Automated PR - 2026-06-17

packages/ltx-core/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ltx-core"
-version = "1.1.6"
+version = "v1.1.6"
 description = "Core implementation of Lightricks' LTX-2 model"
 readme = "README.md"
 requires-python = ">=3.10"

packages/ltx-pipelines/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ltx-pipelines"
-version = "1.1.6"
+version = "v1.1.6"
 description = "Pipelines implementation for Lightricks' LTX-2 model"
 readme = "README.md"
 requires-python = ">=3.10"

packages/ltx-trainer/.gitignore

@@ -1,3 +1,4 @@
+configs/*.yaml
 datasets
 outputs
 wandb

packages/ltx-trainer/configs/a2a_ic_lora.yaml

@@ -1,340 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/a2v_lora.yaml

@@ -1,344 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/audio_extend_lora.yaml

@@ -1,343 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/audio_inpainting_lora.yaml

@@ -1,324 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/audio_suffix_lora.yaml

@@ -1,343 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/av2av_ic_lora.yaml

@@ -1,342 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/i2v_lora.yaml

@@ -1,348 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/t2a_lora.yaml

@@ -1,317 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/t2v_lora.yaml

@@ -1,327 +0,0 @@
-# =============================================================================
-# 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 with joint audio-video support.
-#
-# Use this configuration when you want to:
-#   - Fine-tune LTX-2 on your own video dataset
-#   - Train joint audio-video generation from text prompts
-#   - Create custom video generation styles or audiovisual concepts
-#
-# 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 training approach using the unified flexible strategy.
-# This configuration trains both video and audio generation from text prompts.
-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
-  # 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"
-
-  # 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
-# -----------------------------------------------------------------------------
-# 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.
-    - 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"
-
-  # 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", "t2v" ]
-
-  # 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/t2v_lora"

packages/ltx-trainer/configs/t2v_lora_low_vram.yaml

@@ -1,339 +0,0 @@
-# =============================================================================
-# LTX-2 Text-to-Video LoRA Training Configuration (Low VRAM)
-# =============================================================================
-#
-# 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.
-#
-# Memory optimizations applied:
-#   - 8-bit AdamW optimizer (reduces optimizer state memory by ~75%)
-#   - INT8 model quantization (reduces model memory by ~50%)
-#   - Lower LoRA rank (16 vs 32, reduces trainable parameters)
-#   - Gradient checkpointing enabled
-#
-# 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 with limited GPU memory
-#   - Train joint audio-video generation from text prompts
-#   - Create custom video generation styles or audiovisual concepts
-#
-# 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.
-# Using a lower rank (16) to reduce trainable parameters and memory usage.
-# This still provides good capacity for many fine-tuning tasks.
-lora:
-  # Rank of the LoRA matrices (higher = more capacity but more parameters)
-  # Typical values: 8, 16, 32, 64. Using 16 for low VRAM configuration.
-  rank: 16
-
-  # Alpha scaling factor (usually set equal to rank)
-  # The effective scaling is alpha/rank, so alpha=rank means scaling of 1.0
-  alpha: 16
-
-  # 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 training approach using the unified flexible strategy.
-# This configuration trains both video and audio generation from text prompts.
-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
-  # 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"
-
-  # 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
-# -----------------------------------------------------------------------------
-# 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)
-  # Using 8-bit AdamW to reduce optimizer state memory by ~75%
-  optimizer_type: "adamw8bit"
-
-  # 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"
-  # Using INT8 quantization to reduce base model memory consumption by ~50%
-  quantization: "int8-quanto"
-
-  # Load text encoder in 8-bit precision to save memory
-  # Useful when GPU memory is limited
-  load_text_encoder_in_8bit: true
-
-  # 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
-
-# -----------------------------------------------------------------------------
-# 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.
-    - 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"
-
-  # 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, 49 ]
-
-  # 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 modality settings — 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", "t2v", "low-vram" ]
-
-  # 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/t2v_lora_low_vram"

packages/ltx-trainer/configs/v2a_lora.yaml

@@ -1,349 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/v2v_ic_lora.yaml

@@ -1,359 +0,0 @@
-# =============================================================================
-# LTX-2 Video-to-Video (IC-LoRA) Training Configuration
-# =============================================================================
-#
-# This configuration is for training In-Context LoRA (IC-LoRA) adapters that
-# enable video-to-video transformations. IC-LoRA learns to apply visual
-# transformations (e.g., depth-to-video, pose control, style transfer, etc.)
-# by conditioning on reference videos.
-#
-# Key differences from text-to-video LoRA:
-#   - Uses reference videos as conditioning input alongside text prompts
-#   - Requires preprocessed reference latents in addition to target latents
-#   - Validation requires reference videos to demonstrate the transformation
-#
-# Dataset structure for IC-LoRA training:
-#   preprocessed_data_root/
-#   ├── latents/           # Target video latents (what the model learns to generate)
-#   ├── conditions/        # Text embeddings for each video
-#   └── reference_latents/ # Reference video latents (conditioning input)
-#
-# Dataset metadata columns: video, reference_video, 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
-
-  # 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 (not used for video-only IC-LoRA):
-  #   - 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, not used for video-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 IC-LoRA (video-only), we explicitly target video modules.
-  # Including FFN layers often improves transformation quality.
-  target_modules:
-    # Video self-attention
-    - "attn1.to_k"
-    - "attn1.to_q"
-    - "attn1.to_v"
-    - "attn1.to_out.0"
-    # Video cross-attention
-    - "attn2.to_k"
-    - "attn2.to_q"
-    - "attn2.to_v"
-    - "attn2.to_out.0"
-    # Video feed-forward (often improves transformation quality)
-    - "ff.net.0.proj"
-    - "ff.net.2"
-
-# -----------------------------------------------------------------------------
-# Training Strategy Configuration
-# -----------------------------------------------------------------------------
-# 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: "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 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. 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
-# -----------------------------------------------------------------------------
-# 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/, conditions/, and reference_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 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"
-
-  # 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_v"  # "stg_v" for video-only (no audio training)
-
-  # Whether to generate audio in validation samples
-  # Can be enabled even when not training the audio branch
-  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: 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", "v2v" ]
-
-  # 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/v2v_ic_lora"

packages/ltx-trainer/configs/video_extend_lora.yaml

@@ -1,355 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/video_inpainting_lora.yaml

@@ -1,343 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/video_outpainting_lora.yaml

@@ -1,341 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/configs/video_suffix_lora.yaml

@@ -1,355 +0,0 @@
-# =============================================================================
-# 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"

packages/ltx-trainer/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ltx-trainer"
-version = "1.1.6"
+version = "v1.1.6"
 description = "LTX-2 training, democratized."
 readme = "README.md"
 authors = [
@@ -54,7 +54,7 @@ build-backend = "hatchling.build"
 
 
 [tool.ruff]
-target-version = "1.1.6"
+target-version = "v1.1.6"
 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.