# Copyright 2025 Lightricks and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect from dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Tuple, Union import PIL.Image import torch from transformers import T5EncoderModel, T5TokenizerFast from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...image_processor import PipelineImageInput from ...loaders import FromSingleFileMixin, LTXVideoLoraLoaderMixin from ...models.autoencoders import AutoencoderKLLTXVideo from ...models.transformers import LTXVideoTransformer3DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import is_torch_xla_available, logging, replace_example_docstring from ...utils.torch_utils import randn_tensor from ...video_processor import VideoProcessor from ..pipeline_utils import DiffusionPipeline from .pipeline_output import LTXPipelineOutput if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXConditionPipeline, LTXVideoCondition >>> from diffusers.utils import export_to_video, load_video, load_image >>> pipe = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.5", torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> # Load input image and video >>> video = load_video( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4" ... ) >>> image = load_image( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input.jpg" ... ) >>> # Create conditioning objects >>> condition1 = LTXVideoCondition( ... image=image, ... frame_index=0, ... ) >>> condition2 = LTXVideoCondition( ... video=video, ... frame_index=80, ... ) >>> prompt = "The video depicts a long, straight highway stretching into the distance, flanked by metal guardrails. The road is divided into multiple lanes, with a few vehicles visible in the far distance. The surrounding landscape features dry, grassy fields on one side and rolling hills on the other. The sky is mostly clear with a few scattered clouds, suggesting a bright, sunny day. And then the camera switch to a winding mountain road covered in snow, with a single vehicle traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. The landscape is characterized by rugged terrain and a river visible in the distance. The scene captures the solitude and beauty of a winter drive through a mountainous region." >>> negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted" >>> # Generate video >>> generator = torch.Generator("cuda").manual_seed(0) >>> # Text-only conditioning is also supported without the need to pass `conditions` >>> video = pipe( ... conditions=[condition1, condition2], ... prompt=prompt, ... negative_prompt=negative_prompt, ... width=768, ... height=512, ... num_frames=161, ... num_inference_steps=40, ... generator=generator, ... ).frames[0] >>> export_to_video(video, "output.mp4", fps=24) ``` """ @dataclass class LTXVideoCondition: """ Defines a single frame-conditioning item for LTX Video - a single frame or a sequence of frames. Attributes: image (`PIL.Image.Image`): The image to condition the video on. video (`List[PIL.Image.Image]`): The video to condition the video on. frame_index (`int`): The frame index at which the image or video will conditionally effect the video generation. strength (`float`, defaults to `1.0`): The strength of the conditioning effect. A value of `1.0` means the conditioning effect is fully applied. """ image: Optional[PIL.Image.Image] = None video: Optional[List[PIL.Image.Image]] = None frame_index: int = 0 strength: float = 1.0 # from LTX-Video/ltx_video/schedulers/rf.py def linear_quadratic_schedule(num_steps, threshold_noise=0.025, linear_steps=None): if linear_steps is None: linear_steps = num_steps // 2 if num_steps < 2: return torch.tensor([1.0]) linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)] threshold_noise_step_diff = linear_steps - threshold_noise * num_steps quadratic_steps = num_steps - linear_steps quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps**2) linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps**2) const = quadratic_coef * (linear_steps**2) quadratic_sigma_schedule = [ quadratic_coef * (i**2) + linear_coef * i + const for i in range(linear_steps, num_steps) ] sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0] sigma_schedule = [1.0 - x for x in sigma_schedule] return torch.tensor(sigma_schedule[:-1]) # Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift def calculate_shift( image_seq_len, base_seq_len: int = 256, max_seq_len: int = 4096, base_shift: float = 0.5, max_shift: float = 1.15, ): m = (max_shift - base_shift) / (max_seq_len - base_seq_len) b = base_shift - m * base_seq_len mu = image_seq_len * m + b return mu # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = None, **kwargs, ): r""" Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): r""" Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on Section 3.4 from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891). Args: noise_cfg (`torch.Tensor`): The predicted noise tensor for the guided diffusion process. noise_pred_text (`torch.Tensor`): The predicted noise tensor for the text-guided diffusion process. guidance_rescale (`float`, *optional*, defaults to 0.0): A rescale factor applied to the noise predictions. Returns: noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor. """ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg return noise_cfg class LTXConditionPipeline(DiffusionPipeline, FromSingleFileMixin, LTXVideoLoraLoaderMixin): r""" Pipeline for text/image/video-to-video generation. Reference: https://github.com/Lightricks/LTX-Video Args: transformer ([`LTXVideoTransformer3DModel`]): Conditional Transformer architecture to denoise the encoded video latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKLLTXVideo`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`T5EncoderModel`]): [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer (`T5TokenizerFast`): Second Tokenizer of class [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast). """ model_cpu_offload_seq = "text_encoder->transformer->vae" _optional_components = [] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKLLTXVideo, text_encoder: T5EncoderModel, tokenizer: T5TokenizerFast, transformer: LTXVideoTransformer3DModel, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, ) self.vae_spatial_compression_ratio = ( self.vae.spatial_compression_ratio if getattr(self, "vae", None) is not None else 32 ) self.vae_temporal_compression_ratio = ( self.vae.temporal_compression_ratio if getattr(self, "vae", None) is not None else 8 ) self.transformer_spatial_patch_size = ( self.transformer.config.patch_size if getattr(self, "transformer", None) is not None else 1 ) self.transformer_temporal_patch_size = ( self.transformer.config.patch_size_t if getattr(self, "transformer") is not None else 1 ) self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio) self.tokenizer_max_length = ( self.tokenizer.model_max_length if getattr(self, "tokenizer", None) is not None else 128 ) self.default_height = 512 self.default_width = 704 self.default_frames = 121 def _get_t5_prompt_embeds( self, prompt: Union[str, List[str]] = None, num_videos_per_prompt: int = 1, max_sequence_length: int = 256, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): device = device or self._execution_device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt batch_size = len(prompt) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.bool().to(device) untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {max_sequence_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask)[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) prompt_attention_mask = prompt_attention_mask.view(batch_size, -1) prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1) return prompt_embeds, prompt_attention_mask # Copied from diffusers.pipelines.mochi.pipeline_mochi.MochiPipeline.encode_prompt def encode_prompt( self, prompt: Union[str, List[str]], negative_prompt: Optional[Union[str, List[str]]] = None, do_classifier_free_guidance: bool = True, num_videos_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, max_sequence_length: int = 256, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): Whether to use classifier free guidance or not. num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos that should be generated per prompt. torch device to place the resulting embeddings on prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. device: (`torch.device`, *optional*): torch device dtype: (`torch.dtype`, *optional*): torch dtype """ device = device or self._execution_device prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds( prompt=prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) negative_prompt_embeds, negative_prompt_attention_mask = self._get_t5_prompt_embeds( prompt=negative_prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask def check_inputs( self, prompt, conditions, image, video, frame_index, strength, denoise_strength, height, width, callback_on_step_end_tensor_inputs=None, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, negative_prompt_attention_mask=None, ): if height % 32 != 0 or width % 32 != 0: raise ValueError(f"`height` and `width` have to be divisible by 32 but are {height} and {width}.") if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if prompt_embeds is