indigo
e03cc62548
Wire SCAIL-2 driving + in-context mask conditioning into the trainer via the unified FlexibleStrategy, so the widened patchify_proj (Phase 2) can be trained. - flexible.py: new DrivingConditionConfig (driving-latent concat with a RoPE width offset ΔW) and MaskChannelsConditionConfig (semantic masks -> per-token channels), added to the condition union and get_data_sources. Driving is prepended (cond-first, target stays at the tail for loss slicing); mask channels are written onto the driving tokens via Modality.cond_channels, reusing ltx-core encode_mask_channels. The noisy target keeps a zero mask. - model_loader.load_transformer gains mask_conditioning_channels, widening the video patchify_proj with zero-init columns via a new live-module helper (widen_module_patchify_proj_for_mask_channels). ModelConfig exposes the field. - trainer unfreezes patchify_proj in LoRA mode when mask channels are active (the new input columns are new base params LoRA cannot reach). - configs/scail_animation_lora.yaml plus README / training-modes table rows. Verified on CPU (verify_phase3_trainer.py): config round-trips, prepare_training _inputs builds cond_channels [B,T,56] with the mask on the driving tokens, a tiny widened model forwards and compute_loss returns a finite [B] loss, and the widen helper is output-preserving at zero init. Real training needs Linux+GPU+checkpoint; dataset preprocessing (driving latents + semantic masks) and validation-runner wiring are left for later. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
56 lines
5.5 KiB
Markdown
56 lines
5.5 KiB
Markdown
# SCAIL-2 → LTX-2 移植任務追蹤
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狀態圖例：✅ 完成 ｜ 🔄 進行中 ｜ ⬜ 未開始 ｜ ⏸️ 暫緩
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相關計畫見 [`plan.md`](./plan.md)。
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## Phase 1 — Driving 串接 item（推論期 PoC）
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| # | 任務 | 狀態 | 產出 / 備註 |
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| 1.1 | 新增 `VideoConditionByDrivingLatent` + `DrivingMode` | ✅ | `packages/ltx-core/src/ltx_core/conditioning/types/driving_video_cond.py`，以 `reference_video_cond.py` 為藍本，實作 ΔW width 偏移、時間對齊 target、`max_pos` 防呆、token 數防呆 |
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| 1.2 | 匯出新 conditioning 類別 | ✅ | `conditioning/types/__init__.py`、`conditioning/__init__.py` |
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| 1.3 | 免-GPU 資料流驗證腳本 | ✅ | 序列長度、ΔW 不重疊、時間對齊、frozen denoise_mask、attention_mask=None、`clear_conditioning` 剝除、超界/token 數防呆 — 全通過；ruff clean |
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| 1.4 | （選配）端到端 smoke run | ✅ | 本機 CPU-only、無 checkpoint，改用小型真實 `LTXModel`（隨機權重）跑 pipeline 實走路徑：`create_noised_state`（含 driving cond）→ `modality_from_latent_state` → **真 transformer forward**（seq 160=target 80+driving 80）→ `clear_conditioning`（→80）。ANIMATION/REPLACEMENT 皆通過：不 crash、輸出 finite、driving frozen 且正確剝除。**僅驗證整合，不評估畫質** |
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## Phase 2 — In-context mask channel（checkpoint 手術）
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> **決策**：mask channel 時間編碼採**忠實堆疊**——每 latent 幀對應 8 個 pixel 子幀沿 channel 堆疊，`8×(K+1)=56` channel（LTX 時間因子 8，K=6）。非 Wan 的 4×(K+1)=28。
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| # | 任務 | 狀態 | 備註 |
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| 2.1 | `patchify_proj` 加寬（config-gated） | ✅ | `LTXModel(mask_conditioning_channels=0)`；>0 時 `patchify_proj=Linear(in+mask, inner)`。預設不變 |
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| 2.2 | `LatentState`/`Modality` 帶 `cond_channels` 欄位 | ✅ | `types.py`、`modality.py` 加 `cond_channels: Tensor\|None=None`（patchified [B,T,C]）；`tools.clear_conditioning` 裁切；`helpers.modality_from_latent_state` 帶入 |
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| 2.3 | 投影前 concat/zero-pad cond_channels | ✅ | `transformer_args.py` `_apply_patchify_proj`：寬度不足時用 cond_channels 補齊，None 則補零 |
