + + +# Default model served by ``scripts/serve_captioner.py``. The captioner does not +# download or load this model itself -- it just sends requests to the vLLM +# server, which already has the model loaded. +DEFAULT_QWEN_MODEL = "Qwen/Qwen3-Omni-30B-A3B-Thinking" +DEFAULT_VLLM_BASE_URL = "http://127.0.0.1:8001/v1" + +# Key the combined-caption prompt asks the model to return its caption under. +_CAPTION_JSON_KEY = "combined_caption_english" class CaptionerType(str, Enum): """Enum for different types of media captioners.""" - QWEN_OMNI = "qwen_omni" # Local Qwen2.5-Omni model (audio + video) - GEMINI_FLASH = "gemini_flash" # Gemini Flash API (audio + video) + QWEN_OMNI = "qwen_omni" # Qwen3-Omni via local vLLM HTTP server + GEMINI_FLASH = "gemini_flash" # Gemini Flash 3.5 cloud API def create_captioner(captioner_type: CaptionerType, **kwargs) -> "MediaCaptioningModel": - """Factory function to create a media captioner. - Args: - captioner_type: The type of captioner to create - **kwargs: Additional arguments to pass to the captioner constructor - Returns: - An instance of a MediaCaptioningModel - """ + """Factory function to create a media captioner.""" match captioner_type: case CaptionerType.QWEN_OMNI: return QwenOmniCaptioner(**kwargs) @@ -66,336 +124,332 @@ def create_captioner(captioner_type: CaptionerType, **kwargs) -> "MediaCaptionin class MediaCaptioningModel(ABC): """Abstract base class for audio-visual media captioning models.""" + instruction: str | None = None + @abstractmethod def caption(self, path: str | Path, **kwargs) -> str: - """Generate a caption for the given video or image. - Args: - path: Path to the video/image file to caption - Returns: - A string containing the generated caption - """ + """Generate a caption for the given video or image.""" - @property - @abstractmethod - def supports_audio(self) -> bool: - """Whether this captioner supports audio input.""" + def _resolve_instruction(self, path: str | Path) -> str: + """Return the custom instruction, or the image/video default for this input.""" + if self.instruction is not None: + return self.instruction + return DEFAULT_IMAGE_CAPTION_INSTRUCTION if self._is_image_file(path) else DEFAULT_VIDEO_CAPTION_INSTRUCTION @staticmethod def _is_image_file(path: str | Path) -> bool: - """Check if the file is an image based on extension.""" return str(path).lower().endswith((".png", ".jpg", ".jpeg", ".heic", ".heif", ".webp")) @staticmethod def _is_video_file(path: str | Path) -> bool: - """Check if the file is a video based on extension.""" return str(path).lower().endswith((".mp4", ".avi", ".mov", ".mkv", ".webm")) - @staticmethod - def _clean_raw_caption(caption: str) -> str: - """Clean up the raw caption by removing common VLM patterns.""" - start = ["The", "This"] - kind = ["video", "image", "scene", "animated sequence", "clip", "footage"] - act = ["displays", "shows", "features", "depicts", "presents", "showcases", "captures", "contains"] - - for x, y, z in itertools.product(start, kind, act): - caption = caption.replace(f"{x} {y} {z} ", "", 1) - - return caption - class QwenOmniCaptioner(MediaCaptioningModel): - """Audio-visual captioning using Alibaba's Qwen2.5-Omni model. - Qwen2.5-Omni is an end-to-end multimodal model that can perceive text, images, audio, and video. - It uses a Thinker-Talker architecture where the Thinker generates text and the Talker can - generate speech. For captioning, we use only the Thinker component for text generation. - Key features: - - Block-wise processing for streaming multimodal inputs - - TMRoPE (Time-aligned Multimodal RoPE) for synchronizing video and audio timestamps - - Can extract and process audio directly from video files - See: https://huggingface.co/docs/transformers/en/model_doc/qwen2_5_omni - Model: Qwen/Qwen2.5-Omni-7B (7B parameters) + """Audio-visual captioning via a local vLLM server running Qwen3-Omni. + The vLLM server must already be running. See ``scripts/serve_captioner.py`` + for a helper that launches one in an isolated environment (no impact on + this package's dependency tree). + The captioner uses the OpenAI-compatible chat completions API. It sends + a ``file://`` URL pointing at the local video, the default combined-caption + prompt, and parses the JSON-wrapped response. """ - MODEL_ID = "Qwen/Qwen2.5-Omni-7B" - - # Default system prompt required by Qwen2.5-Omni for proper audio processing - DEFAULT_SYSTEM_PROMPT = ( - "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, " - "capable of perceiving auditory and visual inputs, as well as generating text and speech." - ) - def __init__( self, - device: str | torch.device | None = None, - use_8bit: bool = False, + base_url: str = DEFAULT_VLLM_BASE_URL, + model: str = DEFAULT_QWEN_MODEL, + api_key: str = "EMPTY", instruction: str | None = None, + max_tokens: int = 4096, + enable_thinking: bool = False, + timeout_s: float = 600.0, ): - """ - Initialize the Qwen2.5-Omni captioner. + """Initialize the Qwen3-Omni captioner. Args: - device: Device to use for inference (e.g., 'cuda', 'cuda:0', 'cpu') - use_8bit: Whether to use 8-bit quantization for reduced memory usage - instruction: Custom instruction prompt. If None, uses the default instruction + base_url: Base URL of the vLLM OpenAI-compatible server (default + ``http://127.0.0.1:8001/v1``). + model: Model identifier the server is serving. Must match the + server's ``--served-model-name`` (defaults to the HuggingFace + model ID). + api_key: Token sent in the ``Authorization`` header. vLLM accepts + any value by default. + instruction: Custom instruction prompt. If ``None``, uses the + default combined-caption prompt. + max_tokens: Maximum new tokens to generate per caption. 4096 leaves + comfortable headroom for both ``enable_thinking`` modes. + enable_thinking: Whether to let the Thinking model produce a + ``...`` chain-of-thought before the caption. + Off by default: it makes captioning ~5x slower with little + quality benefit and occasionally introduces hallucinations + (e.g., inventing dialogue or background music). + timeout_s: Per-request HTTP timeout. """ - self.device = torch.device(device or ("cuda" if torch.cuda.is_available() else "cpu")) - self.instruction = instruction - self._load_model(use_8bit=use_8bit) + from openai import OpenAI # noqa: PLC0415 - @property - def supports_audio(self) -> bool: - return True + self.model = model + self.instruction = instruction + self.max_tokens = max_tokens + self.enable_thinking = enable_thinking + self._client = OpenAI(base_url=base_url, api_key=api_key, timeout=timeout_s) def caption( self, path: str | Path, - fps: int = 1, - include_audio: bool = True, - clean_caption: bool = True, + fps: int = 2, ) -> str: """Generate a caption for the given video or image. Args: - path: Path to the video/image file to caption - fps: Frames per second to sample from videos - include_audio: Whether to include audio in the captioning (for videos) - clean_caption: Whether to clean up the raw caption by removing common VLM patterns + path: Path to the video/image file to caption. + fps: Frames per second to sample from the video. Passed through to + vLLM's multimodal processor (``mm_processor_kwargs.fps``). + Default 2 is a typical choice for video MLLMs at this resolution. + Ignored for image inputs. Returns: - A string containing the generated caption + The extracted caption string. """ path = Path(path) is_image = self._is_image_file(path) is_video = self._is_video_file(path) + if not (is_image or is_video): + raise ValueError(f"Unsupported media file: {path}") - # Determine if we should process audio - use_audio = include_audio and is_video - - # Use custom instruction if provided, otherwise pick appropriate default - if self.instruction is not None: - instruction = self.instruction - else: - instruction = DEFAULT_CAPTION_INSTRUCTION if use_audio else VIDEO_ONLY_CAPTION_INSTRUCTION - - # Build the user content based on media type - # Based on HuggingFace docs: https://huggingface.co/docs/transformers/en/model_doc/qwen2_5_omni - user_content = [] + instruction = self._resolve_instruction(path) if is_image: - user_content.append({"type": "image", "image": str(path)}) - elif is_video: - user_content.append({"type": "video", "video": str(path)}) + content = [ + {"type": "image_url", "image_url": {"url": f"file://{path.resolve()}"}}, + {"type": "text", "text": instruction}, + ] + return _parse_caption_response(self._chat(content)).strip() - # Add the instruction text - user_content.append({"type": "text", "text": instruction}) + return self._caption_video(path, instruction, fps) - # Build conversation - use the default system prompt required by Qwen2.5-Omni - # Using a custom system prompt causes warnings and may affect audio processing - messages = [ - { - "role": "system", - "content": [{"type": "text", "text": self.DEFAULT_SYSTEM_PROMPT}], - }, - {"role": "user", "content": user_content}, - ] - - # Process inputs using the processor's apply_chat_template - # For videos with audio, use load_audio_from_video=True and use_audio_in_video=True - inputs = self.processor.apply_chat_template( - messages, - load_audio_from_video=use_audio, - add_generation_prompt=True, - tokenize=True, - return_dict=True, - return_tensors="pt", - fps=fps, - padding=True, - use_audio_in_video=use_audio, - ).to(self.model.device) - - # Generate caption (text only, using Thinker-only model) - # Note: For Qwen2_5OmniThinkerForConditionalGeneration, use standard generate params - # (not thinker_ prefixed ones, those are for the full Qwen2_5OmniForConditionalGeneration) - input_len = inputs["input_ids"].shape[1] - - output_tokens = self.model.generate( - **inputs, - use_audio_in_video=use_audio, - do_sample=False, - max_new_tokens=1024, + def _chat(self, content: list[dict], mm_kwargs: dict | None = None) -> str: + """Send one chat-completions request and return the raw response text.""" + extra_body: dict = { + "repetition_penalty": 1.05, + "chat_template_kwargs": {"enable_thinking": self.enable_thinking}, + } + if mm_kwargs: + extra_body["mm_processor_kwargs"] = mm_kwargs + response = self._client.chat.completions.create( + model=self.model, + messages=[{"role": "user", "content": content}], + max_tokens=self.max_tokens, + temperature=0.0, + extra_body=extra_body, ) + return response.choices[0].message.content or "" - # Extract only the generated tokens (exclude the input/prompt tokens) - generated_tokens = output_tokens[:, input_len:] - - # Decode only the generated response - caption_raw = self.processor.batch_decode( - generated_tokens, - skip_special_tokens=True, - clean_up_tokenization_spaces=False, - )[0] - - # Remove hallucinated conversation turns (e.g., "Human\nHuman\n..." or "Human: ...") - # This is a known issue with chat models continuing to generate fake turns - # We look for patterns that are clearly hallucinated chat turns, not legitimate uses of "human" - - # Match "\nHuman" followed by ":", "\n", or end of string (chat turn patterns) - # This won't match "A human walks..." or "...the human body..." - caption_raw = re.split(r"\nHuman(?::|(?:\s*\n)|$)", caption_raw, maxsplit=1)[0] - caption_raw = caption_raw.strip() - - # Clean up caption if requested - return self._clean_raw_caption(caption_raw) if clean_caption else caption_raw - - def _load_model(self, use_8bit: bool) -> None: - """Load the Qwen2.5-Omni model and processor. - Uses the Thinker-only model (Qwen2_5OmniThinkerForConditionalGeneration) for text generation - to save compute by not loading the audio generation components. + def _caption_video(self, path: Path, instruction: str, fps: int) -> str: + """Caption a video, sending its audio track as a separate modality. + vLLM does not extract a video's audio on its own (and its + ``use_audio_in_video`` path is broken server-side), so we pull the audio + into a 16 kHz mono WAV and send it alongside the video -- otherwise the + model only sees frames and fabricates any spoken content. """ - from transformers import ( # noqa: PLC0415 - BitsAndBytesConfig, - Qwen2_5OmniProcessor, - Qwen2_5OmniThinkerForConditionalGeneration, - ) + with tempfile.TemporaryDirectory(prefix="qwencap_") as tmp: + work = Path(tmp) - quantization_config = BitsAndBytesConfig(load_in_8bit=True) if use_8bit else None + # Best-effort: ffmpeg fails (and we send video only) if there's no audio. + audio_url: str | None = None + try: + wav = work / "audio.wav" + _extract_audio_wav(path, wav) + audio_url = f"file://{wav.resolve()}" + except subprocess.CalledProcessError: + pass - # Use Thinker-only model for text generation (saves memory by not loading Talker) - self.model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained( - self.MODEL_ID, - dtype=torch.bfloat16, - low_cpu_mem_usage=True, - quantization_config=quantization_config, - device_map="auto", - ) + def content(video: Path) -> list[dict]: + parts: list[dict] = [{"type": "video_url", "video_url": {"url": f"file://{video.resolve()}"}}] + if audio_url: + parts.append({"type": "audio_url", "audio_url": {"url": audio_url}}) + parts.append({"type": "text", "text": instruction}) + return parts - self.processor = Qwen2_5OmniProcessor.from_pretrained(self.MODEL_ID) + mm_kwargs = {"fps": fps} + try: + raw = self._chat(content(path), mm_kwargs) + except Exception as e: + # Raw / variable-frame-rate videos over-report their frame count, which + # breaks the server's frame sampler ("... frames from video"). Re-encode + # to a constant frame rate and retry once. + if "frames from video" not in str(e): + raise + cfr = work / "video_cfr.mp4" + _transcode_cfr(path, cfr) + raw = self._chat(content(cfr), mm_kwargs) + + return _parse_caption_response(raw).strip() class GeminiFlashCaptioner(MediaCaptioningModel): - """Audio-visual captioning using Google's Gemini Flash API. - Gemini Flash is a cloud-based multimodal model that natively supports - audio and video understanding. Requires a Google API key. - Note: This captioner requires the `google-generativeai` package and a valid API key. - Set the GEMINI_API_KEY or GOOGLE_API_KEY environment variable, or pass the key directly. + """Audio-visual captioning using Google's Gemini via the Google Gen AI SDK. + Uses the ``google-genai`` package (the current SDK; ``google-generativeai`` + is deprecated). Auth is resolved automatically: + 1. If an API key is given (``api_key`` argument, or ``GEMINI_API_KEY`` / + ``GOOGLE_API_KEY`` in the environment) -> the Gemini Developer API (AI Studio). + 2. Otherwise, if Google Cloud Application Default Credentials are available + (an attached service account or ``gcloud auth application-default login``) + -> Vertex AI. The project comes from ADC (or ``GOOGLE_CLOUD_PROJECT``) and + the location defaults to ``global`` (override with ``GOOGLE_CLOUD_LOCATION``). + This means it "just works" on a gcloud-authed GCP VM with no env vars. + If neither is available, a clear error explains how to authenticate. + Media is sent inline (``Part.from_bytes``), which works on both backends. """ - MODEL_ID = "gemini-flash-lite-latest" + MODEL_ID = "gemini-3.5-flash" + + _MIME_TYPES: ClassVar[dict[str, str]] = { + ".jpg": "image/jpeg", + ".jpeg": "image/jpeg", + ".png": "image/png", + ".webp": "image/webp", + ".heic": "image/heic", + ".heif": "image/heif", + ".mp4": "video/mp4", + ".mov": "video/quicktime", + ".avi": "video/x-msvideo", + ".mkv": "video/x-matroska", + ".webm": "video/webm", + } def __init__( self, api_key: str | None = None, instruction: str | None = None, + model: str | None = None, ): - """Initialize the Gemini Flash captioner. + """Initialize the Gemini captioner. Args: - api_key: Google API key. If not provided, will look for - GEMINI_API_KEY or GOOGLE_API_KEY environment variable. - instruction: Custom instruction prompt. If None, uses the default instruction + api_key: Gemini Developer API key. If ``None``, falls back to + ``GEMINI_API_KEY`` / ``GOOGLE_API_KEY``; if no key is set at all, + uses Vertex AI via Application Default Credentials. + instruction: Custom instruction prompt. If ``None``, uses the default + image or video prompt depending on the input. + model: Override the served model id (defaults to ``MODEL_ID``). """ self.instruction = instruction - self._init_client(api_key) - - @property - def supports_audio(self) -> bool: - return True + self.model = model or self.MODEL_ID + self._client = self._make_client(api_key) def caption( self, path: str | Path, - fps: int = 3, # noqa: ARG002 - kept for API compatibility - include_audio: bool = True, - clean_caption: bool = True, + fps: int = 2, # noqa: ARG002 - kept for API compatibility ) -> str: - """Generate a caption for the given video or image. - Args: - path: Path to the video/image file to caption - fps: Frames per second (not used for Gemini, kept for API compatibility) - include_audio: Whether to include audio content in the caption - clean_caption: Whether to clean up the raw caption - Returns: - A string containing the generated caption - """ - import time # noqa: PLC0415 + from google.genai import types # noqa: PLC0415 path = Path(path) - is_video = self._is_video_file(path) - use_audio = include_audio and is_video + instruction = self._resolve_instruction(path) + media = types.Part.from_bytes(data=path.read_bytes(), mime_type=self._mime_type(path)) + response = self._client.models.generate_content( + model=self.model, + contents=[media, instruction], + config=types.GenerateContentConfig(temperature=0.0), + ) - # Use custom instruction if provided, otherwise pick appropriate default - if self.instruction is not None: - instruction = self.instruction - else: - instruction = DEFAULT_CAPTION_INSTRUCTION if use_audio else VIDEO_ONLY_CAPTION_INSTRUCTION + # Gemini may also return JSON if it followed our prompt format. + return _parse_caption_response(response.text or "").strip() - # Upload the file to Gemini - uploaded_file = self._genai.upload_file(path) + @classmethod + def _mime_type(cls, path: Path) -> str: + try: + return cls._MIME_TYPES[path.suffix.lower()] + except KeyError: + raise ValueError(f"Unsupported media type for Gemini: {path.suffix}") from None - # Wait for processing to complete (videos need time to process) - while uploaded_file.state.name == "PROCESSING": - time.sleep(1) - uploaded_file = self._genai.get_file(uploaded_file.name) + def _make_client(self, api_key: str | None): # noqa: ANN202 - genai.Client type is lazy-imported + from google import genai # noqa: PLC0415 - if uploaded_file.state.name == "FAILED": - raise RuntimeError(f"File processing failed: {uploaded_file.state.name}") + # 1. API key (explicit arg or env) -> Gemini Developer API. + key = api_key or os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY") + if key: + return genai.Client(api_key=key) - # Generate caption - response = self._model.generate_content([uploaded_file, instruction]) - - caption_raw = response.text - - # Clean up the uploaded file - self._genai.delete_file(uploaded_file.name) - - # Clean up caption if requested - return self._clean_raw_caption(caption_raw) if clean_caption else caption_raw - - def _init_client(self, api_key: str | None) -> None: - """Initialize the Gemini API client.""" - import os # noqa: PLC0415 + # 2. No key -> Vertex AI via Application Default Credentials (gcloud / service account). + import google.auth # noqa: PLC0415 try: - import google.generativeai as genai # noqa: PLC0415 - except ImportError as e: - raise ImportError( - "The `google-generativeai` package is required for Gemini Flash captioning. " - "Install it with: `uv pip install google-generativeai`" + _, adc_project = google.auth.default() + except Exception as e: + raise ValueError( + "No Gemini credentials found. Provide an API key (--api-key, or " + "GEMINI_API_KEY / GOOGLE_API_KEY), or set up Google Cloud credentials " + "for Vertex AI (e.g. `gcloud auth application-default login` or an " + "attached service account)." ) from e - # Get API key from argument or environment - # GEMINI_API_KEY is the recommended variable, GOOGLE_API_KEY also works - resolved_api_key = api_key or os.environ.get("GEMINI_API_KEY") or os.environ.get("GOOGLE_API_KEY") - - if not resolved_api_key: - raise ValueError( - "Gemini API key is required. Provide it via the `api_key` argument " - "or set the GEMINI_API_KEY or GOOGLE_API_KEY environment variable." - ) - - # Configure the genai library with the API key - genai.configure(api_key=resolved_api_key) - - # Store reference to genai module for file operations - self._genai = genai - - # Initialize the model - self._model = genai.GenerativeModel(self.MODEL_ID) + project = os.environ.get("GOOGLE_CLOUD_PROJECT") or adc_project + location = os.environ.get("GOOGLE_CLOUD_LOCATION", "global") + return genai.Client(vertexai=True, project=project, location=location) -def example() -> None: - """Example usage of the captioning module.""" - import sys # noqa: PLC0415 +def _parse_caption_response(raw: str) -> str: + """Extract the caption text from a model response. + Backend-agnostic: works for any model that follows the combined-caption + prompt. Handles the formats a model may produce: + - Plain caption text + - JSON ``{"combined_caption_english": "..."}`` + - ``...`` chain-of-thought followed by either of the above + - Truncated JSON (when generation hits a token limit mid-string) + """ + text = re.sub(r"[\s\S]*?", "", raw).strip() - if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [captioner_type]") # noqa: T201 - print(" captioner_type: qwen_omni (default) or gemini_flash") # noqa: T201 - sys.exit(1) + # Thinking models (e.g. Qwen3-Omni-*-Thinking) emit the reasoning trace + # without an opening ```` tag, because the chat template injects it + # for them -- so the response starts mid-thought and is terminated by a lone + # ```` before the real answer. Drop everything up to that closer. + if "" in text: + text = text.rsplit("", 1)[1].strip() - video_path = sys.argv[1] - captioner_type = CaptionerType(sys.argv[2]) if len(sys.argv) > 2 else CaptionerType.QWEN_OMNI + if not text: + return raw.strip() - print(f"Using {captioner_type.value} captioner:") # noqa: T201 - captioner = create_captioner(captioner_type) - caption = captioner.caption(video_path) - print(f"CAPTION: {caption}") # noqa: T201 + try: + parsed = json.loads(text) + if isinstance(parsed, dict) and _CAPTION_JSON_KEY in parsed: + return parsed[_CAPTION_JSON_KEY] + except (json.JSONDecodeError, ValueError): + pass + + match = re.search(rf"\{{[^{{}}]*\"{_CAPTION_JSON_KEY}\"[^{{}}]*\}}", text) + if match: + try: + parsed = json.loads(match.group()) + if isinstance(parsed, dict) and _CAPTION_JSON_KEY in parsed: + return parsed[_CAPTION_JSON_KEY] + except (json.JSONDecodeError, ValueError): + pass + + # Truncated JSON: extract the string value even if the closing quote/brace is missing. + match = re.search(rf'"{_CAPTION_JSON_KEY}"\s*:\s*"((?:[^"\\]|\\.)