From ca1623ad2afa780cb3722a4a7ddc580596012e16 Mon Sep 17 00:00:00 2001 From: sync-bot Date: Thu, 29 Jan 2026 18:42:17 +0000 Subject: [PATCH] Automated PR - 2026-01-29 --- packages/ltx-core/pyproject.toml | 2 +- .../src/ltx_core/components/guiders.py | 86 +- .../src/ltx_core/conditioning/__init__.py | 7 +- .../ltx_core/conditioning/types/__init__.py | 2 + .../types/reference_video_cond.py | 78 ++ .../loader/single_gpu_model_builder.py | 2 +- .../ltx_core/model/transformer/transformer.py | 10 +- .../ltx_core/text_encoders/gemma/config.py | 75 ++ .../gemma/encoders/av_encoder.py | 64 +- .../gemma/encoders/base_encoder.py | 40 +- packages/ltx-core/src/ltx_core/utils.py | 11 + packages/ltx-pipelines/README.md | 87 +- .../src/ltx_pipelines/ic_lora.py | 62 +- .../ltx_pipelines/keyframe_interpolation.py | 42 +- .../src/ltx_pipelines/ti2vid_one_stage.py | 42 +- .../src/ltx_pipelines/ti2vid_two_stages.py | 42 +- .../src/ltx_pipelines/utils/args.py | 134 ++- .../src/ltx_pipelines/utils/constants.py | 18 +- .../src/ltx_pipelines/utils/helpers.py | 114 ++ .../src/ltx_pipelines/utils/model_ledger.py | 10 +- .../ltx-trainer/configs/ltx2_v2v_ic_lora.yaml | 6 + packages/ltx-trainer/docs/training-modes.md | 90 +- .../ltx-trainer/scripts/process_dataset.py | 44 +- .../ltx-trainer/scripts/process_videos.py | 44 + .../ltx-trainer/src/ltx_trainer/config.py | 43 + .../src/ltx_trainer/model_loader.py | 10 +- .../ltx-trainer/src/ltx_trainer/trainer.py | 23 +- .../training_strategies/base_strategy.py | 9 + .../training_strategies/video_to_video.py | 81 +- .../src/ltx_trainer/validation_sampler.py | 51 +- uv.lock | 1057 ++++++++--------- 31 files changed, 1723 insertions(+), 663 deletions(-) create mode 100644 packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py create mode 100644 packages/ltx-core/src/ltx_core/text_encoders/gemma/config.py diff --git a/packages/ltx-core/pyproject.toml b/packages/ltx-core/pyproject.toml index 1be24dc..94eb744 100644 --- a/packages/ltx-core/pyproject.toml +++ b/packages/ltx-core/pyproject.toml @@ -9,7 +9,7 @@ dependencies = [ "torchaudio", "einops", "numpy", - "transformers", + "transformers~=4.57.0", "safetensors", "accelerate", "scipy>=1.14", diff --git a/packages/ltx-core/src/ltx_core/components/guiders.py b/packages/ltx-core/src/ltx_core/components/guiders.py index c4d774b..d262efa 100644 --- a/packages/ltx-core/src/ltx_core/components/guiders.py +++ b/packages/ltx-core/src/ltx_core/components/guiders.py @@ -1,4 +1,5 @@ -from dataclasses import dataclass +import math +from dataclasses import dataclass, field import torch @@ -189,6 +190,89 @@ class LegacyStatefulAPGGuider(GuiderProtocol): return self.scale != 0.0 +@dataclass(frozen=True) +class MultiModalGuiderParams: + """ + Parameters for the multi-modal guider. + """ + + cfg_scale: float = 1.0 + "CFG (Classifier-free guidance) scale controlling how strongly the model adheres to the prompt." + stg_scale: float = 0.0 + "STG (Spatio-Temporal Guidance) scale controls how strongly the model reacts to the perturbation of the modality." + stg_blocks: list[int] | None = field(default_factory=list) + "Which transformer blocks to perturb for STG." + rescale_scale: float = 0.0 + "Rescale scale controlling how strongly the model rescales the modality after applying other guidance." + modality_scale: float = 1.0 + "Modality scale controlling how strongly the model reacts to the perturbation of the modality." + skip_step: int = 0 + "Skip step controlling how often the model skips the step." + + +@dataclass(frozen=True) +class MultiModalGuider: + """ + Multi-modal guider. + """ + + params: MultiModalGuiderParams + negative_context: torch.Tensor | None = None + + def calculate( + self, + cond: torch.Tensor, + uncond_text: torch.Tensor | float, + uncond_perturbed: torch.Tensor | float, + uncond_modality: torch.Tensor | float, + ) -> torch.Tensor: + """ + The guider calculates the guidance delta as (scale - 1) * (cond - uncond) for cfg and modality cfg, + and as scale * (cond - uncond) for stg, steering the denoising process away from the unconditioned + prediction. + """ + pred = ( + cond + + (self.params.cfg_scale - 1) * (cond - uncond_text) + + self.params.stg_scale * (cond - uncond_perturbed) + + (self.params.modality_scale - 1) * (cond - uncond_modality) + ) + + if self.params.rescale_scale != 0: + factor = cond.std() / pred.std() + factor = self.params.rescale_scale * factor + (1 - self.params.rescale_scale) + pred = pred * factor + + return pred + + def do_unconditional_generation(self) -> bool: + """ + Returns True if the guider is doing unconditional generation. + """ + return not math.isclose(self.params.cfg_scale, 1.0) + + def do_perturbed_generation(self) -> bool: + """ + Returns True if the guider is doing perturbed generation. + """ + return not math.isclose(self.params.stg_scale, 0.0) + + def do_isolated_modality_generation(self) -> bool: + """ + Returns True if the guider is doing isolated modality generation. + """ + return not math.isclose(self.params.modality_scale, 1.0) + + def should_skip_step(self, step: int) -> bool: + """ + Returns True if the guider should skip the step. + """ + if self.params.skip_step == 0: + return False + + return step % (self.params.skip_step + 1) != 0 + + def projection_coef(to_project: torch.Tensor, project_onto: torch.Tensor) -> torch.Tensor: batch_size = to_project.shape[0] positive_flat = to_project.reshape(batch_size, -1) diff --git a/packages/ltx-core/src/ltx_core/conditioning/__init__.py b/packages/ltx-core/src/ltx_core/conditioning/__init__.py index 8d0871c..78f403f 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/__init__.py +++ b/packages/ltx-core/src/ltx_core/conditioning/__init__.py @@ -2,11 +2,16 @@ from ltx_core.conditioning.exceptions import ConditioningError from ltx_core.conditioning.item import ConditioningItem -from ltx_core.conditioning.types import VideoConditionByKeyframeIndex, VideoConditionByLatentIndex +from ltx_core.conditioning.types import ( + VideoConditionByKeyframeIndex, + VideoConditionByLatentIndex, + VideoConditionByReferenceLatent, +) __all__ = [ "ConditioningError", "ConditioningItem", "VideoConditionByKeyframeIndex", "VideoConditionByLatentIndex", + "VideoConditionByReferenceLatent", ] diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py b/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py index 867d73f..93d12ba 100644 --- a/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py +++ b/packages/ltx-core/src/ltx_core/conditioning/types/__init__.py @@ -2,8 +2,10 @@ from ltx_core.conditioning.types.keyframe_cond import VideoConditionByKeyframeIndex from ltx_core.conditioning.types.latent_cond import VideoConditionByLatentIndex +from ltx_core.conditioning.types.reference_video_cond import VideoConditionByReferenceLatent __all__ = [ "VideoConditionByKeyframeIndex", "VideoConditionByLatentIndex", + "VideoConditionByReferenceLatent", ] diff --git a/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py b/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py new file mode 100644 index 0000000..f6bd235 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/conditioning/types/reference_video_cond.py @@ -0,0 +1,78 @@ +"""Reference video conditioning for IC-LoRA inference.""" + +import torch + +from ltx_core.components.patchifiers import get_pixel_coords +from ltx_core.conditioning.item import ConditioningItem +from ltx_core.tools import VideoLatentTools +from ltx_core.types import LatentState, VideoLatentShape + + +class VideoConditionByReferenceLatent(ConditioningItem): + """ + Conditions video generation on a reference video latent for IC-LoRA inference. + IC-LoRAs are trained by concatenating reference (control signal) and target tokens, + learning to attend across both. This class replicates that setup at inference by + appending reference tokens to the latent sequence. + IC-LoRAs can be trained with lower-resolution references than the target (e.g., 384px + reference for 768px output) for efficiency and better generalization. The + `downscale_factor` scales reference positions to match target coordinates, preserving + the learned positional relationships. This must match the factor used during training + (stored in LoRA metadata). + Args: + latent: Reference video latents [B, C, F, H, W] + downscale_factor: Target/reference resolution ratio (e.g., 2 = half-resolution + reference). Spatial positions are scaled by this factor. + strength: Conditioning strength. 1.0 = full (reference kept clean), + 0.0 = none (reference denoised). Default 1.0. + """ + + def __init__( + self, + latent: torch.Tensor, + downscale_factor: int = 1, + strength: float = 1.0, + ): + self.latent = latent + self.downscale_factor = downscale_factor + self.strength = strength + + def apply_to( + self, + latent_state: LatentState, + latent_tools: VideoLatentTools, + ) -> LatentState: + """Append reference video tokens with scaled positions.""" + tokens = latent_tools.patchifier.patchify(self.latent) + + # Compute positions for the reference video's actual dimensions + latent_coords = latent_tools.patchifier.get_patch_grid_bounds( + output_shape=VideoLatentShape.from_torch_shape(self.latent.shape), + device=self.latent.device, + ) + positions = get_pixel_coords( + latent_coords=latent_coords, + scale_factors=latent_tools.scale_factors, + causal_fix=latent_tools.causal_fix, + ) + positions = positions.to(dtype=torch.float32) + positions[:, 0, ...] /= latent_tools.fps + + # Scale spatial positions to match target coordinate space + if self.downscale_factor != 1: + positions[:, 1, ...] *= self.downscale_factor # height axis + positions[:, 2, ...] *= self.downscale_factor # width axis + + denoise_mask = torch.full( + size=(*tokens.shape[:2], 1), + fill_value=1.0 - self.strength, + device=self.latent.device, + dtype=self.latent.dtype, + ) + + return LatentState( + latent=torch.cat([latent_state.latent, tokens], dim=1), + denoise_mask=torch.cat([latent_state.denoise_mask, denoise_mask], dim=1), + positions=torch.cat([latent_state.positions, positions], dim=2), + clean_latent=torch.cat([latent_state.clean_latent, tokens], dim=1), + ) diff --git a/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py b/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py index 9e8853a..673d8c8 100644 --- a/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py +++ b/packages/ltx-core/src/ltx_core/loader/single_gpu_model_builder.py @@ -73,7 +73,7 @@ class SingleGPUModelBuilder(Generic[ModelType], ModelBuilderProtocol[ModelType], device = torch.device("cuda") if device is None else device config = self.model_config() meta_model = self.meta_model(config, self.module_ops) - model_paths = self.model_path if isinstance(self.model_path, tuple) else [self.model_path] + model_paths = list(self.model_path) if isinstance(self.model_path, tuple) else [self.model_path] model_state_dict = self.load_sd(model_paths, sd_ops=self.model_sd_ops, registry=self.registry, device=device) lora_strengths = [lora.strength for lora in self.loras] diff --git a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py index 047faaa..03979b3 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py @@ -140,7 +140,11 @@ class BasicAVTransformerBlock(torch.nn.Module): audio: TransformerArgs | None, perturbations: BatchedPerturbationConfig | None = None, ) -> tuple[TransformerArgs | None, TransformerArgs | None]: - batch_size = video.x.shape[0] + if video is None and audio is None: + raise ValueError("At least one of video or audio must be provided") + + batch_size = (video or audio).x.shape[0] + if perturbations is None: perturbations = BatchedPerturbationConfig.empty(batch_size) @@ -211,7 +215,7 @@ class BasicAVTransformerBlock(torch.nn.Module): video.cross_gate_timestep, ) - if run_a2v: + if run_a2v and not perturbations.all_in_batch(PerturbationType.SKIP_A2V_CROSS_ATTN, self.idx): vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_a2v) + shift_ca_video_hidden_states_a2v ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_a2v) + shift_ca_audio_hidden_states_a2v a2v_mask = perturbations.mask_like(PerturbationType.SKIP_A2V_CROSS_ATTN, self.idx, vx) @@ -226,7 +230,7 @@ class BasicAVTransformerBlock(torch.nn.Module): * a2v_mask ) - if run_v2a: + if run_v2a and not perturbations.all_in_batch(PerturbationType.SKIP_V2A_CROSS_ATTN, self.idx): ax_scaled = ax_norm3 * (1 + scale_ca_audio_hidden_states_v2a) + shift_ca_audio_hidden_states_v2a vx_scaled = vx_norm3 * (1 + scale_ca_video_hidden_states_v2a) + shift_ca_video_hidden_states_v2a v2a_mask = perturbations.mask_like(PerturbationType.SKIP_V2A_CROSS_ATTN, self.idx, ax) diff --git a/packages/ltx-core/src/ltx_core/text_encoders/gemma/config.py b/packages/ltx-core/src/ltx_core/text_encoders/gemma/config.py new file mode 100644 index 0000000..8b23e91 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/text_encoders/gemma/config.py @@ -0,0 +1,75 @@ +from dataclasses import asdict, dataclass, field + + +@dataclass +class Gemma3RopeScaling: + factor: float = 8.0 + rope_type: str = "linear" + + +@dataclass +class Gemma3TextConfig: + attention_bias: bool = False + attention_dropout: float = 0.0 + attn_logit_softcapping: float | None = None + cache_implementation: str = "hybrid" + final_logit_softcapping: float | None = None + head_dim: int = 256 + hidden_activation: str = "gelu_pytorch_tanh" + hidden_size: int = 3840 + initializer_range: float = 0.02 + intermediate_size: int = 15360 + max_position_embeddings: int = 131072 + model_type: str = "gemma3_text" + num_attention_heads: int = 16 + num_hidden_layers: int = 48 + num_key_value_heads: int = 8 + query_pre_attn_scalar: int = 256 + rms_norm_eps: float = 1e-06 + rope_local_base_freq: int = 10000 + rope_scaling: Gemma3RopeScaling = field(default_factory=Gemma3RopeScaling) + rope_theta: int = 1000000 + sliding_window: int = 1024 + sliding_window_pattern: int = 6 + torch_dtype: str = "float32" + use_cache: bool = True + vocab_size: int = 262208 + + +@dataclass +class Gemma3VisionConfig: + attention_dropout: float = 0.0 + hidden_act: str = "gelu_pytorch_tanh" + hidden_size: int = 1152 + image_size: int = 896 + intermediate_size: int = 4304 + layer_norm_eps: float = 1e-06 + model_type: str = "siglip_vision_model" + num_attention_heads: int = 16 + num_channels: int = 3 + num_hidden_layers: int = 27 + patch_size: int = 14 + torch_dtype: str = "float32" + vision_use_head: bool = False + + +@dataclass +class Gemma3ConfigData: + architectures: list[str] = field(default_factory=lambda: ["Gemma3ForConditionalGeneration"]) + boi_token_index: int = 255999 + eoi_token_index: int = 256000 + eos_token_id: list[int] = field(default_factory=lambda: [1, 106]) + image_token_index: int = 262144 + initializer_range: float = 0.02 + mm_tokens_per_image: int = 256 + model_type: str = "gemma3" + text_config: Gemma3TextConfig = field(default_factory=Gemma3TextConfig) + torch_dtype: str = "bfloat16" + transformers_version: str = "4.51.0" + vision_config: Gemma3VisionConfig = field(default_factory=Gemma3VisionConfig) + + def to_dict(self) -> dict: + return asdict(self) + + +GEMMA3_CONFIG_FOR_LTX = Gemma3ConfigData() diff --git a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/av_encoder.py b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/av_encoder.py index 11697bc..f061254 100644 --- a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/av_encoder.py +++ b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/av_encoder.py @@ -1,15 +1,22 @@ from typing import NamedTuple import torch +from transformers import Gemma3Config +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS from transformers.models.gemma3 import Gemma3ForConditionalGeneration +from ltx_core.loader import KeyValueOperationResult +from ltx_core.loader.module_ops import ModuleOps from ltx_core.loader.sd_ops import SDOps from ltx_core.model.model_protocol import ModelConfigurator +from ltx_core.text_encoders.gemma.config import GEMMA3_CONFIG_FOR_LTX from ltx_core.text_encoders.gemma.embeddings_connector import ( Embeddings1DConnector, Embeddings1DConnectorConfigurator, ) -from ltx_core.text_encoders.gemma.encoders.base_encoder import GemmaTextEncoderModelBase +from ltx_core.text_encoders.gemma.encoders.base_encoder import ( + GemmaTextEncoderModelBase, +) from ltx_core.text_encoders.gemma.feature_extractor import GemmaFeaturesExtractorProjLinear from ltx_core.text_encoders.gemma.tokenizer import LTXVGemmaTokenizer @@ -76,7 +83,11 @@ class AVGemmaTextEncoderModelConfigurator(ModelConfigurator[AVGemmaTextEncoderMo feature_extractor_linear = GemmaFeaturesExtractorProjLinear.from_config(config) embeddings_connector = Embeddings1DConnectorConfigurator.from_config(config) audio_embeddings_connector = Embeddings1DConnectorConfigurator.from_config(config) + gemma_config = Gemma3Config.from_dict(GEMMA3_CONFIG_FOR_LTX.to_dict()) + with torch.device("meta"): + model = Gemma3ForConditionalGeneration(gemma_config) return AVGemmaTextEncoderModel( + model=model, feature_extractor_linear=feature_extractor_linear, embeddings_connector=embeddings_connector, audio_embeddings_connector=audio_embeddings_connector, @@ -85,10 +96,57 @@ class AVGemmaTextEncoderModelConfigurator(ModelConfigurator[AVGemmaTextEncoderMo AV_GEMMA_TEXT_ENCODER_KEY_OPS = ( SDOps("AV_GEMMA_TEXT_ENCODER_KEY_OPS") + # 1. Map the feature extractor .with_matching(prefix="text_embedding_projection.") - .with_matching(prefix="model.diffusion_model.audio_embeddings_connector.") - .with_matching(prefix="model.diffusion_model.video_embeddings_connector.") .with_replacement("text_embedding_projection.", "feature_extractor_linear.") + # 2. Map the connectors (fixing the swapped prefixes from before) + .with_matching(prefix="model.diffusion_model.video_embeddings_connector.") .with_replacement("model.diffusion_model.video_embeddings_connector.", "embeddings_connector.") + .with_matching(prefix="model.diffusion_model.audio_embeddings_connector.") .with_replacement("model.diffusion_model.audio_embeddings_connector.", "audio_embeddings_connector.") + # 3. Map language model layers (note the double .model prefix) + .with_matching(prefix="language_model.model.") + .with_replacement("language_model.model.", "model.model.language_model.") + # 4. Map the Vision Tower + .with_matching(prefix="vision_tower.") + .with_replacement("vision_tower.", "model.model.vision_tower.") + # 5. Map the Multi-Modal Projector + .with_matching(prefix="multi_modal_projector.") + .with_replacement("multi_modal_projector.", "model.model.multi_modal_projector.") + .with_kv_operation( + operation=lambda key, value: [ + KeyValueOperationResult(key, value), + KeyValueOperationResult("model.lm_head.weight", value), + ], + key_prefix="model.model.language_model.embed_tokens.weight", + ) +) + + +def create_and_populate(module: AVGemmaTextEncoderModel) -> AVGemmaTextEncoderModel: + model = module.model + v_model = model.model.vision_tower.vision_model + l_model = model.model.language_model + + config = model.config.text_config + dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + base = config.rope_local_base_freq + local_rope_freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(dtype=torch.float) / dim)) + inv_freqs, _ = ROPE_INIT_FUNCTIONS[config.rope_scaling["rope_type"]](config) + + positions_length = len(v_model.embeddings.position_ids[0]) + position_ids = torch.arange(positions_length, dtype=torch.long, device="cpu").unsqueeze(0) + v_model.embeddings.register_buffer("position_ids", position_ids) + embed_scale = torch.tensor(model.config.text_config.hidden_size**0.5, device="cpu") + l_model.embed_tokens.register_buffer("embed_scale", embed_scale) + l_model.rotary_emb_local.register_buffer("inv_freq", local_rope_freqs) + l_model.rotary_emb.register_buffer("inv_freq", inv_freqs) + + return module + + +GEMMA_MODEL_OPS = ModuleOps( + name="GemmaModel", + matcher=lambda module: hasattr(module, "model") and isinstance(module.model, Gemma3ForConditionalGeneration), + mutator=create_and_populate, ) diff --git a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py index 9fca260..3976d59 100644 --- a/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py +++ b/packages/ltx-core/src/ltx_core/text_encoders/gemma/encoders/base_encoder.py @@ -8,6 +8,7 @@ from transformers import AutoImageProcessor, Gemma3ForConditionalGeneration, Gem from ltx_core.loader.module_ops import ModuleOps from ltx_core.text_encoders.gemma.feature_extractor import GemmaFeaturesExtractorProjLinear from ltx_core.text_encoders.gemma.tokenizer import LTXVGemmaTokenizer +from ltx_core.utils import find_matching_file class GemmaTextEncoderModelBase(torch.nn.Module): @@ -77,7 +78,7 @@ class GemmaTextEncoderModelBase(torch.nn.Module): messages: list[dict[str, str]], image: torch.Tensor | None = None, max_new_tokens: int = 512, - seed: int = 42, + seed: int = 10, ) -> str: text = self.processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) @@ -107,7 +108,7 @@ class GemmaTextEncoderModelBase(torch.nn.Module): prompt: str, max_new_tokens: int = 512, system_prompt: str | None = None, - seed: int = 42, + seed: int = 10, ) -> str: """Enhance a text prompt for T2V generation.""" @@ -126,7 +127,7 @@ class GemmaTextEncoderModelBase(torch.nn.Module): image: torch.Tensor, max_new_tokens: int = 512, system_prompt: str | None = None, - seed: int = 42, + seed: int = 10, ) -> str: """Enhance a text prompt for I2V generation using a reference image.""" system_prompt = system_prompt or self.default_gemma_i2v_system_prompt @@ -219,44 +220,21 @@ def _load_system_prompt(prompt_name: str) -> str: return f.read() -def _find_matching_dir(root_path: str, pattern: str) -> str: - """ - Recursively search for files matching a glob pattern and return the parent directory of the first match. - """ - - matches = list(Path(root_path).rglob(pattern)) - if not matches: - raise FileNotFoundError(f"No files matching pattern '{pattern}' found under {root_path}") - return str(matches[0].parent) - - def module_ops_from_gemma_root(gemma_root: str) -> tuple[ModuleOps, ...]: - gemma_path = _find_matching_dir(gemma_root, "model*.safetensors") - tokenizer_path = _find_matching_dir(gemma_root, "tokenizer.model") - processor_path = _find_matching_dir(gemma_root, "preprocessor_config.json") - - def load_gemma(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase: - module.model = Gemma3ForConditionalGeneration.from_pretrained( - gemma_path, local_files_only=True, torch_dtype=torch.bfloat16 - ) - return module + tokenizer_root = str(find_matching_file(gemma_root, "tokenizer.model").parent) + processor_root = str(find_matching_file(gemma_root, "preprocessor_config.json").parent) def load_tokenizer(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase: - module.tokenizer = LTXVGemmaTokenizer(tokenizer_path, 1024) + module.tokenizer = LTXVGemmaTokenizer(tokenizer_root, 1024) return module def load_processor(module: GemmaTextEncoderModelBase) -> GemmaTextEncoderModelBase: - image_processor = AutoImageProcessor.from_pretrained(processor_path, local_files_only=True) + image_processor = AutoImageProcessor.from_pretrained(processor_root, local_files_only=True) if not module.tokenizer: raise ValueError("Tokenizer model operation must be performed before processor model operation") module.processor = Gemma3Processor(image_processor=image_processor, tokenizer=module.tokenizer.tokenizer) return module - gemma_load_ops = ModuleOps( - "GemmaLoad", - matcher=lambda module: isinstance(module, GemmaTextEncoderModelBase) and module.model is None, - mutator=load_gemma, - ) tokenizer_load_ops = ModuleOps( "TokenizerLoad", matcher=lambda module: isinstance(module, GemmaTextEncoderModelBase) and module.tokenizer is None, @@ -267,7 +245,7 @@ def module_ops_from_gemma_root(gemma_root: str) -> tuple[ModuleOps, ...]: matcher=lambda module: isinstance(module, GemmaTextEncoderModelBase) and module.processor is None, mutator=load_processor, ) - return (gemma_load_ops, tokenizer_load_ops, processor_load_ops) + return (tokenizer_load_ops, processor_load_ops) def encode_text(text_encoder: GemmaTextEncoderModelBase, prompts: list[str]) -> list[tuple[torch.Tensor, torch.Tensor]]: diff --git a/packages/ltx-core/src/ltx_core/utils.py b/packages/ltx-core/src/ltx_core/utils.py index a7a3acb..21f7d60 100644 --- a/packages/ltx-core/src/ltx_core/utils.py +++ b/packages/ltx-core/src/ltx_core/utils.py @@ -1,3 +1,4 @@ +from pathlib import Path from typing import Any import torch @@ -49,3 +50,13 @@ def to_denoised( if isinstance(sigma, torch.Tensor): sigma = sigma.to(calc_dtype) return (sample.to(calc_dtype) - velocity.to(calc_dtype) * sigma).to(sample.dtype) + + +def find_matching_file(root_path: str, pattern: str) -> Path: + """ + Recursively search for files matching a glob pattern and return the first match. + """ + matches = list(Path(root_path).rglob(pattern)) + if not matches: + raise FileNotFoundError(f"No files matching pattern '{pattern}' found under {root_path}") + return matches[0] diff --git a/packages/ltx-pipelines/README.md b/packages/ltx-pipelines/README.md index f6bc808..5e27172 100644 --- a/packages/ltx-pipelines/README.md +++ b/packages/ltx-pipelines/README.md @@ -89,7 +89,7 @@ Do you need to condition on existing images/videos? ### Features Comparison -| Pipeline | Stages | CFG | Upsampling | Conditioning | Best For | +| Pipeline | Stages | [Multimodal Guidance](#%EF%B8%8F-multimodal-guidance) | Upsampling | Conditioning | Best For | | -------- | ------ | --- | ---------- | ------------- | -------- | | **TI2VidTwoStagesPipeline** | 2 | ✅ | ✅ | Image | **Production quality** (recommended) | | **TI2VidOneStagePipeline** | 1 | ✅ | ❌ | Image | Educational, prototyping | @@ -107,7 +107,7 @@ Do you need to condition on existing images/videos? **Source**: [`src/ltx_pipelines/ti2vid_two_stages.py`](src/ltx_pipelines/ti2vid_two_stages.py) -Two-stage generation: Stage 1 generates low-resolution video with CFG guidance, Stage 2 upsamples to 2x resolution with distilled LoRA refinement. Supports image conditioning. Highest quality output, slower than one-stage but significantly better quality. +Two-stage generation: Stage 1 generates low-resolution video with [multimodal guidance](#%EF%B8%8F-multimodal-guidance), Stage 2 upsamples to 2x resolution with distilled LoRA refinement. Supports image conditioning. Highest quality output, slower than one-stage but significantly better quality. **Use when:** Production-quality video generation, higher resolution needed, quality over speed, text-to-video with image conditioning. @@ -121,7 +121,7 @@ Two-stage generation: Stage 1 generates low-resolution video with CFG guidance, > **⚠️ Important:** This pipeline is primarily for educational purposes. For production-quality results, use `TI2VidTwoStagesPipeline` or other two-stage pipelines. -Single-stage generation (no upsampling) with CFG guidance and image conditioning support. Faster inference but lower resolution output (typically 512x768). +Single-stage generation (no upsampling) with [multimodal guidance](#%EF%B8%8F-multimodal-guidance) and image conditioning support. Faster inference but lower resolution output (typically 512x768). **Use when:** Learning how the pipeline works, quick prototyping, testing, or when high resolution is not needed. @@ -133,7 +133,7 @@ Single-stage generation (no upsampling) with CFG guidance and image conditioning **Source**: [`src/ltx_pipelines/distilled.py`](src/ltx_pipelines/distilled.py) -Two-stage generation with 8 predefined sigmas (8 steps in stage 1, 4 steps in stage 2). No CFG guidance required. Fastest inference among all pipelines. Supports image conditioning. Requires spatial upsampler. +Two-stage generation with 8 predefined sigmas (8 steps in stage 1, 4 steps in stage 2). No guidance required. Fastest inference among all pipelines. Supports image conditioning. Requires spatial upsampler. **Use when:** Fastest inference is critical, batch processing many videos, or when you have a distilled model checkpoint. @@ -157,7 +157,7 @@ Two-stage generation with IC-LoRA support. Can condition on reference videos (vi **Source**: [`src/ltx_pipelines/keyframe_interpolation.py`](src/ltx_pipelines/keyframe_interpolation.py) -Two-stage generation with keyframe interpolation. Uses guiding latents (additive conditioning) instead of replacing latents for smoother transitions. CFG guidance in stage 1, upsampling in stage 2. +Two-stage generation with keyframe interpolation. Uses guiding latents (additive conditioning) instead of replacing latents for smoother transitions. [Multimodal guidance](#%EF%B8%8F-multimodal-guidance) in stage 1, upsampling in stage 2. **Use when:** You have keyframe images and want to interpolate between them, creating smooth transitions, or animation/motion interpolation tasks. @@ -190,6 +190,61 @@ All pipelines support image conditioning, but with different methods: --- +## 🎛️ Multimodal Guidance + +LTX-2 pipelines use **multimodal guidance** to steer the diffusion process for both video and audio modalities. Each modality (video, audio) has its own guider with independent parameters, allowing fine-grained control over generation quality and adherence to prompts. + +### Guidance Parameters + +The `MultiModalGuiderParams` dataclass controls guidance behavior: + +| Parameter | Description | +| --------- | ----------- | +| `cfg_scale` | **Classifier-Free Guidance** scale. Higher values make the output adhere more strongly to the text prompt. Typical values: 2.0–5.0. Set to **1.0** to disable. | +| `stg_scale` | **Spatio-Temporal Guidance** scale. Controls perturbation-based guidance for improved temporal coherence. Typical values: 0.5–1.5. Set to **0.0** to disable. | +| `stg_blocks` | Which transformer blocks to perturb for STG (e.g., `[29]` for the last block). Set to **`[]`** to disable STG. | +| `rescale_scale` | Rescales the guided prediction to match the variance of the conditional prediction. Helps prevent over-saturation. Typical values: 0.5–0.7. Set to **0.0** to disable. | +| `modality_scale` | **Modality CFG** scale. Steers the model away from unsynced video and audio results, improving audio-visual coherence. Set to **1.0** to disable. | +| `skip_step` | Skip guidance every N steps. Can speed up inference with minimal quality loss. Set to **0** to disable (never skip). | + +### How It Works + +The multimodal guider combines three guidance signals during each denoising step: + +1. **CFG (Text Guidance)**: Steers generation toward the text prompt by computing `(cond - uncond_text)`. +2. **STG (Perturbation Guidance)**: Improves structural coherence by perturbing specific transformer blocks and steering away from the perturbed prediction. +3. **Modality CFG**: For joint audio-video generation, steers the model away from unsynced video and audio results. + +### Example Configuration + +```python +from ltx_core.components.guiders import MultiModalGuiderParams + +# Video guider: moderate CFG, STG enabled, modality isolation +video_guider_params = MultiModalGuiderParams( + cfg_scale=3.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + stg_blocks=[29], +) + +# Audio guider: higher CFG for stronger prompt adherence +audio_guider_params = MultiModalGuiderParams( + cfg_scale=7.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + stg_blocks=[29], +) +``` + +> **Tip:** Start with the default values from [`constants.py`](src/ltx_pipelines/utils/constants.py) and adjust based on your use case. Higher `cfg_scale` = stronger prompt adherence but potentially less natural motion; higher `stg_scale` = better temporal coherence but slower inference (requires extra forward passes). +> +> **Tip:** When generating video with audio, set `modality_scale` > 1.0 (e.g., 3.0) to improve audio-visual sync. If generating video-only, set it to 1.0 to disable. + +--- + ## ⚡ Optimization Tips @@ -276,6 +331,7 @@ This allows you to use **20-30 steps instead of 40** while maintaining quality. ```python from ltx_core.loader import LTXV_LORA_COMFY_RENAMING_MAP, LoraPathStrengthAndSDOps from ltx_pipelines.ti2vid_two_stages import TI2VidTwoStagesPipeline +from ltx_core.components.guiders import MultiModalGuiderParams distilled_lora = [ LoraPathStrengthAndSDOps( @@ -293,6 +349,24 @@ pipeline = TI2VidTwoStagesPipeline( loras=[], ) +video_guider_params = MultiModalGuiderParams( + cfg_scale=3.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + skip_step=0, + stg_blocks=[29], +) + +audio_guider_params = MultiModalGuiderParams( + cfg_scale=7.