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ltx-v2v-reference-conditioning / packages /ltx-trainer /docs /configuration-reference.md
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# Configuration Reference
The trainer uses structured Pydantic models for configuration, making it easy to customize training parameters.
This guide covers all available configuration options and their usage.
## 📋 Overview
The main configuration class is [`LtxTrainerConfig`](../src/ltx_trainer/config.py), which includes the following
sub-configurations:
- **ModelConfig**: Base model and training mode settings
- **LoraConfig**: LoRA training parameters
- **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
- **ValidationConfig**: Validation and inference settings
- **CheckpointsConfig**: Checkpoint saving frequency and retention settings
- **HubConfig**: Hugging Face Hub integration settings
- **WandbConfig**: Weights & Biases logging settings
- **FlowMatchingConfig**: Timestep sampling parameters
## 📄 Example Configuration Files
Check out our example configurations in the `configs` directory:
- 📄 [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.
```yaml
model:
 model_path: "/path/to/ltx-2-model.safetensors" # Local path to model checkpoint
 text_encoder_path: "/path/to/gemma-model" # Path to Gemma text encoder directory
 training_mode: "lora" # "lora" or "full"
 load_checkpoint: null # Path to checkpoint to resume from
```
**Key parameters:**
| Parameter | Description |
|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `model_path` | **Required.** Local path to the LTX-2 model checkpoint (`.safetensors` file). URLs are not supported. |
| `text_encoder_path` | **Required.** Path to the Gemma text encoder model directory. Download from [HuggingFace](https://huggingface.co/google/gemma-3-12b-it-qat-q4_0-unquantized/). |
| `training_mode` | Training approach - `"lora"` for LoRA training or `"full"` for full-rank fine-tuning. |
| `load_checkpoint` | Optional path to resume training from a checkpoint file or directory. |
> [!NOTE]
> LTX-2 requires both a model checkpoint and a Gemma text encoder. Both must be local paths.
### LoraConfig
LoRA-specific fine-tuning parameters (only used when `training_mode: "lora"`).
```yaml
lora:
 rank: 32 # LoRA rank (higher = more parameters)
 alpha: 32 # LoRA alpha scaling factor
 dropout: 0.0 # Dropout probability (0.0-1.0)
 target_modules: # Modules to apply LoRA to
 - "to_k"
 - "to_q"
 - "to_v"
 - "to_out.0"
```
**Key parameters:**
| Parameter | Description |
|------------------|---------------------------------------------------------------------------------|
| `rank` | LoRA rank - higher values mean more trainable parameters (typical range: 8-128) |
| `alpha` | Alpha scaling factor - typically set equal to rank |
| `dropout` | Dropout probability for regularization |
| `target_modules` | List of transformer modules to apply LoRA adapters to (see below) |
#### Understanding Target Modules
The LTX-2 transformer has separate attention and feed-forward blocks for video and audio, as well as cross-attention
modules that enable the two modalities to exchange information. Choosing the right `target_modules` is critical for
achieving good results, especially when training with audio.
**Video-only modules:**
| Module Pattern | Description |
|------------------------------------------------------------|---------------------------------|
| `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 network |
**Audio-only modules:**
| Module Pattern | Description |
|------------------------------------------------------------------------------------|---------------------------------|
| `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 network |
**Audio-video cross-attention modules:**
These modules enable bidirectional information flow between the audio and video modalities:
| Module Pattern | Description |
|--------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------|
| `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` | Video attends to audio (Q from video, K/V from audio) |
| `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` | Audio attends to video (Q from audio, K/V from video) |
**Recommended configurations:**
For **video-only training**, target the video attention layers:
```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"
```
For **audio-video training**, use patterns that match both branches:
```yaml
target_modules:
 - "to_k"
 - "to_q"
 - "to_v"
 - "to_out.0"
```
> [!NOTE]
> Using shorter patterns like `"to_k"` will match all attention modules including `attn1.to_k`, `audio_attn1.to_k`,
> `audio_to_video_attn.to_k`, and `video_to_audio_attn.to_k`, effectively training video, audio, and cross-modal
> attention branches together.