*)', text) + if match: + try: + return json.loads('"' + match.group(1) + '"') + except (json.JSONDecodeError, ValueError): + return match.group(1) + + return text -if __name__ == "__main__": - example() +def _run_ffmpeg(args: list[str]) -> None: + """Run the ffmpeg binary bundled with ``imageio-ffmpeg`` (a dependency).""" + import imageio_ffmpeg # noqa: PLC0415 + + cmd = [imageio_ffmpeg.get_ffmpeg_exe(), "-y", "-loglevel", "error", *args] + subprocess.run(cmd, check=True, capture_output=True) + + +def _extract_audio_wav(src: Path, dest: Path) -> None: + """Extract the audio track to a 16 kHz mono PCM WAV (matches pretraining). + Raises ``CalledProcessError`` when the video has no audio stream. + """ + _run_ffmpeg(["-i", str(src), "-vn", "-ac", "1", "-ar", "16000", "-c:a", "pcm_s16le", str(dest)]) + + +def _transcode_cfr(src: Path, dest: Path) -> None: + """Re-encode the video to a constant frame rate so the server's frame sampler can + read every requested index (raw / variable-frame-rate videos over-report frames).""" + _run_ffmpeg(["-i", str(src), "-fps_mode", "cfr", "-c:v", "libx264", "-pix_fmt", "yuv420p", "-an", str(dest)]) diff --git a/packages/ltx-trainer/src/ltx_trainer/config.py b/packages/ltx-trainer/src/ltx_trainer/config.py index 3220b4d..c4d3009 100644 --- a/packages/ltx-trainer/src/ltx_trainer/config.py +++ b/packages/ltx-trainer/src/ltx_trainer/config.py @@ -1,10 +1,11 @@ from pathlib import Path -from typing import Annotated, Literal +from typing import Annotated, Literal, Union from pydantic import BaseModel, ConfigDict, Discriminator, Field, Tag, ValidationInfo, field_validator, model_validator from ltx_trainer.quantization import QuantizationOptions from ltx_trainer.training_strategies.base_strategy import TrainingStrategyConfigBase +from ltx_trainer.training_strategies.flexible import FlexibleStrategyConfig from ltx_trainer.training_strategies.text_to_video import TextToVideoConfig from ltx_trainer.training_strategies.video_to_video import VideoToVideoConfig @@ -13,6 +14,226 @@ class ConfigBaseModel(BaseModel): model_config = ConfigDict(extra="forbid") +# ============================================================================= +# Validation Condition Types +# ============================================================================= + + +class FirstFrameConditionConfig(ConfigBaseModel): + """First-frame conditioning (intrinsic, latent_idx=0). Always targets video. + If image_or_video points to a video file, the first frame is automatically extracted. + """ + + type: Literal["first_frame"] = "first_frame" + image_or_video: str | Path + + +class PrefixConditionConfig(ConfigBaseModel): + """Prefix conditioning for temporal extension (intrinsic). Exactly one of video/audio must be set.""" + + type: Literal["prefix"] = "prefix" + video: str | None = None + audio: str | None = None + num_frames: int | None = Field( + default=None, + ge=1, + description="Number of pixel frames for video prefix. Must satisfy num_frames %% 8 == 1.", + ) + duration: float | None = Field(default=None, gt=0, description="Duration in seconds for audio prefix") + + @model_validator(mode="after") + def validate_exactly_one_modality(self) -> "PrefixConditionConfig": + if (self.video is None) == (self.audio is None): + raise ValueError("Exactly one of 'video' or 'audio' must be set for prefix condition") + return self + + @model_validator(mode="after") + def validate_num_frames_constraint(self) -> "PrefixConditionConfig": + if self.video is not None and self.num_frames is not None and self.num_frames % 8 != 1: + raise ValueError( + f"num_frames ({self.num_frames}) must satisfy num_frames % 8 == 1 " + f"for video prefix (e.g., 1, 9, 17, 25, ...)" + ) + return self + + +class SuffixConditionConfig(ConfigBaseModel): + """Suffix conditioning for temporal extension (intrinsic). Exactly one of video/audio must be set.""" + + type: Literal["suffix"] = "suffix" + video: str | None = None + audio: str | None = None + num_frames: int | None = Field( + default=None, + ge=1, + description="Number of pixel frames for video suffix. Must satisfy num_frames %% 8 == 0.", + ) + duration: float | None = Field(default=None, gt=0, description="Duration in seconds for audio suffix") + + @model_validator(mode="after") + def validate_exactly_one_modality(self) -> "SuffixConditionConfig": + if (self.video is None) == (self.audio is None): + raise ValueError("Exactly one of 'video' or 'audio' must be set for suffix condition") + return self + + @model_validator(mode="after") + def validate_num_frames_constraint(self) -> "SuffixConditionConfig": + if self.video is not None and self.num_frames is not None and self.num_frames % 8 != 0: + raise ValueError( + f"num_frames ({self.num_frames}) must satisfy num_frames % 8 == 0 " + f"for video suffix (e.g., 8, 16, 24, 32, ...)" + ) + return self + + +class SpatialCropConditionConfig(ConfigBaseModel): + """Spatial crop conditioning for outpainting (intrinsic, video only).""" + + type: Literal["spatial_crop"] = "spatial_crop" + video: str + spatial_region: tuple[int, int, int, int] = Field( + ..., description="Spatial crop region as (y1, x1, y2, x2) in pixel coordinates" + ) + + +class MaskConditionConfig(ConfigBaseModel): + """Mask-based conditioning for inpainting (intrinsic). Exactly one of video/audio must be set.""" + + type: Literal["mask"] = "mask" + video: str | None = None + audio: str | None = None + mask: str + + @model_validator(mode="after") + def validate_exactly_one_modality(self) -> "MaskConditionConfig": + if (self.video is None) == (self.audio is None): + raise ValueError("Exactly one of 'video' or 'audio' must be set for mask condition") + return self + + +class ReferenceConditionConfig(ConfigBaseModel): + """Reference conditioning (IC-LoRA style concatenation). Exactly one of video/audio must be set.""" + + type: Literal["reference"] = "reference" + video: str | None = None + audio: str | None = None + downscale_factor: int = Field(default=1, ge=1) + temporal_scale_factor: int = Field(default=1, ge=1) + include_in_output: bool = False + + @model_validator(mode="after") + def validate_exactly_one_modality(self) -> "ReferenceConditionConfig": + if (self.video is None) == (self.audio is None): + raise ValueError("Exactly one of 'video' or 'audio' must be set for reference condition") + return self + + +class VideoToAudioConditionConfig(ConfigBaseModel): + """Video-to-audio — video is provided as frozen cross-modal conditioning. + The video is kept clean (sigma=0) and influences audio generation via cross-modal attention. + """ + + type: Literal["video_to_audio"] = "video_to_audio" + video: str + + +class AudioToVideoConditionConfig(ConfigBaseModel): + """Audio-to-video — audio is provided as frozen cross-modal conditioning. + The audio is kept clean (sigma=0) and influences video generation via cross-modal attention. + """ + + type: Literal["audio_to_video"] = "audio_to_video" + audio: str + + +ValidationCondition = Annotated[ + Union[ + FirstFrameConditionConfig, + PrefixConditionConfig, + SuffixConditionConfig, + SpatialCropConditionConfig, + MaskConditionConfig, + ReferenceConditionConfig, + VideoToAudioConditionConfig, + AudioToVideoConditionConfig, + ], + Field(discriminator="type"), +] + + +def _condition_targets_video(cond: ValidationCondition) -> bool: + """Check if a validation condition targets the video modality.""" + if cond.type in ("first_frame", "spatial_crop", "video_to_audio"): + return True + if cond.type in ("prefix", "suffix", "mask", "reference"): + return getattr(cond, "video", None) is not None + return False + + +def _condition_targets_audio(cond: ValidationCondition) -> bool: + """Check if a validation condition targets the audio modality.""" + if cond.type == "audio_to_video": + return True + if cond.type in ("prefix", "suffix", "mask", "reference"): + return getattr(cond, "audio", None) is not None + return False + + +class ValidationSample(ConfigBaseModel): + """Configuration for a single validation sample — fully self-describing.""" + + prompt: str + conditions: list[ValidationCondition] = Field(default_factory=list) + + video_dims: tuple[int, int, int] | None = Field( + default=None, + description="Per-sample override for (width, height, frames). None = inherit from ValidationConfig.", + ) + seed: int | None = Field( + default=None, + description="Per-sample override for random seed. None = inherit from ValidationConfig.", + ) + + @field_validator("video_dims") + @classmethod + def validate_video_dims(cls, v: tuple[int, int, int] | None) -> tuple[int, int, int] | None: + if v is None: + return v + width, height, frames = v + if width % 32 != 0: + raise ValueError(f"Width ({width}) must be divisible by 32") + if height % 32 != 0: + raise ValueError(f"Height ({height}) must be divisible by 32") + if frames % 8 != 1: + raise ValueError(f"Frames ({frames}) must satisfy frames % 8 == 1 for LTX-2 (e.g., 1, 9, 17, 25, ...)") + return v + + @model_validator(mode="after") + def validate_frozen_modality_conflicts(self) -> "ValidationSample": + frozen_types = {c.type for c in self.conditions if c.type in ("video_to_audio", "audio_to_video")} + + if "video_to_audio" in frozen_types and "audio_to_video" in frozen_types: + raise ValueError( + "Cannot have both video_to_audio and audio_to_video conditions — nothing would be generated" + ) + + if "video_to_audio" in frozen_types: + for c in self.conditions: + if c.type != "video_to_audio" and _condition_targets_video(c): + raise ValueError( + f"Cannot use video-targeting '{c.type}' condition when video is frozen (video_to_audio)" + ) + + if "audio_to_video" in frozen_types: + for c in self.conditions: + if c.type != "audio_to_video" and _condition_targets_audio(c): + raise ValueError( + f"Cannot use audio-targeting '{c.type}' condition when audio is frozen (audio_to_video)" + ) + + return self + + class ModelConfig(ConfigBaseModel): """Configuration for the base model and training mode""" @@ -89,7 +310,9 @@ def _get_strategy_discriminator(v: dict | TrainingStrategyConfigBase) -> str: # Union type for all strategy configs with discriminator TrainingStrategyConfig = Annotated[ - Annotated[TextToVideoConfig, Tag("text_to_video")] | Annotated[VideoToVideoConfig, Tag("video_to_video")], + Annotated[TextToVideoConfig, Tag("text_to_video")] + | Annotated[VideoToVideoConfig, Tag("video_to_video")] + | Annotated[FlexibleStrategyConfig, Tag("flexible")], Discriminator(_get_strategy_discriminator), ] @@ -191,13 +414,32 @@ class DataConfig(ConfigBaseModel): ge=0, ) + @field_validator("preprocessed_data_root") + @classmethod + def validate_preprocessed_data_root(cls, v: str) -> str: + """Validate that preprocessed_data_root exists.""" + path = Path(v).expanduser().resolve() + if not path.exists(): + raise ValueError(f"Dataset path does not exist: {v}") + if not path.is_dir(): + raise ValueError(f"Dataset path is not a directory: {v}") + return str(path) + class ValidationConfig(ConfigBaseModel): """Configuration for validation during training""" + # Per-sample configuration (new format — preferred) + samples: list[ValidationSample] = Field( + default_factory=list, + description="List of validation samples. Each sample is fully self-describing with its own " + "prompt, conditions, and optional overrides. Replaces prompts/images/reference_videos.", + ) + + # Legacy fields (deprecated — converted to samples internally via convert_legacy_format) prompts: list[str] = Field( default_factory=list, - description="List of prompts to use for validation", + description="[DEPRECATED: use 'samples' instead] List of prompts to use for validation", ) negative_prompt: str = Field( @@ -207,19 +449,22 @@ class ValidationConfig(ConfigBaseModel): images: list[str] | None = Field( default=None, - description="List of image paths to use for validation. " + description="[DEPRECATED: use 'samples' with first_frame conditions] " + "List of image paths to use for validation. " "One image path must be provided for each validation prompt", ) reference_videos: list[str] | None = Field( default=None, - description="List of reference video paths to use for validation. " + description="[DEPRECATED: use 'samples' with reference conditions] " + "List of reference video paths to use for validation. " "One video path must be provided for each validation prompt", ) reference_downscale_factor: int = Field( default=1, - description="Downscale factor for reference videos in IC-LoRA validation. " + description="[DEPRECATED: use downscale_factor on ReferenceCondition] " + "Downscale factor for reference videos in IC-LoRA validation. " "When > 1, reference videos are processed at 1/n resolution (e.g., 2 means half resolution). " "Must match the factor used during dataset preprocessing.", ge=1, @@ -301,6 +546,13 @@ class ValidationConfig(ConfigBaseModel): "in validation even when not training the audio branch.", ) + generate_video: bool = Field( + default=True, + description="Whether to generate video in validation samples. " + "Set to False for audio-only or v2a validation to save VRAM by skipping video VAE decoder loading. " + "When False, validation will only generate audio (requires generate_audio=True).", + ) + skip_initial_validation: bool = Field( default=False, description="Skip validation video sampling at step 0 (beginning of training)", @@ -308,7 +560,8 @@ class ValidationConfig(ConfigBaseModel): include_reference_in_output: bool = Field( default=False, - description="For video-to-video training: concatenate the original reference video side-by-side " + description="[DEPRECATED: use include_in_output on ReferenceCondition] " + "For video-to-video training: concatenate the original reference video side-by-side " "with the generated output. The reference comes from the input video, not from the model's output.", ) @@ -346,13 +599,33 @@ class ValidationConfig(ConfigBaseModel): return v + @model_validator(mode="after") + def convert_legacy_format(self) -> "ValidationConfig": + """Convert deprecated prompts/images/reference_videos to the new samples format.""" + if self.prompts and not self.samples: + samples = [] + for i, prompt in enumerate(self.prompts): + conditions: list[ValidationCondition] = [] + if self.images and i < len(self.images): + conditions.append(FirstFrameConditionConfig(image_or_video=self.images[i])) + if self.reference_videos and i < len(self.reference_videos): + conditions.append( + ReferenceConditionConfig( + video=self.reference_videos[i], + downscale_factor=self.reference_downscale_factor, + include_in_output=self.include_reference_in_output, + ) + ) + samples.append(ValidationSample(prompt=prompt, conditions=conditions)) + self.samples = samples + return self + @model_validator(mode="after") def validate_scaled_reference_dimensions(self) -> "ValidationConfig": """Validate that scaled reference dimensions are valid when reference_downscale_factor > 1.""" if self.reference_downscale_factor > 1: width, height, _frames = self.video_dims - # Validate that downscale factor evenly divides the target dimensions if width % self.reference_downscale_factor != 0: raise ValueError( f"Width {width} is not evenly divisible by reference_downscale_factor " @@ -367,7 +640,6 @@ class ValidationConfig(ConfigBaseModel): scaled_width = width // self.reference_downscale_factor scaled_height = height // self.reference_downscale_factor - # Validate scaled dimensions are divisible by 32 if scaled_width % 32 != 0: raise ValueError( f"Scaled reference width {scaled_width} (from {width} / {self.reference_downscale_factor}) " @@ -381,6 +653,16 @@ class ValidationConfig(ConfigBaseModel): return self + @model_validator(mode="after") + def validate_output_modality_requirements(self) -> "ValidationConfig": + """Validate output modality settings when validation is configured.""" + has_validation = bool(self.prompts) or bool(self.samples) + if has_validation and not self.generate_video and not self.generate_audio: + raise ValueError( + "At least one of generate_video or generate_audio must be True when validation is configured." + ) + return self + class CheckpointsConfig(ConfigBaseModel): """Configuration for model checkpointing during training""" @@ -514,19 +796,31 @@ class LtxTrainerConfig(ConfigBaseModel): """Expand user home directory in output path.""" return str(Path(v).expanduser().resolve()) + def _validate_data_dirs_exist(self) -> None: + """Verify that every directory declared by the training strategy exists under the data root.""" + data_root = Path(self.data.preprocessed_data_root) + for dir_name in self.training_strategy.get_data_sources(): + dir_path = data_root / dir_name + if not dir_path.is_dir(): + raise ValueError( + f"Required data directory '{dir_name}' does not exist under preprocessed_data_root: {dir_path}" + ) + @model_validator(mode="after") def validate_strategy_compatibility(self) -> "LtxTrainerConfig": """Validate that training strategy and other configurations are compatible.""" + self._validate_data_dirs_exist() # Check that reference videos are provided when using video_to_video strategy - if ( - self.training_strategy.name == "video_to_video" - and self.validation.interval - and not self.validation.reference_videos - ): - raise ValueError( - "reference_videos must be provided in validation config when using video_to_video strategy" + if self.training_strategy.name == "video_to_video" and self.validation.interval: + has_reference = bool(self.validation.reference_videos) or any( + cond.type == "reference" for sample in self.validation.samples for cond in sample.conditions ) + if not has_reference: + raise ValueError( + "reference_videos or samples with reference conditions must be provided " + "in validation config when using video_to_video strategy" + ) # Check that LoRA config is provided when training mode is lora if self.model.training_mode == "lora" and self.lora is None: diff --git a/packages/ltx-trainer/src/ltx_trainer/progress.py b/packages/ltx-trainer/src/ltx_trainer/progress.py index 78fde56..114b2db 100644 --- a/packages/ltx-trainer/src/ltx_trainer/progress.py +++ b/packages/ltx-trainer/src/ltx_trainer/progress.py @@ -54,60 +54,6 @@ class SamplingContext: self._progress.update(self._task, visible=False) -class StandaloneSamplingProgress: - """Standalone progress display for inference scripts. - Unlike SamplingContext (which integrates with TrainingProgress), this class - manages its own Rich Progress instance for use in standalone inference scripts. - Usage: - with StandaloneSamplingProgress(num_steps=30) as ctx: - for step in range(30): - # ... denoising step ... - ctx.advance_step() - """ - - def __init__(self, num_steps: int, description: str = "Generating"): - """Initialize standalone sampling progress. - Args: - num_steps: Total number of denoising steps - description: Description to show in progress bar - """ - self._num_steps = num_steps - self._description = description - self._progress: Progress | None = None - self._task: TaskID | None = None - - def __enter__(self) -> "StandaloneSamplingProgress": - """Start the progress display.""" - self._progress = Progress( - TextColumn("[progress.description]{task.description}"), - BarColumn(bar_width=40, style="blue"), - TextColumn("{task.fields[info]}", style="cyan"), - TimeElapsedColumn(), - TextColumn("ETA:"), - TimeRemainingColumn(compact=True), - ) - self._progress.__enter__() - self._task = self._progress.add_task( - self._description, - total=self._num_steps, - info=f"step 0/{self._num_steps}", - ) - return self - - def __exit__(self, *args) -> None: - """Stop the progress display.""" - if self._progress is not None: - self._progress.__exit__(*args) - - def advance_step(self) -> None: - """Advance the denoising step by one.""" - if self._progress is None or self._task is None: - return - self._progress.advance(self._task) - completed = int(self._progress.tasks[self._task].completed) - self._progress.update(self._task, info=f"step {completed}/{self._num_steps}") - - class TrainingProgress: """Manages Rich progress display for training and validation. This class encapsulates all progress bar logic, providing a clean interface diff --git a/packages/ltx-trainer/src/ltx_trainer/trainer.py b/packages/ltx-trainer/src/ltx_trainer/trainer.py index 92cfec4..8eb3fbc 100644 --- a/packages/ltx-trainer/src/ltx_trainer/trainer.py +++ b/packages/ltx-trainer/src/ltx_trainer/trainer.py @@ -12,8 +12,8 @@ from typing import Any, Callable import torch import wandb import yaml -from accelerate import Accelerator, DistributedDataParallelKwargs, DistributedType -from accelerate.utils import gather_object, set_seed +from accelerate import Accelerator, DistributedType +from accelerate.utils import DistributedDataParallelKwargs, gather_object, set_seed from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict from peft.tuners.tuners_utils import BaseTunerLayer from peft.utils import ModulesToSaveWrapper @@ -30,26 +30,22 @@ from torch.optim.lr_scheduler import ( StepLR, ) from torch.utils.data import DataLoader -from torchvision.transforms import functional as F # noqa: N812 from ltx_core.text_encoders.gemma import convert_to_additive_mask from ltx_trainer import logger from ltx_trainer.config import LtxTrainerConfig from ltx_trainer.config_display import print_config from ltx_trainer.datasets import PrecomputedDataset -from ltx_trainer.gpu_utils import free_gpu_memory, free_gpu_memory_context, get_gpu_memory_gb +from ltx_trainer.gpu_utils import free_gpu_memory, get_gpu_memory_gb from ltx_trainer.hf_hub_utils import push_to_hub -from ltx_trainer.model_loader import load_embeddings_processor, load_text_encoder -from ltx_trainer.model_loader import load_model as load_ltx_model +from ltx_trainer.model_loader import load_embeddings_processor, load_transformer from ltx_trainer.progress import TrainingProgress from ltx_trainer.quantization import quantize_model from ltx_trainer.sigma_tracker import SigmaBucketTracker from ltx_trainer.timestep_samplers import SAMPLERS from ltx_trainer.training_state import ConfigFingerprint, RngStates, TrainingState from ltx_trainer.training_strategies import get_training_strategy -from ltx_trainer.utils import open_image_as_srgb, save_image -from ltx_trainer.validation_sampler import CachedPromptEmbeddings, GenerationConfig, ValidationSampler -from ltx_trainer.video_utils import read_video, save_video +from ltx_trainer.validation_runner import ValidationRunner # Disable irrelevant warnings from transformers os.environ["TOKENIZERS_PARALLELISM"] = "true" @@ -66,7 +62,7 @@ if not IS_MAIN_PROCESS: disable_progress_bar() -StepCallback = Callable[[int, int, list[Path] | None], None] # (step, total, sampled paths or None) -> None +StepCallback = Callable[[int, int, list[Path]], None] # (step, total, list[sampled_video_path]) -> None MEMORY_CHECK_INTERVAL = 200 @@ -96,7 +92,16 @@ class LtxvTrainer: if IS_MAIN_PROCESS: print_config(trainer_config) self._training_strategy = get_training_strategy(self._config.training_strategy) - self._cached_validation_embeddings = self._load_text_encoder_and_cache_embeddings() + + # ValidationRunner loads its own models (text encoder, VAE encoder/decoder, etc.), + # caches prompt embeddings and conditioning media, then unloads encoders. + self._validation_runner = ValidationRunner( + config=self._config.validation, + model_path=self._config.model.model_path, + text_encoder_path=self._config.model.text_encoder_path, + load_text_encoder_in_8bit=self._config.acceleration.load_text_encoder_in_8bit, + ) + self._load_models() self._setup_accelerator() self._collect_trainable_params() @@ -108,8 +113,8 @@ class LtxvTrainer: self._checkpoint_paths: list[Path] = [] self._training_state_paths: list[Path] = [] self._training_state_size_warned = False - self._wandb_run = None self._sigma_tracker = SigmaBucketTracker() + self._wandb_run = None def train( # noqa: PLR0912, PLR0915 self, @@ -190,7 +195,7 @@ class LtxvTrainer: with progress: if cfg.validation.interval and not cfg.validation.skip_initial_validation: with self._offloaded_optimizer_state(): - sampled_videos_paths = self._run_distributed_validation(progress) + sampled_videos_paths = self._run_validation(progress) self._accelerator.wait_for_everyone() @@ -223,7 +228,7 @@ class LtxvTrainer: if self._lr_scheduler is not None: self._lr_scheduler.step() - # Run validation if needed + # Run validation if needed (handles DDP/FSDP work distribution internally) if ( cfg.validation.interval and self._global_step > 0 @@ -231,7 +236,7 @@ class LtxvTrainer: and is_optimization_step ): with self._offloaded_optimizer_state(): - sampled_videos_paths = self._run_distributed_validation(progress) + sampled_videos_paths = self._run_validation(progress) # Save checkpoint if needed if ( @@ -376,111 +381,39 @@ class LtxvTrainer: perturbations=None, ) - # Use strategy to compute loss + # Use strategy to compute loss (returns per-element [B,] for sigma-bucket tracking) loss = self._training_strategy.compute_loss(video_pred, audio_pred, model_inputs) - sigma = model_inputs.video.sigma.detach() if model_inputs.video.enabled else model_inputs.audio.sigma.detach() + + # Sigma comes from whichever modality is generated (video preferred, else audio). + if model_inputs.video is not None and model_inputs.video.enabled: + sigma = model_inputs.video.sigma.detach() + else: + sigma = model_inputs.audio.sigma.detach() return TrainingStepOutput(loss=loss, sigma=sigma) - @free_gpu_memory_context(after=True) - def _load_text_encoder_and_cache_embeddings(self) -> list[CachedPromptEmbeddings] | None: - """Load text encoder + embeddings processor, compute and cache validation embeddings.""" + def _load_models(self) -> None: + """Load the transformer and embeddings processor for training.""" + logger.debug("Loading transformer...") + self._transformer = load_transformer( + checkpoint_path=self._config.model.model_path, + device="cpu", + dtype=torch.bfloat16, + ) - # This method: - # 1. Loads the pure Gemma text encoder on GPU - # 2. Loads the embeddings processor (feature extractor + connectors) - # 3. If validation prompts are configured, computes and caches their embeddings - # 4. Unloads the Gemma model entirely, keeps the embeddings processor for training - - # Load text encoder (pure Gemma LLM) on GPU — LOCAL_RANK before Accelerator exists + # DDP-safe: LOCAL_RANK is set by accelerate before trainer init. Loading on bare + # "cuda" would resolve to cuda:0 on every rank and crash with a device mismatch. local_rank = int(os.environ.get("LOCAL_RANK", "0")) init_device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu") - logger.debug("Loading text encoder...") - text_encoder = load_text_encoder( - gemma_model_path=self._config.model.text_encoder_path, - device=init_device, - dtype=torch.bfloat16, - load_in_8bit=self._config.acceleration.load_text_encoder_in_8bit, - ) - - # Load embeddings processor (feature extractor + connectors) logger.debug("Loading embeddings processor...") self._embeddings_processor = load_embeddings_processor( checkpoint_path=self._config.model.model_path, device=init_device, dtype=torch.bfloat16, ) - - # Cache validation embeddings if prompts are configured - cached_embeddings = None - if self._config.validation.prompts: - logger.info(f"Pre-computing embeddings for {len(self._config.validation.prompts)} validation prompts...") - cached_embeddings = [] - with torch.inference_mode(): - for prompt in self._config.validation.prompts: - pos_hs, pos_mask = text_encoder.encode(prompt) - pos_out = self._embeddings_processor.process_hidden_states(pos_hs, pos_mask) - - neg_hs, neg_mask = text_encoder.encode(self._config.validation.negative_prompt) - neg_out = self._embeddings_processor.process_hidden_states(neg_hs, neg_mask) - - cached_embeddings.append( - CachedPromptEmbeddings( - video_context_positive=pos_out.video_encoding.cpu(), - audio_context_positive=pos_out.audio_encoding.cpu(), - video_context_negative=neg_out.video_encoding.cpu(), - audio_context_negative=( - neg_out.audio_encoding.cpu() if neg_out.audio_encoding is not None else None - ), - ) - ) - - # Unload Gemma model and feature extractor, keep only connectors for training - del text_encoder self._embeddings_processor.feature_extractor = None - logger.debug("Validation prompt embeddings cached. Gemma model unloaded") - return cached_embeddings - - def _load_models(self) -> None: - """Load the LTX-2 model components.""" - # Load audio components if: - # 1. Training strategy requires audio (training the audio branch), OR - # 2. Validation is configured to generate audio (even if not training audio) - load_audio = self._training_strategy.requires_audio or self._config.validation.generate_audio - - # Check if we need VAE encoder (for image or reference video conditioning) - need_vae_encoder = ( - self._config.validation.images is not None or self._config.validation.reference_videos is not None - ) - - # Load all model components (except text encoder - already handled) - components = load_ltx_model( - checkpoint_path=self._config.model.model_path, - device="cpu", - dtype=torch.bfloat16, - with_video_vae_encoder=need_vae_encoder, # Needed for image conditioning - with_video_vae_decoder=True, # Needed for validation sampling - with_audio_vae_decoder=load_audio, - with_vocoder=load_audio, - with_text_encoder=False, # Text encoder handled separately - ) - - # Extract components - self._transformer = components.transformer - self._vae_decoder = components.video_vae_decoder.to(dtype=torch.bfloat16) - self._vae_encoder = components.video_vae_encoder - if self._vae_encoder is not None: - self._vae_encoder = self._vae_encoder.to(dtype=torch.bfloat16) - self._scheduler = components.scheduler - self._audio_vae = components.audio_vae_decoder - self._vocoder = components.vocoder - # Note: self._embeddings_processor was set in _load_text_encoder_and_cache_embeddings - - # Determine initial dtype based on training mode. - # Note: For FSDP + LoRA, we'll cast to FP32 later in _prepare_models_for_training() - # after the accelerator is set up, and we can detect FSDP. transformer_dtype = torch.bfloat16 if self._config.model.training_mode == "lora" else torch.float32 self._transformer = self._transformer.to(dtype=transformer_dtype) @@ -494,16 +427,7 @@ class LtxvTrainer: precision=self._config.acceleration.quantization, ) - # Freeze all models. We later unfreeze the transformer based on training mode. - # Note: embedding_connectors are already frozen (they come from the frozen text encoder) - self._vae_decoder.requires_grad_(False) - if self._vae_encoder is not None: - self._vae_encoder.requires_grad_(False) self._transformer.requires_grad_(False) - if self._audio_vae is not None: - self._audio_vae.requires_grad_(False) - if self._vocoder is not None: - self._vocoder.requires_grad_(False) def _collect_trainable_params(self) -> None: """Collect trainable parameters based on training mode.""" @@ -692,13 +616,6 @@ class LtxvTrainer: transformer.set_gradient_checkpointing(self._config.optimization.enable_gradient_checkpointing) - # Keep frozen models on CPU for memory efficiency - self._vae_decoder = self._vae_decoder.to("cpu") - if self._vae_encoder is not None: - self._vae_encoder = self._vae_encoder.to("cpu") - - # Embedding connectors are already on GPU from _load_text_encoder_and_cache_embeddings - # noinspection PyTypeChecker self._transformer = self._accelerator.prepare(self._transformer) @@ -740,7 +657,7 @@ class LtxvTrainer: """Initialize the training data loader using the strategy's data sources.""" if self._dataset is None: # Get data sources from the training strategy - data_sources = self._training_strategy.get_data_sources() + data_sources = self._config.training_strategy.get_data_sources() self._dataset = PrecomputedDataset(self._config.data.preprocessed_data_root, data_sources=data_sources) logger.debug(f"Loaded dataset with {len(self._dataset):,} samples from sources: {list(data_sources)}") @@ -785,41 +702,6 @@ class LtxvTrainer: # noinspection PyTypeChecker self._optimizer, self._lr_scheduler = self._accelerator.prepare(optimizer, lr_scheduler) - @contextlib.contextmanager - def _offloaded_optimizer_state(self) -> Iterator[None]: - """Context manager that offloads optimizer state to CPU during validation. - Opt-in via `acceleration.offload_optimizer_during_validation`. Frees VRAM for - validation video generation when optimizer state is large (e.g. full fine-tune - AdamW, high-rank LoRA). No-op for FSDP (sharded state -- manual `.cpu()` breaks - metadata). - """ - enabled = ( - self._config.acceleration.offload_optimizer_during_validation - and self._accelerator.distributed_type != DistributedType.FSDP - ) - - # Track exactly which tensors we move so we don't promote ones that were - # intentionally on CPU (e.g. AdamW's `step` scalar on recent PyTorch). - offloaded: list[tuple[dict, str]] = [] - if enabled: - offloaded_bytes = 0 - for state in self._optimizer.state.values(): - for k, v in state.items(): - if isinstance(v, torch.Tensor) and v.is_cuda: - offloaded.append((state, k)) - offloaded_bytes += v.nbytes - if offloaded: - logger.info(f"Offloading optimizer state to CPU ({offloaded_bytes / 1e9:.1f} GB)") - for state, k in offloaded: - state[k] = state[k].cpu() - - try: - yield - finally: - device = self._accelerator.device - for state, k in offloaded: - state[k] = state[k].to(device) - def _create_scheduler(self, optimizer: torch.optim.Optimizer) -> LRScheduler | None: """Create learning rate scheduler based on config.""" scheduler_type = self._config.optimization.scheduler_type @@ -920,164 +802,104 @@ class LtxvTrainer: "Monitor training stability and consider disabling quantization if issues arise." ) - def _run_distributed_validation(self, progress: TrainingProgress) -> list[Path]: - """Run validation across all ranks and log gathered results on rank 0. - Each rank generates only its assigned subset of prompts (see `_sample_videos`), - so all GPUs stay busy and no rank idles long enough to trigger NCCL timeouts. - Paths are gathered across ranks so rank 0 has the full list for W&B logging. + @contextlib.contextmanager + def _offloaded_optimizer_state(self) -> Iterator[None]: + """Context manager that offloads optimizer state to CPU during validation. + Opt-in via `acceleration.offload_optimizer_during_validation`. Frees VRAM for + validation video generation when optimizer state is large (e.g. full fine-tune + AdamW, high-rank LoRA). No-op for FSDP (sharded state -- manual `.cpu()` breaks + metadata). + """ + enabled = ( + self._config.acceleration.offload_optimizer_during_validation + and self._accelerator.distributed_type != DistributedType.FSDP + ) + + # Track exactly which tensors we move so we don't promote ones that were + # intentionally on CPU (e.g. AdamW's `step` scalar on recent PyTorch). + offloaded: list[tuple[dict, str]] = [] + if enabled: + offloaded_bytes = 0 + for state in self._optimizer.state.values(): + for k, v in state.items(): + if isinstance(v, torch.Tensor) and v.is_cuda: + offloaded.append((state, k)) + offloaded_bytes += v.nbytes + if offloaded: + logger.info(f"Offloading optimizer state to CPU ({offloaded_bytes / 1e9:.1f} GB)") + for state, k in offloaded: + state[k] = state[k].cpu() + + try: + yield + finally: + device = self._accelerator.device + for state, k in offloaded: + state[k] = state[k].to(device) + + def _run_validation(self, progress: TrainingProgress) -> list[Path]: + """Run distributed validation by delegating to the ValidationRunner. + Each rank generates its assigned subset of validation samples (round-robin by + `process_index`/`num_processes`), so all GPUs stay busy and no rank idles long + enough to trigger NCCL timeouts. Paths are gathered across ranks so rank 0 has + the full list for W&B logging. + Under FSDP with multiple processes, ranks pad with extra generate passes + (same sample, no disk write) so every rank runs the same number of forwards -- + avoids collective mismatch. Note: Multi-node training requires a shared filesystem so rank 0 can read videos written by other ranks. """ - sampled = self._sample_videos(progress) + self._optimizer.zero_grad(set_to_none=True) + free_gpu_memory() - if self._accelerator.num_processes > 1: - # gather_object returns a flat list from all ranks + num_samples = len(self._config.validation.samples) + if num_samples == 0: + return [] + + rank = self._accelerator.process_index + world_size = self._accelerator.num_processes + + rank_indices = list(range(rank, num_samples, world_size)) + work_items: list[tuple[int, bool]] = [(i, True) for i in rank_indices] + if self._accelerator.distributed_type == DistributedType.FSDP and world_size > 1: + # FSDP forwards run collective ops; pad short ranks with no-save duplicates so + # every rank executes the same number of forwards. A rank with empty + # rank_indices (world_size > num_samples) still pads with sample 0 to stay in + # sync with the others. + max_per_rank = math.ceil(num_samples / world_size) + pad_seed = rank_indices[-1] if rank_indices else 0 + work_items += [(pad_seed, False)] * (max_per_rank - len(work_items)) + + # W&B logging is handled by the trainer (after gathering across ranks), + # so we always pass wandb_run=None to the runner. + sampled = self._validation_runner.run( + transformer=self._transformer, + step=self._global_step, + output_dir=Path(self._config.output_dir), + device=self._accelerator.device, + progress=progress, + wandb_run=None, + work_items=work_items, + ) + + if world_size > 1: sampled = sorted(gather_object(sampled), key=lambda x: x[0]) paths = [p for _, p in sampled] - if self._accelerator.is_main_process and paths: - self._log_validation_samples(paths, self._config.validation.prompts) + if ( + self._accelerator.is_main_process + and paths + and self._config.wandb.log_validation_videos + and self._wandb_run is not None + ): + self._validation_runner.log_to_wandb(self._wandb_run, paths, self._global_step) # Non-main ranks must not reach checkpoint collectives while main is still logging to W&B. self._accelerator.wait_for_everyone() return paths - # Note: Use @torch.no_grad() instead of @torch.inference_mode() to avoid FSDP inplace update errors after validation - @torch.no_grad() - @free_gpu_memory_context(after=True) - def _sample_videos(self, progress: TrainingProgress) -> list[tuple[int, Path]]: - """Run validation by generating videos from this rank's share of the validation prompts. - Prompts are split round-robin across ranks via `process_index` / `num_processes`, - which collapses to "all prompts" when running on a single GPU. Returns - (prompt_idx, path) tuples so the caller can reconstruct global order without - relying on filename conventions. - Under FSDP with multiple processes, ranks pad with extra generate passes (same prompt, - no disk write) so every rank runs the same number of forwards — avoids collective mismatch. - """ - use_images = self._config.validation.images is not None - use_reference_videos = self._config.validation.reference_videos is not None - generate_audio = self._config.validation.generate_audio - inference_steps = self._config.validation.inference_steps - - # Zero gradients and free GPU memory to reclaim memory before validation sampling - self._optimizer.zero_grad(set_to_none=True) - free_gpu_memory() - - prompts = self._config.validation.prompts - rank = self._accelerator.process_index - world_size = self._accelerator.num_processes - rank_indices = list(range(rank, len(prompts), world_size)) - - # FSDP: every rank must run the same number of forwards; pad with duplicate generates (no save). - work: list[tuple[int, bool]] = [(i, True) for i in rank_indices] - if self._accelerator.distributed_type == DistributedType.FSDP and world_size > 1: - max_per_rank = math.ceil(len(prompts) / world_size) - pad_seed = rank_indices[-1] if rank_indices else 0 - work += [(pad_seed, False)] * (max_per_rank - len(work)) - - sampling_ctx = progress.start_sampling( - num_prompts=len(work), - num_steps=inference_steps, - ) - - # Create a validation sampler with loaded models and progress tracking - sampler = ValidationSampler( - transformer=self._transformer, - vae_decoder=self._vae_decoder, - vae_encoder=self._vae_encoder, - text_encoder=None, - audio_decoder=self._audio_vae if generate_audio else None, - vocoder=self._vocoder if generate_audio else None, - sampling_context=sampling_ctx, - ) - - output_dir = Path(self._config.output_dir) / "samples" - output_dir.mkdir(exist_ok=True, parents=True) - - results: list[tuple[int, Path]] = [] - width, height, num_frames = self._config.validation.video_dims - - for local_i, (prompt_idx, save_output) in enumerate(work): - prompt = prompts[prompt_idx] - sampling_ctx.start_video(local_i) - - # Load conditioning image if provided - condition_image = None - if use_images: - image_path = self._config.validation.images[prompt_idx] - image = open_image_as_srgb(image_path) - # Convert PIL image to tensor [C, H, W] in [0, 1] - condition_image = F.to_tensor(image) - - # Load reference video if provided (for IC-LoRA) - reference_video = None - if use_reference_videos: - ref_video_path = self._config.validation.reference_videos[prompt_idx] - # read_video returns [F, C, H, W] in [0, 1] - reference_video, _ = read_video(ref_video_path, max_frames=num_frames) - - # Get cached embeddings for this prompt if available - cached_embeddings = ( - self._cached_validation_embeddings[prompt_idx] - if self._cached_validation_embeddings is not None - else None - ) - - # Create generation config - gen_config = GenerationConfig( - prompt=prompt, - negative_prompt=self._config.validation.negative_prompt, - height=height, - width=width, - num_frames=num_frames, - frame_rate=self._config.validation.frame_rate, - num_inference_steps=inference_steps, - guidance_scale=self._config.validation.guidance_scale, - seed=self._config.validation.seed, - condition_image=condition_image, - reference_video=reference_video, - reference_downscale_factor=self._config.validation.reference_downscale_factor, - generate_audio=generate_audio, - include_reference_in_output=self._config.validation.include_reference_in_output, - cached_embeddings=cached_embeddings, - stg_scale=self._config.validation.stg_scale, - stg_blocks=self._config.validation.stg_blocks, - stg_mode=self._config.validation.stg_mode, - ) - - # Generate sample - video, audio = sampler.generate( - config=gen_config, - device=self._accelerator.device, - ) - - if not save_output: - continue - - # Save output (image for single frame, video otherwise) - ext = "png" if num_frames == 1 else "mp4" - output_path = output_dir / f"step_{self._global_step:06d}_{prompt_idx + 1:02d}.{ext}" - if num_frames == 1: - save_image(video, output_path) - else: - save_video( - video_tensor=video, - output_path=output_path, - fps=self._config.validation.frame_rate, - audio=audio, - audio_sample_rate=self._vocoder.output_sampling_rate if audio is not None else None, - ) - results.append((prompt_idx, output_path)) - - # Clean up progress tasks - sampling_ctx.cleanup() - - rel_outputs_path = output_dir.relative_to(self._config.output_dir) - logger.info(f"🎥 Validation samples for step {self._global_step} saved in {rel_outputs_path}") - return results - @staticmethod def _log_training_stats(stats: TrainingStats) -> None: """Log training statistics.""" @@ -1166,8 +988,8 @@ class LtxvTrainer: def _save_training_state(self, save_dir: Path) -> None: """Save training state alongside checkpoint for resume. Respects checkpoints.save_training_state config: - - "full": optimizer + scheduler + RNG + step + wandb_run_id - - "minimal": scheduler + RNG + step + wandb_run_id + - "full": optimizer + scheduler + RNG + step + - "minimal": scheduler + RNG + step only - "off": skip entirely """ if not IS_MAIN_PROCESS: @@ -1270,7 +1092,7 @@ class LtxvTrainer: logger.info(f"💾 Training configuration saved to: {config_path.relative_to(self._config.output_dir)}") def _init_wandb(self, resume_run_id: str | None = None) -> None: - """Initialize Weights & Biases run.""" + """Initialize Weights & Biases run, resuming an existing run if its id is provided.""" if not self._config.wandb.enabled or not IS_MAIN_PROCESS: self._wandb_run = None return @@ -1285,7 +1107,7 @@ class LtxvTrainer: } if resume_run_id is not None: init_kwargs["id"] = resume_run_id - init_kwargs["resume"] = "allow" + init_kwargs["resume"] = "must" run = wandb.init(**init_kwargs) self._wandb_run = run @@ -1293,22 +1115,3 @@ class LtxvTrainer: """Log metrics to Weights & Biases.""" if self._wandb_run is not None: self._wandb_run.log(metrics) - - def _log_validation_samples(self, sample_paths: list[Path], prompts: list[str]) -> None: - """Log validation samples (videos or images) to Weights & Biases.""" - if not self._config.wandb.log_validation_videos or self._wandb_run is None: - return - - # Determine if outputs are images or videos based on file extension - is_image = sample_paths and sample_paths[0].suffix.lower() in (".png", ".jpg", ".jpeg", ".heic", ".webp") - - if is_image: - samples = [ - wandb.Image(str(path), caption=prompt) for path, prompt in zip(sample_paths, prompts, strict=True) - ] - else: - samples = [ - wandb.Video(str(path), caption=prompt, format=path.suffix.lower().lstrip(".")) - for path, prompt in zip(sample_paths, prompts, strict=True) - ] - self._wandb_run.log({"validation_samples": samples}, step=self._global_step) diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/__init__.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/__init__.py index d62408f..71857c7 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/__init__.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/__init__.py @@ -1,10 +1,13 @@ """Training strategies for different conditioning modes. This package implements the Strategy Pattern to handle different training modes: -- Text-to-video training (standard generation, optionally with audio) -- Video-to-video training (IC-LoRA mode with reference videos) +- Text-to-video training (standard generation, optionally with audio) [DEPRECATED] +- Video-to-video training (IC-LoRA mode with reference videos) [DEPRECATED] +- Flexible training (unified conditioning framework supporting all scenarios) [RECOMMENDED] Each strategy encapsulates the specific logic for preparing model inputs and computing loss. """ +import warnings + from ltx_trainer import logger from ltx_trainer.training_strategies.base_strategy import ( DEFAULT_FPS, @@ -13,15 +16,18 @@ from ltx_trainer.training_strategies.base_strategy import ( TrainingStrategy, TrainingStrategyConfigBase, ) +from ltx_trainer.training_strategies.flexible import FlexibleStrategy, FlexibleStrategyConfig from ltx_trainer.training_strategies.text_to_video import TextToVideoConfig, TextToVideoStrategy from ltx_trainer.training_strategies.video_to_video import VideoToVideoConfig, VideoToVideoStrategy # Type alias for all strategy config types -TrainingStrategyConfig = TextToVideoConfig | VideoToVideoConfig +TrainingStrategyConfig = TextToVideoConfig | VideoToVideoConfig | FlexibleStrategyConfig __all__ = [ "DEFAULT_FPS", "VIDEO_SCALE_FACTORS", + "FlexibleStrategy", + "FlexibleStrategyConfig", "ModelInputs", "TextToVideoConfig", "TextToVideoStrategy", @@ -43,16 +49,42 @@ def get_training_strategy(config: TrainingStrategyConfig) -> TrainingStrategy: The appropriate training strategy instance Raises: ValueError: If strategy name is not supported + Note: + The `text_to_video` and `video_to_video` strategies are deprecated. + Please use the `flexible` strategy instead. """ match config: case TextToVideoConfig(): + warnings.warn( + "The 'text_to_video' training strategy is deprecated and will be removed " + "in a future version. Please migrate to the 'flexible' strategy. " + "See the migration guide in the documentation.", + DeprecationWarning, + stacklevel=2, + ) strategy = TextToVideoStrategy(config) case VideoToVideoConfig(): + warnings.warn( + "The 'video_to_video' training strategy is deprecated and will be removed " + "in a future version. Please migrate to the 'flexible' strategy. " + "See the migration guide in the documentation.", + DeprecationWarning, + stacklevel=2, + ) strategy = VideoToVideoStrategy(config) + case FlexibleStrategyConfig(): + strategy = FlexibleStrategy(config) case _: raise ValueError(f"Unknown training strategy config type: {type(config).__name__}") - audio_mode = "(audio enabled 🔈)" if getattr(config, "with_audio", False) else "(audio disabled 🔇)" + # Determine audio mode for logging + if hasattr(config, "with_audio"): + audio_mode = "(audio enabled 🔈)" if config.with_audio else "(audio disabled 🔇)" + elif hasattr(config, "audio") and config.audio is not None: + audio_mode = "(audio enabled 🔈)" + else: + audio_mode = "(audio disabled 🔇)" + logger.debug(f"🎯 Using {strategy.__class__.