LTX-2/packages/ltx-trainer/docs/training-modes.md

Training Modes Guide

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.

Key Concepts

Before diving into individual modes, here are the core ideas behind the flexible strategy:

• 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.

Tip

If you are using an agent-enabled environment with repository skills and are unsure which mode to choose, ask for the train-model 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
I2V	Generated	Generated	first_frame	i2v_lora
Video Extension	Generated	Generated	prefix/suffix	video_extend_lora
V2V IC-LoRA	Generated	—	reference	v2v_ic_lora
A2V	Generated	Frozen	—	a2v_lora
V2A (Foley)	Frozen	Generated	—	v2a_lora
Video Inpainting	Generated	—	mask	video_inpainting_lora
Video Outpainting	Generated	—	spatial_crop	video_outpainting_lora
T2A	—	Generated	—	t2a_lora
Audio Extension	—	Generated	prefix/suffix	audio_extend_lora
Audio Inpainting	—	Generated	mask	audio_inpainting_lora
A2A IC-LoRA	—	Generated	reference	a2a_ic_lora
AV2AV IC-LoRA	Generated	Generated	reference (both)	av2av_ic_lora

---

🎯 Text-to-Video (T2V)

Generate video and audio from text prompts. Both modalities are denoised with no additional conditions.

training_strategy:
  name: "flexible"
  video:
    is_generated: true
    latents_dir: "latents"
  audio:
    is_generated: true
    latents_dir: "audio_latents"

Example config: 📄 t2v_lora.yaml

---

🖼️ Image-to-Video (I2V)

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.

training_strategy:
  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 config: 📄 i2v_lora.yaml

---

⏩ 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).

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"

Note

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.

Example configs: 📄 video_extend_lora.yaml (forward), 📄 video_suffix_lora.yaml (backward)

---

🔄 IC-LoRA / Video-to-Video (V2V)

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.

training_strategy:
  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

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 when both video and audio references should be trained jointly.

Example config: 📄 v2v_ic_lora.yaml

Dataset Requirements

• 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 below)
• Both must be preprocessed before training

Dataset structure:

preprocessed_data_root/
├── latents/            # Target video latents
├── conditions/         # Text embeddings
└── reference_latents/  # Reference video latents (conditioning input)

Generating Reference Videos

Use the compute_reference.py script to generate reference videos (e.g., Canny edge maps) for a dataset:

uv run python scripts/compute_reference.py scenes_output_dir/ \
    --output scenes_output_dir/dataset.json

To compute a different condition (depth maps, pose skeletons, etc.), modify the compute_reference() function in the script.

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, 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 — shorter sequence lengths
• Faster inference — reduced memory usage
• Same aspect ratio maintained between reference and target

Preprocess with the --reference-downscale-factor option:

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-downscale-factor 2

Note

The reference_video column is auto-detected by convention — no --reference-column flag needed.

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:

validation:
  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 values are 1 (no scaling), 2 (half resolution), or 4 (quarter resolution).

---

🔊 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.

training_strategy:
  name: "flexible"
  video:
    is_generated: true
    latents_dir: "latents"
  audio:
    is_generated: false
    latents_dir: "audio_latents"

Example config: 📄 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.

training_strategy:
  name: "flexible"
  video:
    is_generated: false
    latents_dir: "latents"
  audio:
    is_generated: true
    latents_dir: "audio_latents"

Example config: 📄 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.

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

---

🌅 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.

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

---

🔈 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.

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 below.

Example config: 📄 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.

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, 📄 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.

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

---

🔄 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.

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

---

🔄 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.

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

---

🔥 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.

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 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):

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 for detailed guidance.

For video-only IC-LoRA — explicitly target video modules (including FFN layers for better transformation quality):

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:

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

After training, use the ltx-pipelines package for production inference with your trained LoRAs:

Training Mode	Recommended Pipeline
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 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

Once you've chosen your training mode:

• Set up your dataset using Dataset Preparation
• Configure your training parameters in Configuration Reference
• Start training with the Training Guide

Tip

Need a training mode that's not covered here? First check whether it can be expressed by composing existing flexible conditions. Use Implementing Custom Training Strategies only for custom losses, noising rules, model outputs, or preprocessing that cannot be represented by configuration.