> [!TIP]
> You can also target the feed-forward (FFN) modules (`ff.net.0.proj`, `ff.net.2` for video,
> `audio_ff.net.0.proj`, `audio_ff.net.2` for audio) to increase the LoRA's capacity and potentially
> help it capture the target distribution better.
### TrainingStrategyConfig
Configures the training strategy. The recommended strategy is `"flexible"`, which supports all conditioning scenarios through configuration.
#### 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: "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)
```
**ModalityConfig parameters:**
| 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. |
**Condition types:**
| 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
Training optimization parameters including learning rates, batch sizes, and schedulers.
```yaml
optimization:
 learning_rate: 1e-4 # Learning rate
 steps: 2000 # Total training steps
 batch_size: 1 # Batch size per GPU
 gradient_accumulation_steps: 1 # Steps to accumulate gradients
 max_grad_norm: 1.0 # Gradient clipping threshold
 optimizer_type: "adamw" # "adamw" or "adamw8bit"
 scheduler_type: "linear" # Scheduler type
 scheduler_params: { } # Additional scheduler parameters
 enable_gradient_checkpointing: true # Memory optimization
```
**Key parameters:**
| Parameter | Description |
|---------------------------------|----------------------------------------------------------------------------------------------|
| `learning_rate` | Learning rate for optimization (typical range: 1e-5 to 1e-3) |
| `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"`, `"step"` |
| `enable_gradient_checkpointing` | Trade training speed for GPU memory savings (recommended for large models) |
### AccelerationConfig
Hardware acceleration and compute optimization settings.
```yaml
acceleration:
 mixed_precision_mode: "bf16" # "no", "fp16", or "bf16"
 quantization: null # Quantization options
 load_text_encoder_in_8bit: false # Load text encoder in 8-bit
 offload_optimizer_during_validation: false # Offload optimizer state to CPU during validation
```
**Key parameters:**
| Parameter | Description |
|---------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `mixed_precision_mode` | Precision mode - `"bf16"` recommended for modern GPUs |
| `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. |
### DataConfig
Data loading and processing configuration.
```yaml
data:
 preprocessed_data_root: "/path/to/preprocessed/data" # Path to precomputed dataset
 num_dataloader_workers: 2 # Background data loading workers
```
**Key parameters:**
| Parameter | Description |
|--------------------------|--------------------------------------------------------------------------------------------|
| `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 samples use a self-describing
format where each sample specifies its own prompt and conditions.
```yaml
validation:
 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 |
|--------------------------|--------------------------------------------------------------------------------------------------------------------------|
| `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
Model checkpointing configuration.
```yaml
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:**
| Parameter | Description |
|---------------|-------------------------------------------------------------------------------|
| `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
Hugging Face Hub integration for automatic model uploads.
```yaml
hub:
 push_to_hub: false # Enable Hub uploading
 hub_model_id: "username/model-name" # Hub repository ID
```
**Key parameters:**
| Parameter | Description |
|----------------|------------------------------------------------------------------|
| `push_to_hub` | Whether to automatically push trained models to Hugging Face Hub |
| `hub_model_id` | Repository ID in format `"username/repository-name"` |
### WandbConfig
Weights & Biases logging configuration.
```yaml
wandb:
 enabled: false # Enable W&B logging
 project: "ltx-2-trainer" # W&B project name
 entity: null # W&B username or team
 tags: [ ] # Tags for the run
 log_validation_videos: true # Log validation videos to W&B
```
**Key parameters:**
| Parameter | Description |
|-------------------------|--------------------------------------------------|
| `enabled` | Whether to enable W&B logging |
| `project` | W&B project name |
| `entity` | W&B username or team (null uses default account) |
| `log_validation_videos` | Whether to log validation videos to W&B |
### FlowMatchingConfig
Flow matching training configuration for timestep sampling.
```yaml
flow_matching:
 timestep_sampling_mode: "shifted_logit_normal" # Timestep sampling strategy
 timestep_sampling_params: { } # Additional sampling parameters
```
**Key parameters:**
| Parameter | Description |
|----------------------------|------------------------------------------------------------|
| `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:
- Set up your dataset using [Dataset Preparation](dataset-preparation.md)
- Choose your training approach in [Training Modes](training-modes.md)
- Start training with the [Training Guide](training-guide.md)