# Copyright 2025 The HunyuanVideo 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 inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image import torch from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, LlamaTokenizerFast, LlavaForConditionalGeneration, ) from ...callbacks import MultiPipelineCallbacks, PipelineCallback from ...loaders import HunyuanVideoLoraLoaderMixin from ...models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel 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 HunyuanVideoPipelineOutput 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: ```python >>> import torch >>> from diffusers import HunyuanVideoImageToVideoPipeline, HunyuanVideoTransformer3DModel >>> from diffusers.utils import load_image, export_to_video >>> # Available checkpoints: hunyuanvideo-community/HunyuanVideo-I2V, hunyuanvideo-community/HunyuanVideo-I2V-33ch >>> model_id = "hunyuanvideo-community/HunyuanVideo-I2V" >>> transformer = HunyuanVideoTransformer3DModel.from_pretrained( ... model_id, subfolder="transformer", torch_dtype=torch.bfloat16 ... ) >>> pipe = HunyuanVideoImageToVideoPipeline.from_pretrained( ... model_id, transformer=transformer, torch_dtype=torch.float16 ... ) >>> pipe.vae.enable_tiling() >>> pipe.to("cuda") >>> prompt = "A man with short gray hair plays a red electric guitar." >>> image = load_image( ... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ... ) >>> # If using hunyuanvideo-community/HunyuanVideo-I2V >>> output = pipe(image=image, prompt=prompt, guidance_scale=6.0).frames[0] >>> # If using hunyuanvideo-community/HunyuanVideo-I2V-33ch >>> output = pipe(image=image, prompt=prompt, guidance_scale=1.0, true_cfg_scale=1.0).frames[0] >>> export_to_video(output, "output.mp4", fps=15) ``` """ DEFAULT_PROMPT_TEMPLATE = { "template": ( "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " "1. The main content and theme of the video." "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." "4. background environment, light, style and atmosphere." "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" "<|start_header_id|>assistant<|end_header_id|>\n\n" ), "crop_start": 103, "image_emb_start": 5, "image_emb_end": 581, "image_emb_len": 576, "double_return_token_id": 271, } def _expand_input_ids_with_image_tokens( text_input_ids, prompt_attention_mask, max_sequence_length, image_token_index, image_emb_len, image_emb_start, image_emb_end, pad_token_id, ): special_image_token_mask = text_input_ids == image_token_index num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1) batch_indices, non_image_indices = torch.where(text_input_ids != image_token_index) max_expanded_length = max_sequence_length + (num_special_image_tokens.max() * (image_emb_len - 1)) new_token_positions = torch.cumsum((special_image_token_mask * (image_emb_len - 1) + 1), -1) - 1 text_to_overwrite = new_token_positions[batch_indices, non_image_indices] expanded_input_ids = torch.full( (text_input_ids.shape[0], max_expanded_length), pad_token_id, dtype=text_input_ids.dtype, device=text_input_ids.device, ) expanded_input_ids[batch_indices, text_to_overwrite] = text_input_ids[batch_indices, non_image_indices] expanded_input_ids[batch_indices, image_emb_start:image_emb_end] = image_token_index expanded_attention_mask = torch.zeros( (text_input_ids.shape[0], max_expanded_length), dtype=prompt_attention_mask.dtype, device=prompt_attention_mask.device, ) attn_batch_indices, attention_indices = torch.where(expanded_input_ids != pad_token_id) expanded_attention_mask[attn_batch_indices, attention_indices] = 1.0 expanded_attention_mask = expanded_attention_mask.to(prompt_attention_mask.dtype) position_ids = (expanded_attention_mask.cumsum(-1) - 1).masked_fill_((expanded_attention_mask == 0), 1) return { "input_ids": expanded_input_ids, "attention_mask": expanded_attention_mask, "position_ids": position_ids, } # 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") class HunyuanVideoImageToVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin): r""" Pipeline for image-to-video generation using HunyuanVideo. 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: text_encoder ([`LlavaForConditionalGeneration`]): [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). tokenizer (`LlamaTokenizer`): Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). transformer ([`HunyuanVideoTransformer3DModel`]): Conditional Transformer to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKLHunyuanVideo`]): Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. text_encoder_2 ([`CLIPTextModel`]): [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. tokenizer_2 (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer). """ model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" _callback_tensor_inputs = ["latents", "prompt_embeds"] def __init__( self, text_encoder: LlavaForConditionalGeneration, tokenizer: LlamaTokenizerFast, transformer: HunyuanVideoTransformer3DModel, vae: AutoencoderKLHunyuanVideo, scheduler: FlowMatchEulerDiscreteScheduler, text_encoder_2: CLIPTextModel, tokenizer_2: CLIPTokenizer, image_processor: CLIPImageProcessor, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, image_processor=image_processor, ) self.vae_scaling_factor = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.476986 self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4 self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 8 