mirror of
https://git.datalinker.icu/kijai/ComfyUI-CogVideoXWrapper.git
synced 2025-12-08 20:34:23 +08:00
801 lines
42 KiB
Python
801 lines
42 KiB
Python
# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
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# All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Callable, Dict, List, Optional, Tuple, Union
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import torch
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import math
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline
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from diffusers.schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler
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from diffusers.utils import logging
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from diffusers.utils.torch_utils import randn_tensor
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#from diffusers.models.embeddings import get_3d_rotary_pos_embed
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from diffusers.loaders import CogVideoXLoraLoaderMixin
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from .embeddings import get_3d_rotary_pos_embed
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from .custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
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from comfy.utils import ProgressBar
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def get_resize_crop_region_for_grid(src, tgt_width, tgt_height):
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tw = tgt_width
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th = tgt_height
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h, w = src
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r = h / w
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if r > (th / tw):
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resize_height = th
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resize_width = int(round(th / h * w))
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else:
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resize_width = tw
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resize_height = int(round(tw / w * h))
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crop_top = int(round((th - resize_height) / 2.0))
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crop_left = int(round((tw - resize_width) / 2.0))
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return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
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def retrieve_timesteps(
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scheduler,
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num_inference_steps: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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timesteps: Optional[List[int]] = None,
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sigmas: Optional[List[float]] = None,
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**kwargs,
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):
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"""
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Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
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custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
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Args:
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scheduler (`SchedulerMixin`):
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The scheduler to get timesteps from.
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num_inference_steps (`int`):
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The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
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must be `None`.
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device (`str` or `torch.device`, *optional*):
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The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
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timesteps (`List[int]`, *optional*):
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Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
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`num_inference_steps` and `sigmas` must be `None`.
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sigmas (`List[float]`, *optional*):
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Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
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`num_inference_steps` and `timesteps` must be `None`.
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Returns:
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`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
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second element is the number of inference steps.
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"""
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if timesteps is not None and sigmas is not None:
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raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
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if timesteps is not None:
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accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accepts_timesteps:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" timestep schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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elif sigmas is not None:
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accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
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if not accept_sigmas:
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raise ValueError(
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f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
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f" sigmas schedules. Please check whether you are using the correct scheduler."
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)
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scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
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timesteps = scheduler.timesteps
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num_inference_steps = len(timesteps)
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else:
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scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
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timesteps = scheduler.timesteps
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return timesteps, num_inference_steps
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class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
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r"""
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Pipeline for text-to-video generation using CogVideoX.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Args:
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transformer ([`CogVideoXTransformer3DModel`]):
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A text conditioned `CogVideoXTransformer3DModel` to denoise the encoded video latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
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"""
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_optional_components = ["tokenizer", "text_encoder"]
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model_cpu_offload_seq = "text_encoder->transformer->vae"
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def __init__(
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self,
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transformer: CogVideoXTransformer3DModel,
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scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler],
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dtype: torch.dtype = torch.bfloat16,
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is_fun_inpaint: bool = False,
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):
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super().__init__()
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self.register_modules(transformer=transformer, scheduler=scheduler)
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self.vae_scale_factor_spatial = 8
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self.vae_scale_factor_temporal = 4
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self.vae_latent_channels = 16
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self.vae_dtype = dtype
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self.is_fun_inpaint = is_fun_inpaint
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self.input_with_padding = True
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def prepare_latents(
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self, batch_size, num_channels_latents, num_frames, height, width, device, generator, timesteps, denoise_strength,
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num_inference_steps, latents=None, freenoise=True, context_size=None, context_overlap=None
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):
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shape = (
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batch_size,
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(num_frames - 1) // self.vae_scale_factor_temporal + 1,
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num_channels_latents,
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height // self.vae_scale_factor_spatial,
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width // self.vae_scale_factor_spatial,
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)
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noise = randn_tensor(shape, generator=generator, device=torch.device("cpu"), dtype=self.vae_dtype)
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if freenoise:
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logger.info("Applying FreeNoise")
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# code and comments from AnimateDiff-Evolved by Kosinkadink (https://github.com/Kosinkadink/ComfyUI-AnimateDiff-Evolved)
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video_length = num_frames // 4
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delta = context_size - context_overlap
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for start_idx in range(0, video_length-context_size, delta):
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# start_idx corresponds to the beginning of a context window
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# goal: place shuffled in the delta region right after the end of the context window
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# if space after context window is not enough to place the noise, adjust and finish
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place_idx = start_idx + context_size
