# Copyright 2025 The Helios Team 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 html import math from typing import Any, Callable import regex as re import torch import torch.nn.functional as F from transformers import AutoTokenizer, UMT5EncoderModel from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...image_processor import PipelineImageInput from ...loaders import HeliosLoraLoaderMixin from ...models import AutoencoderKLWan, HeliosTransformer3DModel from ...schedulers import HeliosDMDScheduler, HeliosScheduler from ...utils import is_ftfy_available, 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 HeliosPipelineOutput 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 if is_ftfy_available(): import ftfy EXAMPLE_DOC_STRING = """ Examples: ```python >>> import torch >>> from diffusers.utils import export_to_video >>> from diffusers import AutoencoderKLWan, HeliosPyramidPipeline >>> # Available models: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled >>> model_id = "BestWishYsh/Helios-Base" >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) >>> pipe = HeliosPyramidPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16) >>> pipe.to("cuda") >>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window." >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" >>> output = pipe( ... prompt=prompt, ... negative_prompt=negative_prompt, ... height=384, ... width=640, ... num_frames=132, ... guidance_scale=5.0, ... ).frames[0] >>> export_to_video(output, "output.mp4", fps=24) ``` """ def optimized_scale(positive_flat, negative_flat): # Calculate dot production dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True) # Squared norm of uncondition squared_norm = torch.sum(negative_flat**2, dim=1, keepdim=True) + 1e-8 # st_star = v_cond^T * v_uncond / ||v_uncond||^2 st_star = dot_product / squared_norm return st_star def basic_clean(text): text = ftfy.fix_text(text) text = html.unescape(html.unescape(text)) return text.strip() def whitespace_clean(text): text = re.sub(r"\s+", " ", text) text = text.strip() return text def prompt_clean(text): text = whitespace_clean(basic_clean(text)) return text # 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 class HeliosPyramidPipeline(DiffusionPipeline, HeliosLoraLoaderMixin): r""" Pipeline for text-to-video / image-to-video / video-to-video generation using Helios. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Args: tokenizer ([`T5Tokenizer`]): Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant. text_encoder ([`T5EncoderModel`]): [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant. transformer ([`HeliosTransformer3DModel`]): Conditional Transformer to denoise the input latents. scheduler ([`HeliosScheduler`, `HeliosDMDScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKLWan`]): Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. """ model_cpu_offload_seq = "text_encoder->transformer->vae" _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _optional_components = ["transformer"] def __init__( self, tokenizer: AutoTokenizer, text_encoder: UMT5EncoderModel, vae: AutoencoderKLWan, scheduler: HeliosScheduler | HeliosDMDScheduler, transformer: HeliosTransformer3DModel, is_cfg_zero_star: bool = False, is_distilled: bool = False, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, ) self.register_to_config(is_cfg_zero_star=is_cfg_zero_star) self.register_to_config(is_distilled=is_distilled) self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4 self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8 self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) # Copied from diffusers.pipelines.helios.pipeline_helios.HeliosPipeline._get_t5_prompt_embeds def _get_t5_prompt_embeds( self, prompt: str | list[str] = None, num_videos_per_prompt: int = 1, max_sequence_length: int = 226, device: torch.device | None = None, dtype: torch.dtype | None = None, ): device = device or self._execution_device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt prompt = [prompt_clean(u) for u in 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_attention_mask=True, return_tensors="pt", ) text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask seq_lens = mask.gt(0).sum(dim=1).long() prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)] prompt_embeds = torch.stack( [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0 ) # 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) return