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| 2.4 | checkpoint zero-init 加寬轉換 | ✅ | `mask_channels_checkpoint.py` `widen_patchify_proj_for_mask_channels`：尾端補零欄，載入舊權重行為不變 |
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| 2.5 | `VideoConditionByMaskChannels`（56ch） | ✅ | `mask_channels_cond.py`：1 環境開關 + K=6 綁定槽 → 空間下採樣 + 時間 8× 堆疊(causal 首幀複製)=56ch，寫入尾端 driving token，target 保持零 |
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| 2.6 | token-append conditioning 延伸 cond_channels | ✅ | `cond_channels.py` `extend_cond_channels`；reference_video/reference_audio/driving/keyframe 皆接上 |
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| 2.7 | Phase 2 驗證（免訓練） | ✅ | `verify_mask_channels.py`：mask=0 向後相容；zero-init 加寬 forward == baseline（任意 cond_channels）；driving+mask pipeline forward 不 crash、cond_channels 形狀/placement 正確、`clear_conditioning` 剝除 |
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| 2.8 | Replacement Mode z_ref 高度位移 ΔH_ref | ⬜ | 仍暫緩（Phase 1 divergence，需獨立 reference token group） |
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## Phase 3 — 訓練整合（ltx-trainer）
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> **決策**：完整整合到 `FlexibleStrategy`；訓練方式 = **LoRA + 解凍 patchify_proj**（新 mask 欄位無法純 LoRA 訓練）。**本機無 GPU/Linux/checkpoint，只做到 CPU 單元驗證**，實訓需在 GPU 機器跑。
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| # | 任務 | 狀態 | 備註 |
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| 3.1 | 新增 Driving/MaskChannels ConditionConfig | ✅ | `flexible.py`：`DrivingConditionConfig`(latents_dir/mode/width_offset) + `MaskChannelsConditionConfig`(mask_dir/num_slots)，加入 union + `get_data_sources` |
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| 3.2 | strategy 接線 driving concat + cond_channels | ✅ | `_apply_driving_condition`(cond-first concat + ΔW width 偏移) + `_build_mask_channels`(重用 `encode_mask_channels`，寫前 N driving token) → `Modality.cond_channels` |
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| 3.3 | model_loader + ModelConfig 支援加寬 | ✅ | `widen_module_patchify_proj_for_mask_channels`(live module zero-init) + `load_transformer(mask_conditioning_channels=)` + `ModelConfig.mask_conditioning_channels` |
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| 3.4 | LoRA 模式解凍 patchify_proj | ✅ | `trainer._unfreeze_patchify_proj`：mask_channels>0 時把 video patchify_proj 設 trainable，讓新欄位隨 LoRA 一起訓 |
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| 3.5 | 範例 config + docs | ✅ | `configs/scail_animation_lora.yaml`；`configs/README.md` 與 `docs/training-modes.md` 表格 row |
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| 3.6 | CPU 單元驗證 | ✅ | `verify_phase3_trainer.py`：config round-trip、prepare_training_inputs 建 cond_channels[B,T,56]、driving 前置/mask placement、widened model forward + compute_loss finite、widen helper zero-init 等價 |
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| 3.7 | dataset 前處理（產 driving latents + 語意 mask） | ⬜ | 需 process_dataset 產出 `driving_latents/`（同 target 形狀）與 `char_masks/`(mask=[K+1,F_pix,H,W])；語意 mask 需分割模型，屬資料工程，未做 |
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| 3.8 | validation runner 接 driving/mask | ⬜ | 驗證期取樣尚未接 SCAIL 條件（config 內 validation 先停用），與 Phase 4 一起 |
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| 3.9 | 實機訓練跑通 | ⬜ | 需 Linux + GPU + checkpoint，本機無法 |
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## Phase 4 — Pipeline + CLI
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| # | 任務 | 狀態 | 備註 |
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| 4.1 | `scail_animation.py` pipeline + arg parser | ⬜ | 仿 `lipdub.py` |
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## 決議紀錄
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- **範圍**：先只做 Phase 1（推論期 PoC）。Phase 2+ 待 Phase 1 驗證後再討論。
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- **架構前提**：SCAIL-2 建構於 Wan 2.1，本移植為跨架構移植。
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