# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 import math from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F from einops import rearrange from transformers import T5EncoderModel, T5Tokenizer from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.models import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel from diffusers.models.embeddings import get_3d_rotary_pos_embed from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler from diffusers.utils import BaseOutput, logging, replace_example_docstring from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor from diffusers.image_processor import VaeImageProcessor from einops import rearrange logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```python >>> import torch >>> from diffusers import CogVideoX_Fun_Pipeline >>> from diffusers.utils import export_to_video >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b" >>> pipe = CogVideoX_Fun_Pipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda") >>> prompt = ( ... "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " ... "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " ... "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " ... "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " ... "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " ... "atmosphere of this unique musical performance." ... ) >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] >>> export_to_video(video, "output.mp4", fps=8) ``` """ # Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): tw = tgt_width th = tgt_height h, w = src r = h / w if r > (th / tw): resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) # 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, ): """ 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 def resize_mask(mask, latent, process_first_frame_only=True): latent_size = latent.size() batch_size, channels, num_frames, height, width = mask.shape if process_first_frame_only: target_size = list(latent_size[2:]) target_size[0] = 1 first_frame_resized = F.interpolate( mask[:, :, 0:1, :, :], size=target_size, mode='trilinear', align_corners=False ) target_size = list(latent_size[2:]) target_size[0] = target_size[0] - 1 if target_size[0] != 0: remaining_frames_resized = F.interpolate( mask[:, :, 1:, :, :], size=target_size, mode='trilinear', align_corners=False ) resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2) else: resized_mask = first_frame_resized else: target_size = list(latent_size[2:]) resized_mask = F.interpolate( mask, size=target_size, mode='trilinear', align_corners=False ) return resized_mask @dataclass class CogVideoX_Fun_PipelineOutput(BaseOutput): r""" Output class for CogVideo pipelines. Args: video (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. """ videos: torch.Tensor class CogVideoX_Fun_Pipeline_Inpaint(DiffusionPipeline): r""" Pipeline for text-to-video generation using CogVideoX. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. text_encoder ([`T5EncoderModel`]): Frozen text-encoder. CogVideoX_Fun uses [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. tokenizer (`T5Tokenizer`): Tokenizer of class [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). transformer ([`CogVideoXTransformer3DModel`]): A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `transformer` to denoise the encoded video latents. """ _optional_components = [] model_cpu_offload_seq = "text_encoder->vae->transformer->vae" _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", ] def __init__( self, tokenizer: T5Tokenizer, text_encoder: T5EncoderModel, vae: AutoencoderKLCogVideoX, transformer: CogVideoXTransformer3DModel, scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler], ): super().__init__() self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler ) self.vae_scale_factor_spatial = ( 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 ) self.vae_scale_factor_temporal = ( self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4 ) self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True ) def _get_t5_prompt_embeds( self, prompt: Union[str, List[str]] = None, num_videos_per_prompt: int = 1, max_sequence_length: int = 226, 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 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))[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) return prompt_embeds 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, max_sequence_length: int = 226, 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 = 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 prepare_latents( self, batch_size, num_channels_latents, height, width, video_length, dtype, device, generator, latents=None, video=None, timestep=None, is_strength_max=True, return_noise=False, return_video_latents=False, ): shape = ( batch_size, (video_length - 1) // self.vae_scale_factor_temporal + 1, num_channels_latents, height // self.vae_scale_factor_spatial, 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." ) if return_video_latents or (latents is None and not is_strength_max): video = video.to(device=device, dtype=self.vae.dtype) bs = 1 new_video = [] for i in range(0, video.shape[0], bs): video_bs = video[i : i + bs] video_bs = self.vae.encode(video_bs)[0] video_bs = video_bs.sample() new_video.append(video_bs) video = torch.cat(new_video, dim = 0) video = video * self.vae.config.scaling_factor video_latents = video.repeat(batch_size // video.shape[0], 1, 1, 1, 1) video_latents = video_latents.to(device=device, dtype=dtype) video_latents = rearrange(video_latents, "b c f h w -> b f c h w") if latents is None: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # if strength is 1. then initialise the latents to noise, else initial to image + noise latents = noise if is_strength_max else self.scheduler.add_noise(video_latents, noise, timestep) # if pure noise then scale the initial latents by the Scheduler's init sigma latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents else: noise = latents.to(device) latents = noise * self.scheduler.init_noise_sigma # scale the initial noise by the standard deviation required by the scheduler outputs = (latents,) if return_noise: outputs += (noise,) if return_video_latents: outputs += (video_latents,) return outputs def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision if mask is not None: mask = mask.to(device=device, dtype=self.vae.dtype) bs = 1 new_mask = [] for i in range(0, mask.shape[0], bs): mask_bs = mask[i : i + bs] mask_bs = self.vae.encode(mask_bs)[0] mask_bs = mask_bs.mode() new_mask.append(mask_bs) mask = torch.cat(new_mask, dim = 0) mask = mask * self.vae.config.scaling_factor if masked_image is not None: masked_image = masked_image.to(device=device, dtype=self.vae.dtype) bs = 1 new_mask_pixel_values = [] for i in range(0, masked_image.shape[0], bs): mask_pixel_values_bs = masked_image[i : i + bs] mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0] mask_pixel_values_bs = mask_pixel_values_bs.mode() new_mask_pixel_values.append(mask_pixel_values_bs) masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0) masked_image_latents = masked_image_latents * self.vae.config.scaling_factor else: masked_image_latents = None return mask, masked_image_latents def decode_latents(self, latents: torch.Tensor) -> torch.Tensor: latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] latents = 1 / self.vae.config.scaling_factor * latents frames = self.vae.decode(latents).sample frames = (frames / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 frames = frames.cpu().float().numpy() return frames # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs def check_inputs( self, prompt, height, width, negative_prompt, callback_on_step_end_tensor_inputs, prompt_embeds=None, negative_prompt_embeds=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 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 is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if 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`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) 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}." ) def fuse_qkv_projections(self) -> None: r"""Enables fused QKV projections.""" self.fusing_transformer = True self.transformer.fuse_qkv_projections() def unfuse_qkv_projections(self) -> None: r"""Disable QKV projection fusion if enabled.""" if not self.fusing_transformer: logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.") else: self.transformer.unfuse_qkv_projections() self.fusing_transformer = False def _prepare_rotary_positional_embeddings( self, height: int, width: int, num_frames: int, device: torch.device, ) -> Tuple[torch.Tensor, torch.Tensor]: grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) base_size_width = 720 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) base_size_height = 480 // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size_width, base_size_height ) freqs_cos, freqs_sin = get_3d_rotary_pos_embed( embed_dim=self.transformer.config.attention_head_dim, crops_coords=grid_crops_coords, grid_size=(grid_height, grid_width), temporal_size=num_frames, use_real=True, ) freqs_cos = freqs_cos.to(device=device) freqs_sin = freqs_sin.to(device=device) return freqs_cos, freqs_sin @property def guidance_scale(self): return self._guidance_scale @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Optional[Union[str, List[str]]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, height: int = 480, width: int = 720, video: Union[torch.FloatTensor] = None, mask_video: Union[torch.FloatTensor] = None, masked_video_latents: Union[torch.FloatTensor] = None, num_frames: int = 49, num_inference_steps: int = 50, timesteps: Optional[List[int]] = None, guidance_scale: float = 6, use_dynamic_cfg: bool = False, num_videos_per_prompt: int = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: str = "numpy", return_dict: bool = False, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], max_sequence_length: int = 226, strength: float = 1, comfyui_progressbar: bool = False, ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]: """ Function invoked when calling 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. 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`). height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_frames (`int`, defaults to `48`): Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will contain 1 extra frame because CogVideoX_Fun is conditioned with (num_seconds * fps + 1) frames where num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that needs to be satisfied is that of divisibility mentioned above. 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`, *optional*, defaults to 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). 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 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.FloatTensor`, *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.FloatTensor`, *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.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. 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.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. 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 `226`): Maximum sequence length in encoded prompt. Must be consistent with `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. Examples: Returns: [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] or `tuple`: [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoX_Fun_PipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ if num_frames > 49: raise ValueError( "The number of frames must be less than 49 for now due to static positional embeddings. This will be updated in the future to remove this limitation." ) if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial num_videos_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, negative_prompt, callback_on_step_end_tensor_inputs, prompt_embeds, negative_prompt_embeds, ) self._guidance_scale = guidance_scale self._interrupt = False # 2. Default 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] device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, negative_prompt, 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, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) # 4. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps=num_inference_steps, strength=strength, device=device ) self._num_timesteps = len(timesteps) if comfyui_progressbar: from comfy.utils import ProgressBar pbar = ProgressBar(num_inference_steps + 2) # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt) # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise is_strength_max = strength == 1.0 # 5. Prepare latents. if video is not None: video_length = video.shape[2] init_video = self.image_processor.preprocess(rearrange(video, "b c f h w -> (b f) c h w"), height=height, width=width) init_video = init_video.to(dtype=torch.float32) init_video = rearrange(init_video, "(b f) c h w -> b c f h w", f=video_length) else: init_video = None num_channels_latents = self.vae.config.latent_channels num_channels_transformer = self.transformer.config.in_channels return_image_latents = num_channels_transformer == num_channels_latents latents_outputs = self.prepare_latents( batch_size * num_videos_per_prompt, num_channels_latents, height, width, video_length, prompt_embeds.dtype, device, generator, latents, video=init_video, timestep=latent_timestep, is_strength_max=is_strength_max, return_noise=True, return_video_latents=return_image_latents, ) if return_image_latents: latents, noise, image_latents = latents_outputs else: latents, noise = latents_outputs if comfyui_progressbar: pbar.update(1) if mask_video is not None: if (mask_video == 255).all(): mask_latents = torch.zeros_like(latents)[:, :, :1].to(latents.device, latents.dtype) masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype) else: # Prepare mask latent variables video_length = video.shape[2] mask_condition = self.mask_processor.preprocess(rearrange(mask_video, "b c f h w -> (b f) c h w"), height=height, width=width) mask_condition = mask_condition.to(dtype=torch.float32) mask_condition = rearrange(mask_condition, "(b f) c h w -> b c f h w", f=video_length) if num_channels_transformer != num_channels_latents: mask_condition_tile = torch.tile(mask_condition, [1, 3, 1, 1, 1]) if masked_video_latents is None: masked_video = init_video * (mask_condition_tile < 0.5) + torch.ones_like(init_video) * (mask_condition_tile > 0.5) * -1 else: masked_video = masked_video_latents _, masked_video_latents = self.prepare_mask_latents( None, masked_video, batch_size, height, width, prompt_embeds.dtype, device, generator, do_classifier_free_guidance, ) mask_latents = resize_mask(1 - mask_condition, masked_video_latents) mask_latents = mask_latents.to(masked_video_latents.device) * self.vae.config.scaling_factor mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1]) mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype) mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents ) mask = rearrange(mask, "b c f h w -> b f c h w") mask_input = rearrange(mask_input, "b c f h w -> b f c h w") masked_video_latents_input = rearrange(masked_video_latents_input, "b c f h w -> b f c h w") inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype) else: mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1]) mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype) mask = rearrange(mask, "b c f h w -> b f c h w") inpaint_latents = None else: if num_channels_transformer != num_channels_latents: mask = torch.zeros_like(latents).to(latents.device, latents.dtype) masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) mask_input = torch.cat([mask] * 2) if do_classifier_free_guidance else mask masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype) else: mask = torch.zeros_like(init_video[:, :1]) mask = torch.tile(mask, [1, num_channels_latents, 1, 1, 1]) mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype) mask = rearrange(mask, "b c f h w -> b f c h w") inpaint_latents = None if comfyui_progressbar: pbar.update(1) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Create rotary embeds if required image_rotary_emb = ( self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) if self.transformer.config.use_rotary_positional_embeddings else None ) # 8. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) with self.progress_bar(total=num_inference_steps) as progress_bar: # for DPM-solver++ old_pred_original_sample = None for i, t in enumerate(timesteps): if self.interrupt: continue latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) # predict noise model_output noise_pred = self.transformer( hidden_states=latent_model_input, encoder_hidden_states=prompt_embeds, timestep=timestep, image_rotary_emb=image_rotary_emb, return_dict=False, inpaint_latents=inpaint_latents, )[0] noise_pred = noise_pred.float() # perform guidance if use_dynamic_cfg: self._guidance_scale = 1 + guidance_scale * ( (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 ) if 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) # compute the previous noisy sample x_t -> x_t-1 if not isinstance(self.scheduler, CogVideoXDPMScheduler): latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] else: latents, old_pred_original_sample = self.scheduler.step( noise_pred, old_pred_original_sample, t, timesteps[i - 1] if i > 0 else None, latents, **extra_step_kwargs, return_dict=False, ) latents = latents.to(prompt_embeds.dtype) # call the callback, if provided 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 comfyui_progressbar: pbar.update(1) if output_type == "numpy": video = self.decode_latents(latents) elif not output_type == "latent": video = self.decode_latents(latents) video = self.video_processor.postprocess_video(video=video, output_type=output_type) else: video = latents # Offload all models self.maybe_free_model_hooks() if not return_dict: video = torch.from_numpy(video) return CogVideoX_Fun_PipelineOutput(videos=video)