__name__} training strategy {audio_mode}") return strategy diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py index c0ad765..d74083f 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py @@ -34,29 +34,36 @@ class TrainingStrategyConfigBase(BaseModel): model_config = ConfigDict(extra="forbid") - name: Literal["text_to_video", "video_to_video"] = Field( + name: Literal["text_to_video", "video_to_video", "flexible"] = Field( description="Unique name identifying the training strategy type" ) + @abstractmethod + def get_data_sources(self) -> dict[str, str]: + """Get the required data sources for this strategy. + Returns a mapping of directory name (relative to ``preprocessed_data_root``) + to the dataset output key under which that directory's contents are exposed. + This is the single source of truth for which directories the strategy needs: + it drives both dataset wiring (in the trainer) and existence validation + (in ``LtxTrainerConfig``). + """ + @dataclass class ModelInputs: """Container for model inputs using the Modality-based interface.""" - video: Modality + video: Modality | None audio: Modality | None # Training targets (for loss computation) - video_targets: Tensor + video_targets: Tensor | None audio_targets: Tensor | None - # Masks for loss computation - video_loss_mask: Tensor # Boolean mask: True = compute loss for this token + # Masks for loss computation (True = compute loss for this token) + video_loss_mask: Tensor | None audio_loss_mask: Tensor | None - # Metadata needed for loss computation in some strategies - ref_seq_len: int | None = None # For IC-LoRA: length of reference sequence - class TrainingStrategy(ABC): """Abstract base class for training strategies. @@ -73,24 +80,6 @@ class TrainingStrategy(ABC): self._video_patchifier = VideoLatentPatchifier(patch_size=1) self._audio_patchifier = AudioPatchifier(patch_size=1) - @property - def requires_audio(self) -> bool: - """Whether this training strategy requires audio components. - Override this property in subclasses that support audio training. - The trainer uses this to determine whether to load audio VAE and vocoder. - Returns: - True if audio components should be loaded, False otherwise. - """ - return False - - @abstractmethod - def get_data_sources(self) -> list[str] | dict[str, str]: - """Get the required data sources for this training strategy. - Returns: - Either a list of data directory names (where output keys match directory names) - or a dictionary mapping data directory names to custom output keys for the dataset - """ - @abstractmethod def prepare_training_inputs( self, @@ -145,7 +134,6 @@ class TrainingStrategy(ABC): batch_size: int, fps: float, device: torch.device, - dtype: torch.dtype, ) -> Tensor: """Generate video position embeddings using ltx_core's native implementation. Args: @@ -155,9 +143,8 @@ class TrainingStrategy(ABC): batch_size: Batch size fps: Frames per second device: Target device - dtype: Target dtype Returns: - Position tensor of shape [B, 3, seq_len, 2] + Position tensor of shape [B, 3, seq_len, 2] (float32) """ latent_coords = self._video_patchifier.get_patch_grid_bounds( output_shape=VideoLatentShape( @@ -175,7 +162,7 @@ class TrainingStrategy(ABC): latent_coords=latent_coords, scale_factors=VIDEO_SCALE_FACTORS, causal_fix=True, - ).to(dtype) + ).float() # Scale temporal dimension by 1/fps to get time in seconds pixel_coords[:, 0, ...] = pixel_coords[:, 0, ...] / fps @@ -187,14 +174,12 @@ class TrainingStrategy(ABC): num_time_steps: int, batch_size: int, device: torch.device, - dtype: torch.dtype, ) -> Tensor: """Generate audio position embeddings using ltx_core's native implementation. Args: num_time_steps: Number of audio time steps (T, not T*mel_bins) batch_size: Batch size device: Target device - dtype: Target dtype Returns: Position tensor of shape [B, 1, num_time_steps, 2] Note: @@ -204,7 +189,7 @@ class TrainingStrategy(ABC): """ mel_bins = 16 - latent_coords = self._audio_patchifier.get_patch_grid_bounds( + return self._audio_patchifier.get_patch_grid_bounds( output_shape=AudioLatentShape( frames=num_time_steps, mel_bins=mel_bins, @@ -214,8 +199,6 @@ class TrainingStrategy(ABC): device=device, ) - return latent_coords.to(dtype) - @staticmethod def _create_per_token_timesteps(conditioning_mask: Tensor, sampled_sigma: Tensor) -> Tensor: """Create per-token timesteps based on conditioning mask. diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/flexible.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/flexible.py new file mode 100644 index 0000000..82d0d9a --- /dev/null +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/flexible.py @@ -0,0 +1,718 @@ +"""Flexible training strategy for a unified conditioning framework. +This strategy implements the Unified Conditioning Framework that supports: +- Simple fine-tuning with text conditioning (text-to-video/audio) +- Intrinsic conditioning (first_frame, prefix, suffix, spatial_crop, mask) +- Extrinsic conditioning (concatenation-based, IC-LoRA style) +The flexible strategy replaces TextToVideoStrategy and VideoToVideoStrategy by expressing +all conditioning scenarios through configuration rather than code. +""" + +from dataclasses import dataclass +from pathlib import Path +from typing import Annotated, Any, Literal, Union + +import torch +from pydantic import BaseModel, ConfigDict, Field, model_validator +from torch import Tensor + +from ltx_core.model.transformer.modality import Modality +from ltx_trainer.timestep_samplers import TimestepSampler +from ltx_trainer.training_strategies.base_strategy import ( + DEFAULT_FPS, + VIDEO_SCALE_FACTORS, + ModelInputs, + TrainingStrategy, + TrainingStrategyConfigBase, +) + +# ============================================================================= +# Configuration Classes +# ============================================================================= + + +class IntrinsicConditionBase(BaseModel): + """Base for intrinsic conditioning — tokens get clean latents, timestep=0, no loss.""" + + model_config = ConfigDict(extra="forbid") + + probability: float = Field( + default=1.0, + ge=0.0, + le=1.0, + description="Probability of applying this condition", + ) + + +class FirstFrameConditionConfig(IntrinsicConditionBase): + """First frame conditioning — frame 0 is clean, excluded from loss.""" + + type: Literal["first_frame"] = "first_frame" + + +class PrefixConditionConfig(IntrinsicConditionBase): + """Prefix conditioning — first N temporal units are clean, excluded from loss.""" + + type: Literal["prefix"] = "prefix" + temporal_boundary: int = Field( + ..., + ge=1, + description="Number of temporal units for prefix region. " + "For video: number of latent frames. For audio: number of audio latent timesteps.", + ) + + +class SuffixConditionConfig(IntrinsicConditionBase): + """Suffix conditioning — last N temporal units are clean, excluded from loss.""" + + type: Literal["suffix"] = "suffix" + temporal_boundary: int = Field( + ..., + ge=1, + description="Number of temporal units for suffix region. " + "For video: number of latent frames. For audio: number of audio latent timesteps.", + ) + + +class SpatialCropConditionConfig(IntrinsicConditionBase): + """Spatial crop conditioning — rectangular pixel region is clean, excluded from loss.""" + + type: Literal["spatial_crop"] = "spatial_crop" + spatial_region: tuple[int, int, int, int] = Field( + ..., + description="Spatial crop region as (y1, x1, y2, x2) in pixel coordinates", + ) + + +class MaskConditionConfig(IntrinsicConditionBase): + """Mask conditioning — per-sample binary mask determines conditioning tokens.""" + + type: Literal["mask"] = "mask" + mask_dir: str = Field( + ..., + description="Directory containing per-sample masks", + ) + + +class ReferenceConditionConfig(BaseModel): + """Reference conditioning (IC-LoRA style concatenation). + External reference latents are concatenated to the target sequence. + Reference tokens are clean (timestep=0), excluded from loss, and + participate in bidirectional self-attention. + """ + + model_config = ConfigDict(extra="forbid") + + type: Literal["reference"] = "reference" + latents_dir: str = Field(..., description="Directory for reference latents") + probability: float = Field(default=1.0, ge=0.0, le=1.0, description="Probability of applying this condition") + + +# Discriminated union for condition configs +ConditionConfig = Annotated[ + Union[ + FirstFrameConditionConfig, + PrefixConditionConfig, + SuffixConditionConfig, + SpatialCropConditionConfig, + MaskConditionConfig, + ReferenceConditionConfig, + ], + Field(discriminator="type"), +] + + +class ModalityConfig(BaseModel): + """Configuration for a single modality (video or audio).""" + + model_config = ConfigDict(extra="forbid") + + is_generated: bool = Field( + ..., + description="True = generated modality (denoised, contributes to loss), False = conditioning-only modality", + ) + + latents_dir: str = Field( + ..., + description="Directory for latents", + ) + + conditions: list[ConditionConfig] = Field( + default_factory=list, + description="List of conditions (e.g. first_frame, prefix, reference). Text conditioning is always applied.", + ) + + +class FlexibleStrategyConfig(TrainingStrategyConfigBase): + """Configuration for the flexible training strategy. + This strategy supports all conditioning scenarios through configuration: + - Text-to-video/audio with simple fine-tuning + - Intrinsic conditioning like first-frame, extension, outpainting + - Reference conditioning like IC-LoRA (concatenation-based reference) + """ + + name: Literal["flexible"] = "flexible" + + video: ModalityConfig | None = Field( + default=None, + description="Video modality configuration", + ) + + audio: ModalityConfig | None = Field( + default=None, + description="Audio modality configuration", + ) + + @model_validator(mode="after") + def validate_at_least_one_generated(self) -> "FlexibleStrategyConfig": + """Ensure at least one modality has is_generated=true.""" + has_video_target = self.video is not None and self.video.is_generated + has_audio_target = self.audio is not None and self.audio.is_generated + if not has_video_target and not has_audio_target: + raise ValueError("At least one modality must have is_generated=true") + return self + + @model_validator(mode="after") + def validate_audio_intrinsic_regions(self) -> "FlexibleStrategyConfig": + """Reject video-only intrinsic regions on the audio modality.""" + if self.audio is None: + return self + for cond in self.audio.conditions: + if isinstance(cond, (FirstFrameConditionConfig, SpatialCropConditionConfig)): + raise ValueError( + f"Intrinsic condition '{cond.type}' is not supported for audio. " + f"Audio supports: prefix, suffix, mask." + ) + return self + + def get_data_sources(self) -> dict[str, str]: + """Dynamically determine required data sources from config. + Returns a mapping of directory name (under ``preprocessed_data_root``) to + the dataset output key. + """ + sources: dict[str, str] = {"conditions": "conditions"} + + if self.video is not None: + sources[self.video.latents_dir] = "video_latents" + if self.audio is not None: + sources[self.audio.latents_dir] = "audio_latents" + + for modality_config in (self.video, self.audio): + if modality_config is None: + continue + for cond in modality_config.conditions: + if isinstance(cond, ReferenceConditionConfig): + sources[cond.latents_dir] = cond.latents_dir + elif isinstance(cond, MaskConditionConfig): + sources[cond.mask_dir] = cond.mask_dir + + return sources + + +# ============================================================================= +# Helper Data Structures +# ============================================================================= + + +@dataclass +class ModalityProcessingResult: + """Result of processing a single modality.""" + + modality: Modality + targets: Tensor | None + loss_mask: Tensor | None + + +@dataclass +class LatentData: + """Loaded and patchified latents with metadata.""" + + latents: Tensor # [B, seq_len, C] + num_frames: int + height: int + width: int + fps: float + + +# ============================================================================= +# FlexibleStrategy Implementation +# ============================================================================= + + +class FlexibleStrategy(TrainingStrategy): + """Unified training strategy supporting all conditioning scenarios. + This strategy implements the Unified Conditioning Framework, allowing + any training scenario to be expressed through configuration. + """ + + config: FlexibleStrategyConfig + + def __init__(self, config: FlexibleStrategyConfig): + """Initialize strategy with configuration. + Args: + config: Flexible strategy configuration + """ + super().__init__(config) + self.config = config + self.reference_spatial_scale_factor, self.reference_temporal_scale_factor = ( + self._infer_reference_scale_factors_from_config() + ) + + def prepare_training_inputs( + self, + batch: dict[str, Any], + timestep_sampler: TimestepSampler, + ) -> ModelInputs: + """Prepare training inputs by processing video and audio modalities.""" + video_result = self._process_modality(self.config.video, batch, "video", timestep_sampler) + audio_result = self._process_modality(self.config.audio, batch, "audio", timestep_sampler) + + return ModelInputs( + video=video_result.modality if video_result else None, + audio=audio_result.modality if audio_result else None, + video_targets=video_result.targets if video_result else None, + audio_targets=audio_result.targets if audio_result else None, + video_loss_mask=video_result.loss_mask if video_result else None, + audio_loss_mask=audio_result.loss_mask if audio_result else None, + ) + + def compute_loss( + self, + video_pred: Tensor | None, + audio_pred: Tensor | None, + inputs: ModelInputs, + ) -> Tensor: + """Compute masked MSE loss for video and audio predictions. Returns [B,].""" + total_loss = None + + if video_pred is not None and inputs.video_targets is not None: + video_loss = self._compute_modality_loss( + pred=video_pred, + targets=inputs.video_targets, + loss_mask=inputs.video_loss_mask, + ) + total_loss = video_loss + + if audio_pred is not None and inputs.audio_targets is not None: + audio_loss = self._compute_modality_loss( + pred=audio_pred, + targets=inputs.audio_targets, + loss_mask=inputs.audio_loss_mask, + ) + total_loss = audio_loss if total_loss is None else total_loss + audio_loss + + if total_loss is None: + raise ValueError("No valid predictions and targets provided for loss computation") + + return total_loss + + def get_checkpoint_metadata(self) -> dict[str, Any]: + """Include reference scale factors in checkpoint metadata for inference pipelines.""" + metadata: dict[str, Any] = {} + spatial = self.reference_spatial_scale_factor + temporal = self.reference_temporal_scale_factor + if spatial is not None and spatial != 1: + metadata["reference_spatial_scale_factor"] = spatial + metadata["reference_downscale_factor"] = spatial # backward compat + if temporal is not None and temporal != 1: + metadata["reference_temporal_scale_factor"] = temporal + return metadata + + def _infer_reference_scale_factors_from_config(self) -> tuple[int | None, int | None]: + """Infer spatial and temporal scale factors by peeking at one sample pair.""" + if self.config.video is None: + return None, None + for cond in self.config.video.conditions: + if not isinstance(cond, ReferenceConditionConfig): + continue + target_dir = Path(self.config.video.latents_dir) + ref_dir = Path(cond.latents_dir) + for sample_file in target_dir.rglob("*.pt"): + ref_file = ref_dir / sample_file.relative_to(target_dir) + if not ref_file.exists(): + continue + target_data = torch.load(sample_file, map_location="cpu", weights_only=True) + ref_data = torch.load(ref_file, map_location="cpu", weights_only=True) + if "height" not in ref_data or "height" not in target_data: + continue + spatial = self._infer_scale_factor( + ref_data["height"], + ref_data["width"], + target_data["height"], + target_data["width"], + ) + temporal = self._infer_temporal_scale_factor( + ref_data["num_frames"], + target_data["num_frames"], + ) + return spatial, temporal + return None, None + + def _process_modality( + self, + modality_config: ModalityConfig | None, + batch: dict[str, Any], + modality_key: str, + timestep_sampler: TimestepSampler, + ) -> ModalityProcessingResult | None: + """Process a single modality: load latents, add noise, apply conditions, build Modality.""" + if modality_config is None: + return None + + # Step 1: Load and patchify latents + data = self._patchify_latent_data(batch[f"{modality_key}_latents"], modality_key) + latents = data.latents + + batch_size, seq_len, _ = latents.shape + device = latents.device + dtype = latents.dtype + + # Step 2: Get text embeddings + conditions = batch["conditions"] + prompt_embeds = conditions[f"{modality_key}_prompt_embeds"] + prompt_attention_mask = conditions["prompt_attention_mask"] + + # Step 3: Initialize noise, timesteps, and loss mask based on is_generated flag + if modality_config.is_generated: + noisy_latents, targets, timesteps, loss_mask, sigmas = self._initialize_noisy_target( + latents, timestep_sampler + ) + else: + # Conditioning modality: keep clean (sigma=0), no loss + noisy_latents = latents + targets = None + timesteps = torch.zeros(batch_size, seq_len, device=device, dtype=dtype) + loss_mask = None + sigmas = torch.zeros(batch_size, device=device, dtype=dtype) + + # Step 4: Generate positions + if modality_key == "video": + positions = self._get_video_positions( + num_frames=data.num_frames, + height=data.height, + width=data.width, + batch_size=batch_size, + fps=data.fps, + device=device, + ) + else: + positions = self._get_audio_positions( + num_time_steps=seq_len, + batch_size=batch_size, + device=device, + ) + + # Step 5: Apply conditions (intrinsic first, then extrinsic) + for cond in modality_config.conditions: + if isinstance(cond, IntrinsicConditionBase) and modality_config.is_generated: + noisy_latents, timesteps, loss_mask = self._apply_intrinsic_condition( + noisy_latents=noisy_latents, + clean_latents=latents, + timesteps=timesteps, + loss_mask=loss_mask, + config=cond, + height=data.height, + width=data.width, + batch=batch, + ) + + for cond in modality_config.conditions: + if isinstance(cond, ReferenceConditionConfig): + noisy_latents, positions, timesteps, loss_mask, targets = self._apply_reference_condition( + noisy_latents=noisy_latents, + positions=positions, + timesteps=timesteps, + loss_mask=loss_mask, + targets=targets, + batch=batch, + config=cond, + modality_key=modality_key, + ) + + # Step 6: Build Modality + modality = Modality( + enabled=True, + latent=noisy_latents, + sigma=sigmas, + timesteps=timesteps, + positions=positions, + context=prompt_embeds, + context_mask=prompt_attention_mask, + ) + + return ModalityProcessingResult( + modality=modality, + targets=targets, + loss_mask=loss_mask, + ) + + @staticmethod + def _initialize_noisy_target( + latents: Tensor, + timestep_sampler: TimestepSampler, + ) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: + """Add noise to latents and create training targets. Returns (noisy, targets, timesteps, mask, sigmas).""" + batch_size, seq_len, _ = latents.shape + sigmas = timestep_sampler.sample_for(latents) + noise = torch.randn_like(latents) + sigmas_expanded = sigmas.view(-1, 1, 1) + noisy_latents = (1 - sigmas_expanded) * latents + sigmas_expanded * noise + targets = noise - latents # velocity prediction + timesteps = sigmas.view(-1, 1).expand(batch_size, seq_len).clone() + loss_mask = torch.ones(batch_size, seq_len, dtype=torch.bool, device=latents.device) + return noisy_latents, targets, timesteps, loss_mask, sigmas + + def _apply_intrinsic_condition( + self, + noisy_latents: Tensor, + clean_latents: Tensor, + timesteps: Tensor, + loss_mask: Tensor, + config: IntrinsicConditionBase, + height: int, + width: int, + batch: dict[str, Any], + ) -> tuple[Tensor, Tensor, Tensor]: + """Apply intrinsic conditioning using a binary mask. + For each token, the mask value determines conditioning strength: + - mask=1: conditioned (clean latent, timestep=0, excluded from loss) + - mask=0: generated (noisy latent, original timestep, contributes to loss) + The conditioning decision is drawn independently per batch element so the training + signal across samples in a batch is i.i.d. -- a single batch-wide draw would + correlate gradient updates across the batch. + """ + batch_size, seq_len, _ = noisy_latents.shape + device = noisy_latents.device + + # Per-sample Bernoulli draw -- each element is independently conditioned. + apply_per_sample = torch.rand(batch_size, device=device) < config.probability + if not apply_per_sample.any(): + return noisy_latents, timesteps, loss_mask + + if isinstance(config, FirstFrameConditionConfig): + mask = self._compute_temporal_mask(batch_size, seq_len, height, width, 1, False, device) + elif isinstance(config, PrefixConditionConfig): + mask = self._compute_temporal_mask( + batch_size, seq_len, height, width, config.temporal_boundary, False, device + ) + elif isinstance(config, SuffixConditionConfig): + mask = self._compute_temporal_mask( + batch_size, seq_len, height, width, config.temporal_boundary, True, device + ) + elif isinstance(config, SpatialCropConditionConfig): + mask = self._compute_spatial_crop_mask(batch_size, seq_len, height, width, config.spatial_region, device) + elif isinstance(config, MaskConditionConfig): + # Binarize to match inference, which thresholds masks at load time + # (validation_runner._load_and_downsample_mask / _load_audio_mask). + mask = (batch[config.mask_dir]["mask"].reshape(batch_size, seq_len) > 0.5).float() + else: + raise ValueError(f"Unknown intrinsic condition type: {type(config).