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[LTX0.9.5] Refactor LTXConditionPipeline for text-only conditioning (#11174)
* Refactor `LTXConditionPipeline` to add text-only conditioning * style * up * Refactor `LTXConditionPipeline` to streamline condition handling and improve clarity * Improve condition checks * Simplify latents handling based on conditioning type * Refactor rope_interpolation_scale preparation for clarity and efficiency * Update LTXConditionPipeline docstring to clarify supported input types * Add LTX Video 0.9.5 model to documentation * Clarify documentation to indicate support for text-only conditioning without passing `conditions` * refactor: comment out unused parameters in LTXConditionPipeline * fix: restore previously commented parameters in LTXConditionPipeline * fix: remove unused parameters from LTXConditionPipeline * refactor: remove unnecessary lines in LTXConditionPipeline
This commit is contained in:
Tolga Cangöz
GitHub
@@ -32,6 +32,7 @@ Available models:
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|:-------------:|:-----------------:|
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| [`LTX Video 0.9.0`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.safetensors) | `torch.bfloat16` |
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| [`LTX Video 0.9.1`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.1.safetensors) | `torch.bfloat16` |
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| [`LTX Video 0.9.5`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltx-video-2b-v0.9.5.safetensors) | `torch.bfloat16` |
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Note: The recommended dtype is for the transformer component. The VAE and text encoders can be either `torch.float32`, `torch.bfloat16` or `torch.float16` but the recommended dtype is `torch.bfloat16` as used in the original repository.
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@@ -14,7 +14,7 @@
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import inspect
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, Optional, Union
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import PIL.Image
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import torch
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@@ -75,6 +75,7 @@ EXAMPLE_DOC_STRING = """
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>>> # Generate video
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>>> generator = torch.Generator("cuda").manual_seed(0)
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>>> # Text-only conditioning is also supported without the need to pass `conditions`
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>>> video = pipe(
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... conditions=[condition1, condition2],
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... prompt=prompt,
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@@ -223,7 +224,7 @@ def retrieve_latents(
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class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin):
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r"""
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Pipeline for image-to-video generation.
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Pipeline for text/image/video-to-video generation.
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Reference: https://github.com/Lightricks/LTX-Video
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@@ -482,9 +483,6 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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if conditions is not None and (image is not None or video is not None):
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raise ValueError("If `conditions` is provided, `image` and `video` must not be provided.")
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if conditions is None and (image is None and video is None):
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raise ValueError("If `conditions` is not provided, `image` or `video` must be provided.")
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if conditions is None:
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if isinstance(image, list) and isinstance(frame_index, list) and len(image) != len(frame_index):
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raise ValueError(
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@@ -642,9 +640,9 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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def prepare_latents(
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self,
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conditions: List[torch.Tensor],
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condition_strength: List[float],
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condition_frame_index: List[int],
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conditions: Optional[List[torch.Tensor]] = None,
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condition_strength: Optional[List[float]] = None,
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condition_frame_index: Optional[List[int]] = None,
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batch_size: int = 1,
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num_channels_latents: int = 128,
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height: int = 512,
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@@ -654,7 +652,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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generator: Optional[torch.Generator] = None,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
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num_latent_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
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latent_height = height // self.vae_spatial_compression_ratio
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latent_width = width // self.vae_spatial_compression_ratio
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@@ -662,77 +660,80 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
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latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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condition_latent_frames_mask = torch.zeros((batch_size, num_latent_frames), device=device, dtype=torch.float32)
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if len(conditions) > 0:
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condition_latent_frames_mask = torch.zeros(
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(batch_size, num_latent_frames), device=device, dtype=torch.float32
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)
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extra_conditioning_latents = []
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extra_conditioning_video_ids = []
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extra_conditioning_mask = []
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extra_conditioning_num_latents = 0
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for data, strength, frame_index in zip(conditions, condition_strength, condition_frame_index):
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condition_latents = retrieve_latents(self.vae.encode(data), generator=generator)
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condition_latents = self._normalize_latents(
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condition_latents, self.vae.latents_mean, self.vae.latents_std
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).to(device, dtype=dtype)
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extra_conditioning_latents = []
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extra_conditioning_video_ids = []
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extra_conditioning_mask = []
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extra_conditioning_num_latents = 0
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for data, strength, frame_index in zip(conditions, condition_strength, condition_frame_index):
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condition_latents = retrieve_latents(self.vae.encode(data), generator=generator)
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condition_latents = self._normalize_latents(