self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) def _get_llama_prompt_embeds( self, image: torch.Tensor, prompt: Union[str, List[str]], prompt_template: Dict[str, Any], num_videos_per_prompt: int = 1, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, max_sequence_length: int = 256, num_hidden_layers_to_skip: int = 2, image_embed_interleave: int = 2, ) -> Tuple[torch.Tensor, torch.Tensor]: device = device or self._execution_device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt prompt = [prompt_template["template"].format(p) for p in prompt] crop_start = prompt_template.get("crop_start", None) image_emb_len = prompt_template.get("image_emb_len", 576) image_emb_start = prompt_template.get("image_emb_start", 5) image_emb_end = prompt_template.get("image_emb_end", 581) double_return_token_id = prompt_template.get("double_return_token_id", 271) if crop_start is None: prompt_template_input = self.tokenizer( prompt_template["template"], padding="max_length", return_tensors="pt", return_length=False, return_overflowing_tokens=False, return_attention_mask=False, ) crop_start = prompt_template_input["input_ids"].shape[-1] # Remove <|start_header_id|>, <|end_header_id|>, assistant, <|eot_id|>, and placeholder {} crop_start -= 5 max_sequence_length += crop_start text_inputs = self.tokenizer( prompt, max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt", return_length=False, return_overflowing_tokens=False, return_attention_mask=True, ) text_input_ids = text_inputs.input_ids.to(device=device) prompt_attention_mask = text_inputs.attention_mask.to(device=device) image_embeds = self.image_processor(image, return_tensors="pt").pixel_values.to(device) image_token_index = self.text_encoder.config.image_token_index pad_token_id = self.text_encoder.config.pad_token_id expanded_inputs = _expand_input_ids_with_image_tokens( text_input_ids, prompt_attention_mask, max_sequence_length, image_token_index, image_emb_len, image_emb_start, image_emb_end, pad_token_id, ) prompt_embeds = self.text_encoder( **expanded_inputs, pixel_values=image_embeds, output_hidden_states=True, ).hidden_states[-(num_hidden_layers_to_skip + 1)] prompt_embeds = prompt_embeds.to(dtype=dtype) if crop_start is not None and crop_start > 0: text_crop_start = crop_start - 1 + image_emb_len batch_indices, last_double_return_token_indices = torch.where(text_input_ids == double_return_token_id) if last_double_return_token_indices.shape[0] == 3: # in case the prompt is too long last_double_return_token_indices = torch.cat( (last_double_return_token_indices, torch.tensor([text_input_ids.shape[-1]])) ) batch_indices = torch.cat((batch_indices, torch.tensor([0]))) last_double_return_token_indices = last_double_return_token_indices.reshape(text_input_ids.shape[0], -1)[ :, -1 ] batch_indices = batch_indices.reshape(text_input_ids.shape[0], -1)[:, -1] assistant_crop_start = last_double_return_token_indices - 1 + image_emb_len - 4 assistant_crop_end = last_double_return_token_indices - 1 + image_emb_len attention_mask_assistant_crop_start = last_double_return_token_indices - 4 attention_mask_assistant_crop_end = last_double_return_token_indices prompt_embed_list = [] prompt_attention_mask_list = [] image_embed_list = [] image_attention_mask_list = [] for i in range(text_input_ids.shape[0]): prompt_embed_list.append( torch.cat( [ prompt_embeds[i, text_crop_start : assistant_crop_start[i].item()], prompt_embeds[i, assistant_crop_end[i].item() :], ] ) ) prompt_attention_mask_list.append( torch.cat( [ prompt_attention_mask[i, crop_start : attention_mask_assistant_crop_start[i].item()], prompt_attention_mask[i, attention_mask_assistant_crop_end[i].item() :], ] ) ) image_embed_list.append(prompt_embeds[i, image_emb_start:image_emb_end]) image_attention_mask_list.append( torch.ones(image_embed_list[-1].shape[0]).to(prompt_embeds.device).to(prompt_attention_mask.dtype) ) prompt_embed_list = torch.stack(prompt_embed_list) prompt_attention_mask_list = torch.stack(prompt_attention_mask_list) image_embed_list = torch.stack(image_embed_list) image_attention_mask_list = torch.stack(image_attention_mask_list) if 0 < image_embed_interleave < 6: image_embed_list = image_embed_list[:, ::image_embed_interleave, :] image_attention_mask_list = image_attention_mask_list[:, ::image_embed_interleave] assert ( prompt_embed_list.shape[0] == prompt_attention_mask_list.shape[0] and image_embed_list.shape[0] == image_attention_mask_list.shape[0] ) prompt_embeds = torch.cat([image_embed_list, prompt_embed_list], dim=1) prompt_attention_mask = torch.cat([image_attention_mask_list, prompt_attention_mask_list], dim=1) return prompt_embeds, prompt_attention_mask def _get_clip_prompt_embeds( self, prompt: Union[str, List[str]], num_videos_per_prompt: int = 1, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, max_sequence_length: int = 77, ) -> torch.Tensor: device = device or self._execution_device dtype = dtype or self.text_encoder_2.dtype prompt = [prompt] if isinstance(prompt, str) else prompt text_inputs = self.tokenizer_2( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer_2(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_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {max_sequence_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output return prompt_embeds def encode_prompt( self, image: torch.Tensor, prompt: Union[str, List[str]], prompt_2: Union[str, List[str]] = None, prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, num_videos_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, pooled_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, max_sequence_length: int = 256, image_embed_interleave: int = 2, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: if prompt_embeds is None: prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds( image, prompt, prompt_template, num_videos_per_prompt, device=device, dtype=dtype, max_sequence_length=max_sequence_length, image_embed_interleave=image_embed_interleave, ) if pooled_prompt_embeds is None: if prompt_2 is None: prompt_2 = prompt pooled_prompt_embeds = self._get_clip_prompt_embeds( prompt, num_videos_per_prompt, device=device, dtype=dtype, max_sequence_length=77, ) return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask def check_inputs( self, prompt, prompt_2, height, width, prompt_embeds=None, callback_on_step_end_tensor_inputs=None, prompt_template=None, true_cfg_scale=1.0, guidance_scale=1.0, ): 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 prompt_2 is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt_2`: {prompt_2} 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)}") elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") if prompt_template is not None: if not isinstance(prompt_template, dict): raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}") if "template" not in prompt_template: raise ValueError( f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}" ) if true_cfg_scale > 1.0 and guidance_scale > 1.0: logger.warning( "Both `true_cfg_scale` and `guidance_scale` are greater than 1.0. This will result in both " "classifier-free guidance and embedded-guidance to be applied. This is not recommended " "as it may lead to higher memory usage, slower inference and potentially worse results." ) def prepare_latents( self, image: torch.Tensor, batch_size: int, num_channels_latents: int = 32, height: int = 720, width: int = 1280, num_frames: int = 129, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, image_condition_type: str = "latent_concat", ) -> torch.Tensor: 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." ) num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 latent_height, latent_width = height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width) image = image.unsqueeze(2) # [B, C, 1, H, W] if isinstance(generator, list): image_latents = [ retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i], "argmax") for i in range(batch_size) ] else: image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator, "argmax") for img in image] image_latents = torch.cat(image_latents, dim=0).to(dtype) * self.vae_scaling_factor image_latents = image_latents.repeat(1, 1, num_latent_frames, 1, 1) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device=device, dtype=dtype) t = torch.tensor([0.999]).to(device=device) latents = latents * t + image_latents * (1 - t) if image_condition_type == "token_replace": image_latents = image_latents[:, :, :1] return latents, image_latents def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() @property def guidance_scale(self): return self._guidance_scale @property def num_timesteps(self): return self._num_timesteps @property def attention_kwargs(self): return self._attention_kwargs @property def current_timestep(self): return self._current_timestep @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, image: PIL.Image.Image, prompt: Union[str, List[str]] = None, prompt_2: Union[str, List[str]] = None, negative_prompt: Union[str, List[str]] = None, negative_prompt_2: Union[str, List[str]] = None, height: int = 720, width: int = 1280, num_frames: int = 129, num_inference_steps: int = 50, sigmas: List[float] = None, true_cfg_scale: float = 1.0, guidance_scale: float = 1.0, 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, pooled_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_pooled_prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE, max_sequence_length: int = 256, image_embed_interleave: Optional[int] = None, ): 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, one has to pass `prompt_embeds`. instead. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is will be used instead. 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 `true_cfg_scale` is not greater than `1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders. height (`int`, defaults to `720`): The height in pixels of the generated image. width (`int`, defaults to `1280`): The width in pixels of the generated image. num_frames (`int`, defaults to `129`): The number of frames in the generated video. num_inference_steps (`int`, defaults to `50`): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. true_cfg_scale (`float`, *optional*, defaults to 1.0): When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance. guidance_scale (`float`, defaults to `1.0`): 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. Note that the only available HunyuanVideo model is CFG-distilled, which means that traditional guidance between unconditional and conditional latent is not applied. 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. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *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. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): 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 [`HunyuanVideoPipelineOutput`] 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). clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. 