__name__}") + + # Zero the mask for samples the per-sample draw did not select. + mask = mask * apply_per_sample.view(-1, 1).to(mask.dtype) + + # Apply binary mask: clean conditioned tokens, noisy generated tokens. + m = mask.unsqueeze(-1) + noisy_latents = m * clean_latents + (1 - m) * noisy_latents + timesteps = (1 - mask) * timesteps + loss_mask = loss_mask & (mask == 0) + + return noisy_latents, timesteps, loss_mask + + @staticmethod + def _compute_temporal_mask( + batch_size: int, + seq_len: int, + height: int, + width: int, + num_frames: int, + from_end: bool, + device: torch.device, + ) -> Tensor: + """Compute float mask for temporal region (prefix or suffix). Returns [B, seq_len] in {0, 1}.""" + tokens_per_frame = height * width + num_tokens = num_frames * tokens_per_frame + mask = torch.zeros(batch_size, seq_len, device=device) + if from_end: + mask[:, -num_tokens:] = 1.0 + else: + mask[:, :num_tokens] = 1.0 + return mask + + @staticmethod + def _compute_spatial_crop_mask( + batch_size: int, + seq_len: int, + height: int, + width: int, + region: tuple[int, int, int, int], + device: torch.device, + ) -> Tensor: + """Compute float mask for spatial crop region (y1, x1, y2, x2) in pixel coords. + Returns [B, seq_len] in {0, 1}. + """ + y1, x1, y2, x2 = region + num_frames = seq_len // (height * width) + + # Convert pixel to latent coordinates and clamp (per-axis VAE scale factor). + def to_latent(v: int, scale: int, max_v: int) -> int: + return max(0, min(v // scale, max_v)) + + ly1 = to_latent(y1, VIDEO_SCALE_FACTORS.height, height) + ly2 = to_latent(y2, VIDEO_SCALE_FACTORS.height, height) + lx1 = to_latent(x1, VIDEO_SCALE_FACTORS.width, width) + lx2 = to_latent(x2, VIDEO_SCALE_FACTORS.width, width) + + # Create spatial mask and tile across frames + spatial_mask = torch.zeros(height, width, device=device) + spatial_mask[ly1:ly2, lx1:lx2] = 1.0 + full_mask = spatial_mask.flatten().repeat(num_frames) + + return full_mask.unsqueeze(0).expand(batch_size, -1) + + def _patchify_latent_data(self, latent_data: dict[str, Any], modality_key: str) -> LatentData: + """Patchify latent data and extract metadata.""" + latents = latent_data["latents"] + + if modality_key == "video": + num_frames = latent_data["num_frames"][0].item() + height = latent_data["height"][0].item() + width = latent_data["width"][0].item() + fps = latent_data.get("fps") + fps = fps[0].item() if fps is not None else DEFAULT_FPS + latents = self._video_patchifier.patchify(latents) + else: + num_frames = latent_data.get("num_frames", [latents.shape[2]])[0] + if isinstance(num_frames, Tensor): + num_frames = num_frames.item() + height = 1 + width = 1 + fps = 1.0 + latents = self._audio_patchifier.patchify(latents) + + return LatentData(latents=latents, num_frames=num_frames, height=height, width=width, fps=fps) + + def _apply_reference_condition( + self, + noisy_latents: Tensor, + positions: Tensor, + timesteps: Tensor, + loss_mask: Tensor | None, + targets: Tensor | None, + batch: dict[str, Any], + config: ReferenceConditionConfig, + modality_key: str, + ) -> tuple[Tensor, Tensor, Tensor, Tensor | None, Tensor | None]: + """Concatenate reference latents to target sequence for reference conditioning (IC-LoRA style). + The apply/skip decision is batch-wide (reference conditioning changes the sequence + length, so it cannot be applied to only part of a batch) but is drawn from the torch + RNG so runs are reproducible under ``torch.manual_seed`` — mirroring the intrinsic + per-sample draw rather than Python's unseeded ``random``. + """ + if torch.rand((), device=noisy_latents.device).item() >= config.probability: + return noisy_latents, positions, timesteps, loss_mask, targets + + # Load and patchify condition latents + cond = self._patchify_latent_data(batch[config.latents_dir], modality_key) + cond_latents = cond.latents + + batch_size, cond_seq_len, _ = cond_latents.shape + device = cond_latents.device + dtype = cond_latents.dtype + + # Generate condition positions + if modality_key == "video": + cond_positions = self._get_video_positions( + num_frames=cond.num_frames, + height=cond.height, + width=cond.width, + batch_size=batch_size, + fps=cond.fps, + device=device, + ) + else: + cond_positions = self._get_audio_positions( + num_time_steps=cond_seq_len, + batch_size=batch_size, + device=device, + ) + + # Translate / rescale ref positions into the target's frame (video only). + if modality_key == "video": + spatial_sf = self.reference_spatial_scale_factor or 1 + temporal_sf = self.reference_temporal_scale_factor or 1 + if spatial_sf != 1 or temporal_sf != 1: + cond_positions = cond_positions.clone() + if temporal_sf != 1: + # Ref positions are already at the ref's effective fps (source_fps / S, + # stored by process_videos.py). Shift by (S - 1) / target_fps so ref's + # last patch aligns with target's last; clamp the causal patch at 0. + t_target = positions[:, 0, 0:1, 1:2] # = 1 / target_fps + cond_positions[:, 0, ...] = torch.clamp( + cond_positions[:, 0, ...] - (temporal_sf - 1) * t_target, min=0 + ) + if spatial_sf != 1: + cond_positions[:, 1, ...] *= spatial_sf + cond_positions[:, 2, ...] *= spatial_sf + + # Condition tokens: clean, timestep=0, no loss + cond_timesteps = torch.zeros(batch_size, cond_seq_len, device=device, dtype=dtype) + cond_loss_mask = torch.zeros(batch_size, cond_seq_len, dtype=torch.bool, device=device) + + # Concatenate condition and target sequences (condition first, then target) + combined_latents = torch.cat([cond_latents, noisy_latents], dim=1) + combined_positions = torch.cat([cond_positions, positions], dim=2) + combined_timesteps = torch.cat([cond_timesteps, timesteps], dim=1) + + combined_loss_mask = torch.cat([cond_loss_mask, loss_mask], dim=1) if loss_mask is not None else None + + # Targets remain unchanged (only for target portion, not condition portion) + + return combined_latents, combined_positions, combined_timesteps, combined_loss_mask, targets + + @staticmethod + def _compute_modality_loss(pred: Tensor, targets: Tensor, loss_mask: Tensor) -> Tensor: + """Compute per-element MSE loss for a single modality. Returns [B,].""" + # Slice prediction to match targets length (removes any prepended condition tokens) + target_len = targets.shape[1] + pred = pred[:, -target_len:, :] + mask = loss_mask[:, -target_len:] + + # Compute masked MSE loss, reduce per-element [B,] over (seq, channels) + mask_expanded = mask.unsqueeze(-1).float() + squared_error = (pred - targets).pow(2) + masked_loss = squared_error * mask_expanded + return masked_loss.mean(dim=[-2, -1]) / mask_expanded.mean(dim=[-2, -1]).clamp(min=1e-8) + + @staticmethod + def _infer_scale_factor(cond_height: int, cond_width: int, target_height: int, target_width: int) -> int: + """Infer spatial scale factor between condition and target resolutions.""" + if target_height == cond_height and target_width == cond_width: + return 1 + scale_h = target_height // cond_height if cond_height > 0 else 1 + scale_w = target_width // cond_width if cond_width > 0 else 1 + if scale_h != scale_w: + raise ValueError( + f"Non-uniform scale factors between condition and target: height={scale_h}, width={scale_w}. " + "Condition and target resolutions must scale uniformly." + ) + return scale_h + + @staticmethod + def _infer_temporal_scale_factor(cond_num_frames: int, target_num_frames: int) -> int: + """Infer temporal scale factor between condition and target latent frame counts. + The first latent frame encodes a single pixel frame (the VAE's causal structure), + so the temporal groups count is (num_frames - 1). The scale factor is the ratio + of target groups to condition groups. + """ + if target_num_frames == cond_num_frames: + return 1 + target_groups = target_num_frames - 1 + cond_groups = cond_num_frames - 1 + if cond_groups <= 0 or target_groups <= 0: + return 1 + if target_groups % cond_groups != 0: + raise ValueError( + f"Target temporal groups ({target_groups}) is not evenly divisible by " + f"condition temporal groups ({cond_groups})." + ) + return target_groups // cond_groups diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/text_to_video.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/text_to_video.py index 83a1a0d..66cf229 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/text_to_video.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/text_to_video.py @@ -45,6 +45,18 @@ class TextToVideoConfig(TrainingStrategyConfigBase): description="Directory name for audio latents when with_audio is True", ) + def get_data_sources(self) -> dict[str, str]: + """Text-to-video training requires latents and text conditions. + When ``with_audio`` is True, also requires audio latents. + """ + sources = { + "latents": "latents", + "conditions": "conditions", + } + if self.with_audio: + sources[self.audio_latents_dir] = "audio_latents" + return sources + class TextToVideoStrategy(TrainingStrategy): """Text-to-video training strategy. @@ -64,26 +76,6 @@ class TextToVideoStrategy(TrainingStrategy): """ super().__init__(config) - @property - def requires_audio(self) -> bool: - """Whether this training strategy requires audio components.""" - return self.config.with_audio - - def get_data_sources(self) -> list[str] | dict[str, str]: - """ - Text-to-video training requires latents and text conditions. - When with_audio is True, also requires audio latents. - """ - sources = { - "latents": "latents", - "conditions": "conditions", - } - - if self.config.with_audio: - sources[self.config.audio_latents_dir] = "audio_latents" - - return sources - def prepare_training_inputs( self, batch: dict[str, Any], @@ -119,7 +111,6 @@ class TextToVideoStrategy(TrainingStrategy): batch_size = video_latents.shape[0] video_seq_len = video_latents.shape[1] device = video_latents.device - dtype = video_latents.dtype # Create conditioning mask (first frame conditioning) video_conditioning_mask = self._create_first_frame_conditioning_mask( @@ -157,7 +148,6 @@ class TextToVideoStrategy(TrainingStrategy): batch_size=batch_size, fps=fps, device=device, - dtype=dtype, ) # Create video Modality @@ -187,7 +177,6 @@ class TextToVideoStrategy(TrainingStrategy): prompt_attention_mask=prompt_attention_mask, batch_size=batch_size, device=device, - dtype=dtype, ) return ModelInputs( @@ -207,7 +196,6 @@ class TextToVideoStrategy(TrainingStrategy): prompt_attention_mask: Tensor, batch_size: int, device: torch.device, - dtype: torch.dtype, ) -> tuple[Modality, Tensor, Tensor]: """Prepare audio inputs for joint audio-video training. Args: @@ -217,7 +205,6 @@ class TextToVideoStrategy(TrainingStrategy): prompt_attention_mask: Attention mask for context batch_size: Batch size device: Target device - dtype: Target dtype Returns: Tuple of (audio_modality, audio_targets, audio_loss_mask) """ @@ -248,7 +235,6 @@ class TextToVideoStrategy(TrainingStrategy): num_time_steps=audio_seq_len, batch_size=batch_size, device=device, - dtype=dtype, ) # Create audio Modality diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py index c07b7c1..2d133b3 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py @@ -39,6 +39,14 @@ class VideoToVideoConfig(TrainingStrategyConfigBase): description="Directory name for latents of reference videos", ) + def get_data_sources(self) -> dict[str, str]: + """IC-LoRA training requires latents, conditions, and reference latents.""" + return { + "latents": "latents", + "conditions": "conditions", + self.reference_latents_dir: "ref_latents", + } + class VideoToVideoStrategy(TrainingStrategy): """Video-to-video training strategy for IC-LoRA. @@ -62,14 +70,6 @@ class VideoToVideoStrategy(TrainingStrategy): super().__init__(config) self.reference_downscale_factor = None # Will be inferred from first batch - def get_data_sources(self) -> dict[str, str]: - """IC-LoRA training requires latents, conditions, and reference latents.""" - return { - "latents": "latents", - "conditions": "conditions", - self.config.reference_latents_dir: "ref_latents", - } - def prepare_training_inputs( # noqa: PLR0915 self, batch: dict[str, Any], @@ -133,7 +133,6 @@ class VideoToVideoStrategy(TrainingStrategy): ref_seq_len = ref_latents.shape[1] target_seq_len = target_latents.shape[1] device = target_latents.device - dtype = target_latents.dtype # Create conditioning mask # Reference tokens are always conditioning (timestep=0) @@ -164,7 +163,7 @@ class VideoToVideoStrategy(TrainingStrategy): target_conditioning_mask_expanded = target_conditioning_mask.unsqueeze(-1) noisy_target = torch.where(target_conditioning_mask_expanded, target_latents, noisy_target) - # Targets for loss computation + # Targets for loss computation (velocity prediction) - only for target portion targets = noise - target_latents # Concatenate reference (clean) and target (noisy) @@ -181,7 +180,6 @@ class VideoToVideoStrategy(TrainingStrategy): batch_size=batch_size, fps=fps, device=device, - dtype=dtype, ) # Scale reference positions to match target coordinate space @@ -200,7 +198,6 @@ class VideoToVideoStrategy(TrainingStrategy): batch_size=batch_size, fps=fps, device=device, - dtype=dtype, ) # Concatenate positions along sequence dimension @@ -231,7 +228,6 @@ class VideoToVideoStrategy(TrainingStrategy): audio_targets=None, video_loss_mask=video_loss_mask, audio_loss_mask=None, - ref_seq_len=ref_seq_len, ) def compute_loss( @@ -240,13 +236,11 @@ class VideoToVideoStrategy(TrainingStrategy): _audio_pred: Tensor | None, inputs: ModelInputs, ) -> Tensor: - """Compute masked loss only on target portion. Returns [B,].""" - # Extract target portion of prediction - ref_seq_len = inputs.ref_seq_len - target_pred = video_pred[:, ref_seq_len:, :] - - # Get target portion of loss mask - target_loss_mask = inputs.video_loss_mask[:, ref_seq_len:] + """Compute masked loss on target portion only. Returns [B,].""" + # Slice prediction to match targets length (removes prepended reference tokens) + target_len = inputs.video_targets.shape[1] + target_pred = video_pred[:, -target_len:, :] + target_loss_mask = inputs.video_loss_mask[:, -target_len:] # Compute per-element loss [B,] loss = (target_pred - inputs.video_targets).pow(2) diff --git a/packages/ltx-trainer/src/ltx_trainer/validation_runner.py b/packages/ltx-trainer/src/ltx_trainer/validation_runner.py new file mode 100644 index 0000000..090d311 --- /dev/null +++ b/packages/ltx-trainer/src/ltx_trainer/validation_runner.py @@ -0,0 +1,1208 @@ +"""Self-contained validation runner for LTX-2 training. +Encapsulates all validation concerns: model loading, embedding caching, +conditioning media encoding, sample generation (denoising + decoding), +output saving, and W&B logging. +The trainer only needs to call ``run()`` with the current transformer state. +""" + +import os +from collections.abc import Callable +from dataclasses import dataclass, field, replace +from pathlib import Path +from typing import TYPE_CHECKING, Literal + +import torch +from einops import rearrange +from torch import Tensor +from torchvision.transforms import InterpolationMode +from torchvision.transforms import functional as TF # noqa: N812 +from torchvision.transforms.functional import to_tensor + +from ltx_core.components.diffusion_steps import EulerDiffusionStep +from ltx_core.components.guiders import CFGGuider, STGGuider +from ltx_core.components.noisers import GaussianNoiser +from ltx_core.components.patchifiers import AudioPatchifier, VideoLatentPatchifier +from ltx_core.components.schedulers import LTX2Scheduler +from ltx_core.conditioning.types.latent_cond import VideoConditionByLatentIndex +from ltx_core.conditioning.types.mask_cond import VideoConditionByMask +from ltx_core.conditioning.types.reference_video_cond import VideoConditionByReferenceLatent +from ltx_core.guidance.perturbations import ( + BatchedPerturbationConfig, + Perturbation, + PerturbationConfig, + PerturbationType, +) +from ltx_core.model.audio_vae.audio_vae import encode_audio as ltx_encode_audio +from ltx_core.model.transformer.modality import Modality +from ltx_core.model.transformer.model import X0Model +from ltx_core.model.video_vae import SpatialTilingConfig, TemporalTilingConfig, TilingConfig +from ltx_core.tools import AudioLatentTools, VideoLatentTools +from ltx_core.types import ( + Audio, + AudioLatentShape, + LatentState, + SpatioTemporalScaleFactors, + VideoLatentShape, + VideoPixelShape, +) +from ltx_trainer import logger +from ltx_trainer.config import ValidationConfig, ValidationSample +from ltx_trainer.gpu_utils import free_gpu_memory_context +from ltx_trainer.model_loader import ( + load_audio_vae_decoder, + load_audio_vae_encoder, + load_embeddings_processor, + load_text_encoder, + load_video_vae_decoder, + load_video_vae_encoder, + load_vocoder, +) +from ltx_trainer.progress import SamplingContext, TrainingProgress +from ltx_trainer.utils import open_image_as_srgb, save_image +from ltx_trainer.video_utils import read_video, save_video + +if TYPE_CHECKING: + from ltx_core.model.transformer import LTXModel + +VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default() +_DEFAULT_TILING = TilingConfig( + spatial_config=SpatialTilingConfig(tile_size_in_pixels=192, tile_overlap_in_pixels=64), + temporal_config=TemporalTilingConfig(tile_size_in_frames=48, tile_overlap_in_frames=24), +) + + +def _local_rank_device() -> torch.device: + """Per-rank CUDA device for early init (before Accelerator exists). + DDP-safe: ``LOCAL_RANK`` is set by accelerate before trainer init; loading on bare + ``"cuda"`` would resolve to ``cuda:0`` on every rank and crash with a device mismatch. + """ + if not torch.cuda.is_available(): + return torch.device("cpu") + local_rank = int(os.environ.get("LOCAL_RANK", "0")) + return torch.device(f"cuda:{local_rank}") + + +# ============================================================================= +# Data classes +# ============================================================================= + + +@dataclass +class CachedPromptEmbeddings: + """Pre-computed text embeddings for a validation prompt.""" + + video_context_positive: Tensor + audio_context_positive: Tensor + video_context_negative: Tensor | None = None + audio_context_negative: Tensor | None = None + + +@dataclass +class CachedConditionMedia: + """Pre-encoded media for a single validation condition.""" + + latent: Tensor + pixels: Tensor | None = None + mask: Tensor | None = None + + +@dataclass +class CachedSampleMedia: + """Pre-encoded conditioning media for one validation sample. + Keyed by condition index (position in the sample's conditions list). + """ + + conditions: dict[int, CachedConditionMedia] = field(default_factory=dict) + + +# ============================================================================= +# ValidationRunner +# ============================================================================= + + +class ValidationRunner: + """Self-contained validation: loads models, caches media, generates samples. + Lifecycle: + 1. ``__init__``: loads text encoder + embeddings processor, caches prompt + embeddings, unloads both. Loads VAE encoder, encodes conditioning media, + unloads. Loads VAE decoder / audio decoder / vocoder and keeps them on CPU. + 2. ``run()``: called at each validation step with the current transformer. + Generates all samples, saves outputs, returns file paths. + """ + + def __init__( + self, + config: ValidationConfig, + model_path: str | Path, + text_encoder_path: str | Path | None, + load_text_encoder_in_8bit: bool = False, + ): + self._config = config + self._model_path = Path(model_path) + + self._video_patchifier = VideoLatentPatchifier(patch_size=1) + self._audio_patchifier = AudioPatchifier(patch_size=1) + + self._cached_embeddings = self._cache_prompt_embeddings(text_encoder_path, load_text_encoder_in_8bit) + self._cached_media = self._encode_conditioning_media() + self._load_decoder_components() + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + + @torch.no_grad() + @free_gpu_memory_context(after=True) + def run( + self, + transformer: "LTXModel", + step: int, + output_dir: Path, + device: torch.device, + progress: TrainingProgress, + wandb_run: object | None = None, + work_items: list[tuple[int, bool]] | None = None, + ) -> list[tuple[int, Path]]: + """Generate validation samples, save outputs, and optionally log to W&B. + Args: + work_items: Optional list of ``(sample_index, save_output)`` tuples for distributed + validation. Each rank passes its assigned indices so prompts are split across + ranks. ``save_output=False`` runs the generation but skips writing to disk; this + is used for FSDP padding so every rank executes the same number of forwards and + collective ops stay aligned. When ``None``, all samples are generated and saved. + Returns: + List of ``(sample_index, path)`` tuples for samples that were saved. Callers can + ``gather_object`` and sort by index to reconstruct the global ordering. + """ + samples = self._config.samples + if not samples: + return [] + + if work_items is None: + work_items = [(i, True) for i in range(len(samples))] + if not work_items: + return [] + + inference_steps = self._config.inference_steps + sampling_ctx = progress.start_sampling(num_prompts=len(work_items), num_steps=inference_steps) + + samples_dir = output_dir / "samples" + samples_dir.mkdir(exist_ok=True, parents=True) + + results: list[tuple[int, Path]] = [] + + for local_i, (sample_idx, save_output) in enumerate(work_items): + sample = samples[sample_idx] + sampling_ctx.start_video(local_i) + + cached_embeddings = self._cached_embeddings[sample_idx] if self._cached_embeddings else None + cached_media = self._cached_media[sample_idx] if self._cached_media else CachedSampleMedia() + + video, audio = self._generate_sample( + sample=sample, + cached_embeddings=cached_embeddings, + cached_media=cached_media, + transformer=transformer, + device=device, + sampling_ctx=sampling_ctx, + ) + + if not save_output: + continue + + dims = sample.video_dims or self._config.video_dims + num_frames = dims[2] + ext = "png" if num_frames == 1 else "mp4" + out_path = samples_dir / f"step_{step:06d}_{sample_idx + 1}.{ext}" + + if video is not None: + if num_frames == 1: + save_image(video, out_path) + else: + save_video( + video_tensor=video, + output_path=out_path, + fps=self._config.frame_rate, + audio=audio, + audio_sample_rate=self._vocoder.output_sampling_rate if audio is not None else None, + video_format="CFHW", + ) + results.append((sample_idx, out_path)) + elif audio is not None: + out_path = out_path.with_suffix(".wav") + self._save_audio(audio, out_path) + results.append((sample_idx, out_path)) + + sampling_ctx.cleanup() + + rel_path = samples_dir.relative_to(output_dir) + logger.info(f"🎥 Validation samples for step {step} saved in {rel_path}") + + if wandb_run is not None and results: + self.log_to_wandb(wandb_run, [p for _, p in results], step) + + return results + + # --- Initialization: caching --- + + @torch.no_grad() + @free_gpu_memory_context(after=True) + def _cache_prompt_embeddings( + self, + text_encoder_path: str | Path | None, + load_in_8bit: bool, + ) -> list[CachedPromptEmbeddings]: + """Load text encoder, encode all validation prompts, cache on CPU, then unload.""" + prompts = [s.prompt for s in self._config.samples] + if not prompts: + return [] + + init_device = _local_rank_device() + + logger.debug("Loading text encoder for validation embedding caching...") + text_encoder = load_text_encoder( + gemma_model_path=text_encoder_path, device=init_device, dtype=torch.bfloat16, load_in_8bit=load_in_8bit + ) + + logger.debug("Loading embeddings processor for validation embedding caching...") + embeddings_processor = load_embeddings_processor( + checkpoint_path=self._model_path, device=init_device, dtype=torch.bfloat16 + ) + + logger.info(f"Pre-computing embeddings for {len(prompts)} validation prompts...") + cached: list[CachedPromptEmbeddings] = [] + neg_hs, neg_mask = text_encoder.encode([self._config.negative_prompt])[0] + neg_out = embeddings_processor.process_hidden_states(neg_hs, neg_mask) + + for prompt in prompts: + pos_hs, pos_mask = text_encoder.encode([prompt])[0] + pos_out = embeddings_processor.process_hidden_states(pos_hs, pos_mask) + + cached.append( + CachedPromptEmbeddings( + video_context_positive=pos_out.video_encoding.cpu(), + audio_context_positive=pos_out.audio_encoding.cpu(), + video_context_negative=neg_out.video_encoding.cpu(), + audio_context_negative=( + neg_out.audio_encoding.cpu() if neg_out.audio_encoding is not None else None + ), + ) + ) + + del text_encoder, embeddings_processor + logger.debug("Validation prompt embeddings cached. Text encoder unloaded.") + return cached + + @torch.no_grad() + @free_gpu_memory_context(after=True) + def _encode_conditioning_media(self) -> list[CachedSampleMedia]: + """Load VAE encoders, encode all conditioning media, cache on CPU, then unload.""" + samples = self._config.samples + if not samples: + return [] + + def _cond_needs_video_encoder(cond: object) -> bool: + if getattr(cond, "type", None) in ("first_frame", "video_to_audio", "spatial_crop"): + return True + return getattr(cond, "video", None) is not None + + def _cond_needs_audio_encoder(cond: object) -> bool: + if getattr(cond, "type", None) == "audio_to_video": + return True + return getattr(cond, "type", None) in ("prefix", "suffix", "mask", "reference") and ( + getattr(cond, "audio", None) is not None + ) + + needs_video_encoder = any(_cond_needs_video_encoder(c) for s in samples for c in s.conditions) + needs_audio_encoder = any(_cond_needs_audio_encoder(c) for s in samples for c in s.conditions) + + if not needs_video_encoder and not needs_audio_encoder: + return [CachedSampleMedia() for _ in samples] + + device = _local_rank_device() + vae_encoder = None + audio_encoder = None + + if needs_video_encoder: + logger.debug("Loading VAE encoder for validation media encoding...") + vae_encoder = load_video_vae_encoder(self._model_path, device="cpu", dtype=torch.bfloat16) + if needs_audio_encoder: + logger.debug("Loading audio VAE encoder for validation media encoding...") + audio_encoder = load_audio_vae_encoder(self._model_path, device="cpu", dtype=torch.bfloat16) + + logger.info(f"Pre-encoding conditioning media for {len(samples)} validation