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + skip_step=0, + stg_blocks=[29], +) + # Generate video from image pipeline( prompt="A serene landscape with mountains in the background", @@ -303,7 +377,8 @@ pipeline( num_frames=121, frame_rate=25.0, num_inference_steps=40, - cfg_guidance_scale=3.0, + video_guider_params=video_guider_params, + audio_guider_params=audio_guider_params, images=[("input_image.jpg", 0, 1.0)], # Image at frame 0, strength 1.0 ) ``` diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py index 42b66ae..339e0ea 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py @@ -2,11 +2,12 @@ import logging from collections.abc import Iterator import torch +from safetensors import safe_open from ltx_core.components.diffusion_steps import EulerDiffusionStep from ltx_core.components.noisers import GaussianNoiser from ltx_core.components.protocols import DiffusionStepProtocol -from ltx_core.conditioning import ConditioningItem, VideoConditionByKeyframeIndex +from ltx_core.conditioning import ConditioningItem, VideoConditionByReferenceLatent from ltx_core.loader import LoraPathStrengthAndSDOps from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.upsampler import upsample_video @@ -81,6 +82,21 @@ class ICLoraPipeline: ) self.device = device + # Read reference downscale factor from LoRA metadata. + # IC-LoRAs trained with low-resolution reference videos store this factor + # so inference can resize reference videos to match training conditions. + self.reference_downscale_factor = 1 + for lora in loras: + scale = _read_lora_reference_downscale_factor(lora.path) + if scale != 1: + if self.reference_downscale_factor not in (1, scale): + raise ValueError( + f"Conflicting reference_downscale_factor values in LoRAs: " + f"already have {self.reference_downscale_factor}, but {lora.path} " + f"specifies {scale}. Cannot combine LoRAs with different reference scales." + ) + self.reference_downscale_factor = scale + @torch.inference_mode() def __call__( self, @@ -249,17 +265,34 @@ class ICLoraPipeline: device=self.device, ) + # Calculate scaled dimensions for reference video conditioning. + # IC-LoRAs trained with downscaled reference videos expect the same ratio at inference. + scale = self.reference_downscale_factor + if scale != 1 and (height % scale != 0 or width % scale != 0): + raise ValueError( + f"Output dimensions ({height}x{width}) must be divisible by reference_downscale_factor ({scale})" + ) + ref_height = height // scale + ref_width = width // scale + for video_path, strength in video_conditioning: + # Load video at scaled-down resolution (if scale > 1) video = load_video_conditioning( video_path=video_path, - height=height, - width=width, + height=ref_height, + width=ref_width, frame_cap=num_frames, dtype=self.dtype, device=self.device, ) encoded_video = video_encoder(video) - conditionings.append(VideoConditionByKeyframeIndex(keyframes=encoded_video, frame_idx=0, strength=strength)) + conditionings.append( + VideoConditionByReferenceLatent( + latent=encoded_video, + downscale_factor=scale, + strength=strength, + ) + ) return conditionings @@ -307,5 +340,26 @@ def main() -> None: ) +def _read_lora_reference_downscale_factor(lora_path: str) -> int: + """Read reference_downscale_factor from LoRA safetensors metadata. + Some IC-LoRA models are trained with reference videos at lower resolution than + the target output. This allows for more efficient training and can improve + generalization. The downscale factor indicates the ratio between target and + reference resolutions (e.g., factor=2 means reference is half the resolution). + Args: + lora_path: Path to the LoRA .safetensors file + Returns: + The reference downscale factor (1 if not specified in metadata, meaning + reference and target have the same resolution) + """ + try: + with safe_open(lora_path, framework="pt") as f: + metadata = f.metadata() or {} + return int(metadata.get("reference_downscale_factor", 1)) + except Exception as e: + logging.warning(f"Failed to read metadata from LoRA file '{lora_path}': {e}") + return 1 + + if __name__ == "__main__": main() diff --git a/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py b/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py index 30648be..cc522b0 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py @@ -4,7 +4,7 @@ from collections.abc import Iterator import torch from ltx_core.components.diffusion_steps import EulerDiffusionStep -from ltx_core.components.guiders import CFGGuider +from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams from ltx_core.components.noisers import GaussianNoiser from ltx_core.components.protocols import DiffusionStepProtocol from ltx_core.components.schedulers import LTX2Scheduler @@ -28,8 +28,8 @@ from ltx_pipelines.utils.helpers import ( euler_denoising_loop, generate_enhanced_prompt, get_device, - guider_denoising_func, image_conditionings_by_adding_guiding_latent, + multi_modal_guider_denoising_func, simple_denoising_func, ) from ltx_pipelines.utils.media_io import encode_video @@ -86,7 +86,8 @@ class KeyframeInterpolationPipeline: num_frames: int, frame_rate: float, num_inference_steps: int, - cfg_guidance_scale: float, + video_guider_params: MultiModalGuiderParams, + audio_guider_params: MultiModalGuiderParams, images: list[tuple[str, int, float]], tiling_config: TilingConfig | None = None, enhance_prompt: bool = False, @@ -96,7 +97,6 @@ class KeyframeInterpolationPipeline: generator = torch.Generator(device=self.device).manual_seed(seed) noiser = GaussianNoiser(generator=generator) stepper = EulerDiffusionStep() - cfg_guider = CFGGuider(cfg_guidance_scale) dtype = torch.bfloat16 text_encoder = self.stage_1_model_ledger.text_encoder() @@ -125,12 +125,17 @@ class KeyframeInterpolationPipeline: video_state=video_state, audio_state=audio_state, stepper=stepper, - denoise_fn=guider_denoising_func( - cfg_guider, - v_context_p, - v_context_n, - a_context_p, - a_context_n, + denoise_fn=multi_modal_guider_denoising_func( + video_guider=MultiModalGuider( + params=video_guider_params, + negative_context=v_context_n, + ), + audio_guider=MultiModalGuider( + params=audio_guider_params, + negative_context=a_context_n, + ), + v_context=v_context_p, + a_context=a_context_p, transformer=transformer, # noqa: F821 ), ) @@ -256,7 +261,22 @@ def main() -> None: num_frames=args.num_frames, frame_rate=args.frame_rate, num_inference_steps=args.num_inference_steps, - cfg_guidance_scale=args.cfg_guidance_scale, + video_guider_params=MultiModalGuiderParams( + cfg_scale=args.video_cfg_guidance_scale, + stg_scale=args.video_stg_guidance_scale, + rescale_scale=args.video_rescale_scale, + modality_scale=args.a2v_guidance_scale, + skip_step=args.video_skip_step, + stg_blocks=args.video_stg_blocks, + ), + audio_guider_params=MultiModalGuiderParams( + cfg_scale=args.audio_cfg_guidance_scale, + stg_scale=args.audio_stg_guidance_scale, + rescale_scale=args.audio_rescale_scale, + modality_scale=args.v2a_guidance_scale, + skip_step=args.audio_skip_step, + stg_blocks=args.audio_stg_blocks, + ), images=args.images, tiling_config=tiling_config, ) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py index 40c5d60..36defea 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py @@ -4,7 +4,7 @@ from collections.abc import Iterator import torch from ltx_core.components.diffusion_steps import EulerDiffusionStep -from ltx_core.components.guiders import CFGGuider +from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams from ltx_core.components.noisers import GaussianNoiser from ltx_core.components.protocols import DiffusionStepProtocol from ltx_core.components.schedulers import LTX2Scheduler @@ -23,8 +23,8 @@ from ltx_pipelines.utils.helpers import ( euler_denoising_loop, generate_enhanced_prompt, get_device, - guider_denoising_func, image_conditionings_by_replacing_latent, + multi_modal_guider_denoising_func, ) from ltx_pipelines.utils.media_io import encode_video from ltx_pipelines.utils.types import PipelineComponents @@ -73,7 +73,8 @@ class TI2VidOneStagePipeline: num_frames: int, frame_rate: float, num_inference_steps: int, - cfg_guidance_scale: float, + video_guider_params: MultiModalGuiderParams, + audio_guider_params: MultiModalGuiderParams, images: list[tuple[str, int, float]], enhance_prompt: bool = False, ) -> tuple[Iterator[torch.Tensor], torch.Tensor]: @@ -82,7 +83,6 @@ class TI2VidOneStagePipeline: generator = torch.Generator(device=self.device).manual_seed(seed) noiser = GaussianNoiser(generator=generator) stepper = EulerDiffusionStep() - cfg_guider = CFGGuider(cfg_guidance_scale) dtype = torch.bfloat16 text_encoder = self.model_ledger.text_encoder() @@ -111,12 +111,17 @@ class TI2VidOneStagePipeline: video_state=video_state, audio_state=audio_state, stepper=stepper, - denoise_fn=guider_denoising_func( - cfg_guider, - v_context_p, - v_context_n, - a_context_p, - a_context_n, + denoise_fn=multi_modal_guider_denoising_func( + video_guider=MultiModalGuider( + params=video_guider_params, + negative_context=v_context_n, + ), + audio_guider=MultiModalGuider( + params=audio_guider_params, + negative_context=a_context_n, + ), + v_context=v_context_p, + a_context=a_context_p, transformer=transformer, # noqa: F821 ), ) @@ -175,7 +180,22 @@ def main() -> None: num_frames=args.num_frames, frame_rate=args.frame_rate, num_inference_steps=args.num_inference_steps, - cfg_guidance_scale=args.cfg_guidance_scale, + video_guider_params=MultiModalGuiderParams( + cfg_scale=args.video_cfg_guidance_scale, + stg_scale=args.video_stg_guidance_scale, + rescale_scale=args.video_rescale_scale, + modality_scale=args.a2v_guidance_scale, + skip_step=args.video_skip_step, + stg_blocks=args.video_stg_blocks, + ), + audio_guider_params=MultiModalGuiderParams( + cfg_scale=args.audio_cfg_guidance_scale, + stg_scale=args.audio_stg_guidance_scale, + rescale_scale=args.audio_rescale_scale, + modality_scale=args.v2a_guidance_scale, + skip_step=args.audio_skip_step, + stg_blocks=args.audio_stg_blocks, + ), images=args.images, ) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py index 528096d..6119de1 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py @@ -4,7 +4,7 @@ from collections.abc import Iterator import torch from ltx_core.components.diffusion_steps import EulerDiffusionStep -from ltx_core.components.guiders import CFGGuider +from ltx_core.components.guiders import MultiModalGuider, MultiModalGuiderParams from ltx_core.components.noisers import GaussianNoiser from ltx_core.components.protocols import DiffusionStepProtocol from ltx_core.components.schedulers import LTX2Scheduler @@ -28,8 +28,8 @@ from ltx_pipelines.utils.helpers import ( euler_denoising_loop, generate_enhanced_prompt, get_device, - guider_denoising_func, image_conditionings_by_replacing_latent, + multi_modal_guider_denoising_func, simple_denoising_func, ) from ltx_pipelines.utils.media_io import encode_video @@ -88,7 +88,8 @@ class TI2VidTwoStagesPipeline: num_frames: int, frame_rate: float, num_inference_steps: int, - cfg_guidance_scale: float, + video_guider_params: MultiModalGuiderParams, + audio_guider_params: MultiModalGuiderParams, images: list[tuple[str, int, float]], tiling_config: TilingConfig | None = None, enhance_prompt: bool = False, @@ -98,7 +99,6 @@ class TI2VidTwoStagesPipeline: generator = torch.Generator(device=self.device).manual_seed(seed) noiser = GaussianNoiser(generator=generator) stepper = EulerDiffusionStep() - cfg_guider = CFGGuider(cfg_guidance_scale) dtype = torch.bfloat16 text_encoder = self.stage_1_model_ledger.text_encoder() @@ -127,12 +127,17 @@ class TI2VidTwoStagesPipeline: video_state=video_state, audio_state=audio_state, stepper=stepper, - denoise_fn=guider_denoising_func( - cfg_guider, - v_context_p, - v_context_n, - a_context_p, - a_context_n, + denoise_fn=multi_modal_guider_denoising_func( + video_guider=MultiModalGuider( + params=video_guider_params, + negative_context=v_context_n, + ), + audio_guider=MultiModalGuider( + params=audio_guider_params, + negative_context=a_context_n, + ), + v_context=v_context_p, + a_context=a_context_p, transformer=transformer, # noqa: F821 ), ) @@ -259,7 +264,22 @@ def main() -> None: num_frames=args.num_frames, frame_rate=args.frame_rate, num_inference_steps=args.num_inference_steps, - cfg_guidance_scale=args.cfg_guidance_scale, + video_guider_params=MultiModalGuiderParams( + cfg_scale=args.video_cfg_guidance_scale, + stg_scale=args.video_stg_guidance_scale, + rescale_scale=args.video_rescale_scale, + modality_scale=args.a2v_guidance_scale, + skip_step=args.video_skip_step, + stg_blocks=args.video_stg_blocks, + ), + audio_guider_params=MultiModalGuiderParams( + cfg_scale=args.audio_cfg_guidance_scale, + stg_scale=args.audio_stg_guidance_scale, + rescale_scale=args.audio_rescale_scale, + modality_scale=args.v2a_guidance_scale, + skip_step=args.audio_skip_step, + stg_blocks=args.audio_stg_blocks, + ), images=args.images, tiling_config=tiling_config, ) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py index c5bb8f5..2fa0163 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py @@ -7,13 +7,14 @@ from ltx_pipelines.utils.constants import ( DEFAULT_1_STAGE_WIDTH, DEFAULT_2_STAGE_HEIGHT, DEFAULT_2_STAGE_WIDTH, - DEFAULT_CFG_GUIDANCE_SCALE, + DEFAULT_AUDIO_GUIDER_PARAMS, DEFAULT_FRAME_RATE, DEFAULT_LORA_STRENGTH, DEFAULT_NEGATIVE_PROMPT, DEFAULT_NUM_FRAMES, DEFAULT_NUM_INFERENCE_STEPS, DEFAULT_SEED, + DEFAULT_VIDEO_GUIDER_PARAMS, ) @@ -185,16 +186,6 @@ def basic_arg_parser() -> argparse.ArgumentParser: def default_1_stage_arg_parser() -> argparse.ArgumentParser: parser = basic_arg_parser() - parser.add_argument( - "--cfg-guidance-scale", - type=float, - default=DEFAULT_CFG_GUIDANCE_SCALE, - help=( - f"Classifier-free guidance (CFG) scale controlling how strongly " - f"the model adheres to the prompt. Higher values increase prompt " - f"adherence but may reduce diversity (default: {DEFAULT_CFG_GUIDANCE_SCALE})." - ), - ) parser.add_argument( "--negative-prompt", type=str, @@ -205,7 +196,126 @@ def default_1_stage_arg_parser() -> argparse.ArgumentParser: "Default: a comprehensive negative prompt covering common artifacts and quality issues." ), ) - + parser.add_argument( + "--video-cfg-guidance-scale", + type=float, + default=DEFAULT_VIDEO_GUIDER_PARAMS.cfg_scale, + help=( + f"Classifier-free guidance (CFG) scale controlling how strongly " + f"the model adheres to the video prompt. Higher values increase prompt " + "adherence but may reduce diversity. 1.0 means no effect " + f"(default: {DEFAULT_VIDEO_GUIDER_PARAMS.cfg_scale})." + ), + ) + parser.add_argument( + "--video-stg-guidance-scale", + type=float, + default=DEFAULT_VIDEO_GUIDER_PARAMS.stg_scale, + help=( + f"STG (Spatio-Temporal Guidance) scale controlling how strongly " + f"the model reacts to the perturbation of the video modality. Higher values increase " + f"the effect but may reduce quality. 0.0 means no effect " + f"(default: {DEFAULT_VIDEO_GUIDER_PARAMS.stg_scale})." + ), + ) + parser.add_argument( + "--video-rescale-scale", + type=float, + default=DEFAULT_VIDEO_GUIDER_PARAMS.rescale_scale, + help=( + f"Rescale scale controlling how strongly " + f"the model rescales the video modality after applying other guidance. Higher values tend to decrease " + f"oversaturation effects. 0.0 means no effect (default: {DEFAULT_VIDEO_GUIDER_PARAMS.rescale_scale})." + ), + ) + parser.add_argument( + "--video-stg-blocks", + type=int, + nargs="*", + default=DEFAULT_VIDEO_GUIDER_PARAMS.stg_blocks, + help=(f"Which transformer blocks to perturb for STG. Default: {DEFAULT_VIDEO_GUIDER_PARAMS.stg_blocks}."), + ) + parser.add_argument( + "--a2v-guidance-scale", + type=float, + default=DEFAULT_VIDEO_GUIDER_PARAMS.modality_scale, + help=( + f"A2V (Audio-to-Video) guidance scale controlling how strongly " + f"the model reacts to the perturbation of the audio-to-video cross-attention. Higher values may increase " + f"lipsync quality. 1.0 means no effect (default: {DEFAULT_VIDEO_GUIDER_PARAMS.modality_scale})." + ), + ) + parser.add_argument( + "--video-skip-step", + type=int, + default=DEFAULT_VIDEO_GUIDER_PARAMS.skip_step, + help=( + "Video skip step N controls periodic skipping during the video diffusion process: " + "only steps where step_index % (N + 1) == 0 are processed, all others are skipped " + f"(e.g., 0 = no skipping; 1 = skip every other step; 2 = skip 2 of every 3 steps; " + f"default: {DEFAULT_VIDEO_GUIDER_PARAMS.skip_step})." + ), + ) + parser.add_argument( + "--audio-cfg-guidance-scale", + type=float, + default=DEFAULT_AUDIO_GUIDER_PARAMS.cfg_scale, + help=( + f"Audio CFG (Classifier-free guidance) scale controlling how strongly " + f"the model adheres to the audio prompt. Higher values increase prompt " + f"adherence but may reduce diversity. 1.0 means no effect " + f"(default: {DEFAULT_AUDIO_GUIDER_PARAMS.cfg_scale})." + ), + ) + parser.add_argument( + "--audio-stg-guidance-scale", + type=float, + default=DEFAULT_AUDIO_GUIDER_PARAMS.stg_scale, + help=( + f"Audio STG (Spatio-Temporal Guidance) scale controlling how strongly " + f"the model reacts to the perturbation of the audio modality. Higher values increase " + f"the effect but may reduce quality. 0.0 means no effect " + f"(default: {DEFAULT_AUDIO_GUIDER_PARAMS.stg_scale})." + ), + ) + parser.add_argument( + "--audio-rescale-scale", + type=float, + default=DEFAULT_AUDIO_GUIDER_PARAMS.rescale_scale, + help=( + f"Audio rescale scale controlling how strongly " + f"the model rescales the audio modality after applying other guidance. " + f"Experimental. 0.0 means no effect (default: {DEFAULT_AUDIO_GUIDER_PARAMS.rescale_scale})." + ), + ) + parser.add_argument( + "--audio-stg-blocks", + type=int, + nargs="*", + default=DEFAULT_AUDIO_GUIDER_PARAMS.stg_blocks, + help=(f"Which transformer blocks to perturb for Audio STG. Default: {DEFAULT_AUDIO_GUIDER_PARAMS.stg_blocks}."), + ) + parser.add_argument( + "--v2a-guidance-scale", + type=float, + default=DEFAULT_AUDIO_GUIDER_PARAMS.modality_scale, + help=( + f"V2A (Video-to-Audio) guidance scale controlling how strongly " + f"the model reacts to the perturbation of the video-to-audio cross-attention. Higher values may increase " + f"lipsync quality. 1.0 means no effect (default: {DEFAULT_AUDIO_GUIDER_PARAMS.modality_scale})." + ), + ) + parser.add_argument( + "--audio-skip-step", + type=int, + default=DEFAULT_AUDIO_GUIDER_PARAMS.skip_step, + help=( + "Audio skip step N controls periodic skipping during the audio diffusion process: " + "only steps where step_index % (N + 1) == 0 are processed, all others are skipped " + f"(e.g., 0 = no skipping; 1 = skip every other step; 2 = skip 2 of every 3 steps; " + f"default: {DEFAULT_AUDIO_GUIDER_PARAMS.skip_step})." + ), + ) return parser diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/constants.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/constants.py index 737135b..4928423 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/constants.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/constants.py @@ -4,6 +4,7 @@ # Noise schedule for the distilled pipeline. These sigma values control noise # levels at each denoising step and were tuned to match the distillation process. +from ltx_core.components.guiders import MultiModalGuiderParams from ltx_core.types import SpatioTemporalScaleFactors DISTILLED_SIGMA_VALUES = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0] @@ -24,13 +25,28 @@ DEFAULT_2_STAGE_WIDTH = DEFAULT_1_STAGE_WIDTH * 2 DEFAULT_NUM_FRAMES = 121 DEFAULT_FRAME_RATE = 24.0 DEFAULT_NUM_INFERENCE_STEPS = 40 -DEFAULT_CFG_GUIDANCE_SCALE = 4.0 +DEFAULT_VIDEO_GUIDER_PARAMS = MultiModalGuiderParams( + cfg_scale=3.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + skip_step=0, + stg_blocks=[29], +) # ============================================================================= # Audio # ============================================================================= +DEFAULT_AUDIO_GUIDER_PARAMS = MultiModalGuiderParams( + cfg_scale=7.0, + stg_scale=1.0, + rescale_scale=0.7, + modality_scale=3.0, + skip_step=0, + stg_blocks=[29], +) AUDIO_SAMPLE_RATE = 24000 diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/helpers.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/helpers.py index 867db18..98577d4 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/helpers.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/helpers.py @@ -5,6 +5,7 @@ from dataclasses import replace import torch from tqdm import tqdm +from ltx_core.components.guiders import MultiModalGuider from ltx_core.components.noisers import Noiser from ltx_core.components.protocols import DiffusionStepProtocol, GuiderProtocol from ltx_core.conditioning import ( @@ -12,6 +13,12 @@ from ltx_core.conditioning import ( VideoConditionByKeyframeIndex, VideoConditionByLatentIndex, ) +from ltx_core.guidance.perturbations import ( + BatchedPerturbationConfig, + Perturbation, + PerturbationConfig, + PerturbationType, +) from ltx_core.model.transformer import Modality, X0Model from ltx_core.model.video_vae import VideoEncoder from ltx_core.text_encoders.gemma import GemmaTextEncoderModelBase @@ -376,6 +383,113 @@ def guider_denoising_func( return guider_denoising_step +def multi_modal_guider_denoising_func( + video_guider: MultiModalGuider, + audio_guider: MultiModalGuider, + v_context: torch.Tensor, + a_context: torch.Tensor, + transformer: X0Model, +) -> DenoisingFunc: + last_denoised_video = None + last_denoised_audio = None + + def guider_denoising_step( + video_state: LatentState, audio_state: LatentState, sigmas: torch.Tensor, step_index: int + ) -> tuple[torch.Tensor, torch.Tensor]: + nonlocal last_denoised_video, last_denoised_audio + + if video_guider.should_skip_step(step_index) and audio_guider.should_skip_step(step_index): + return last_denoised_video, last_denoised_audio + + sigma = sigmas[step_index] + pos_video_modality = modality_from_latent_state( + video_state, v_context, sigma, enabled=not video_guider.should_skip_step(step_index) + ) + pos_audio_modality = modality_from_latent_state( + audio_state, a_context, sigma, enabled=not audio_guider.should_skip_step(step_index) + ) + + denoised_video, denoised_audio = transformer( + video=pos_video_modality, audio=pos_audio_modality, perturbations=None + ) + neg_denoised_video, neg_denoised_audio = 0.0, 0.0 + if video_guider.do_unconditional_generation() or audio_guider.do_unconditional_generation(): + if video_guider.do_unconditional_generation() and video_guider.negative_context is None: + raise ValueError("Negative context is required for unconditioned denoising") + if audio_guider.do_unconditional_generation() and audio_guider.negative_context is None: + raise ValueError("Negative context is required for unconditioned denoising") + neg_video_modality = modality_from_latent_state( + video_state, + video_guider.negative_context + if video_guider.negative_context is not None + else pos_video_modality.context, + sigma, + ) + neg_audio_modality = modality_from_latent_state( + audio_state, + audio_guider.negative_context + if audio_guider.negative_context is not None + else pos_audio_modality.context, + sigma, + ) + + neg_denoised_video, neg_denoised_audio = transformer( + video=neg_video_modality, audio=neg_audio_modality, perturbations=None + ) + + ptb_denoised_video, ptb_denoised_audio = 0.0, 0.0 + if video_guider.do_perturbed_generation() or