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. Examples: Returns: [`~HunyuanVideoPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] 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. """ 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_2, height, width, prompt_embeds, callback_on_step_end_tensor_inputs, prompt_template, true_cfg_scale, guidance_scale, ) image_condition_type = self.transformer.config.image_condition_type has_neg_prompt = negative_prompt is not None or ( negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None ) do_true_cfg = true_cfg_scale > 1 and has_neg_prompt image_embed_interleave = ( image_embed_interleave if image_embed_interleave is not None else ( 2 if image_condition_type == "latent_concat" else 4 if image_condition_type == "token_replace" else 1 ) ) self._guidance_scale = guidance_scale self._attention_kwargs = attention_kwargs self._current_timestep = None self._interrupt = False device = self._execution_device # 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. Prepare latent variables vae_dtype = self.vae.dtype image_tensor = self.video_processor.preprocess(image, height, width).to(device, vae_dtype) if image_condition_type == "latent_concat": num_channels_latents = (self.transformer.config.in_channels - 1) // 2 elif image_condition_type == "token_replace": num_channels_latents = self.transformer.config.in_channels latents, image_latents = self.prepare_latents( image_tensor, batch_size * num_videos_per_prompt, num_channels_latents, height, width, num_frames, torch.float32, device, generator, latents, image_condition_type, ) if image_condition_type == "latent_concat": image_latents[:, :, 1:] = 0 mask = image_latents.new_ones(image_latents.shape[0], 1, *image_latents.shape[2:]) mask[:, :, 1:] = 0 # 4. Encode input prompt transformer_dtype = self.transformer.dtype prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt( image=image, prompt=prompt, prompt_2=prompt_2, prompt_template=prompt_template, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, prompt_attention_mask=prompt_attention_mask, device=device, max_sequence_length=max_sequence_length, image_embed_interleave=image_embed_interleave, ) prompt_embeds = prompt_embeds.to(transformer_dtype) prompt_attention_mask = prompt_attention_mask.to(transformer_dtype) pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype) if do_true_cfg: black_image = PIL.Image.new("RGB", (width, height), 0) negative_prompt_embeds, negative_pooled_prompt_embeds, negative_prompt_attention_mask = self.encode_prompt( image=black_image, prompt=negative_prompt, prompt_2=negative_prompt_2, prompt_template=prompt_template, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=negative_pooled_prompt_embeds, prompt_attention_mask=negative_prompt_attention_mask, device=device, max_sequence_length=max_sequence_length, ) negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype) negative_prompt_attention_mask = negative_prompt_attention_mask.to(transformer_dtype) negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype) # 5. Prepare timesteps sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas) # 6. Prepare guidance condition guidance = None if self.transformer.config.guidance_embeds: guidance = ( torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0 ) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) 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 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latents.shape[0]).to(latents.dtype) if image_condition_type == "latent_concat": latent_model_input = torch.cat([latents, image_latents, mask], dim=1).to(transformer_dtype) elif image_condition_type == "token_replace": latent_model_input = torch.cat([image_latents, latents[:, :, 1:]], dim=2).to(transformer_dtype) noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds, encoder_attention_mask=prompt_attention_mask, pooled_projections=pooled_prompt_embeds, guidance=guidance, attention_kwargs=attention_kwargs, return_dict=False, )[0] if do_true_cfg: neg_noise_pred = self.transformer( hidden_states=latent_model_input, timestep=timestep, encoder_hidden_states=negative_prompt_embeds, encoder_attention_mask=negative_prompt_attention_mask, pooled_projections=negative_pooled_prompt_embeds, guidance=guidance, attention_kwargs=attention_kwargs, return_dict=False, )[0] noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred) # compute the previous noisy sample x_t -> x_t-1 if image_condition_type == "latent_concat": latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] elif image_condition_type == "token_replace": latents = latents = self.scheduler.step( noise_pred[:, :, 1:], t, latents[:, :, 1:], return_dict=False )[0] latents = torch.cat([image_latents, latents], dim=2) 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() self._current_timestep = None if not output_type == "latent": latents = latents.to(self.vae.dtype) / self.vae_scaling_factor video = self.vae.decode(latents, return_dict=False)[0] if image_condition_type == "latent_concat": video = video[:, :, 4:, :, :] video = self.video_processor.postprocess_video(video, output_type=output_type) else: if image_condition_type == "latent_concat": video = latents[:, :, 1:, :, :] else: video = latents # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return HunyuanVideoPipelineOutput(frames=video)