samples...") + cached: list[CachedSampleMedia] = [] + + for sample in samples: + sample_dims = sample.video_dims or self._config.video_dims + cached.append(self._encode_sample_conditions(sample, sample_dims, vae_encoder, audio_encoder, device)) + + del vae_encoder, audio_encoder + logger.info("Validation conditioning media cached on CPU.") + return cached + + def _encode_sample_conditions( + self, + sample: ValidationSample, + dims: tuple[int, int, int], + vae_encoder: torch.nn.Module | None, + audio_encoder: torch.nn.Module | None, + device: torch.device, + ) -> CachedSampleMedia: + """Encode all conditioning media for a single validation sample.""" + s_width, s_height, s_num_frames = dims + sample_media = CachedSampleMedia() + + for cond_idx, cond in enumerate(sample.conditions): + if cond.type == "first_frame": + image = self._load_first_frame(Path(cond.image_or_video)) + latent = self._encode_image(image, s_height, s_width, vae_encoder, device) + sample_media.conditions[cond_idx] = CachedConditionMedia(latent=latent) + + elif cond.type == "reference" and cond.video is not None: + ref_video, _ = read_video(cond.video, max_frames=s_num_frames) + preprocessed, pixels = self._preprocess_reference( + ref_video, s_height, s_width, cond.downscale_factor, cond.temporal_scale_factor + ) + latent = self._encode_video(preprocessed, vae_encoder, device) + sample_media.conditions[cond_idx] = CachedConditionMedia( + latent=latent, pixels=pixels if cond.include_in_output else None + ) + + elif cond.type == "reference" and getattr(cond, "audio", None) is not None: + video_duration = s_num_frames / self._config.frame_rate + latent = self._encode_audio(cond.audio, audio_encoder, device, max_duration=video_duration) + sample_media.conditions[cond_idx] = CachedConditionMedia(latent=latent) + + elif cond.type in ("prefix", "suffix", "mask", "spatial_crop", "video_to_audio"): + latent = self._encode_temporal_condition_media( + cond=cond, + target_width=s_width, + target_height=s_height, + max_frames=s_num_frames, + vae_encoder=vae_encoder, + audio_encoder=audio_encoder, + device=device, + ) + mask_tensor = None + if latent is not None and cond.type == "mask" and hasattr(cond, "mask") and cond.mask is not None: + if getattr(cond, "audio", None) is not None: + mask_tensor = self._load_audio_mask(cond.mask, s_num_frames, self._config.frame_rate) + else: + mask_tensor = self._load_and_downsample_mask(cond.mask, s_width, s_height, s_num_frames) + if latent is not None: + sample_media.conditions[cond_idx] = CachedConditionMedia(latent=latent, mask=mask_tensor) + + elif cond.type == "audio_to_video": + video_duration = s_num_frames / self._config.frame_rate + latent = self._encode_audio(cond.audio, audio_encoder, device, max_duration=video_duration) + sample_media.conditions[cond_idx] = CachedConditionMedia(latent=latent) + + return sample_media + + def _load_decoder_components(self) -> None: + """Load VAE decoder, audio decoder, and vocoder. Kept on CPU until generation.""" + self._vae_decoder = None + needs_video_decoder = self._config.generate_video or any( + c.type == "video_to_audio" for s in self._config.samples for c in s.conditions + ) + if needs_video_decoder: + logger.debug("Loading video VAE decoder for validation...") + self._vae_decoder = load_video_vae_decoder(self._model_path, device="cpu", dtype=torch.bfloat16) + if self._vae_decoder is not None: + self._vae_decoder.requires_grad_(False) + + self._audio_decoder = None + self._vocoder = None + needs_audio_decoder = self._config.generate_audio or any( + c.type == "audio_to_video" for s in self._config.samples for c in s.conditions + ) + if needs_audio_decoder: + logger.debug("Loading audio decoder and vocoder for validation...") + self._audio_decoder = load_audio_vae_decoder(self._model_path, device="cpu", dtype=torch.bfloat16) + if self._audio_decoder is not None: + self._audio_decoder.requires_grad_(False) + self._vocoder = load_vocoder(self._model_path, device="cpu", dtype=torch.bfloat16) + if self._vocoder is not None: + self._vocoder.requires_grad_(False) + + def _encode_temporal_condition_media( + self, + cond: object, + *, + target_width: int, + target_height: int, + max_frames: int, + vae_encoder: torch.nn.Module | None, + audio_encoder: torch.nn.Module | None, + device: torch.device, + ) -> Tensor | None: + """Encode media for prefix/suffix/mask/spatial_crop/frozen-video conditions.""" + video_path = getattr(cond, "video", None) + audio_path = getattr(cond, "audio", None) + if video_path is None and audio_path is not None: + max_dur = getattr(cond, "duration", None) + if max_dur is None and cond.type == "mask": + max_dur = max_frames / self._config.frame_rate + return self._encode_audio( + audio_path, + audio_encoder, + device, + max_duration=max_dur, + from_end=cond.type == "suffix", + ) + if video_path is None: + return None + + video, _ = read_video(video_path, max_frames=max_frames) + video = self._resize_and_center_crop(video, target_height, target_width) + requested_frames = getattr(cond, "num_frames", None) + + if cond.type == "suffix" and requested_frames is not None: + # The VAE encoder has temporal receptive fields, so encoding a short + # suffix clip independently produces different latents than encoding the + # full video. Encode the full clip and extract the last N latent frames. + video = rearrange(video, "f c h w -> 1 c f h w") + valid_frames = (video.shape[2] - 1) // 8 * 8 + 1 + video = video[:, :, :valid_frames] + latent = self._encode_video(video * 2.0 - 1.0, vae_encoder, device) + num_suffix_latent_frames = requested_frames // 8 + return latent[:, :, -num_suffix_latent_frames:] + + if requested_frames is not None: + requested_frames = min(requested_frames, video.shape[0]) + video = video[:requested_frames] + video = rearrange(video, "f c h w -> 1 c f h w") + valid_frames = (video.shape[2] - 1) // 8 * 8 + 1 + video = video[:, :, :valid_frames] + return self._encode_video(video * 2.0 - 1.0, vae_encoder, device) + + # ------------------------------------------------------------------ + # Unified generation pipeline + # ------------------------------------------------------------------ + + def _generate_sample( + self, + sample: ValidationSample, + cached_embeddings: CachedPromptEmbeddings | None, + cached_media: CachedSampleMedia, + transformer: "LTXModel", + device: torch.device, + sampling_ctx: SamplingContext, + ) -> tuple[Tensor | None, Tensor | None]: + """Generate one sample: build conditioned states, denoise, decode.""" + dims = sample.video_dims or self._config.video_dims + width, height, num_frames = dims + seed = sample.seed or self._config.seed + generate_audio = self._config.generate_audio + generate_video = self._config.generate_video + + # Determine frozen modalities from conditions + video_frozen = any(c.type == "video_to_audio" for c in sample.conditions) + audio_frozen = any(c.type == "audio_to_video" for c in sample.conditions) + + # 1. Prompt embeddings + v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg = self._get_prompt_embeddings(cached_embeddings, device) + + # 2. Build video state + generator = torch.Generator(device=device).manual_seed(seed) + noiser = GaussianNoiser(generator=generator) + + video_state: LatentState | None = None + video_clean: LatentState | None = None + video_tools: VideoLatentTools | None = None + + if generate_video or video_frozen: + video_tools = self._create_video_tools(width, height, num_frames) + + if video_frozen: + frozen_media = self._find_condition_media(sample, cached_media, "video_to_audio") + video_state = video_tools.create_initial_state( + device=device, dtype=torch.bfloat16, initial_latent=frozen_media.latent.to(device) + ) + video_state = replace(video_state, denoise_mask=torch.zeros_like(video_state.denoise_mask)) + video_clean = video_state + else: + video_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16) + video_state = self._apply_video_conditionings(video_state, video_tools, sample, cached_media, device) + video_clean = video_state + video_state = noiser(video_state, noise_scale=1.0) + + # 3. Build audio state + audio_state: LatentState | None = None + audio_clean: LatentState | None = None + audio_tools: AudioLatentTools | None = None + + if generate_audio or audio_frozen: + if audio_frozen: + frozen_media = self._find_condition_media(sample, cached_media, "audio_to_video") + frozen_latent = frozen_media.latent.to(device) + audio_tools = AudioLatentTools( + patchifier=self._audio_patchifier, + target_shape=AudioLatentShape.from_torch_shape(frozen_latent.shape), + ) + audio_state = audio_tools.create_initial_state( + device=device, dtype=torch.bfloat16, initial_latent=frozen_latent + ) + audio_state = replace(audio_state, denoise_mask=torch.zeros_like(audio_state.denoise_mask)) + audio_clean = audio_state + else: + audio_tools = self._create_audio_tools(num_frames, self._config.frame_rate) + audio_state = audio_tools.create_initial_state(device=device, dtype=torch.bfloat16) + audio_state = self._apply_audio_conditionings(audio_state, audio_tools, sample, cached_media, device) + audio_clean = audio_state + audio_state = noiser(audio_state, noise_scale=1.0) + + # 4. Denoising loop + video_state, audio_state = self._run_denoising( + transformer=transformer, + video_state=video_state, + audio_state=audio_state, + video_clean=video_clean, + audio_clean=audio_clean, + video_frozen=video_frozen, + audio_frozen=audio_frozen, + v_ctx_pos=v_ctx_pos, + a_ctx_pos=a_ctx_pos, + v_ctx_neg=v_ctx_neg, + a_ctx_neg=a_ctx_neg, + device=device, + sampling_ctx=sampling_ctx, + ) + + # 5. Decode modalities (both generated and frozen — frozen audio/video is included in output) + video_output = self._finalize_modality(video_state, video_tools, self._decode_video, device) + audio_output = self._finalize_modality(audio_state, audio_tools, self._decode_audio, device) + + # 6. Side-by-side reference output + if video_output is not None: + video_output = self._apply_reference_side_by_side(video_output, sample, cached_media) + + return video_output, audio_output + + # ------------------------------------------------------------------ + # Conditioning application + # ------------------------------------------------------------------ + + @staticmethod + def _apply_video_conditionings( + state: LatentState, + tools: VideoLatentTools, + sample: ValidationSample, + cached_media: CachedSampleMedia, + device: torch.device, + ) -> LatentState: + """Apply all video-targeting conditionings from the sample's conditions list.""" + for cond_idx, cond in enumerate(sample.conditions): + media = cached_media.conditions.get(cond_idx) + if media is None: + continue + + latent = media.latent.to(device=device, dtype=torch.bfloat16) + + if cond.type == "first_frame" or (cond.type == "prefix" and getattr(cond, "video", None) is not None): + state = VideoConditionByLatentIndex(latent=latent, strength=1.0, latent_idx=0).apply_to(state, tools) + + elif cond.type == "suffix" and getattr(cond, "video", None) is not None: + _, _, tgt_frames, _, _ = tools.target_shape.to_torch_shape() + _, _, cond_frames, _, _ = latent.shape + suffix_start = tgt_frames - cond_frames + state = VideoConditionByLatentIndex(latent=latent, strength=1.0, latent_idx=suffix_start).apply_to( + state, tools + ) + + elif cond.type == "reference" and cond.video is not None: + state = VideoConditionByReferenceLatent( + latent=latent, + downscale_factor=cond.downscale_factor, + temporal_scale_factor=cond.temporal_scale_factor, + strength=1.0, + ).apply_to(state, tools) + + elif cond.type == "mask": + mask = media.mask.to(device=device) + state = VideoConditionByMask(latent=latent, mask=mask, strength=1.0).apply_to(state, tools) + + elif cond.type == "spatial_crop": + mask = _build_spatial_crop_mask(cond.spatial_region, tools.target_shape, device) + state = VideoConditionByMask(latent=latent, mask=mask, strength=1.0).apply_to(state, tools) + + return state + + @staticmethod + def _apply_audio_conditionings( + state: LatentState, + tools: AudioLatentTools, + sample: ValidationSample, + cached_media: CachedSampleMedia, + device: torch.device, + ) -> LatentState: + """Apply all audio-targeting conditionings from the sample's conditions list.""" + for cond_idx, cond in enumerate(sample.conditions): + media = cached_media.conditions.get(cond_idx) + if media is None: + continue + + latent = media.latent.to(device=device, dtype=torch.bfloat16) + + if cond.type in ("prefix", "suffix") and getattr(cond, "audio", None) is not None: + tokens = tools.patchifier.patchify(latent) + num_cond_tokens = tokens.shape[1] + + state = state.clone() + start = 0 if cond.type == "prefix" else tools.target_shape.token_count() - num_cond_tokens + stop = start + num_cond_tokens + + state.clean_latent[:, start:stop] = tokens + state.denoise_mask[:, start:stop] = 0.0 + + elif cond.type == "reference" and getattr(cond, "audio", None) is not None: + tokens = tools.patchifier.patchify(latent) + ref_shape = AudioLatentShape.from_torch_shape(latent.shape) + + positions = tools.patchifier.get_patch_grid_bounds( + output_shape=ref_shape, + device=device, + ) + + denoise_mask = torch.zeros( + *tokens.shape[:2], + 1, + device=device, + dtype=torch.float32, + ) + + state = LatentState( + latent=torch.cat([state.latent, torch.zeros_like(tokens, dtype=state.latent.dtype)], dim=1), + denoise_mask=torch.cat([state.denoise_mask, denoise_mask], dim=1), + positions=torch.cat([state.positions, positions], dim=2), + clean_latent=torch.cat([state.clean_latent, tokens], dim=1), + attention_mask=None, + ) + + elif cond.type == "mask" and getattr(cond, "audio", None) is not None: + tokens = tools.patchifier.patchify(latent) + target_len = state.latent.shape[1] + tokens = tokens[:, :target_len] + + mask = media.mask.to(device=device) + if mask.shape[-1] != target_len: + mask = torch.nn.functional.interpolate( + mask.unsqueeze(0).float(), size=target_len, mode="nearest" + ).squeeze(0) + mask = (mask > 0.5).float() + if mask.dim() == 2: + mask = mask.unsqueeze(-1) + + m = mask.to(dtype=state.latent.dtype) + inv = 1 - m + + state = LatentState( + latent=state.latent, + denoise_mask=state.denoise_mask * inv, + positions=state.positions, + clean_latent=state.clean_latent * inv + tokens * m, + attention_mask=state.attention_mask, + ) + + return state + + @staticmethod + def _find_condition_media( + sample: ValidationSample, cached_media: CachedSampleMedia, condition_type: str + ) -> CachedConditionMedia: + """Look up the cached media for the first condition matching the given type.""" + for cond_idx, cond in enumerate(sample.conditions): + if cond.type == condition_type and cond_idx in cached_media.conditions: + return cached_media.conditions[cond_idx] + raise ValueError(f"No cached media found for condition type '{condition_type}'") + + @staticmethod + def _apply_reference_side_by_side( + video_output: Tensor, sample: ValidationSample, cached_media: CachedSampleMedia + ) -> Tensor: + """Concatenate reference video pixels side-by-side with generated output if requested.""" + for cond_idx, cond in enumerate(sample.conditions): + if cond.type == "reference" and cond.include_in_output: + media = cached_media.conditions.get(cond_idx) + if media is not None and media.pixels is not None: + ref_pixels = media.pixels + output_frames = video_output.shape[1] + ref_frames = ref_pixels.shape[1] + if ref_frames < output_frames: + temporal_sf = cond.temporal_scale_factor + if temporal_sf > 1: + # VAE-aligned stretch mirroring training-time subsampling: + # first frame preserved once (aligns with VAE's causal + # first latent), each subsequent ref frame held for + # `temporal_sf` output frames. + indices = torch.tensor([0] + [1 + (i - 1) // temporal_sf for i in range(1, output_frames)]) + indices = indices.clamp(max=ref_frames - 1) + else: + indices = torch.linspace(0, ref_frames - 1, output_frames).round().long() + ref_pixels = ref_pixels[:, indices] + video_output = _concatenate_videos_side_by_side(ref_pixels, video_output) + return video_output + + # ------------------------------------------------------------------ + # Denoising loop + # ------------------------------------------------------------------ + + def _run_denoising( # noqa: PLR0913 + self, + transformer: "LTXModel", + video_state: LatentState | None, + audio_state: LatentState | None, + video_clean: LatentState | None, + audio_clean: LatentState | None, + *, + video_frozen: bool, + audio_frozen: bool, + v_ctx_pos: Tensor, + a_ctx_pos: Tensor, + v_ctx_neg: Tensor | None, + a_ctx_neg: Tensor | None, + device: torch.device, + sampling_ctx: SamplingContext, + ) -> tuple[LatentState | None, LatentState | None]: + """Run the Euler denoising loop with CFG/STG, handling frozen modalities.""" + cfg = self._config + scheduler = LTX2Scheduler() + sigmas = scheduler.execute(steps=cfg.inference_steps).to(device).float() + stepper = EulerDiffusionStep() + cfg_guider = CFGGuider(cfg.guidance_scale) + stg_guider = STGGuider(cfg.stg_scale) + + stg_perturbation_config = ( + self._build_stg_perturbation_config(cfg.stg_blocks, cfg.stg_mode) if stg_guider.enabled() else None + ) + + x0_model = X0Model(transformer) + + for step_idx, sigma in enumerate(sigmas[:-1]): + v_sigma = torch.zeros_like(sigma) if video_frozen else sigma + a_sigma = torch.zeros_like(sigma) if audio_frozen else sigma + + video = ( + self._modality_from_latent_state(video_state, v_ctx_pos, v_sigma.unsqueeze(0)) + if video_state is not None + else None + ) + audio = ( + self._modality_from_latent_state(audio_state, a_ctx_pos, a_sigma.unsqueeze(0)) + if audio_state is not None + else None + ) + + pos_video, pos_audio = x0_model(video=video, audio=audio, perturbations=None) + denoised_video, denoised_audio = pos_video, pos_audio + + # CFG + if cfg_guider.enabled() and v_ctx_neg is not None: + video_neg = replace(video, context=v_ctx_neg) if video is not None else None + audio_neg = replace(audio, context=a_ctx_neg) if audio is not None else None + neg_video, neg_audio = x0_model(video=video_neg, audio=audio_neg, perturbations=None) + + if not video_frozen and denoised_video is not None: + denoised_video = denoised_video + cfg_guider.delta(pos_video, neg_video) + if not audio_frozen and denoised_audio is not None: + denoised_audio = denoised_audio + cfg_guider.delta(pos_audio, neg_audio) + + # STG + if stg_perturbation_config is not None: + ptb_video, ptb_audio = x0_model(video=video, audio=audio, perturbations=stg_perturbation_config) + if not video_frozen and denoised_video is not None: + denoised_video = denoised_video + stg_guider.delta(pos_video, ptb_video) + if not audio_frozen and denoised_audio is not None and ptb_audio is not None: + denoised_audio = denoised_audio + stg_guider.delta(pos_audio, ptb_audio) + + # Re-apply conditioning mask + if denoised_video is not None and video_clean is not None: + denoised_video = self._post_process_latent( + denoised_video, + video_state.denoise_mask, + video_clean.clean_latent, + ) + if denoised_audio is not None and audio_clean is not None: + denoised_audio = self._post_process_latent( + denoised_audio, + audio_state.denoise_mask, + audio_clean.clean_latent, + ) + + # Euler step (skip for frozen modalities) + if video_state is not None and not video_frozen: + video_state = replace( + video_state, + latent=stepper.step(video.latent, denoised_video, sigmas, step_idx), + ) + if audio_state is not None and not audio_frozen: + audio_state = replace( + audio_state, + latent=stepper.step(audio.latent, denoised_audio, sigmas, step_idx), + ) + + sampling_ctx.advance_step() + + return video_state, audio_state + + # --- Finalization (decode) --- + + def _finalize_modality( + self, + state: LatentState | None, + tools: VideoLatentTools | AudioLatentTools | None, + decode_fn: Callable[[LatentState, torch.device], Tensor], + device: torch.device, + ) -> Tensor | None: + """Clear conditioning tokens, unpatchify, and decode a modality (generated or frozen).""" + if state is None or tools is None: + return None + state = tools.clear_conditioning(state) + state = tools.unpatchify(state) + return decode_fn(state, device) + + def _decode_video(self, video_state: LatentState, device: torch.device) -> Tensor: + """Decode video latents to pixels using tiled VAE decoding.""" + self._vae_decoder.to(device) + latent = video_state.latent.to(dtype=torch.bfloat16) + + chunks = list(self._vae_decoder.tiled_decode(latent, tiling_config=_DEFAULT_TILING)) + decoded_video = torch.cat(chunks, dim=2) + + decoded_video = ((decoded_video + 1.0) / 2.0).clamp(0.0, 1.0) + self._vae_decoder.to("cpu") + return decoded_video[0].float().cpu() + + def _decode_audio(self, audio_state: LatentState, device: torch.device) -> Tensor: + """Decode audio latents to waveform via audio VAE + vocoder.""" + self._audio_decoder.to(device) + first_param = next(self._audio_decoder.parameters(), None) + decoder_dtype = first_param.dtype if first_param is not None else audio_state.latent.dtype + latent = audio_state.latent.to(dtype=decoder_dtype, device=device) + decoded_audio = self._audio_decoder(latent) + self._audio_decoder.to("cpu") + + self._vocoder.to(device) + audio_waveform = self._vocoder(decoded_audio) + self._vocoder.to("cpu") + + return audio_waveform.squeeze(0).float().cpu() + + # ------------------------------------------------------------------ + # Helpers + # ------------------------------------------------------------------ + + def _save_audio(self, audio: Tensor, path: Path) -> None: + """Save an audio waveform tensor to a .wav file using stdlib wave.""" + import wave # noqa: PLC0415 + + if audio.dim() == 1: + audio = audio.unsqueeze(0) + pcm = (audio.clamp(-1.0, 1.0).cpu() * 32767).to(torch.int16) + with wave.open(str(path), "wb") as wf: + wf.setnchannels(pcm.shape[0]) + wf.setsampwidth(2) + wf.setframerate(self._vocoder.output_sampling_rate) + wf.writeframes(pcm.T.contiguous().numpy().tobytes()) + + @staticmethod + def _encode_audio( + audio_path: str, + audio_encoder: torch.nn.Module, + device: torch.device, + max_duration: float | None = None, + *, + from_end: bool = False, + ) -> Tensor: + """Encode an audio file through the audio VAE encoder. Returns latent on CPU.""" + import torchaudio # noqa: PLC0415 + + waveform, sr = torchaudio.load(audio_path) + if max_duration is not None: + num_samples = min(round(max_duration * sr), waveform.shape[-1]) + waveform = waveform[:, -num_samples:] if from_end else waveform[:, :num_samples] + audio = Audio(waveform=waveform.unsqueeze(0), sampling_rate=sr) + audio_encoder.to(device) + latent = ltx_encode_audio(audio, audio_encoder) + audio_encoder.to("cpu") + return latent.cpu() + + @staticmethod + def _load_and_downsample_mask( + mask_path: str | Path, + target_width: int, + target_height: int, + target_num_frames: int, + ) -> Tensor: + """Load a mask image/video and downsample to latent-space dimensions. + Returns a binary float tensor of shape [1, F', H', W'] where F', H', W' are + the latent-space dimensions corresponding to the target video dims. + """ + from ltx_core.types import SpatioTemporalScaleFactors # noqa: PLC0415 + + sf = SpatioTemporalScaleFactors.default() + latent_f = (target_num_frames - 1) // sf.time + 1 + + mask_path = Path(mask_path) + if mask_path.suffix.lower() in (".png", ".jpg", ".jpeg", ".bmp", ".webp"): + img = to_tensor(open_image_as_srgb(mask_path)).mean(dim=0, keepdim=True) # [1, H, W] + img = torch.nn.functional.interpolate( + img.unsqueeze(0), size=(target_height, target_width), mode="nearest" + ).squeeze(0) # [1, H, W] + mask_pixels = img.expand(target_num_frames, -1, -1) # [F, H, W] + else: + frames, _ = read_video(str(mask_path), max_frames=target_num_frames) # [F, C, H, W] + frames = frames[:target_num_frames].mean(dim=1) # grayscale [F, H, W] + mask_pixels = torch.nn.functional.interpolate( + frames.unsqueeze(1), size=(target_height, target_width), mode="nearest" + ).squeeze(1) # [F, H, W] + + mask_latent = torch.nn.functional.avg_pool2d( + mask_pixels.unsqueeze(1), kernel_size=(sf.height, sf.width) + ).squeeze(1) # [F, H', W'] + + # Temporal: max-pool over groups of sf.time frames (conservative — any masked frame masks the group) + f_spatial = mask_latent.shape[0] + pad_f = (sf.time - f_spatial % sf.time) % sf.time + if pad_f > 0: + mask_latent = torch.nn.functional.pad(mask_latent, (0, 0, 0, 0, 0, pad_f)) + h_prime, w_prime = mask_latent.shape[1], mask_latent.shape[2] + mask_latent = mask_latent.reshape(-1, sf.time, h_prime, w_prime).amax(dim=1)[:latent_f] + + mask_latent = (mask_latent > 0.5).float() + return mask_latent.unsqueeze(0) # [1, F', H', W'] — unpatchified latent space + + @staticmethod + def _load_audio_mask( + mask_path: str | Path, + target_num_frames: int, + frame_rate: float, + ) -> Tensor: + """Load an audio mask (.pt or .wav) and resample to target audio latent length. + Returns a float tensor of shape [1, num_tokens] in patchified token space. + """ + mask_path = Path(mask_path) + if mask_path.suffix == ".pt": + raw = torch.load(mask_path, map_location="cpu", weights_only=True) + if isinstance(raw, dict): + raw = raw.get("mask", next(iter(raw.values()))) + raw = raw.float().flatten() + else: + import torchaudio # noqa: PLC0415 + + waveform, _sr = torchaudio.load(mask_path) + raw = waveform.abs().mean(dim=0) + + target_duration = target_num_frames / frame_rate + target_shape = AudioLatentShape.from_duration(batch=1, duration=target_duration) + target_len = target_shape.frames + + resampled = torch.nn.functional.interpolate( + raw.unsqueeze(0).unsqueeze(0), size=target_len, mode="nearest" + ).squeeze() + mask = (resampled > 0.5).float() + return mask.unsqueeze(0) # [1, num_tokens] + + def _create_video_tools(self, width: int, height: int, num_frames: int) -> VideoLatentTools: + """Create VideoLatentTools for the given output dimensions.""" + pixel_shape = VideoPixelShape( + batch=1, frames=num_frames, height=height, width=width, fps=self._config.frame_rate + ) + return VideoLatentTools( + patchifier=self._video_patchifier, + target_shape=VideoLatentShape.from_pixel_shape(shape=pixel_shape), + fps=self._config.frame_rate, + scale_factors=VIDEO_SCALE_FACTORS, + causal_fix=True, + ) + + def _create_audio_tools(self, num_frames: int, frame_rate: float) -> AudioLatentTools: + """Create AudioLatentTools for the given video duration.""" + return AudioLatentTools( + patchifier=self._audio_patchifier, + target_shape=AudioLatentShape.from_duration(batch=1, duration=num_frames / frame_rate), + ) + + @staticmethod + def _get_prompt_embeddings( + cached: CachedPromptEmbeddings | None, device: torch.device + ) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]: + """Move cached prompt embeddings to device, returning (v_pos, a_pos, v_neg, a_neg).""" + if cached is None: + raise ValueError("Cached prompt embeddings are required for validation generation") + return ( + cached.video_context_positive.to(device), + cached.audio_context_positive.to(device), + cached.video_context_negative.to(device) if cached.video_context_negative is not None else None, + cached.audio_context_negative.to(device) if cached.audio_context_negative is not None else None, + ) + + @staticmethod + def log_to_wandb(wandb_run: object, sample_paths: list[Path], step: int) -> None: + """Log validation outputs (images, videos, or audio) to Weights & Biases.""" + import wandb # noqa: PLC0415 + + suffix = sample_paths[0].suffix.lower() + if suffix in (".png", ".jpg", ".jpeg", ".heic", ".webp"): + media = [wandb.Image(str(path)) for path in sample_paths] + elif suffix in (".wav", ".mp3", ".flac", ".ogg"): + media = [wandb.Audio(str(path)) for path in sample_paths] + else: + media = [wandb.Video(str(path), format=suffix.lstrip(".")) for path in sample_paths] + wandb_run.log({"validation_samples": media}, step=step) + + @staticmethod + def _post_process_latent(denoised: Tensor, denoise_mask: Tensor, clean_latent: Tensor) -> Tensor: + """Blend denoised output with clean latent according to the denoise mask.""" + return (denoised * denoise_mask + clean_latent.float() * (1 - denoise_mask)).to(denoised.dtype) + + @staticmethod + def _modality_from_latent_state(state: LatentState, context: Tensor, sigma: Tensor) -> Modality: + """Build a Modality object from a LatentState, text context, and sigma.""" + return Modality( + enabled=True, + latent=state.latent, + sigma=sigma, + timesteps=state.denoise_mask * sigma, + positions=state.positions, + context=context, + context_mask=None, + ) + + @staticmethod + def _build_stg_perturbation_config( + stg_blocks: list[int] | None, stg_mode: Literal["stg_av", "stg_v"] + ) -> BatchedPerturbationConfig: + """Build STG perturbation config that skips self-attention in the specified blocks.""" + perturbations: list[Perturbation] = [ + Perturbation(type=PerturbationType.SKIP_VIDEO_SELF_ATTN, blocks=stg_blocks) + ] + if stg_mode == "stg_av": + perturbations.append(Perturbation(type=PerturbationType.SKIP_AUDIO_SELF_ATTN, blocks=stg_blocks)) + return BatchedPerturbationConfig(perturbations=[PerturbationConfig(perturbations=perturbations)]) + + @staticmethod + def _load_first_frame(media_path: Path) -> Tensor: + """Load the first frame from an image or video file as [C, H, W] in [0, 1].""" + video_extensions = {".mp4", ".avi", ".mov", ".mkv", ".webm"} + if media_path.suffix.lower() in video_extensions: + frames, _ = read_video(str(media_path), max_frames=1) + return frames[0] + image = open_image_as_srgb(str(media_path)) + return TF.to_tensor(image) + + @staticmethod + def _resize_and_center_crop(tensor: Tensor, target_height: int, target_width: int) -> Tensor: + """Resize [N, C, H, W] tensor to cover target dims (preserving aspect ratio) and center-crop.""" + current_height, current_width = tensor.shape[2:] + if current_height == target_height and current_width == target_width: + return tensor + + aspect_ratio = current_width / current_height + target_aspect_ratio = target_width / target_height + if aspect_ratio > target_aspect_ratio: + resize_height, resize_width = target_height, int(target_height * aspect_ratio) + else: + resize_height, resize_width = int(target_width / aspect_ratio), target_width + + tensor = TF.resize(tensor, size=[resize_height, resize_width], interpolation=InterpolationMode.BICUBIC) + tensor = tensor.clamp(0, 1) + h_start = (resize_height - target_height) // 2 + w_start = (resize_width - target_width) // 2 + return tensor[:, :, h_start : h_start + target_height, w_start : w_start + target_width] + + @staticmethod + def _encode_image( + image: Tensor, target_height: int, target_width: int, vae_encoder: torch.nn.Module, device: torch.device + ) -> Tensor: + """Encode a conditioning image to latent space. Returns [B, C, 1, H', W'] on CPU.""" + image = ValidationRunner._resize_and_center_crop(image.unsqueeze(0), target_height, target_width) + image = image.unsqueeze(2) # [B, C, 1, H, W] + image = (image * 2.0 - 1.0).to(device=device, dtype=torch.bfloat16) + vae_encoder.to(device) + encoded = vae_encoder(image) + vae_encoder.to("cpu") + return encoded.cpu() + + @staticmethod + def _preprocess_reference( + video: Tensor, + target_height: int, + target_width: int, + downscale_factor: int = 1, + temporal_scale_factor: int = 1, + ) -> tuple[Tensor, Tensor]: + """Preprocess reference video. Returns (preprocessed [-1,1], pixels [0,1]).""" + ref_height = target_height // downscale_factor + ref_width = target_width // downscale_factor + if ref_height % 32 != 0 or ref_width % 32 != 0: + raise ValueError( + f"Scaled reference dimensions ({ref_height}x{ref_width}) must be divisible by 32. " + f"Original: {target_height}x{target_width}, downscale_factor: {downscale_factor}" + ) + + video = ValidationRunner._resize_and_center_crop(video, ref_height, ref_width) + video = rearrange(video, "f c h w -> 1 c f h w") + valid_frames = (video.shape[2] - 1) // 8 * 8 + 1 + video = video[:, :, :valid_frames] + + # VAE-aligned temporal subsampling: keep frame 0, then every Nth frame + if temporal_scale_factor > 1: + indices = [0, *list(range(1, video.shape[2], temporal_scale_factor))] + video = video[:, :, indices] + + pixels = video[0].clone() + preprocessed = video * 2.0 - 1.0 + return preprocessed, pixels + + @staticmethod + def _encode_video(video: Tensor, vae_encoder: torch.nn.Module, device: torch.device) -> Tensor: + """Encode a [B, C, F, H, W] video tensor through the VAE. Returns latent on CPU.""" + vae_encoder.to(device) + latent = vae_encoder.tiled_encode(video.to(dtype=torch.bfloat16), TilingConfig.default()) + vae_encoder.to("cpu") + return latent.cpu() + + +def _build_spatial_crop_mask( + region: tuple[int, int, int, int], target_shape: VideoLatentShape, device: torch.device +) -> Tensor: + """Build a binary mask from a pixel-space spatial region (y1, x1, y2, x2).""" + y1, x1, y2, x2 = region + _, _, frames, height, width = target_shape.to_torch_shape() + + def to_latent(v: int, scale: int, max_v: int) -> int: + return max(0, min(v // scale, max_v)) + + ly1 = to_latent(y1, VIDEO_SCALE_FACTORS.height, height) + ly2 = to_latent(y2, VIDEO_SCALE_FACTORS.height, height) + lx1 = to_latent(x1, VIDEO_SCALE_FACTORS.width, width) + lx2 = to_latent(x2, VIDEO_SCALE_FACTORS.width, width) + + spatial_mask = torch.zeros(height, width, dtype=torch.float32, device=device) + spatial_mask[ly1:ly2, lx1:lx2] = 1.0 + + return spatial_mask.unsqueeze(0).unsqueeze(0).expand(1, frames, -1, -1) # [1, F, H, W] + + +def _concatenate_videos_side_by_side(left_video: Tensor, right_video: Tensor) -> Tensor: + """Concatenate two [C, F, H, W] videos horizontally, matching height and padding frames.""" + left_height, left_width = left_video.shape[2], left_video.shape[3] + right_height = right_video.shape[2] + if left_height != right_height: + scale = right_height / left_height + new_width = int(left_width * scale) + c, f, h, w = left_video.shape + left_video = left_video.reshape(c * f, 1, h, w) + left_video = TF.resize(left_video, size=[right_height, new_width], interpolation=InterpolationMode.BICUBIC) + left_video = left_video.clamp(0, 1) + left_video = left_video.reshape(c, f, right_height, new_width) + left_frames, right_frames = left_video.shape[1], right_video.shape[1] + if left_frames < right_frames: + padding = left_video[:, -1:].expand(-1, right_frames - left_frames, -1, -1) + left_video = torch.cat([left_video, padding], dim=1) + elif right_frames < left_frames: + padding = right_video[:, -1:].expand(-1, left_frames - right_frames, -1, -1) + right_video = torch.cat([right_video, padding], dim=1) + return torch.cat([left_video, right_video], dim=3) diff --git a/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py b/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py deleted file mode 100644 index b756cf1..0000000 --- a/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py +++ /dev/null @@ -1,874 +0,0 @@ -"""Validation sampling for LTX-2 training using ltx-core components. -This module provides a simplified validation pipeline for generating samples during training, -using the new ltx-core components (VideoLatentTools, AudioLatentTools, LatentState, etc.). -""" - -from dataclasses import dataclass, replace -from typing import TYPE_CHECKING, Literal - -import torch -from einops import rearrange -from torch import Tensor - -from ltx_core.components.diffusion_steps import EulerDiffusionStep -from ltx_core.components.guiders import CFGGuider, STGGuider -from ltx_core.components.noisers import GaussianNoiser -from ltx_core.components.patchifiers import ( - AudioPatchifier, - VideoLatentPatchifier, - get_pixel_coords, -) -from ltx_core.components.schedulers import LTX2Scheduler -from ltx_core.guidance.perturbations import ( - BatchedPerturbationConfig, - Perturbation, - PerturbationConfig, - PerturbationType, -) -from ltx_core.model.transformer.modality import Modality -from ltx_core.model.transformer.model import X0Model -from ltx_core.model.video_vae import SpatialTilingConfig, TemporalTilingConfig, TilingConfig -from ltx_core.tools import AudioLatentTools, VideoLatentTools -from ltx_core.types import AudioLatentShape, LatentState, SpatioTemporalScaleFactors, VideoLatentShape, VideoPixelShape -from ltx_trainer.progress import SamplingContext - -if TYPE_CHECKING: - from ltx_core.model.audio_vae import AudioDecoder, Vocoder - from ltx_core.model.transformer import LTXModel - from ltx_core.model.video_vae import VideoDecoder, VideoEncoder - from ltx_core.text_encoders.gemma import GemmaTextEncoder - from ltx_core.text_encoders.gemma.embeddings_processor import EmbeddingsProcessor - -VIDEO_SCALE_FACTORS = SpatioTemporalScaleFactors.default() - - -@dataclass -class CachedPromptEmbeddings: - """Pre-computed text embeddings for a validation prompt. - These embeddings are computed once at training start and reused for all validation runs, - avoiding the need to load the full Gemma text encoder during validation. - """ - - video_context_positive: Tensor # [1, seq_len, hidden_dim] - audio_context_positive: Tensor # [1, seq_len, hidden_dim] - video_context_negative: Tensor | None = None - audio_context_negative: Tensor | None = None - - -@dataclass -class TiledDecodingConfig: - """Configuration for tiled video decoding to reduce VRAM usage. - Tiled decoding splits the latent tensor into overlapping tiles, decodes each - tile individually, and blends them together. This significantly reduces peak - VRAM usage at the cost of slightly slower decoding. - Defaults match the recommended values from ltx-core tests. - """ - - enabled: bool = True # Whether to use tiled decoding (enabled by default) - tile_size_pixels: int = 192 # Spatial tile size in pixels (must be ≥64 and divisible by 32) - tile_overlap_pixels: int = 64 # Spatial tile overlap in pixels (must be divisible by 32) - tile_size_frames: int = 48 # Temporal tile size in frames (must be ≥16 and divisible by 8) - tile_overlap_frames: int = 24 # Temporal tile overlap in frames (must be divisible by 8) - - -@dataclass -class GenerationConfig: - """Configuration for video/audio generation.""" - - prompt: str # Text prompt for generation - negative_prompt: str = "" # Negative prompt to avoid unwanted artifacts - height: int = 544 # Output video height in pixels - width: int = 960 # Output video width in pixels - num_frames: int = 97 # Number of frames to generate - frame_rate: float = 25.0 # Frame rate for temporal position scaling - num_inference_steps: int = 30 # Number of denoising steps - guidance_scale: float = 4.0 # CFG guidance scale - seed: int = 42 # Random seed for reproducibility - condition_image: Tensor | None = None # Optional first frame image for image-to-video - reference_video: Tensor | None = None # For IC-LoRA: [F, C, H, W] in [0, 1] - reference_downscale_factor: int = 1 # For IC-LoRA: downscale factor (1 = same resolution, 2 = half resolution) - generate_audio: bool = True # Whether to generate audio alongside video - include_reference_in_output: bool = False # For IC-LoRA: concatenate original reference with generated output - cached_embeddings: CachedPromptEmbeddings | None = None # Pre-computed text embeddings (avoids loading Gemma) - stg_scale: float = 0.0 # STG strength (0.0 = disabled, recommended: 1.0) - stg_blocks: list[int] | None = None # Transformer blocks to perturb (None = all, recommended: [29]) - stg_mode: Literal["stg_av", "stg_v"] = "stg_av" # STG mode: "stg_av" (audio+video) or "stg_v" (video only) - # Tiled decoding config: None = use defaults (enabled), False = disable, or TiledDecodingConfig for custom settings - tiled_decoding: TiledDecodingConfig | Literal[False] | None = None - - def __post_init__(self) -> None: - """Apply default tiled decoding config if not provided.""" - if self.tiled_decoding is None: - # Use default config with tiling enabled - object.__setattr__(self, "tiled_decoding", TiledDecodingConfig()) - elif self.tiled_decoding is False: - # Explicitly disabled - use config with enabled=False - object.__setattr__(self, "tiled_decoding", TiledDecodingConfig(enabled=False)) - - -class ValidationSampler: - """Generates validation samples during training using ltx-core components. - This class provides a simplified interface for generating video (and optionally audio) - samples during training validation. It supports: - - Text-to-video generation - - Image-to-video generation (first frame conditioning) - - Video-to-video generation (IC-LoRA reference video conditioning) - - Optional audio generation - The implementation follows the patterns from ltx_pipelines.single_stage. - Text embeddings can be provided either via: - - A full text_encoder (encodes prompts on-the-fly) - - Pre-computed cached_embeddings (avoids loading Gemma during validation) - """ - - def __init__( - self, - transformer: "LTXModel", - vae_decoder: "VideoDecoder", - vae_encoder: "VideoEncoder | None", - text_encoder: "GemmaTextEncoder | None" = None, - audio_decoder: "AudioDecoder | None" = None, - vocoder: "Vocoder | None" = None, - sampling_context: SamplingContext | None = None, - embeddings_processor: "EmbeddingsProcessor | None" = None, - ): - """Initialize the validation sampler. - Args: - transformer: LTX-2 transformer model - vae_decoder: Video VAE decoder - vae_encoder: Video VAE encoder (for image/video conditioning), can be None if not needed - text_encoder: Gemma text encoder (optional if cached_embeddings in config) - audio_decoder: Optional audio VAE decoder (for audio generation) - vocoder: Optional vocoder (for audio generation) - sampling_context: Optional SamplingContext for progress display during denoising - embeddings_processor: Optional embeddings processor (required if text_encoder provided) - """ - self._transformer = transformer - self._vae_decoder = vae_decoder - self._vae_encoder = vae_encoder - self._text_encoder = text_encoder - self._embeddings_processor = embeddings_processor - self._audio_decoder = audio_decoder - self._vocoder = vocoder - self._sampling_context = sampling_context - - # Patchifiers - self._video_patchifier = VideoLatentPatchifier(patch_size=1) - self._audio_patchifier = AudioPatchifier(patch_size=1) - - # Note: Use @torch.no_grad() instead of @torch.inference_mode() to avoid FSDP inplace update errors after validation - @torch.no_grad() - def generate( - self, - config: GenerationConfig, - device: torch.device | str = "cuda", - ) -> tuple[Tensor, Tensor | None]: - """Generate a video (and optionally audio) sample. - Args: - config: Generation configuration - device: Device to run generation on - Returns: - Tuple of: - - video: Video tensor [C, F, H, W] in [0, 1] (float32) - - audio: Audio waveform tensor [C, samples] or None - """ - device = torch.device(device) if isinstance(device, str) else device - self._validate_config(config) - - # Route to appropriate generation method - if config.reference_video is not None: - return self._generate_with_reference(config, device) - return self._generate_standard(config, device) - - def _generate_standard(self, config: GenerationConfig, device: torch.device) -> tuple[Tensor, Tensor | None]: - """Standard generation (text-to-video or image-to-video).""" - # Get prompt embeddings (from cache or encode on-the-fly) - v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg = self._get_prompt_embeddings(config, device) - - # Setup generator - generator = torch.Generator(device=device).manual_seed(config.seed) - - # Create latent tools - video_tools = self._create_video_latent_tools(config) - audio_tools = self._create_audio_latent_tools(config) if config.generate_audio else None - - # Create initial states - video_clean_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16) - audio_clean_state = ( - audio_tools.create_initial_state(device=device, dtype=torch.bfloat16) if audio_tools else None - ) - - # Apply image conditioning if provided - if config.condition_image is not None: - video_clean_state = self._apply_image_conditioning( - video_clean_state, config.condition_image, config, device - ) - - # Add noise - noiser = GaussianNoiser(generator=generator) - video_state = noiser(latent_state=video_clean_state, noise_scale=1.0) - audio_state = noiser(latent_state=audio_clean_state, noise_scale=1.0) if audio_clean_state else None - - # Run denoising loop - video_state, audio_state = self._run_denoising( - config=config, - video_state=video_state, - audio_state=audio_state, - video_clean_state=video_clean_state, - audio_clean_state=audio_clean_state, - v_ctx_pos=v_ctx_pos, - a_ctx_pos=a_ctx_pos, - v_ctx_neg=v_ctx_neg, - a_ctx_neg=a_ctx_neg, - device=device, - ) - - # Decode outputs - video_state = video_tools.clear_conditioning(video_state) - video_state = video_tools.unpatchify(video_state) - video_output = self._decode_video(video_state, device, config.tiled_decoding) - - audio_output = None - if audio_state is not None and audio_tools is not None: - audio_state = audio_tools.clear_conditioning(audio_state) - audio_state = audio_tools.unpatchify(audio_state) - audio_output = self._decode_audio(audio_state, device) - - return video_output, audio_output - - def _generate_with_reference(self, config: GenerationConfig, device: torch.device) -> tuple[Tensor, Tensor | None]: - """Generate with reference video conditioning (IC-LoRA style). - For IC-LoRA: - - Reference video latents are concatenated with target latents - - Reference latents have timestep=0 (clean, not denoised) - - Target latents are denoised normally - - If condition_image is also provided, the first frame of the target is conditioned - - If include_reference_in_output is True, the preprocessed reference video - is concatenated side-by-side with the generated video - """ - # Get prompt embeddings (from cache or encode on-the-fly) - v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg = self._get_prompt_embeddings(config, device) - - # Setup generator - generator = torch.Generator(device=device).manual_seed(config.seed) - - # Preprocess and encode reference video - ref_video_preprocessed = self._preprocess_reference_video(config) - ref_latent, ref_positions = self._encode_video(ref_video_preprocessed, config.frame_rate, device) - ref_seq_len = ref_latent.shape[1] - - # Scale reference positions to match target coordinate space - # Position tensor shape: [B, 3, seq_len, 2] where dim 1 is (time, height, width) - if config.reference_downscale_factor != 1: - ref_positions = ref_positions.clone() - ref_positions[:, 1, ...] *= config.reference_downscale_factor # height axis - ref_positions[:, 2, ...] *= config.reference_downscale_factor # width axis - # Time axis (index 0) remains unchanged - - # Create target video state - video_tools = self._create_video_latent_tools(config) - target_clean_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16) - - # Apply first-frame image conditioning to target if provided - if config.condition_image is not None: - target_clean_state = self._apply_image_conditioning( - target_clean_state, config.condition_image, config, device - ) - - # Create combined state (reference + target) - # denoise_mask shape is [B, seq_len, 1] after patchification - ref_denoise_mask = torch.zeros(1, ref_seq_len, 1, device=device, dtype=torch.float32) - combined_clean_state = LatentState( - latent=torch.cat([ref_latent, target_clean_state.latent], dim=1), - denoise_mask=torch.cat([ref_denoise_mask, target_clean_state.denoise_mask], dim=1), - positions=torch.cat([ref_positions, target_clean_state.positions], dim=2), - clean_latent=torch.cat([ref_latent, target_clean_state.clean_latent], dim=1), - ) - - # Add noise (only to the target portion via denoise_mask) - noiser = GaussianNoiser(generator=generator) - combined_state = noiser(latent_state=combined_clean_state, noise_scale=1.0) - - # Create audio state if needed - audio_tools = self._create_audio_latent_tools(config) if config.generate_audio else None - audio_clean_state = ( - audio_tools.create_initial_state(device=device, dtype=torch.bfloat16) if audio_tools else None - ) - audio_state = noiser(latent_state=audio_clean_state, noise_scale=1.0) if audio_clean_state else None - - # Run denoising loop - combined_state, audio_state = self._run_denoising( - config=config, - video_state=combined_state, - audio_state=audio_state, - video_clean_state=combined_clean_state, - audio_clean_state=audio_clean_state, - v_ctx_pos=v_ctx_pos, - a_ctx_pos=a_ctx_pos, - v_ctx_neg=v_ctx_neg, - a_ctx_neg=a_ctx_neg, - device=device, - ) - - # Extract target portion and decode - target_latent = combined_state.latent[:, ref_seq_len:] - video_output = self._decode_video_latent(target_latent, config, device) - - # Optionally concatenate original reference video side-by-side - if config.include_reference_in_output: - # Use preprocessed reference (already resized/cropped, in pixel space) - # Convert from [B, C, F, H, W] to [C, F, H, W] - ref_video_pixels = ref_video_preprocessed[0].cpu() - # Normalize from [-1, 1] to [0, 1] - ref_video_pixels = ((ref_video_pixels + 1.0) / 2.0).clamp(0.0, 1.0) - video_output = self._concatenate_videos_side_by_side(ref_video_pixels, video_output) - - # Decode audio - audio_output = None - if audio_state is not None and audio_tools is not None: - audio_state = audio_tools.clear_conditioning(audio_state) - audio_state = audio_tools.unpatchify(audio_state) - audio_output = self._decode_audio(audio_state, device) - - return video_output, audio_output - - def _create_video_latent_tools(self, config: GenerationConfig) -> VideoLatentTools: - """Create video latent tools for the given configuration.""" - pixel_shape = VideoPixelShape( - batch=1, - frames=config.num_frames, - height=config.height, - width=config.width, - fps=config.frame_rate, - ) - return VideoLatentTools( - patchifier=self._video_patchifier, - target_shape=VideoLatentShape.from_pixel_shape(shape=pixel_shape), - fps=config.frame_rate, - scale_factors=VIDEO_SCALE_FACTORS, - causal_fix=True, - ) - - def _create_audio_latent_tools(self, config: GenerationConfig) -> AudioLatentTools: - """Create audio latent tools for the given configuration.""" - return AudioLatentTools( - patchifier=self._audio_patchifier, - target_shape=AudioLatentShape.from_duration(batch=1, duration=config.num_frames / config.frame_rate), - ) - - def _apply_image_conditioning( - self, video_state: LatentState, image: Tensor, config: GenerationConfig, device: torch.device - ) -> LatentState: - """Apply first-frame image conditioning to the video state.""" - # Encode the image - encoded_image = self._encode_conditioning_image(image, config.height, config.width, device) - - # Patchify the encoded image (single frame) - patchified_image = self._video_patchifier.patchify(encoded_image) # [1, 1, C] -> [1, num_patches, C] - num_image_tokens = patchified_image.shape[1] - - # Update the first frame tokens in the latent - new_latent = video_state.latent.clone() - new_latent[:, :num_image_tokens] = patchified_image.to(new_latent.dtype) - - # Update clean_latent as well (conditioning image is clean) - new_clean_latent = video_state.clean_latent.clone() - new_clean_latent[:, :num_image_tokens] = patchified_image.to(new_clean_latent.dtype) - - # Set denoise_mask to 0 for conditioned tokens (don't denoise them) - new_denoise_mask = video_state.denoise_mask.clone() - new_denoise_mask[:, :num_image_tokens] = 0.0 - - return LatentState( - latent=new_latent, - denoise_mask=new_denoise_mask, - positions=video_state.positions, - clean_latent=new_clean_latent, - ) - - @staticmethod - def _preprocess_reference_video(config: GenerationConfig) -> Tensor: - """Preprocess reference video: resize, crop, and convert to model input format. - When reference_downscale_factor > 1, the reference video is downscaled to a smaller - resolution for more efficient inference. The positions will be scaled up later - to match the target coordinate space. - Args: - config: Generation configuration - Returns: - Preprocessed video tensor [B, C, F, H, W] in [-1, 1] range - """ - ref_video = config.reference_video # [F, C, H, W] in [0, 1] - scale_factor = config.reference_downscale_factor - - # Target dimensions for reference (scaled down if scale_factor > 1) - target_height = config.height // scale_factor - target_width = config.width // scale_factor - - # Validate scaled dimensions - if target_height % 32 != 0 or target_width % 32 != 0: - raise ValueError( - f"Scaled reference dimensions ({target_height}x{target_width}) must be divisible by 32. " - f"Original: {config.height}x{config.width}, scale_factor: {scale_factor}" - ) - - current_height, current_width = ref_video.shape[2:] - - # Resize maintaining aspect ratio and center crop if needed - if current_height != target_height or current_width != target_width: - aspect_ratio = current_width / current_height - target_aspect_ratio = target_width / target_height - - if aspect_ratio > target_aspect_ratio: - resize_height, resize_width = target_height, int(target_height * aspect_ratio) - else: - resize_height, resize_width = int(target_width / aspect_ratio), target_width - - ref_video = torch.nn.functional.interpolate( - ref_video, size=(resize_height, resize_width), mode="bilinear", align_corners=False - ) - - # Center crop - h_start = (resize_height - target_height) // 2 - w_start = (resize_width - target_width) // 2 - ref_video = ref_video[:, :, h_start : h_start + target_height, w_start : w_start + target_width] - - # Convert to [B, C, F, H, W] and trim to valid frame count (k*8 + 1) - ref_video = rearrange(ref_video, "f c h w -> 1 c f h w") - valid_frames = (ref_video.shape[2] - 1) // 8 * 8 + 1 - ref_video = ref_video[:, :, :valid_frames] - - # Convert to [-1, 1] range - return ref_video * 2.0 - 1.0 - - def _encode_video(self, video: Tensor, fps: float, device: torch.device) -> tuple[Tensor, Tensor]: - """Encode video to patchified latents and compute positions. - Args: - video: Video tensor [B, C, F, H, W] in [-1, 1] range - fps: Frame rate for temporal position scaling - device: Device to run encoding on - Returns: - Tuple of (patchified_latents, positions) - """ - video = video.to(device=device, dtype=torch.float32) - - # Encode with VAE - self._vae_encoder.to(device) - with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16): - latents = self._vae_encoder(video) - self._vae_encoder.to("cpu") - - latents = latents.to(torch.bfloat16) - patchified = self._video_patchifier.patchify(latents) - - # Compute positions - latent_shape = VideoLatentShape( - batch=1, - channels=latents.shape[1], - frames=latents.shape[2], - height=latents.shape[3], - width=latents.shape[4], - ) - latent_coords = self._video_patchifier.get_patch_grid_bounds(output_shape=latent_shape, device=device) - positions = get_pixel_coords(latent_coords, scale_factors=VIDEO_SCALE_FACTORS, causal_fix=True) - positions = positions.to(torch.bfloat16) - positions[:, 0, ...] = positions[:, 0, ...] / fps - - return patchified, positions - - def _run_denoising( - self, - config: GenerationConfig, - video_state: LatentState, - audio_state: LatentState | None, - video_clean_state: LatentState, - audio_clean_state: LatentState | None, - v_ctx_pos: Tensor, - a_ctx_pos: Tensor, - v_ctx_neg: Tensor | None, - a_ctx_neg: Tensor | None, - device: torch.device, - ) -> tuple[LatentState, LatentState | None]: - """Run the denoising loop using X0 prediction with CFG and optional STG.""" - scheduler = LTX2Scheduler() - sigmas = scheduler.execute(steps=config.num_inference_steps).to(device).float() - stepper = EulerDiffusionStep() - cfg_guider = CFGGuider(config.guidance_scale) - stg_guider = STGGuider(config.stg_scale) - - # Build STG perturbation config if STG is enabled - stg_perturbation_config = self._build_stg_perturbation_config(config) if stg_guider.enabled() else None - - # Create initial modalities (will be updated each step via replace()) - video = Modality( - enabled=True, - latent=video_state.latent, - sigma=sigmas[0].repeat(video_state.latent.shape[0]), - timesteps=video_state.denoise_mask, - positions=video_state.positions, - context=v_ctx_pos, - context_mask=None, - ) - - # Audio modality is None when not generating audio - audio: Modality | None = None - if audio_state is not None: - audio = Modality( - enabled=True, - latent=audio_state.latent, - sigma=sigmas[0].repeat(audio_state.latent.shape[0]), - timesteps=audio_state.denoise_mask, - positions=audio_state.positions, - context=a_ctx_pos, - context_mask=None, - ) - - # Wrap transformer with X0Model to convert velocity predictions to denoised outputs - self._transformer.to(device) - x0_model = X0Model(self._transformer) - - with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16): - for step_idx, sigma in enumerate(sigmas[:-1]): - # Update modalities with current state and timesteps - video = replace( - video, - latent=video_state.latent, - sigma=sigma.repeat(video_state.latent.shape[0]), - timesteps=sigma * video_state.denoise_mask, - positions=video_state.positions, - ) - - if audio is not None and audio_state is not None: - audio = replace( - audio, - latent=audio_state.latent, - sigma=sigma.repeat(audio_state.latent.shape[0]), - timesteps=sigma * audio_state.denoise_mask, - positions=audio_state.positions, - ) - - # Run model (positive pass) - X0Model returns denoised outputs - pos_video, pos_audio = x0_model(video=video, audio=audio, perturbations=None) - denoised_video, denoised_audio = pos_video, pos_audio - - # Apply CFG if guidance_scale != 1.0 - if cfg_guider.enabled() and v_ctx_neg is not None: - video_neg = replace(video, context=v_ctx_neg) - audio_neg = replace(audio, context=a_ctx_neg) if audio is not None else None - neg_video, neg_audio = x0_model(video=video_neg, audio=audio_neg, perturbations=None) - - denoised_video = denoised_video + cfg_guider.delta(pos_video, neg_video) - if audio is not None and denoised_audio is not None: - denoised_audio = denoised_audio + cfg_guider.delta(pos_audio, neg_audio) - - # Apply STG if stg_scale != 0.0 - if stg_guider.enabled() and stg_perturbation_config is not None: - perturbed_video, perturbed_audio = x0_model( - video=video, audio=audio, perturbations=stg_perturbation_config - ) - denoised_video = denoised_video + stg_guider.delta(pos_video, perturbed_video) - if audio is not None and denoised_audio is not None and perturbed_audio is not None: - denoised_audio = denoised_audio + stg_guider.delta(pos_audio, perturbed_audio) - - # Apply conditioning mask (keep conditioned tokens clean) - denoised_video = denoised_video * video_state.denoise_mask + video_clean_state.latent.float() * ( - 1 - video_state.denoise_mask - ) - if audio is not None and audio_state is not None and audio_clean_state is not None: - denoised_audio = denoised_audio * audio_state.denoise_mask + audio_clean_state.latent.float() * ( - 1 - audio_state.denoise_mask - ) - - # Euler step - video_state = replace( - video_state, - latent=stepper.step( - sample=video.latent, denoised_sample=denoised_video, sigmas=sigmas, step_index=step_idx - ), - ) - if audio is not None and audio_state is not None: - audio_state = replace( - audio_state, - latent=stepper.step( - sample=audio.latent, denoised_sample=denoised_audio, sigmas=sigmas, step_index=step_idx - ), - ) - - # Update progress - if self._sampling_context is not None: - self._sampling_context.advance_step() - - return video_state, audio_state - - @staticmethod - def _build_stg_perturbation_config(config: GenerationConfig) -> BatchedPerturbationConfig: - """Build the perturbation config for STG based on the stg_mode.""" - # Always skip video self-attention for STG - perturbations: list[Perturbation] = [ - Perturbation(type=PerturbationType.SKIP_VIDEO_SELF_ATTN, blocks=config.stg_blocks) - ] - - # Optionally also skip audio self-attention (stg_av mode) - if config.stg_mode == "stg_av": - perturbations.append(Perturbation(type=PerturbationType.SKIP_AUDIO_SELF_ATTN, blocks=config.stg_blocks)) - - perturbation_config = PerturbationConfig(perturbations=perturbations) - # Batch size is 1 for validation - return BatchedPerturbationConfig(perturbations=[perturbation_config]) - - def _decode_video_latent(self, latent: Tensor, config: GenerationConfig, device: torch.device) -> Tensor: - """Decode patchified video latent to pixel space.""" - # Unpatchify - latent_frames = config.num_frames // VIDEO_SCALE_FACTORS.time + 1 - latent_height = config.height // VIDEO_SCALE_FACTORS.height - latent_width = config.width // VIDEO_SCALE_FACTORS.width - - unpatchified = self._video_patchifier.unpatchify( - latent, - output_shape=VideoLatentShape( - height=latent_height, - width=latent_width, - frames=latent_frames, - batch=1, - channels=128, - ), - ) - - # Decode - ensure bfloat16 to match decoder weights - self._vae_decoder.to(device) - unpatchified = unpatchified.to(dtype=torch.bfloat16) - tiled_config = config.tiled_decoding - - if tiled_config is not None and tiled_config.enabled: - # Use tiled decoding for reduced VRAM - tiling_config = TilingConfig( - spatial_config=SpatialTilingConfig( - tile_size_in_pixels=tiled_config.tile_size_pixels, - tile_overlap_in_pixels=tiled_config.tile_overlap_pixels, - ), - temporal_config=TemporalTilingConfig( - tile_size_in_frames=tiled_config.tile_size_frames, - tile_overlap_in_frames=tiled_config.tile_overlap_frames, - ), - ) - chunks = [] - for video_chunk in self._vae_decoder.tiled_decode( - unpatchified, - tiling_config=tiling_config, - ): - chunks.append(video_chunk) - decoded_video = torch.cat(chunks, dim=2) - else: - # Standard full decoding - decoded_video = self._vae_decoder(unpatchified) - - decoded_video = ((decoded_video + 1.0) / 2.0).clamp(0.0, 1.0) - self._vae_decoder.to("cpu") - - return decoded_video[0].float().cpu() - - def _validate_config(self, config: GenerationConfig) -> None: - """Validate generation configuration.""" - if config.height % 32 != 0 or config.width % 32 != 0: - raise ValueError(f"height and width must be divisible by 32, got {config.height}x{config.width}") - if config.num_frames % 8 != 1: - raise ValueError(f"num_frames must satisfy num_frames % 8 == 1, got {config.num_frames}") - if config.generate_audio and (self._audio_decoder is None or self._vocoder is None): - raise ValueError("Audio generation requires audio_decoder and vocoder") - if config.condition_image is not None and self._vae_encoder is None: - raise ValueError("Image conditioning requires vae_encoder") - if config.reference_video is not None and self._vae_encoder is None: - raise ValueError("Reference video conditioning requires vae_encoder") - - # Validate prompt embedding source - if config.cached_embeddings is None and self._text_encoder is None: - raise ValueError("Either text_encoder or config.cached_embeddings must be provided") - if config.cached_embeddings is None and self._embeddings_processor is None: - raise ValueError("embeddings_processor is required when encoding prompts on-the-fly") - - def _get_prompt_embeddings( - self, config: GenerationConfig, device: torch.device - ) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]: - """Get prompt embeddings from config cache or encode on-the-fly.""" - if config.cached_embeddings is not None: - # Use pre-computed embeddings from config - cached = config.cached_embeddings - v_ctx_pos = cached.video_context_positive.to(device) - a_ctx_pos = cached.audio_context_positive.to(device) - v_ctx_neg = cached.video_context_negative.to(device) if cached.video_context_negative is not None else None - a_ctx_neg = cached.audio_context_negative.to(device) if cached.audio_context_negative is not None else None - return v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg - - # Fall back to encoding on-the-fly - return self._encode_prompts(config, device) - - def _encode_prompts( - self, config: GenerationConfig, device: torch.device - ) -> tuple[Tensor, Tensor, Tensor | None, Tensor | None]: - """Encode positive and negative prompts using the text encoder + embeddings processor.""" - self._text_encoder.to(device) - self._embeddings_processor.to(device) - - pos_hs, pos_mask = self._text_encoder.encode(config.prompt) - pos_out = self._embeddings_processor.process_hidden_states(pos_hs, pos_mask) - v_ctx_pos, a_ctx_pos = pos_out.video_encoding, pos_out.audio_encoding - - v_ctx_neg, a_ctx_neg = None, None - if config.guidance_scale != 1.0: - neg_hs, neg_mask = self._text_encoder.encode(config.negative_prompt) - neg_out = self._embeddings_processor.process_hidden_states(neg_hs, neg_mask) - v_ctx_neg, a_ctx_neg = neg_out.video_encoding, neg_out.audio_encoding - - # Move the base Gemma model to CPU - self._text_encoder.model.to("cpu") - - return v_ctx_pos, a_ctx_pos, v_ctx_neg, a_ctx_neg - - def _decode_video( - self, video_state: LatentState, device: torch.device, tiled_config: TiledDecodingConfig | None = None - ) -> Tensor: - """Decode video latents to pixel space. - Args: - video_state: Video latent state to decode - device: Device to run decoding on - tiled_config: Optional tiled decoding configuration for reduced VRAM usage - Returns: - Decoded video tensor [C, F, H, W] in [0, 1] range - """ - self._vae_decoder.to(device) - # Ensure latent is bfloat16 to match decoder weights - latent = video_state.latent.to(dtype=torch.bfloat16) - - if tiled_config is not None and tiled_config.enabled: - # Use tiled decoding for reduced VRAM - tiling_config = TilingConfig( - spatial_config=SpatialTilingConfig( - tile_size_in_pixels=tiled_config.tile_size_pixels, - tile_overlap_in_pixels=tiled_config.tile_overlap_pixels, - ), - temporal_config=TemporalTilingConfig( - tile_size_in_frames=tiled_config.tile_size_frames, - tile_overlap_in_frames=tiled_config.tile_overlap_frames, - ), - ) - chunks = [] - for video_chunk in self._vae_decoder.tiled_decode( - latent, - tiling_config=tiling_config, - ): - chunks.append(video_chunk) - decoded_video = torch.cat(chunks, dim=2) - else: - # Standard full decoding - decoded_video = self._vae_decoder(latent) - - decoded_video = ((decoded_video + 1.0) / 2.0).clamp(0.0, 1.0) - self._vae_decoder.to("cpu") - return decoded_video[0].float().cpu() - - def _decode_audio(self, audio_state: LatentState, device: torch.device) -> Tensor: - """Decode audio latents to waveform.""" - self._audio_decoder.to(device) - first_param = next(self._audio_decoder.parameters(), None) - decoder_dtype = first_param.dtype if first_param is not None else audio_state.latent.dtype - latent = audio_state.latent.to(dtype=decoder_dtype, device=device) - decoded_audio = self._audio_decoder(latent) - self._audio_decoder.to("cpu") - - self._vocoder.to(device) - audio_waveform = self._vocoder(decoded_audio) - self._vocoder.to("cpu") - - return audio_waveform.squeeze(0).float().cpu() - - @staticmethod - def _concatenate_videos_side_by_side(left_video: Tensor, right_video: Tensor) -> Tensor: - """Concatenate two videos side-by-side (horizontally). - If the videos have different frame counts, the shorter one is padded with - its last frame repeated. - Args: - left_video: Left video tensor [C, F1, H1, W1] in [0, 1] - right_video: Right video tensor [C, F2, H2, W2] in [0, 1] - Returns: - Concatenated video tensor [C, max(F1,F2), H2, W1_scaled+W2] in [0, 1] - """ - left_height, left_width = left_video.shape[2], left_video.shape[3] - right_height = right_video.shape[2] - - # Resize left video to match right video's height if needed - if left_height != right_height: - # Scale width proportionally to maintain aspect ratio - scale = right_height / left_height - new_width = int(left_width * scale) - # Interpolate expects [N, C, H, W], we have [C, F, H, W] - # Reshape to [C*F, 1, H, W] -> interpolate -> reshape back - c, f, h, w = left_video.shape - left_video = left_video.reshape(c * f, 1, h, w) - left_video = torch.nn.functional.interpolate( - left_video, size=(right_height, new_width), mode="bilinear", align_corners=False - ) - left_video = left_video.reshape(c, f, right_height, new_width) - - left_frames = left_video.shape[1] - right_frames = right_video.shape[1] - - # Pad shorter video by repeating last frame - if left_frames < right_frames: - padding = left_video[:, -1:, :, :].expand(-1, right_frames - left_frames, -1, -1) - left_video = torch.cat([left_video, padding], dim=1) - elif right_frames < left_frames: - padding = right_video[:, -1:, :, :].expand(-1, left_frames - right_frames, -1, -1) - right_video = torch.cat([right_video, padding], dim=1) - - # Concatenate along width dimension - return torch.cat([left_video, right_video], dim=3) - - def _encode_conditioning_image( - self, - image: Tensor, - target_height: int, - target_width: int, - device: torch.device, - ) -> Tensor: - """Encode a conditioning image to latent space. - The image is resized to cover the target dimensions while preserving aspect ratio, - then center-cropped to exactly match the target size. - """ - # image is [C, H, W] in [0, 1] # noqa: ERA001 - current_height, current_width = image.shape[1:] - - # Resize maintaining aspect ratio (cover target, then center crop) - if current_height != target_height or current_width != target_width: - aspect_ratio = current_width / current_height - target_aspect_ratio = target_width / target_height - - if aspect_ratio > target_aspect_ratio: - # Image is wider than target - resize to match height, crop width - resize_height = target_height - resize_width = int(target_height * aspect_ratio) - else: - # Image is taller than target - resize to match width, crop height - resize_height = int(target_width / aspect_ratio) - resize_width = target_width - - image = rearrange(image, "c h w -> 1 c h w") - image = torch.nn.functional.interpolate( - image, size=(resize_height, resize_width), mode="bilinear", align_corners=False - ) - - # Center crop to target dimensions - h_start = (resize_height - target_height) // 2 - w_start = (resize_width - target_width) // 2 - image = image[:, :, h_start : h_start + target_height, w_start : w_start + target_width] - else: - image = rearrange(image, "c h w -> 1 c h w") - - # Add frame dimension and convert to [-1, 1] - image = rearrange(image, "b c h w -> b c 1 h w") - image = (image * 2.0 - 1.0).to(device=device, dtype=torch.float32) - - # Encode - self._vae_encoder.to(device) - with torch.autocast(device_type=str(device).split(":")[0], dtype=torch.bfloat16): - encoded = self._vae_encoder(image) - self._vae_encoder.to("cpu") - - return encoded diff --git a/uv.lock b/uv.lock index ae73f33..6b72fe2 100644 --- a/uv.lock +++ b/uv.lock @@ -248,9 +248,9 @@ name = "anyio" version = "4.12.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "exceptiongroup", marker = "python_full_version < '3.11'" }, + { name = "exceptiongroup", marker = "python_full_version < '3.11' or (extra == 'extra-8-ltx-core-fp8-trtllm' and extra == 'extra-8-ltx-core-xformers')" }, { name = "idna" }, - { name = "typing-extensions", marker = "python_full_version < '3.13'" }, + { name = "typing-extensions", marker = "python_full_version < '3.13' or (extra == 'extra-8-ltx-core-fp8-trtllm' and extra == 'extra-8-ltx-core-xformers')" }, ] sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = 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size = 28800, upload-time = "2025-12-16T00:40:30.322Z" }, ] +[[package]] +name = "google-genai" +version = "2.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "distro" }, + { name = "google-auth", extra = ["requests"] }, + { name = "httpx" }, + { name = "pydantic" }, + { name = "requests" }, + { name = "sniffio" }, + { name = "tenacity" }, + { name = "typing-extensions" }, + { name = "websockets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a7/7b/6eb3b3d545b6bb4c374acba1ccf91b0f33b605e551536a6243cfcef2f07f/google_genai-2.7.0.tar.gz", hash = "sha256:3c6f32f5ced9877ededd1b384b5e5b7f09c20046ec3390b662b16d8cd1882ac5", size = 555853, upload-time = "2026-05-28T15:39:24.58Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3c/dd/7a8be39e9d698e80e9db796514efbc6083dbd787bdb9a101e8ba47248e5e/google_genai-2.7.0-py3-none-any.whl", hash = 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