audio_guider.do_perturbed_generation(): + perturbations = [] + if video_guider.do_perturbed_generation(): + perturbations.append( + Perturbation(type=PerturbationType.SKIP_VIDEO_SELF_ATTN, blocks=video_guider.params.stg_blocks) + ) + if audio_guider.do_perturbed_generation(): + perturbations.append( + Perturbation(type=PerturbationType.SKIP_AUDIO_SELF_ATTN, blocks=audio_guider.params.stg_blocks) + ) + perturbation_config = PerturbationConfig(perturbations=perturbations) + ptb_denoised_video, ptb_denoised_audio = transformer( + video=pos_video_modality, + audio=pos_audio_modality, + perturbations=BatchedPerturbationConfig(perturbations=[perturbation_config]), + ) + + mod_denoised_video, mod_denoised_audio = 0.0, 0.0 + if video_guider.do_isolated_modality_generation() or audio_guider.do_isolated_modality_generation(): + perturbations = [ + Perturbation(type=PerturbationType.SKIP_A2V_CROSS_ATTN, blocks=None), + Perturbation(type=PerturbationType.SKIP_V2A_CROSS_ATTN, blocks=None), + ] + perturbation_config = PerturbationConfig(perturbations=perturbations) + mod_denoised_video, mod_denoised_audio = transformer( + video=pos_video_modality, + audio=pos_audio_modality, + perturbations=BatchedPerturbationConfig(perturbations=[perturbation_config]), + ) + + if video_guider.should_skip_step(step_index): + denoised_video = last_denoised_video + else: + denoised_video = video_guider.calculate( + denoised_video, neg_denoised_video, ptb_denoised_video, mod_denoised_video + ) + + if audio_guider.should_skip_step(step_index): + denoised_audio = last_denoised_audio + else: + denoised_audio = audio_guider.calculate( + denoised_audio, neg_denoised_audio, ptb_denoised_audio, mod_denoised_audio + ) + + last_denoised_video = denoised_video + last_denoised_audio = denoised_audio + + return denoised_video, denoised_audio + + return guider_denoising_step + + def denoise_audio_video( # noqa: PLR0913 output_shape: VideoPixelShape, conditionings: list[ConditioningItem], diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py index c507ff4..1ae36f3 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py @@ -35,6 +35,8 @@ from ltx_core.text_encoders.gemma import ( AVGemmaTextEncoderModelConfigurator, module_ops_from_gemma_root, ) +from ltx_core.text_encoders.gemma.encoders.av_encoder import GEMMA_MODEL_OPS +from ltx_core.utils import find_matching_file class ModelLedger: @@ -143,12 +145,16 @@ class ModelLedger: ) if self.gemma_root_path is not None: + module_ops = module_ops_from_gemma_root(self.gemma_root_path) + model_folder = find_matching_file(self.gemma_root_path, "model*.safetensors").parent + weight_paths = [str(p) for p in model_folder.rglob("*.safetensors")] + self.text_encoder_builder = Builder( - model_path=self.checkpoint_path, + model_path=(str(self.checkpoint_path), *weight_paths), model_class_configurator=AVGemmaTextEncoderModelConfigurator, model_sd_ops=AV_GEMMA_TEXT_ENCODER_KEY_OPS, registry=self.registry, - module_ops=module_ops_from_gemma_root(self.gemma_root_path), + module_ops=(GEMMA_MODEL_OPS, *module_ops), ) if self.spatial_upsampler_path is not None: diff --git a/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml b/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml index 7e647ac..6190948 100644 --- a/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml +++ b/packages/ltx-trainer/configs/ltx2_v2v_ic_lora.yaml @@ -200,6 +200,12 @@ validation: - "/path/to/reference_video_1.mp4" - "/path/to/reference_video_2.mp4" + # Downscale factor for reference videos (for efficient IC-LoRA training) + # When > 1, reference videos are processed at 1/n resolution + # Must match the --reference-downscale-factor used during dataset preprocessing + # Examples: 1 = same resolution, 2 = half resolution (384x384 ref for 768x768 target) + reference_downscale_factor: 1 + # Negative prompt to avoid unwanted artifacts negative_prompt: "worst quality, inconsistent motion, blurry, jittery, distorted" diff --git a/packages/ltx-trainer/docs/training-modes.md b/packages/ltx-trainer/docs/training-modes.md index 39df11d..99485cb 100644 --- a/packages/ltx-trainer/docs/training-modes.md +++ b/packages/ltx-trainer/docs/training-modes.md @@ -111,7 +111,8 @@ IC-LoRA enables a wide range of advanced video-to-video applications, such as: - **Colorization**: Convert grayscale reference videos into colorized outputs - **Restoration and enhancement**: Denoise, upscale, or restore old or degraded videos -By providing paired reference and target videos, IC-LoRA can learn complex transformations that go beyond caption-based conditioning. +By providing paired reference and target videos, IC-LoRA can learn complex transformations that go beyond caption-based +conditioning. IC-LoRA training fundamentally differs from standard LoRA and full fine-tuning: @@ -140,8 +141,10 @@ training_strategy: ### Dataset Requirements for IC-LoRA - Your dataset must contain **paired videos** where each target video has a corresponding reference video -- Reference and target videos must have **identical resolution and length** -- Both reference and target videos should be **preprocessed together** using the same resolution buckets +- Reference and target videos must have the **same frame count** (length) +- Reference videos can optionally be at **lower spatial resolution** than target videos ( + see [Scaled Reference Conditioning](#scaled-reference-conditioning) below) +- Both reference and target videos should be **preprocessed** before training **Dataset structure for IC-LoRA training:** @@ -181,30 +184,82 @@ validation: reference_videos: - "/path/to/reference1.mp4" - "/path/to/reference2.mp4" + reference_downscale_factor: 1 # Set to match preprocessing (e.g., 2 for half resolution) include_reference_in_output: true # Show reference side-by-side with output ``` +### Scaled Reference Conditioning + +For more efficient training and inference, you can use **downscaled reference videos** while keeping target videos at +full resolution. This reduces the number of conditioning tokens, leading to: + +- **Faster training** due to shorter sequence lengths +- **Faster inference** with reduced memory usage +- **Same aspect ratio** maintained between reference and target + +#### How It Works + +When the reference video has resolution `H/n × W/n` and the target video has resolution `H × W`, the trainer +automatically detects this scale factor `n` and adjusts the positional encodings so that the reference positions +map to the correct locations in the target coordinate space. + +#### Preprocessing Datasets with Scaled References + +Use the `--reference-downscale-factor` option when running `process_dataset.py`: + +```bash +# Process dataset with scaled reference videos (half resolution) +uv run python scripts/process_dataset.py dataset.json \ + --resolution-buckets 768x768x25 \ + --model-path /path/to/ltx2.safetensors \ + --text-encoder-path /path/to/gemma \ + --reference-column "reference_path" \ + --reference-downscale-factor 2 +``` + +This will: + +- Process target videos at 768×768 resolution +- Process reference videos at 384×384 resolution (768 / 2) +- The trainer will automatically infer the scale factor from the dimension ratio + +**Important**: Set `reference_downscale_factor: 2` in your validation configuration to match the preprocessing: + +```yaml +validation: + reference_downscale_factor: 2 # Must match the preprocessing factor + reference_videos: + - "/path/to/reference1.mp4" + - "/path/to/reference2.mp4" +``` + +> [!NOTE] +> The scale factor must be a positive integer, and all dimensions must be divisible by 32. +> Common scale factors are 1 (no scaling), 2 (half resolution), or 4 (quarter resolution). + ## 📊 Training Mode Comparison -| Aspect | LoRA | Audio-Video LoRA | Full Fine-tuning | IC-LoRA | -|----------------------|------------|------------------|------------------|----------------| -| **Memory Usage** | Low | Low-Medium | High | Medium | -| **Training Speed** | Fast | Fast | Slow | Medium | -| **Output Size** | 100MB-few GB (depends on rank) | 100MB-few GB (depends on rank) | Tens of GB | 100MB-few GB (depends on rank) | -| **Flexibility** | Medium | Medium | High | Specialized | -| **Audio Support** | Optional | Yes | Optional | No | -| **Reference Videos** | No | No | No | Yes (required) | +| Aspect | LoRA | Audio-Video LoRA | Full Fine-tuning | IC-LoRA | +|----------------------|--------------------------------|--------------------------------|------------------|--------------------------------| +| **Memory Usage** | Low | Low-Medium | High | Medium | +| **Training Speed** | Fast | Fast | Slow | Medium | +| **Output Size** | 100MB-few GB (depends on rank) | 100MB-few GB (depends on rank) | Tens of GB | 100MB-few GB (depends on rank) | +| **Flexibility** | Medium | Medium | High | Specialized | +| **Audio Support** | Optional | Yes | Optional | No | +| **Reference Videos** | No | No | No | Yes (required) | ## 🎬 Using Trained Models for Inference -After training, use the [`ltx-pipelines`](../../ltx-pipelines/) package for production inference with your trained LoRAs: +After training, use the [`ltx-pipelines`](../../ltx-pipelines/) package for production inference with your trained +LoRAs: -| Training Mode | Recommended Pipeline | -|---------------|---------------------| +| Training Mode | Recommended Pipeline | +|-------------------------|-------------------------------------------------------| | LoRA / Audio-Video LoRA | `TI2VidOneStagePipeline` or `TI2VidTwoStagesPipeline` | -| IC-LoRA | `ICLoraPipeline` | +| IC-LoRA | `ICLoraPipeline` | -All pipelines support loading custom LoRAs via the `loras` parameter. See the [`ltx-pipelines`](../../ltx-pipelines/) package +All pipelines support loading custom LoRAs via the `loras` parameter. See the [`ltx-pipelines`](../../ltx-pipelines/) +package documentation for detailed usage instructions. ## 🚀 Next Steps @@ -216,6 +271,7 @@ Once you've chosen your training mode: - Start training with the [Training Guide](training-guide.md) > [!TIP] -> Need a training mode that's not covered here? See [Implementing Custom Training Strategies](custom-training-strategies.md) +> Need a training mode that's not covered here? +> See [Implementing Custom Training Strategies](custom-training-strategies.md) > to learn how to create your own strategy for specialized use cases like video inpainting, audio-only training, or > custom conditioning. diff --git a/packages/ltx-trainer/scripts/process_dataset.py b/packages/ltx-trainer/scripts/process_dataset.py index f13fc0c..22c257b 100755 --- a/packages/ltx-trainer/scripts/process_dataset.py +++ b/packages/ltx-trainer/scripts/process_dataset.py @@ -16,13 +16,14 @@ from pathlib import Path import typer from decode_latents import LatentsDecoder from process_captions import compute_captions_embeddings -from process_videos import compute_latents, parse_resolution_buckets +from process_videos import compute_latents, compute_scaled_resolution_buckets, parse_resolution_buckets from rich.console import Console from ltx_trainer import logger from ltx_trainer.gpu_utils import free_gpu_memory_context console = Console() + app = typer.Typer( pretty_exceptions_enable=False, no_args_is_help=True, @@ -46,6 +47,7 @@ def preprocess_dataset( # noqa: PLR0913 device: str, remove_llm_prefixes: bool = False, reference_column: str | None = None, + reference_downscale_factor: int = 1, with_audio: bool = False, load_text_encoder_in_8bit: bool = False, ) -> None: @@ -99,14 +101,33 @@ def preprocess_dataset( # noqa: PLR0913 # Process reference videos if reference_column is provided if reference_column: - logger.info("Processing reference videos for IC-LoRA training...") + # Validate: scaled references with multiple buckets can cause ambiguous bucket matching + if reference_downscale_factor > 1 and len(resolution_buckets) > 1: + raise ValueError( + "When using --reference-downscale-factor > 1, only a single resolution bucket is supported. " + "Using multiple buckets with scaled references can cause ambiguous bucket matching " + "(e.g., a 512x256 reference could match either the scaled-down 1024x512 bucket or the 512x256 " + "bucket). Please use a single resolution bucket or set --reference-downscale-factor to 1." + ) + + # Calculate and validate scaled resolution buckets for reference videos + reference_buckets = compute_scaled_resolution_buckets(resolution_buckets, reference_downscale_factor) + + if reference_downscale_factor > 1: + logger.info( + f"Processing reference videos for IC-LoRA training at 1/{reference_downscale_factor} resolution..." + ) + logger.info(f"Reference resolution buckets: {reference_buckets}") + else: + logger.info("Processing reference videos for IC-LoRA training...") + reference_latents_dir = output_base / "reference_latents" compute_latents( dataset_file=dataset_file, main_media_column=video_column, video_column=reference_column, - resolution_buckets=resolution_buckets, + resolution_buckets=reference_buckets, output_dir=str(reference_latents_dir), model_path=model_path, batch_size=batch_size, @@ -226,6 +247,11 @@ def main( # noqa: PLR0913 default=False, help="Load the Gemma text encoder in 8-bit precision to save GPU memory (requires bitsandbytes)", ), + reference_downscale_factor: int = typer.Option( + default=1, + help="Downscale factor for reference video resolution. When > 1, reference videos are processed at " + "1/n resolution (e.g., 2 means half resolution). Used for efficient IC-LoRA training.", + ), ) -> None: """Preprocess a video dataset by computing and saving latents and text embeddings. The dataset must be a CSV, JSON, or JSONL file with columns for captions and video paths. @@ -242,6 +268,10 @@ def main( # noqa: PLR0913 python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ --reference-column "reference_path" + # Process dataset with scaled reference videos (half resolution) for efficient IC-LoRA + python scripts/process_dataset.py dataset.json --resolution-buckets 768x768x25 \\ + --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ + --reference-column "reference_path" --reference-downscale-factor 2 # Process dataset with audio for audio-video training python scripts/process_dataset.py dataset.json --resolution-buckets 768x512x97 \\ --model-path /path/to/ltx2.safetensors --text-encoder-path /path/to/gemma \\ @@ -255,6 +285,13 @@ def main( # noqa: PLR0913 "When training with multiple resolution buckets, you must use a batch size of 1." ) + # Validate reference_downscale_factor + if reference_downscale_factor < 1: + raise typer.BadParameter("--reference-downscale-factor must be >= 1") + + if reference_downscale_factor > 1 and not reference_column: + logger.warning("--reference-downscale-factor specified but no --reference-column provided. Ignoring.") + preprocess_dataset( dataset_file=dataset_path, caption_column=caption_column, @@ -270,6 +307,7 @@ def main( # noqa: PLR0913 device=device, remove_llm_prefixes=remove_llm_prefixes, reference_column=reference_column, + reference_downscale_factor=reference_downscale_factor, with_audio=with_audio, load_text_encoder_in_8bit=load_text_encoder_in_8bit, ) diff --git a/packages/ltx-trainer/scripts/process_videos.py b/packages/ltx-trainer/scripts/process_videos.py index ca51774..c1b6ed4 100755 --- a/packages/ltx-trainer/scripts/process_videos.py +++ b/packages/ltx-trainer/scripts/process_videos.py @@ -891,6 +891,50 @@ def parse_resolution_buckets(resolution_buckets_str: str) -> list[tuple[int, int return resolution_buckets +def compute_scaled_resolution_buckets( + resolution_buckets: list[tuple[int, int, int]], + scale_factor: int, +) -> list[tuple[int, int, int]]: + """Compute scaled resolution buckets and validate the results.""" + if scale_factor == 1: + return resolution_buckets + + scaled_buckets = [] + for frames, height, width in resolution_buckets: + # Validate that scale factor evenly divides the dimensions + if height % scale_factor != 0: + raise ValueError( + f"Height {height} is not evenly divisible by scale factor {scale_factor}. " + f"Choose a scale factor that divides {height} evenly." + ) + if width % scale_factor != 0: + raise ValueError( + f"Width {width} is not evenly divisible by scale factor {scale_factor}. " + f"Choose a scale factor that divides {width} evenly." + ) + + scaled_height = height // scale_factor + scaled_width = width // scale_factor + + # Validate scaled dimensions are divisible by VAE spatial factor + if scaled_height % VAE_SPATIAL_FACTOR != 0: + raise ValueError( + f"Scaled height {scaled_height} (from {height} / {scale_factor}) " + f"is not divisible by {VAE_SPATIAL_FACTOR}. " + f"Choose a different scale factor or adjust your resolution buckets." + ) + if scaled_width % VAE_SPATIAL_FACTOR != 0: + raise ValueError( + f"Scaled width {scaled_width} (from {width} / {scale_factor}) " + f"is not divisible by {VAE_SPATIAL_FACTOR}. " + f"Choose a different scale factor or adjust your resolution buckets." + ) + + scaled_buckets.append((frames, scaled_height, scaled_width)) + + return scaled_buckets + + @app.command() def main( # noqa: PLR0913 dataset_file: str = typer.Argument( diff --git a/packages/ltx-trainer/src/ltx_trainer/config.py b/packages/ltx-trainer/src/ltx_trainer/config.py index b999665..76cf9bc 100644 --- a/packages/ltx-trainer/src/ltx_trainer/config.py +++ b/packages/ltx-trainer/src/ltx_trainer/config.py @@ -207,6 +207,14 @@ class ValidationConfig(ConfigBaseModel): "One video path must be provided for each validation prompt", ) + reference_downscale_factor: int = Field( + default=1, + description="Downscale factor for reference videos in IC-LoRA validation. " + "When > 1, reference videos are processed at 1/n resolution (e.g., 2 means half resolution). " + "Must match the factor used during dataset preprocessing.", + ge=1, + ) + video_dims: tuple[int, int, int] = Field( default=(960, 544, 97), description="Dimensions of validation videos (width, height, frames). " @@ -334,6 +342,41 @@ class ValidationConfig(ConfigBaseModel): return v + @model_validator(mode="after") + def validate_scaled_reference_dimensions(self) -> "ValidationConfig": + """Validate that scaled reference dimensions are valid when reference_downscale_factor > 1.""" + if self.reference_downscale_factor > 1: + width, height, _frames = self.video_dims + + # Validate that downscale factor evenly divides the target dimensions + if width % self.reference_downscale_factor != 0: + raise ValueError( + f"Width {width} is not evenly divisible by reference_downscale_factor " + f"{self.reference_downscale_factor}. Choose a downscale factor that divides {width} evenly." + ) + if height % self.reference_downscale_factor != 0: + raise ValueError( + f"Height {height} is not evenly divisible by reference_downscale_factor " + f"{self.reference_downscale_factor}. Choose a downscale factor that divides {height} evenly." + ) + + scaled_width = width // self.reference_downscale_factor + scaled_height = height // self.reference_downscale_factor + + # Validate scaled dimensions are divisible by 32 + if scaled_width % 32 != 0: + raise ValueError( + f"Scaled reference width {scaled_width} (from {width} / {self.reference_downscale_factor}) " + f"is not divisible by 32. Choose a different downscale factor or adjust video_dims." + ) + if scaled_height % 32 != 0: + raise ValueError( + f"Scaled reference height {scaled_height} (from {height} / {self.reference_downscale_factor}) " + f"is not divisible by 32. Choose a different downscale factor or adjust video_dims." + ) + + return self + class CheckpointsConfig(ConfigBaseModel): """Configuration for model checkpointing during training""" diff --git a/packages/ltx-trainer/src/ltx_trainer/model_loader.py b/packages/ltx-trainer/src/ltx_trainer/model_loader.py index 3220463..7fd7290 100644 --- a/packages/ltx-trainer/src/ltx_trainer/model_loader.py +++ b/packages/ltx-trainer/src/ltx_trainer/model_loader.py @@ -218,16 +218,22 @@ def load_text_encoder( from ltx_core.loader.single_gpu_model_builder import SingleGPUModelBuilder from ltx_core.text_encoders.gemma.encoders.av_encoder import ( AV_GEMMA_TEXT_ENCODER_KEY_OPS, + GEMMA_MODEL_OPS, AVGemmaTextEncoderModelConfigurator, ) from ltx_core.text_encoders.gemma.encoders.base_encoder import module_ops_from_gemma_root + from ltx_core.utils import find_matching_file torch_device = _to_torch_device(device) + + gemma_model_folder = find_matching_file(str(gemma_model_path), "model*.safetensors").parent + gemma_weight_paths = [str(p) for p in gemma_model_folder.rglob("*.safetensors")] + text_encoder = SingleGPUModelBuilder( - model_path=str(checkpoint_path), + model_path=(str(checkpoint_path), *gemma_weight_paths), model_class_configurator=AVGemmaTextEncoderModelConfigurator, model_sd_ops=AV_GEMMA_TEXT_ENCODER_KEY_OPS, - module_ops=module_ops_from_gemma_root(str(gemma_model_path)), + module_ops=(GEMMA_MODEL_OPS, *module_ops_from_gemma_root(str(gemma_model_path))), ).build(device=torch_device, dtype=dtype) return text_encoder diff --git a/packages/ltx-trainer/src/ltx_trainer/trainer.py b/packages/ltx-trainer/src/ltx_trainer/trainer.py index b9f5f80..974c081 100644 --- a/packages/ltx-trainer/src/ltx_trainer/trainer.py +++ b/packages/ltx-trainer/src/ltx_trainer/trainer.py @@ -795,6 +795,7 @@ class LtxvTrainer: seed=self._config.validation.seed, condition_image=condition_image, reference_video=reference_video, + reference_downscale_factor=self._config.validation.reference_downscale_factor, generate_audio=generate_audio, include_reference_in_output=self._config.validation.include_reference_in_output, cached_embeddings=cached_embeddings, @@ -885,8 +886,11 @@ class LtxvTrainer: # Cast to configured precision state_dict = {k: v.to(save_dtype) if isinstance(v, Tensor) else v for k, v in state_dict.items()} - # Save to disk - save_file(state_dict, saved_weights_path) + # Build metadata for safetensors file + metadata = self._build_checkpoint_metadata() + + # Save to disk with metadata + save_file(state_dict, saved_weights_path, metadata=metadata) else: # Cast to configured precision full_state_dict = {k: v.to(save_dtype) if isinstance(v, Tensor) else v for k, v in full_state_dict.items()} @@ -913,6 +917,21 @@ class LtxvTrainer: # Update the list to only contain kept checkpoints self._checkpoint_paths = self._checkpoint_paths[-self._config.checkpoints.keep_last_n :] + def _build_checkpoint_metadata(self) -> dict[str, str]: + """Build metadata dictionary for safetensors checkpoint. + Delegates to the training strategy to get strategy-specific metadata + that downstream inference pipelines may need. + Returns: + Dictionary of string key-value pairs for safetensors metadata. + Values are converted to strings for safetensors compatibility. + """ + raw_metadata = self._training_strategy.get_checkpoint_metadata() + # Convert all values to strings for safetensors compatibility + metadata = {k: str(v) for k, v in raw_metadata.items()} + if metadata: + logger.info(f"Saving checkpoint metadata: {metadata}") + return metadata + def _save_config(self) -> None: """Save the training configuration as a YAML file in the output directory.""" if not IS_MAIN_PROCESS: diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py index 6035b90..a298b36 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/base_strategy.py @@ -128,6 +128,15 @@ class TrainingStrategy(ABC): Scalar loss tensor """ + def get_checkpoint_metadata(self) -> dict[str, Any]: + """Get strategy-specific metadata to include in checkpoint files. + Override this method in subclasses to add custom metadata, + e.g. any parameters that a downstream inference pipeline may need. + Returns: + Dictionary of metadata key-value pairs (values must be JSON-serializable) + """ + return {} + def _get_video_positions( self, num_frames: int, diff --git a/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py b/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py index bf51331..000aa63 100644 --- a/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py +++ b/packages/ltx-trainer/src/ltx_trainer/training_strategies/video_to_video.py @@ -46,9 +46,13 @@ class VideoToVideoStrategy(TrainingStrategy): - Reference latents (clean) are concatenated with target latents (noised) - Video coordinates handle both reference and target sequences - Loss is computed only on the target portion + Attributes: + reference_downscale_factor: The inferred downscale factor of reference videos. + This is computed from the first batch and cached for metadata export. """ config: VideoToVideoConfig + reference_downscale_factor: int | None def __init__(self, config: VideoToVideoConfig): """Initialize strategy with configuration. @@ -56,6 +60,7 @@ class VideoToVideoStrategy(TrainingStrategy): config: Video-to-video configuration """ super().__init__(config) + self.reference_downscale_factor = None # Will be inferred from first batch def get_data_sources(self) -> dict[str, str]: """IC-LoRA training requires latents, conditions, and reference latents.""" @@ -65,7 +70,7 @@ class VideoToVideoStrategy(TrainingStrategy): self.config.reference_latents_dir: "ref_latents", } - def prepare_training_inputs( + def prepare_training_inputs( # noqa: PLR0915 self, batch: dict[str, Any], timestep_sampler: TimestepSampler, @@ -86,6 +91,26 @@ class VideoToVideoStrategy(TrainingStrategy): ref_height = ref_latents_info["height"][0].item() ref_width = ref_latents_info["width"][0].item() + # Infer reference downscale factor from dimension ratios + # This allows training with downscaled reference videos for efficiency + reference_downscale_factor = self._infer_reference_downscale_factor( + target_height=height, + target_width=width, + ref_height=ref_height, + ref_width=ref_width, + ) + + # Cache the scale factor for metadata export (only on first batch) + if self.reference_downscale_factor is None: + self.reference_downscale_factor = reference_downscale_factor + elif self.reference_downscale_factor != reference_downscale_factor: + raise ValueError( + f"Inconsistent reference downscale factor across batches. " + f"First batch had factor={self.reference_downscale_factor}, " + f"but current batch has factor={reference_downscale_factor}. " + f"All training samples must use the same reference/target resolution ratio." + ) + # Patchify latents: [B, C, F, H, W] -> [B, seq_len, C] target_latents = self._video_patchifier.patchify(target_latents) ref_latents = self._video_patchifier.patchify(ref_latents) @@ -159,6 +184,15 @@ class VideoToVideoStrategy(TrainingStrategy): dtype=dtype, ) + # Scale reference positions to match target coordinate space + # This maps ref positions from (0, ref_H, ref_W) to (0, target_H, target_W) + # Position tensor shape: [B, 3, seq_len, 2] where dim 1 is (time, height, width) + if reference_downscale_factor != 1: + ref_positions = ref_positions.clone() + ref_positions[:, 1, ...] *= reference_downscale_factor # height axis + ref_positions[:, 2, ...] *= reference_downscale_factor # width axis + # Time axis (index 0) remains unchanged + target_positions = self._get_video_positions( num_frames=num_frames, height=height, @@ -221,3 +255,48 @@ class VideoToVideoStrategy(TrainingStrategy): loss = loss.mul(loss_mask).div(loss_mask.mean()) return loss.mean() + + def get_checkpoint_metadata(self) -> dict[str, Any]: + """Get metadata for checkpoint files.""" + metadata: dict[str, Any] = {} + # Always include reference_downscale_factor for IC-LoRAs so inference + # pipelines know the expected scale factor for reference videos. + if self.reference_downscale_factor is not None: + metadata["reference_downscale_factor"] = self.reference_downscale_factor + return metadata + + @staticmethod + def _infer_reference_downscale_factor( + target_height: int, + target_width: int, + ref_height: int, + ref_width: int, + ) -> int: + """Infer the reference downscale factor from target and reference dimensions.""" + # If dimensions match, no scaling needed + if target_height == ref_height and target_width == ref_width: + return 1 + + # Calculate scale factors for each dimension + if target_height % ref_height != 0 or target_width % ref_width != 0: + raise ValueError( + f"Target dimensions ({target_height}x{target_width}) must be exact multiples " + f"of reference dimensions ({ref_height}x{ref_width})" + ) + + scale_h = target_height // ref_height + scale_w = target_width // ref_width + + if scale_h != scale_w: + raise ValueError( + f"Reference scale must be uniform. Got height scale {scale_h} and width scale {scale_w}. " + f"Target: {target_height}x{target_width}, Reference: {ref_height}x{ref_width}" + ) + + if scale_h < 1: + raise ValueError( + f"Reference dimensions ({ref_height}x{ref_width}) cannot be larger than " + f"target dimensions ({target_height}x{target_width})" + ) + + return scale_h diff --git a/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py b/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py index 93f3199..9541a5d 100644 --- a/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py +++ b/packages/ltx-trainer/src/ltx_trainer/validation_sampler.py @@ -85,6 +85,7 @@ class GenerationConfig: seed: int = 42 # Random seed for reproducibility condition_image: Tensor | None = None # Optional first frame image for image-to-video reference_video: Tensor | None = None # For IC-LoRA: [F, C, H, W] in [0, 1] + reference_downscale_factor: int = 1 # For IC-LoRA: downscale factor (1 = same resolution, 2 = half resolution) generate_audio: bool = True # Whether to generate audio alongside video include_reference_in_output: bool = False # For IC-LoRA: concatenate original reference with generated output cached_embeddings: CachedPromptEmbeddings | None = None # Pre-computed text embeddings (avoids loading Gemma) @@ -251,6 +252,14 @@ class ValidationSampler: ref_latent, ref_positions = self._encode_video(ref_video_preprocessed, config.frame_rate, device) ref_seq_len = ref_latent.shape[1] + # Scale reference positions to match target coordinate space + # Position tensor shape: [B, 3, seq_len, 2] where dim 1 is (time, height, width) + if config.reference_downscale_factor != 1: + ref_positions = ref_positions.clone() + ref_positions[:, 1, ...] *= config.reference_downscale_factor # height axis + ref_positions[:, 2, ...] *= config.reference_downscale_factor # width axis + # Time axis (index 0) remains unchanged + # Create target video state video_tools = self._create_video_latent_tools(config) target_clean_state = video_tools.create_initial_state(device=device, dtype=torch.bfloat16) @@ -375,13 +384,28 @@ class ValidationSampler: @staticmethod def _preprocess_reference_video(config: GenerationConfig) -> Tensor: """Preprocess reference video: resize, crop, and convert to model input format. + When reference_downscale_factor > 1, the reference video is downscaled to a smaller + resolution for more efficient inference. The positions will be scaled up later + to match the target coordinate space. Args: - config: Generation configuration with reference_video + config: Generation configuration Returns: Preprocessed video tensor [B, C, F, H, W] in [-1, 1] range """ ref_video = config.reference_video # [F, C, H, W] in [0, 1] - target_height, target_width = config.height, config.width + scale_factor = config.reference_downscale_factor + + # Target dimensions for reference (scaled down if scale_factor > 1) + target_height = config.height // scale_factor + target_width = config.width // scale_factor + + # Validate scaled dimensions + if target_height % 32 != 0 or target_width % 32 != 0: + raise ValueError( + f"Scaled reference dimensions ({target_height}x{target_width}) must be divisible by 32. " + f"Original: {config.height}x{config.width}, scale_factor: {scale_factor}" + ) + current_height, current_width = ref_video.shape[2:] # Resize maintaining aspect ratio and center crop if needed @@ -745,11 +769,28 @@ class ValidationSampler: If the videos have different frame counts, the shorter one is padded with its last frame repeated. Args: - left_video: Left video tensor [C, F1, H, W] in [0, 1] - right_video: Right video tensor [C, F2, H, W] in [0, 1] + left_video: Left video tensor [C, F1, H1, W1] in [0, 1] + right_video: Right video tensor [C, F2, H2, W2] in [0, 1] Returns: - Concatenated video tensor [C, max(F1,F2), H, W*2] in [0, 1] + Concatenated video tensor [C, max(F1,F2), H2, W1_scaled+W2] in [0, 1] """ + left_height, left_width = left_video.shape[2], left_video.shape[3] + right_height = right_video.shape[2] + + # Resize left video to match right video's height if needed + if left_height != right_height: + # Scale width proportionally to maintain aspect ratio + scale = right_height / left_height + new_width = int(left_width * scale) + # Interpolate expects [N, C, H, W], we have [C, F, H, W] + # Reshape to [C*F, 1, H, W] -> interpolate -> reshape back + c, f, h, w = left_video.shape + left_video = left_video.reshape(c * f, 1, h, w) + left_video = torch.nn.functional.interpolate( + left_video, size=(right_height, new_width), mode="bilinear", align_corners=False + ) + left_video = left_video.reshape(c, f, right_height, new_width) + left_frames = left_video.shape[1] right_frames = right_video.shape[1] diff --git a/uv.lock b/uv.lock index 4454371..63afc81 100644 --- a/uv.lock +++ b/uv.lock @@ -2,18 +2,12 @@ version = 1 revision = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux'", - "python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux'", - "python_full_version == '3.11.*' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 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