prompt_embeds, text_inputs.attention_mask.bool() def encode_prompt( self, prompt: str | list[str], negative_prompt: str | list[str] | None = None, do_classifier_free_guidance: bool = True, num_videos_per_prompt: int = 1, prompt_embeds: torch.Tensor | None = None, negative_prompt_embeds: torch.Tensor | None = None, max_sequence_length: int = 226, device: torch.device | None = None, dtype: torch.dtype | None = 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, _ = 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, _ = 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, negative_prompt_embeds def check_inputs( self, prompt, negative_prompt, height, width, prompt_embeds=None, negative_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, image=None, video=None, guidance_scale=None, ): if height % 16 != 0 or width % 16 != 0: raise ValueError(f"`height` and `width` have to be divisible by 16 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 negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_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)}") elif negative_prompt is not None and ( not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list) ): raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}") if image is not None and video is not None: raise ValueError("image and video cannot be provided simultaneously") if guidance_scale > 1.0 and self.config.is_distilled: logger.warning(f"Guidance scale {guidance_scale} is ignored for step-wise distilled models.") def prepare_latents( self, batch_size: int, num_channels_latents: int = 16, height: int = 384, width: int = 640, num_frames: int = 33, dtype: torch.dtype | None = None, device: torch.device | None = None, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.Tensor | None = None, ) -> torch.Tensor: if latents is not None: return latents.to(device=device, dtype=dtype) num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 shape = ( batch_size, num_channels_latents, num_latent_frames, int(height) // self.vae_scale_factor_spatial, int(width) // self.vae_scale_factor_spatial, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) return latents def prepare_image_latents( self, image: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, num_latent_frames_per_chunk: int, dtype: torch.dtype | None = None, device: torch.device | None = None, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.Tensor | None = None, fake_latents: torch.Tensor | None = None, ) -> torch.Tensor: device = device or self._execution_device if latents is None: image = image.unsqueeze(2).to(device=device, dtype=self.vae.dtype) latents = self.vae.encode(image).latent_dist.sample(generator=generator) latents = (latents - latents_mean) * latents_std if fake_latents is None: min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1 fake_video = image.repeat(1, 1, min_frames, 1, 1).to(device=device, dtype=self.vae.dtype) fake_latents_full = self.vae.encode(fake_video).latent_dist.sample(generator=generator) fake_latents_full = (fake_latents_full - latents_mean) * latents_std fake_latents = fake_latents_full[:, :, -1:, :, :] return latents.to(device=device, dtype=dtype), fake_latents.to(device=device, dtype=dtype) def prepare_video_latents( self, video: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, num_latent_frames_per_chunk: int, dtype: torch.dtype | None = None, device: torch.device | None = None, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.Tensor | None = None, ) -> torch.Tensor: device = device or self._execution_device video = video.to(device=device, dtype=self.vae.dtype) if latents is None: num_frames = video.shape[2] min_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1 num_chunks = num_frames // min_frames if num_chunks == 0: raise ValueError( f"Video must have at least {min_frames} frames " f"(got {num_frames} frames). " f"Required: (num_latent_frames_per_chunk - 1) * {self.vae_scale_factor_temporal} + 1 = ({num_latent_frames_per_chunk} - 1) * {self.vae_scale_factor_temporal} + 1 = {min_frames}" ) total_valid_frames = num_chunks * min_frames start_frame = num_frames - total_valid_frames first_frame = video[:, :, 0:1, :, :] first_frame_latent = self.vae.encode(first_frame).latent_dist.sample(generator=generator) first_frame_latent = (first_frame_latent - latents_mean) * latents_std latents_chunks = [] for i in range(num_chunks): chunk_start = start_frame + i * min_frames chunk_end = chunk_start + min_frames video_chunk = video[:, :, chunk_start:chunk_end, :, :] chunk_latents = self.vae.encode(video_chunk).latent_dist.sample(generator=generator) chunk_latents = (chunk_latents - latents_mean) * latents_std latents_chunks.append(chunk_latents) latents = torch.cat(latents_chunks, dim=2) return first_frame_latent.to(device=device, dtype=dtype), latents.to(device=device, dtype=dtype) def sample_block_noise( self, batch_size, channel, num_frames, height, width, patch_size: tuple[int, ...] = (1, 2, 2), device: torch.device | None = None, ): gamma = self.scheduler.config.gamma _, ph, pw = patch_size block_size = ph * pw cov = ( torch.eye(block_size, device=device) * (1 + gamma) - torch.ones(block_size, block_size, device=device) * gamma ) cov += torch.eye(block_size, device=device) * 1e-6 dist = torch.distributions.MultivariateNormal(torch.zeros(block_size, device=device), covariance_matrix=cov) block_number = batch_size * channel * num_frames * (height // ph) * (width // pw) noise = dist.sample((block_number,)) # [block number, block_size] noise = noise.view(batch_size, channel, num_frames, height // ph, width // pw, ph, pw) noise = noise.permute(0, 1, 2, 3, 5, 4, 6).reshape(batch_size, channel, num_frames, height, width) return noise @property def guidance_scale(self): return self._guidance_scale @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 interrupt(self): return self._interrupt @property def attention_kwargs(self): return self._attention_kwargs @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: str | list[str] = None, negative_prompt: str | list[str] = None, height: int = 384, width: int = 640, num_frames: int = 132, sigmas: list[float] = None, guidance_scale: float = 5.0, num_videos_per_prompt: int | None = 1, generator: torch.Generator | list[torch.Generator] | None = None, latents: torch.Tensor | None = None, prompt_embeds: torch.Tensor | None = None, negative_prompt_embeds: torch.Tensor | None = None, output_type: str | None = "np", return_dict: bool = True, attention_kwargs: dict[str, Any] | None = None, callback_on_step_end: Callable[[int, int], None] | PipelineCallback | MultiPipelineCallbacks | None = None, callback_on_step_end_tensor_inputs: list[str] = ["latents"], max_sequence_length: int = 512, # ------------ I2V ------------ image: PipelineImageInput | None = None, image_latents: torch.Tensor | None = None, fake_image_latents: torch.Tensor | None = None, add_noise_to_image_latents: bool = True, image_noise_sigma_min: float = 0.111, image_noise_sigma_max: float = 0.135, # ------------ V2V ------------ video: PipelineImageInput | None = None, video_latents: torch.Tensor | None = None, add_noise_to_video_latents: bool = True, video_noise_sigma_min: float = 0.111, video_noise_sigma_max: float = 0.135, # ------------ Stage 1 ------------ history_sizes: list = [16, 2, 1], num_latent_frames_per_chunk: int = 9, keep_first_frame: bool = True, is_skip_first_chunk: bool = False, # ------------ Stage 2 ------------ pyramid_num_inference_steps_list: list = [10, 10, 10], # ------------ CFG Zero ------------ use_zero_init: bool | None = True, zero_steps: int | None = 1, # ------------ DMD ------------ is_amplify_first_chunk: bool = False, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `list[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead. negative_prompt (`str` or `list[str]`, *optional*): The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale` < `1`). height (`int`, defaults to `384`): The height in pixels of the generated image. width (`int`, defaults to `640`): The width in pixels of the generated image. num_frames (`int`, defaults to `132`): The number of frames in the generated video. guidance_scale (`float`, defaults to `5.0`): 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. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `list[torch.Generator]`, *optional*): A [`torch.Generator`](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 is 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 (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. output_type (`str`, *optional*, defaults to `"np"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`HeliosPipelineOutput`] 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`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. 