not None and prompt_attention_mask is None: raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if prompt_attention_mask.shape != negative_prompt_attention_mask.shape: raise ValueError( "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but" f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`" f" {negative_prompt_attention_mask.shape}." ) if conditions is not None and (image is not None or video is not None): raise ValueError("If `conditions` is provided, `image` and `video` must not be provided.") if conditions is None: if isinstance(image, list) and isinstance(frame_index, list) and len(image) != len(frame_index): raise ValueError( "If `conditions` is not provided, `image` and `frame_index` must be of the same length." ) elif isinstance(image, list) and isinstance(strength, list) and len(image) != len(strength): raise ValueError("If `conditions` is not provided, `image` and `strength` must be of the same length.") elif isinstance(video, list) and isinstance(frame_index, list) and len(video) != len(frame_index): raise ValueError( "If `conditions` is not provided, `video` and `frame_index` must be of the same length." ) elif isinstance(video, list) and isinstance(strength, list) and len(video) != len(strength): raise ValueError("If `conditions` is not provided, `video` and `strength` must be of the same length.") if denoise_strength < 0 or denoise_strength > 1: raise ValueError(f"The value of strength should in [0.0, 1.0] but is {denoise_strength}") @staticmethod def _prepare_video_ids( batch_size: int, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1, device: torch.device = None, ) -> torch.Tensor: latent_sample_coords = torch.meshgrid( torch.arange(0, num_frames, patch_size_t, device=device), torch.arange(0, height, patch_size, device=device), torch.arange(0, width, patch_size, device=device), indexing="ij", ) latent_sample_coords = torch.stack(latent_sample_coords, dim=0) latent_coords = latent_sample_coords.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1) latent_coords = latent_coords.reshape(batch_size, -1, num_frames * height * width) return latent_coords @staticmethod def _scale_video_ids( video_ids: torch.Tensor, scale_factor: int = 32, scale_factor_t: int = 8, frame_index: int = 0, device: torch.device = None, ) -> torch.Tensor: scaled_latent_coords = ( video_ids * torch.tensor([scale_factor_t, scale_factor, scale_factor], device=video_ids.device)[None, :, None] ) scaled_latent_coords[:, 0] = (scaled_latent_coords[:, 0] + 1 - scale_factor_t).clamp(min=0) scaled_latent_coords[:, 0] += frame_index return scaled_latent_coords @staticmethod # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._pack_latents def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor: # Unpacked latents of shape are [B, C, F, H, W] are patched into tokens of shape [B, C, F // p_t, p_t, H // p, p, W // p, p]. # The patch dimensions are then permuted and collapsed into the channel dimension of shape: # [B, F // p_t * H // p * W // p, C * p_t * p * p] (an ndim=3 tensor). # dim=0 is the batch size, dim=1 is the effective video sequence length, dim=2 is the effective number of input features batch_size, num_channels, num_frames, height, width = latents.shape post_patch_num_frames = num_frames // patch_size_t post_patch_height = height // patch_size post_patch_width = width // patch_size latents = latents.reshape( batch_size, -1, post_patch_num_frames, patch_size_t, post_patch_height, patch_size, post_patch_width, patch_size, ) latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3) return latents @staticmethod # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._unpack_latents def _unpack_latents( latents: torch.Tensor, num_frames: int, height: int, width: int, patch_size: int = 1, patch_size_t: int = 1 ) -> torch.Tensor: # Packed latents of shape [B, S, D] (S is the effective video sequence length, D is the effective feature dimensions) # are unpacked and reshaped into a video tensor of shape [B, C, F, H, W]. This is the inverse operation of # what happens in the `_pack_latents` method. batch_size = latents.size(0) latents = latents.reshape(batch_size, num_frames, height, width, -1, patch_size_t, patch_size, patch_size) latents = latents.