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condition_latents, self.vae.latents_mean, self.vae.latents_std
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).to(device, dtype=dtype)
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num_data_frames = data.size(2)
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num_cond_frames = condition_latents.size(2)
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num_data_frames = data.size(2)
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num_cond_frames = condition_latents.size(2)
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if frame_index == 0:
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latents[:, :, :num_cond_frames] = torch.lerp(
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latents[:, :, :num_cond_frames], condition_latents, strength
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)
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condition_latent_frames_mask[:, :num_cond_frames] = strength
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if frame_index == 0:
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latents[:, :, :num_cond_frames] = torch.lerp(
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latents[:, :, :num_cond_frames], condition_latents, strength
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)
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condition_latent_frames_mask[:, :num_cond_frames] = strength
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else:
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if num_data_frames > 1:
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if num_cond_frames < num_prefix_latent_frames:
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raise ValueError(
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f"Number of latent frames must be at least {num_prefix_latent_frames} but got {num_data_frames}."
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)
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else:
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if num_data_frames > 1:
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if num_cond_frames < num_prefix_latent_frames:
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raise ValueError(
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f"Number of latent frames must be at least {num_prefix_latent_frames} but got {num_data_frames}."
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)
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if num_cond_frames > num_prefix_latent_frames:
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start_frame = frame_index // self.vae_temporal_compression_ratio + num_prefix_latent_frames
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end_frame = start_frame + num_cond_frames - num_prefix_latent_frames
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latents[:, :, start_frame:end_frame] = torch.lerp(
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latents[:, :, start_frame:end_frame],
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condition_latents[:, :, num_prefix_latent_frames:],
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strength,
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)
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condition_latent_frames_mask[:, start_frame:end_frame] = strength
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condition_latents = condition_latents[:, :, :num_prefix_latent_frames]
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if num_cond_frames > num_prefix_latent_frames:
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start_frame = frame_index // self.vae_temporal_compression_ratio + num_prefix_latent_frames
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end_frame = start_frame + num_cond_frames - num_prefix_latent_frames
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latents[:, :, start_frame:end_frame] = torch.lerp(
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latents[:, :, start_frame:end_frame],
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condition_latents[:, :, num_prefix_latent_frames:],
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strength,
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)
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condition_latent_frames_mask[:, start_frame:end_frame] = strength
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condition_latents = condition_latents[:, :, :num_prefix_latent_frames]
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noise = randn_tensor(condition_latents.shape, generator=generator, device=device, dtype=dtype)
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condition_latents = torch.lerp(noise, condition_latents, strength)
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noise = randn_tensor(condition_latents.shape, generator=generator, device=device, dtype=dtype)
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condition_latents = torch.lerp(noise, condition_latents, strength)
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condition_video_ids = self._prepare_video_ids(
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batch_size,
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condition_latents.size(2),
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latent_height,
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latent_width,
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patch_size=self.transformer_spatial_patch_size,
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patch_size_t=self.transformer_temporal_patch_size,
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device=device,
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)
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condition_video_ids = self._scale_video_ids(
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condition_video_ids,
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scale_factor=self.vae_spatial_compression_ratio,
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scale_factor_t=self.vae_temporal_compression_ratio,
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frame_index=frame_index,
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device=device,
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)
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condition_latents = self._pack_latents(
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condition_latents,
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self.transformer_spatial_patch_size,
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self.transformer_temporal_patch_size,
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)
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condition_conditioning_mask = torch.full(
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condition_latents.shape[:2], strength, device=device, dtype=dtype
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)
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condition_video_ids = self._prepare_video_ids(
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batch_size,
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condition_latents.size(2),
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latent_height,
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latent_width,
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patch_size=self.transformer_spatial_patch_size,
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patch_size_t=self.transformer_temporal_patch_size,
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device=device,
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)
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condition_video_ids = self._scale_video_ids(
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condition_video_ids,
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scale_factor=self.vae_spatial_compression_ratio,
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scale_factor_t=self.vae_temporal_compression_ratio,
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frame_index=frame_index,
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device=device,
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)
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condition_latents = self._pack_latents(