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 `512`): The maximum sequence length of the text encoder. If the prompt is longer than this, it will be truncated. If the prompt is shorter, it will be padded to this length. Examples: Returns: [`~HeliosPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`HeliosPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ history_sizes = sorted(history_sizes, reverse=True) # From big to small pyramid_num_stages = len(pyramid_num_inference_steps_list) 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, negative_prompt, height, width, prompt_embeds, negative_prompt_embeds, callback_on_step_end_tensor_inputs, image, video, guidance_scale, ) num_frames = max(num_frames, 1) self._guidance_scale = guidance_scale self._attention_kwargs = attention_kwargs self._current_timestep = None self._interrupt = False device = self._execution_device vae_dtype = self.vae.dtype latents_mean = ( torch.tensor(self.vae.config.latents_mean) .view(1, self.vae.config.z_dim, 1, 1, 1) .to(device, self.vae.dtype) ) latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to( device, self.vae.dtype ) # 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] # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = 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, max_sequence_length=max_sequence_length, device=device, ) transformer_dtype = self.transformer.dtype prompt_embeds = prompt_embeds.to(transformer_dtype) if negative_prompt_embeds is not None: negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype) # 4. Prepare image or video if image is not None: image = self.video_processor.preprocess(image, height=height, width=width) image_latents, fake_image_latents = self.prepare_image_latents( image, latents_mean=latents_mean, latents_std=latents_std, num_latent_frames_per_chunk=num_latent_frames_per_chunk, dtype=torch.float32, device=device, generator=generator, latents=image_latents, fake_latents=fake_image_latents, ) if image_latents is not None and add_noise_to_image_latents: image_noise_sigma = ( torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min) + image_noise_sigma_min ) image_latents = ( image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device) + (1 - image_noise_sigma) * image_latents ) fake_image_noise_sigma = ( torch.rand(1, device=device, generator=generator) * (video_noise_sigma_max - video_noise_sigma_min) + video_noise_sigma_min ) fake_image_latents = ( fake_image_noise_sigma * randn_tensor(fake_image_latents.shape, generator=generator, device=device) + (1 - fake_image_noise_sigma) * fake_image_latents ) if video is not None: video = self.video_processor.preprocess_video(video, height=height, width=width) image_latents, video_latents = self.prepare_video_latents( video, latents_mean=latents_mean, latents_std=latents_std, num_latent_frames_per_chunk=num_latent_frames_per_chunk, dtype=torch.float32, device=device, generator=generator, latents=video_latents, ) if video_latents is not None and add_noise_to_video_latents: image_noise_sigma = ( torch.rand(1, device=device, generator=generator) * (image_noise_sigma_max - image_noise_sigma_min) + image_noise_sigma_min ) image_latents = ( image_noise_sigma * randn_tensor(image_latents.shape, generator=generator, device=device) + (1 - image_noise_sigma) * image_latents ) noisy_latents_chunks = [] num_latent_chunks = video_latents.shape[2] // num_latent_frames_per_chunk for i in range(num_latent_chunks): chunk_start = i * num_latent_frames_per_chunk chunk_end = chunk_start + num_latent_frames_per_chunk latent_chunk = video_latents[:, :, chunk_start:chunk_end, :, :] chunk_frames = latent_chunk.shape[2] frame_sigmas = ( torch.rand(chunk_frames, device=device, generator=generator) * (video_noise_sigma_max - video_noise_sigma_min) + video_noise_sigma_min ) frame_sigmas = frame_sigmas.view(1, 1, chunk_frames, 1, 1) noisy_chunk = ( frame_sigmas * randn_tensor(latent_chunk.shape, generator=generator, device=device) + (1 - frame_sigmas) * latent_chunk ) noisy_latents_chunks.append(noisy_chunk) video_latents = torch.cat(noisy_latents_chunks, dim=2) # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_channels window_num_frames = (num_latent_frames_per_chunk - 1) * self.vae_scale_factor_temporal + 1 num_latent_chunk = max(1, (num_frames + window_num_frames - 1) // window_num_frames) num_history_latent_frames = sum(history_sizes) history_video = None total_generated_latent_frames = 0 if not keep_first_frame: history_sizes[-1] = history_sizes[-1] + 1 history_latents = torch.zeros( batch_size, num_channels_latents, num_history_latent_frames, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, device=device, dtype=torch.float32, ) if