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(2, 3) return latents @staticmethod # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._normalize_latents def _normalize_latents( latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0 ) -> torch.Tensor: # Normalize latents across the channel dimension [B, C, F, H, W] latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype) latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype) latents = (latents - latents_mean) * scaling_factor / latents_std return latents @staticmethod # Copied from diffusers.pipelines.ltx.pipeline_ltx.LTXPipeline._denormalize_latents def _denormalize_latents( latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0 ) -> torch.Tensor: # Denormalize latents across the channel dimension [B, C, F, H, W] latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype) latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype) latents = latents * latents_std / scaling_factor + latents_mean return latents def trim_conditioning_sequence(self, start_frame: int, sequence_num_frames: int, target_num_frames: int): """ Trim a conditioning sequence to the allowed number of frames. Args: start_frame (int): The target frame number of the first frame in the sequence. sequence_num_frames (int): The number of frames in the sequence. target_num_frames (int): The target number of frames in the generated video. Returns: int: updated sequence length """ scale_factor = self.vae_temporal_compression_ratio num_frames = min(sequence_num_frames, target_num_frames - start_frame) # Trim down to a multiple of temporal_scale_factor frames plus 1 num_frames = (num_frames - 1) // scale_factor * scale_factor + 1 return num_frames @staticmethod def add_noise_to_image_conditioning_latents( t: float, init_latents: torch.Tensor, latents: torch.Tensor, noise_scale: float, conditioning_mask: torch.Tensor, generator, eps=1e-6, ): """ Add timestep-dependent noise to the hard-conditioning latents. This helps with motion continuity, especially when conditioned on a single frame. """ noise = randn_tensor( latents.shape, generator=generator, device=latents.device, dtype=latents.dtype, ) # Add noise only to hard-conditioning latents (conditioning_mask = 1.0) need_to_noise = (conditioning_mask > 1.0 - eps).unsqueeze(-1) noised_latents = init_latents + noise_scale * noise * (t**2) latents = torch.where(need_to_noise, noised_latents, latents) return latents def prepare_latents( self, conditions: Optional[List[torch.Tensor]] = None, condition_strength: Optional[List[float]] = None, condition_frame_index: Optional[List[int]] = None, batch_size: int = 1, num_channels_latents: int = 128, height: int = 512, width: int = 704, num_frames: int = 161, num_prefix_latent_frames: int = 2, sigma: Optional[torch.Tensor] = None, latents: Optional[torch.Tensor] = None, generator: Optional[torch.Generator] = None, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]: num_latent_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1 latent_height = height // self.vae_spatial_compression_ratio latent_width = width // self.vae_spatial_compression_ratio shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width) noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) if latents is not None and sigma is not None: if latents.shape != shape: raise ValueError( f"Latents shape {latents.shape} does not match expected shape {shape}. Please check the input." ) latents = latents.to(device=device, dtype=dtype) sigma = sigma.to(device=device, dtype=dtype) latents = sigma * noise + (1 - sigma) * latents else: latents = noise if len(conditions) > 0: condition_latent_frames_mask = torch.zeros( (batch_size, num_latent_frames), device=device, dtype=torch.float32 ) extra_conditioning_latents = [] extra_conditioning_video_ids = [] extra_conditioning_mask = [] extra_conditioning_num_latents = 0 for data, strength, frame_index in zip(conditions, condition_strength, condition_frame_index): condition_latents = retrieve_latents(self.vae.encode(data), generator=generator) condition_latents = self._normalize_latents( condition_latents, self.vae.latents_mean, self.vae.latents_std ).to(device, dtype=dtype) num_data_frames = data.size(2) num_cond_frames = condition_latents.size(2) if frame_index == 0: latents[:, :, :num_cond_frames] = torch.lerp( latents[:, :, :num_cond_frames], condition_latents, strength ) condition_latent_frames_mask[:, :num_cond_frames] = strength else: if num_data_frames > 1: if num_cond_frames < num_prefix_latent_frames: raise ValueError( f"Number of latent frames must be at least {num_prefix_latent_frames} but got {num_data_frames}." ) if num_cond_frames > num_prefix_latent_frames: start_frame = frame_index // self.vae_temporal_compression_ratio + num_prefix_latent_frames end_frame = start_frame + num_cond_frames - num_prefix_latent_frames latents[:, :, start_frame:end_frame] = torch.lerp( latents[:, :, start_frame:end_frame], condition_latents[:, :, num_prefix_latent_frames:], strength, ) condition_latent_frames_mask[:, start_frame:end_frame] = strength