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condition_latents,
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self.transformer_spatial_patch_size,
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self.transformer_temporal_patch_size,
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)
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condition_conditioning_mask = torch.full(
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condition_latents.shape[:2], strength, device=device, dtype=dtype
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)
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extra_conditioning_latents.append(condition_latents)
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extra_conditioning_video_ids.append(condition_video_ids)
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extra_conditioning_mask.append(condition_conditioning_mask)
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extra_conditioning_num_latents += condition_latents.size(1)
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extra_conditioning_latents.append(condition_latents)
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extra_conditioning_video_ids.append(condition_video_ids)
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extra_conditioning_mask.append(condition_conditioning_mask)
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extra_conditioning_num_latents += condition_latents.size(1)
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video_ids = self._prepare_video_ids(
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batch_size,
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@@ -743,7 +744,10 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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patch_size=self.transformer_spatial_patch_size,
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device=device,
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)
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conditioning_mask = condition_latent_frames_mask.gather(1, video_ids[:, 0])
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if len(conditions) > 0:
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conditioning_mask = condition_latent_frames_mask.gather(1, video_ids[:, 0])
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else:
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conditioning_mask, extra_conditioning_num_latents = None, 0
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video_ids = self._scale_video_ids(
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video_ids,
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scale_factor=self.vae_spatial_compression_ratio,
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@@ -755,7 +759,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
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)
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if len(extra_conditioning_latents) > 0:
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if len(conditions) > 0 and len(extra_conditioning_latents) > 0:
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latents = torch.cat([*extra_conditioning_latents, latents], dim=1)
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video_ids = torch.cat([*extra_conditioning_video_ids, video_ids], dim=2)
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conditioning_mask = torch.cat([*extra_conditioning_mask, conditioning_mask], dim=1)
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@@ -955,7 +959,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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frame_index = [condition.frame_index for condition in conditions]
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image = [condition.image for condition in conditions]
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video = [condition.video for condition in conditions]
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else:
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elif image is not None or video is not None:
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if not isinstance(image, list):
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image = [image]
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num_conditions = 1
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@@ -999,32 +1003,34 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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vae_dtype = self.vae.dtype
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conditioning_tensors = []
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for condition_image, condition_video, condition_frame_index, condition_strength in zip(
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image, video, frame_index, strength
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):
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if condition_image is not None:
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condition_tensor = (
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self.video_processor.preprocess(condition_image, height, width)
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.unsqueeze(2)
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.to(device, dtype=vae_dtype)
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)
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elif condition_video is not None:
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condition_tensor = self.video_processor.preprocess_video(condition_video, height, width)
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num_frames_input = condition_tensor.size(2)
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num_frames_output = self.trim_conditioning_sequence(
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condition_frame_index, num_frames_input, num_frames
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)
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condition_tensor = condition_tensor[:, :, :num_frames_output]
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condition_tensor = condition_tensor.to(device, dtype=vae_dtype)
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else:
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raise ValueError("Either `image` or `video` must be provided in the `LTXVideoCondition`.")
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is_conditioning_image_or_video = image is not None or video is not None
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if is_conditioning_image_or_video:
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for condition_image, condition_video, condition_frame_index, condition_strength in zip(
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image, video, frame_index, strength
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):
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if condition_image is not None:
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condition_tensor = (
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self.video_processor.preprocess(condition_image, height, width)
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.unsqueeze(2)
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.to(device, dtype=vae_dtype)
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)
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elif condition_video is not None:
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condition_tensor = self.video_processor.preprocess_video(condition_video, height, width)
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num_frames_input = condition_tensor.size(2)
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num_frames_output = self.trim_conditioning_sequence(
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condition_frame_index, num_frames_input, num_frames
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)
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condition_tensor = condition_tensor[:, :, :num_frames_output]
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condition_tensor = condition_tensor.to(device, dtype=vae_dtype)
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else:
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raise ValueError("Either `image` or `video` must be provided for conditioning.")