fake_image_latents is not None: history_latents = torch.cat([history_latents[:, :, :-1, :, :], fake_image_latents], dim=2) total_generated_latent_frames += 1 if video_latents is not None: history_frames = history_latents.shape[2] video_frames = video_latents.shape[2] if video_frames < history_frames: keep_frames = history_frames - video_frames history_latents = torch.cat([history_latents[:, :, :keep_frames, :, :], video_latents], dim=2) else: history_latents = video_latents total_generated_latent_frames += video_latents.shape[2] if keep_first_frame: indices = torch.arange(0, sum([1, *history_sizes, num_latent_frames_per_chunk])) ( indices_prefix, indices_latents_history_long, indices_latents_history_mid, indices_latents_history_1x, indices_hidden_states, ) = indices.split([1, *history_sizes, num_latent_frames_per_chunk], dim=0) indices_latents_history_short = torch.cat([indices_prefix, indices_latents_history_1x], dim=0) else: indices = torch.arange(0, sum([*history_sizes, num_latent_frames_per_chunk])) ( indices_latents_history_long, indices_latents_history_mid, indices_latents_history_short, indices_hidden_states, ) = indices.split([*history_sizes, num_latent_frames_per_chunk], dim=0) indices_hidden_states = indices_hidden_states.unsqueeze(0) indices_latents_history_short = indices_latents_history_short.unsqueeze(0) indices_latents_history_mid = indices_latents_history_mid.unsqueeze(0) indices_latents_history_long = indices_latents_history_long.unsqueeze(0) # 6. Denoising loop for k in range(num_latent_chunk): is_first_chunk = k == 0 is_second_chunk = k == 1 if keep_first_frame: latents_history_long, latents_history_mid, latents_history_1x = history_latents[ :, :, -num_history_latent_frames: ].split(history_sizes, dim=2) if image_latents is None and is_first_chunk: latents_prefix = torch.zeros( ( batch_size, num_channels_latents, 1, latents_history_1x.shape[-2], latents_history_1x.shape[-1], ), device=device, dtype=latents_history_1x.dtype, ) else: latents_prefix = image_latents latents_history_short = torch.cat([latents_prefix, latents_history_1x], dim=2) else: latents_history_long, latents_history_mid, latents_history_short = history_latents[ :, :, -num_history_latent_frames: ].split(history_sizes, dim=2) latents = self.prepare_latents( batch_size, num_channels_latents, height, width, window_num_frames, dtype=torch.float32, device=device, generator=generator, latents=None, ) num_inference_steps = ( sum(pyramid_num_inference_steps_list) * 2 if is_amplify_first_chunk and self.config.is_distilled and is_first_chunk else sum(pyramid_num_inference_steps_list) ) with self.progress_bar(total=num_inference_steps) as progress_bar: _, _, _, pyramid_height, pyramid_width = latents.shape latents = latents.permute(0, 2, 1, 3, 4).reshape( batch_size * num_latent_frames_per_chunk, num_channels_latents, pyramid_height, pyramid_width ) for _ in range(pyramid_num_stages - 1): pyramid_height //= 2 pyramid_width //= 2 latents = ( F.interpolate( latents, size=(pyramid_height, pyramid_width), mode="bilinear", ) * 2 ) latents = latents.reshape( batch_size, num_latent_frames_per_chunk, num_channels_latents, pyramid_height, pyramid_width ).permute(0, 2, 1, 3, 4) start_point_list = None if self.config.is_distilled: start_point_list = [latents] for stage_idx in range(pyramid_num_stages): patch_size = self.transformer.config.patch_size image_seq_len = (latents.shape[-1] * latents.shape[-2] * latents.shape[-3]) // ( patch_size[0] * patch_size[1] * patch_size[2] ) mu = calculate_shift( image_seq_len, self.scheduler.config.get("base_image_seq_len", 256), self.scheduler.config.get("max_image_seq_len", 4096), self.scheduler.config.get("base_shift", 0.5), self.scheduler.config.get("max_shift", 1.15), ) self.scheduler.set_timesteps( pyramid_num_inference_steps_list[stage_idx], stage_idx, device=device, mu=mu, is_amplify_first_chunk=is_amplify_first_chunk and is_first_chunk, ) timesteps = self.scheduler.timesteps num_warmup_steps = 0 self._num_timesteps = len(timesteps) if stage_idx > 0: pyramid_height *= 2 pyramid_width *= 2 num_frames = latents.shape[2] latents = latents.permute(0, 2, 1, 3, 4).reshape( batch_size * num_latent_frames_per_chunk, num_channels_latents, pyramid_height // 2, pyramid_width // 2, ) latents = F.interpolate(latents, size=(pyramid_height, pyramid_width), mode="nearest") latents = latents.reshape( batch_size, num_latent_frames_per_chunk, num_channels_latents, pyramid_height, pyramid_width, ).permute(0, 