condition_latents = condition_latents[:, :, :num_prefix_latent_frames] noise = randn_tensor(condition_latents.shape, generator=generator, device=device, dtype=dtype) condition_latents = torch.lerp(noise, condition_latents, strength) condition_video_ids = self._prepare_video_ids( batch_size, condition_latents.size(2), latent_height, latent_width, patch_size=self.transformer_spatial_patch_size, patch_size_t=self.transformer_temporal_patch_size, device=device, ) condition_video_ids = self._scale_video_ids( condition_video_ids, scale_factor=self.vae_spatial_compression_ratio, scale_factor_t=self.vae_temporal_compression_ratio, frame_index=frame_index, device=device, ) condition_latents = self._pack_latents( condition_latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size, ) condition_conditioning_mask = torch.full( condition_latents.shape[:2], strength, device=device, dtype=dtype ) extra_conditioning_latents.append(condition_latents) extra_conditioning_video_ids.append(condition_video_ids) extra_conditioning_mask.append(condition_conditioning_mask) extra_conditioning_num_latents += condition_latents.size(1) video_ids = self._prepare_video_ids( batch_size, num_latent_frames, latent_height, latent_width, patch_size_t=self.transformer_temporal_patch_size, patch_size=self.transformer_spatial_patch_size, device=device, ) if len(conditions) > 0: conditioning_mask = condition_latent_frames_mask.gather(1, video_ids[:, 0]) else: conditioning_mask, extra_conditioning_num_latents = None, 0 video_ids = self._scale_video_ids( video_ids, scale_factor=self.vae_spatial_compression_ratio, scale_factor_t=self.vae_temporal_compression_ratio, frame_index=0, device=device, ) latents = self._pack_latents( latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size ) if len(conditions) > 0 and len(extra_conditioning_latents) > 0: latents = torch.cat([*extra_conditioning_latents, latents], dim=1) video_ids = torch.cat([*extra_conditioning_video_ids, video_ids], dim=2) conditioning_mask = torch.cat([*extra_conditioning_mask, conditioning_mask], dim=1) return latents, conditioning_mask, video_ids, extra_conditioning_num_latents def get_timesteps(self, sigmas, timesteps, num_inference_steps, strength): num_steps = min(int(num_inference_steps * strength), num_inference_steps) start_index = max(num_inference_steps - num_steps, 0) sigmas = sigmas[start_index:] timesteps = timesteps[start_index:] return sigmas, timesteps, num_inference_steps - start_index @property def guidance_scale(self): return self._guidance_scale @property def guidance_rescale(self): return self._guidance_rescale @property def do_classifier_free_guidance(self): return self._guidance_scale > 1.0 @property def num_timesteps(self): return self._num_timesteps @property def current_timestep(self): return self._current_timestep @property def attention_kwargs(self): return self._attention_kwargs @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, conditions: Union[LTXVideoCondition, List[LTXVideoCondition]] = None, image: Union[PipelineImageInput, List[PipelineImageInput]] = None, video: List[PipelineImageInput] = None, frame_index: Union[int, List[int]] = 0, strength: Union[float, List[float]] = 1.0, denoise_strength: float = 1.0, prompt: Union[str, List[str]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, height: int = 512, width: int = 704, num_frames: int = 161, frame_rate: int = 25, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 3, guidance_rescale: float = 0.0, image_cond_noise_scale: float = 0.15, num_videos_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, decode_timestep: Union[float, List[float]] = 0.0, decode_noise_scale: Optional[Union[float, List[float]]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], max_sequence_length: int = 256, ): r""" Function invoked when calling the pipeline for generation. Args: conditions (`List[LTXVideoCondition], *optional*`): The list of frame-conditioning items for the video generation.If not provided, conditions will be created using `image`, `video`, `frame_index` and `strength`. image (`PipelineImageInput` or `List[PipelineImageInput]`, *optional*): The image or images to condition the video generation. If not provided, one has to pass `video` or `conditions`. video (`List[PipelineImageInput]`, *optional*): The video to condition the video generation. If not provided, one has to pass `image` or `conditions`. frame_index (`int` or `List[int]`, *optional*): The frame index or frame indices at which the image or video will conditionally effect the video generation. If not provided, one has to pass `conditions`. strength (`float` or `List[float]`, *optional*): The strength or strengths of the conditioning effect. If not provided, one has to pass `conditions`. denoise_strength (`float`, defaults to `1.0`): The strength of the noise added to the latents for editing. Higher strength leads to more noise added to the latents, therefore leading to more differences between original video and generated video. This is useful for video-to-video editing. prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, defaults to `512`): The height in pixels of the generated image. This is set to 480 by default for the best results. width (`int`, defaults to `704`): The width in pixels of the generated image. This is set to 848 by default for the best results. num_frames (`int`, defaults to `161`): The number of video frames to generate num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, defaults to `3 `): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when using zero terminal SNR. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of videos to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.Tensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. negative_prompt_attention_mask (`torch.FloatTensor`, *optional*): Pre-generated attention mask for negative text embeddings. decode_timestep (`float`, defaults to `0.0`): The timestep at which generated video is decoded. decode_noise_scale (`float`, defaults to `None`): The interpolation factor between random noise and denoised latents at the decode timestep. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.ltx.LTXPipelineOutput`] instead of a plain tuple. attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int` defaults to `128 `): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.ltx.LTXPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.ltx.LTXPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images. """ if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs # 1. Check inputs. Raise error if not correct self.check_inputs( prompt=prompt, conditions=conditions, image=image, video=video, frame_index=frame_index, strength=strength, denoise_strength=denoise_strength, height=height, width=width, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._attention_kwargs = attention_kwargs self._interrupt = False self._current_timestep = None # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if conditions is not None: if not isinstance(conditions, list): conditions = [conditions] strength = [condition.strength for condition in conditions] frame_index = [condition.frame_index for condition in conditions] image = [condition.image for condition in conditions] video = [condition.video for condition in conditions] elif image is not None or video is not None: if not isinstance(image, list): image = [image] num_conditions = 1 elif isinstance(image, list): num_conditions = len(image) if not isinstance(video, list): video = [video] num_conditions = 1 elif isinstance(video, list): num_conditions = len(video) if not isinstance(frame_index, list): frame_index = [frame_index] * num_conditions if not isinstance(strength, list): strength = [strength] * num_conditions device = self._execution_device vae_dtype = self.vae.dtype # 3. Prepare text embeddings & conditioning image/video ( prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, ) = self.encode_prompt( prompt=prompt, negative_prompt=negative_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, max_sequence_length=max_sequence_length, device=device, ) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) conditioning_tensors = [] is_conditioning_image_or_video = image is not None or video is not None if is_conditioning_image_or_video: for condition_image, condition_video, condition_frame_index, condition_strength in zip( image, video, frame_index, strength ): if condition_image is not None: condition_tensor = ( self.video_processor.preprocess(condition_image, height, width) .unsqueeze(2) .to(device, dtype=vae_dtype) ) elif condition_video is not None: condition_tensor = self.video_processor.preprocess_video(condition_video, height, width) num_frames_input = condition_tensor.size(2) num_frames_output = self.trim_conditioning_sequence( condition_frame_index, num_frames_input, num_frames ) condition_tensor = condition_tensor[:, :, :num_frames_output] condition_tensor = condition_tensor.to(device, dtype=vae_dtype) else: raise ValueError("Either `image` or `video` must be provided for conditioning.") if condition_tensor.size(2) % self.vae_temporal_compression_ratio != 1: raise ValueError( f"Number of frames in the video must be of the form (k * {self.vae_temporal_compression_ratio} + 1) " f"but got {condition_tensor.size(2)} frames." ) conditioning_tensors.append(condition_tensor) # 4. Prepare timesteps latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1 latent_height = height // self.vae_spatial_compression_ratio latent_width = width // self.vae_spatial_compression_ratio if timesteps is None: sigmas = linear_quadratic_schedule(num_inference_steps) timesteps = sigmas * 1000 timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) sigmas = self.scheduler.sigmas num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) latent_sigma = None if denoise_strength < 1: sigmas, timesteps, num_inference_steps = self.get_timesteps( sigmas, timesteps, num_inference_steps, denoise_strength ) latent_sigma = sigmas[:1].repeat(batch_size * num_videos_per_prompt) self._num_timesteps = len(timesteps) # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_channels latents, conditioning_mask, video_coords, extra_conditioning_num_latents = self.prepare_latents( conditioning_tensors, strength, frame_index, batch_size=batch_size * num_videos_per_prompt, num_channels_latents=num_channels_latents, height=height, width=width, num_frames=num_frames, sigma=latent_sigma, latents=latents, generator=generator, device=device, dtype=torch.float32, ) video_coords = video_coords.float() video_coords[:, 0] = video_coords[:, 0] * (1.0 / frame_rate) init_latents = latents.clone() if is_conditioning_image_or_video else None if self.do_classifier_free_guidance: video_coords = torch.cat([video_coords, video_coords], dim=0) # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t if image_cond_noise_scale > 0 and init_latents is not None: # Add timestep-dependent noise to the hard-conditioning latents # This helps with motion continuity, especially when conditioned on a single frame latents = self.add_noise_to_image_conditioning_latents( t / 1000.0, init_latents, latents, image_cond_noise_scale, conditioning_mask, generator, ) latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents if is_conditioning_image_or_video: conditioning_mask_model_input = ( torch.cat([conditioning_mask, conditioning_mask]) if self.do_classifier_free_guidance else conditioning_mask ) latent_model_input = latent_model_input.to(prompt_embeds.dtype) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]).unsqueeze(-1).float() if is_conditioning_image_or_video: timestep = torch.min(timestep, (1 - conditioning_mask_model_input) * 1000.0) with self.transformer.cache_context("cond_uncond"): noise_pred = self.transformer( hidden_states=latent_model_input, encoder_hidden_states=prompt_embeds, timestep=timestep, encoder_attention_mask=prompt_attention_mask, video_coords=video_coords, attention_kwargs=attention_kwargs, return_dict=False, )[0] if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) timestep, _ = timestep.chunk(2) if self.guidance_rescale > 0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg( noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale ) denoised_latents = self.scheduler.step( -noise_pred, t, latents, per_token_timesteps=timestep, return_dict=False )[0] if is_conditioning_image_or_video: tokens_to_denoise_mask = (t / 1000 - 1e-6 < (1.0 - conditioning_mask)).unsqueeze(-1) latents = torch.where(tokens_to_denoise_mask, denoised_latents, latents) else: latents = denoised_latents if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step() if is_conditioning_image_or_video: latents = latents[:, extra_conditioning_num_latents:] latents = self._unpack_latents( latents, latent_num_frames, latent_height, latent_width, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size, ) if output_type == "latent": video = latents else: latents = self._denormalize_latents( latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor ) latents = latents.to(prompt_embeds.dtype) if not self.vae.config.timestep_conditioning: timestep = None else: noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype) if not isinstance(decode_timestep, list): decode_timestep = [decode_timestep] * batch_size if decode_noise_scale is None: decode_noise_scale = decode_timestep elif not isinstance(decode_noise_scale, list): decode_noise_scale = [decode_noise_scale] * batch_size timestep = torch.tensor(decode_timestep, device=device, dtype=latents.dtype) decode_noise_scale = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[ :, None, None, None, None ] latents = (1 - decode_noise_scale) * latents + decode_noise_scale * noise video = self.vae.decode(latents, timestep, return_dict=False)[0] video = self.video_processor.postprocess_video(video, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return LTXPipelineOutput(frames=video)