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if condition_tensor.size(2) % self.vae_temporal_compression_ratio != 1:
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raise ValueError(
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f"Number of frames in the video must be of the form (k * {self.vae_temporal_compression_ratio} + 1) "
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f"but got {condition_tensor.size(2)} frames."
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)
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conditioning_tensors.append(condition_tensor)
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if condition_tensor.size(2) % self.vae_temporal_compression_ratio != 1:
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raise ValueError(
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f"Number of frames in the video must be of the form (k * {self.vae_temporal_compression_ratio} + 1) "
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f"but got {condition_tensor.size(2)} frames."
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)
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conditioning_tensors.append(condition_tensor)
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# 4. Prepare latent variables
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num_channels_latents = self.transformer.config.in_channels
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@@ -1045,7 +1051,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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video_coords = video_coords.float()
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video_coords[:, 0] = video_coords[:, 0] * (1.0 / frame_rate)
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init_latents = latents.clone()
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init_latents = latents.clone() if is_conditioning_image_or_video else None
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if self.do_classifier_free_guidance:
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video_coords = torch.cat([video_coords, video_coords], dim=0)
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@@ -1065,7 +1071,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
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self._num_timesteps = len(timesteps)
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# 7. Denoising loop
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# 6. Denoising loop
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with self.progress_bar(total=num_inference_steps) as progress_bar:
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for i, t in enumerate(timesteps):
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if self.interrupt:
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@@ -1073,7 +1079,7 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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self._current_timestep = t
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if image_cond_noise_scale > 0:
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if image_cond_noise_scale > 0 and init_latents is not None:
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# Add timestep-dependent noise to the hard-conditioning latents
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# This helps with motion continuity, especially when conditioned on a single frame
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latents = self.add_noise_to_image_conditioning_latents(
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@@ -1086,16 +1092,18 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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)
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latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
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conditioning_mask_model_input = (
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torch.cat([conditioning_mask, conditioning_mask])
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if self.do_classifier_free_guidance
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else conditioning_mask
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)
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if is_conditioning_image_or_video:
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conditioning_mask_model_input = (
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torch.cat([conditioning_mask, conditioning_mask])
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if self.do_classifier_free_guidance
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else conditioning_mask
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)
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latent_model_input = latent_model_input.to(prompt_embeds.dtype)
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# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
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timestep = t.expand(latent_model_input.shape[0]).unsqueeze(-1).float()
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timestep = torch.min(timestep, (1 - conditioning_mask_model_input) * 1000.0)
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if is_conditioning_image_or_video:
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timestep = torch.min(timestep, (1 - conditioning_mask_model_input) * 1000.0)
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noise_pred = self.transformer(
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hidden_states=latent_model_input,
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@@ -1115,8 +1123,11 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
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denoised_latents = self.scheduler.step(
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-noise_pred, t, latents, per_token_timesteps=timestep, return_dict=False
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)[0]
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tokens_to_denoise_mask = (t / 1000 - 1e-6 < (1.0 - conditioning_mask)).unsqueeze(-1)
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latents = torch.where(tokens_to_denoise_mask, denoised_latents, latents)
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if is_conditioning_image_or_video:
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tokens_to_denoise_mask = (t / 1000 - 1e-6 < (1.0 - conditioning_mask)).unsqueeze(-1)
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latents = torch.where(tokens_to_denoise_mask, denoised_latents, latents)
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else:
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latents = denoised_latents
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if callback_on_step_end is not None:
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callback_kwargs = {}
|
||||
@@ -1134,7 +1145,9 @@ class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraL
|
||||
if XLA_AVAILABLE:
|
||||
xm.mark_step()
|
||||
|
||||
latents = latents[:, extra_conditioning_num_latents:]
|
||||
if is_conditioning_image_or_video:
|
||||
latents = latents[:, extra_conditioning_num_latents:]
|
||||
|
||||
latents = self._unpack_latents(
|
||||
latents,
|
||||
latent_num_frames,
|
||||
|
||||
Reference in New Issue
Block a user