2, 1, 3, 4) # Fix the stage ori_sigma = 1 - self.scheduler.ori_start_sigmas[stage_idx] # the original coeff of signal gamma = self.scheduler.config.gamma alpha = 1 / (math.sqrt(1 + (1 / gamma)) * (1 - ori_sigma) + ori_sigma) beta = alpha * (1 - ori_sigma) / math.sqrt(gamma) batch_size, channel, num_frames, pyramid_height, pyramid_width = latents.shape noise = self.sample_block_noise( batch_size, channel, num_frames, pyramid_height, pyramid_width, patch_size, device ) noise = noise.to(device=device, dtype=transformer_dtype) latents = alpha * latents + beta * noise # To fix the block artifact if self.config.is_distilled: start_point_list.append(latents) for i, t in enumerate(timesteps): timestep = t.expand(latents.shape[0]).to(torch.int64) latent_model_input = latents.to(transformer_dtype) latents_history_short = latents_history_short.to(transformer_dtype) latents_history_mid = latents_history_mid.to(transformer_dtype) latents_history_long = latents_history_long.to(transformer_dtype) with self.transformer.cache_context("cond"): noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds, indices_hidden_states=indices_hidden_states, indices_latents_history_short=indices_latents_history_short, indices_latents_history_mid=indices_latents_history_mid, indices_latents_history_long=indices_latents_history_long, latents_history_short=latents_history_short, latents_history_mid=latents_history_mid, latents_history_long=latents_history_long, attention_kwargs=attention_kwargs, return_dict=False, )[0] if self.do_classifier_free_guidance: with self.transformer.cache_context("uncond"): noise_uncond = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=negative_prompt_embeds, indices_hidden_states=indices_hidden_states, indices_latents_history_short=indices_latents_history_short, indices_latents_history_mid=indices_latents_history_mid, indices_latents_history_long=indices_latents_history_long, latents_history_short=latents_history_short, latents_history_mid=latents_history_mid, latents_history_long=latents_history_long, attention_kwargs=attention_kwargs, return_dict=False, )[0] if self.config.is_cfg_zero_star: noise_pred_text = noise_pred positive_flat = noise_pred_text.view(batch_size, -1) negative_flat = noise_uncond.view(batch_size, -1) alpha = optimized_scale(positive_flat, negative_flat) alpha = alpha.view(batch_size, *([1] * (len(noise_pred_text.shape) - 1))) alpha = alpha.to(noise_pred_text.dtype) if (stage_idx == 0 and i <= zero_steps) and use_zero_init: noise_pred = noise_pred_text * 0.0 else: noise_pred = noise_uncond * alpha + guidance_scale * ( noise_pred_text - noise_uncond * alpha ) else: noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond) extra_kwargs = ( { "cur_sampling_step": i, "dmd_noisy_tensor": start_point_list[stage_idx] if start_point_list is not None else None, "dmd_sigmas": self.scheduler.sigmas, "dmd_timesteps": self.scheduler.timesteps, "all_timesteps": timesteps, } if self.config.is_distilled else {} ) latents = self.scheduler.step( noise_pred, t, latents, generator=generator, return_dict=False, **extra_kwargs, )[0] 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) negative_prompt_embeds = callback_outputs.pop( "negative_prompt_embeds", negative_prompt_embeds ) 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 keep_first_frame and ( (is_first_chunk and image_latents is None) or (is_skip_first_chunk and is_second_chunk) ): image_latents = latents[:, :, 0:1, :, :] total_generated_latent_frames += latents.shape[2] history_latents = torch.cat([history_latents, latents], dim=2) real_history_latents = history_latents[:, :, -total_generated_latent_frames:] current_latents = ( real_history_latents[:, :, -num_latent_frames_per_chunk:].to(vae_dtype) / latents_std + latents_mean ) current_video = self.vae.decode(current_latents, return_dict=False)[0] if history_video is None: history_video = current_video else: history_video = torch.cat([history_video, current_video], dim=2) self._current_timestep = None if output_type != "latent": generated_frames = history_video.size(2) generated_frames = ( generated_frames - 1 ) // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1 history_video = history_video[:, :, :generated_frames] video = self.video_processor.postprocess_video(history_video, output_type=output_type) else: video = real_history_latents # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return HeliosPipelineOutput(frames=video)