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# if place_idx is outside the valid indexes, we are already finished
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if place_idx >= video_length:
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break
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end_idx = place_idx - 1
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#print("video_length:", video_length, "start_idx:", start_idx, "end_idx:", end_idx, "place_idx:", place_idx, "delta:", delta)
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# if there is not enough room to copy delta amount of indexes, copy limited amount and finish
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if end_idx + delta >= video_length:
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final_delta = video_length - place_idx
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# generate list of indexes in final delta region
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list_idx = torch.tensor(list(range(start_idx,start_idx+final_delta)), device=torch.device("cpu"), dtype=torch.long)
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# shuffle list
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list_idx = list_idx[torch.randperm(final_delta, generator=generator)]
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# apply shuffled indexes
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noise[:, place_idx:place_idx + final_delta, :, :, :] = noise[:, list_idx, :, :, :]
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break
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# otherwise, do normal behavior
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# generate list of indexes in delta region
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list_idx = torch.tensor(list(range(start_idx,start_idx+delta)), device=torch.device("cpu"), dtype=torch.long)
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# shuffle list
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list_idx = list_idx[torch.randperm(delta, generator=generator)]
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# apply shuffled indexes
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#print("place_idx:", place_idx, "delta:", delta, "list_idx:", list_idx)
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noise[:, place_idx:place_idx + delta, :, :, :] = noise[:, list_idx, :, :, :]
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if latents is None:
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latents = noise.to(device)
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else:
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latents = latents.to(device)
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timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, denoise_strength, device)
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latent_timestep = timesteps[:1]
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frames_needed = noise.shape[1]
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current_frames = latents.shape[1]
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if frames_needed > current_frames:
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repeat_factor = frames_needed - current_frames
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additional_frame = torch.randn((latents.size(0), repeat_factor, latents.size(2), latents.size(3), latents.size(4)), dtype=latents.dtype, device=latents.device)
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latents = torch.cat((additional_frame, latents), dim=1)
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self.additional_frames = repeat_factor
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elif frames_needed < current_frames:
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latents = latents[:, :frames_needed, :, :, :]
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latents = self.scheduler.add_noise(latents, noise.to(device), latent_timestep)
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latents = latents * self.scheduler.init_noise_sigma # scale the initial noise by the standard deviation required by the scheduler
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return latents, timesteps
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(self, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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# Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs
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def check_inputs(
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self,
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height,
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width,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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):
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if height % 8 != 0 or width % 8 != 0:
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raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
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f" {negative_prompt_embeds.shape}."
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)
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def get_timesteps(self, num_inference_steps, strength, device):
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# get the original timestep using init_timestep
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init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
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t_start = max(num_inference_steps - init_timestep, 0)
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timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
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if hasattr(self.scheduler, "set_begin_index"):
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self.scheduler.set_begin_index(t_start * self.scheduler.order)
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return timesteps.to(device), num_inference_steps - t_start
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def _prepare_rotary_positional_embeddings(
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self,
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height: int,
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width: int,
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num_frames: int,
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device: torch.device,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
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grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size)
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p = self.transformer.config.patch_size
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p_t = self.transformer.config.patch_size_t
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if p_t is None:
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# CogVideoX 1.0 I2V
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base_size_width = self.transformer.config.sample_width // p
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base_size_height = self.transformer.config.sample_height // p
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grid_crops_coords = get_resize_crop_region_for_grid(
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(grid_height, grid_width), base_size_width, base_size_height
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)
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freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
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embed_dim=self.transformer.config.attention_head_dim,
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crops_coords=grid_crops_coords,
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grid_size=(grid_height, grid_width),
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temporal_size=num_frames,
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)
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else:
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# CogVideoX 1.5 I2V
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base_size_width = self.transformer.config.sample_width // p
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base_size_height = self.transformer.config.sample_height // p
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base_num_frames = (num_frames + p_t - 1) // p_t
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freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
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embed_dim=self.transformer.config.attention_head_dim,
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crops_coords=None,
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grid_size=(grid_height, grid_width),
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temporal_size=base_num_frames,
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grid_type="slice",
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max_size=(base_size_height, base_size_width),
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)
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freqs_cos = freqs_cos.to(device=device)
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freqs_sin = freqs_sin.to(device=device)
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return freqs_cos, freqs_sin
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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@property
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def do_classifier_free_guidance(self):
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return self._guidance_scale > 1
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@property
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def num_timesteps(self):
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return self._num_timesteps
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@property
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def interrupt(self):
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return self._interrupt
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@torch.no_grad()
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def __call__(
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self,
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height: int = 480,
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width: int = 720,
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num_frames: int = 48,
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num_inference_steps: int = 50,
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timesteps: Optional[List[int]] = None,
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guidance_scale: float = 6,
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denoise_strength: float = 1.0,
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sigmas: Optional[List[float]] = None,
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eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.Tensor] = None,
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fun_mask: Optional[torch.Tensor] = None,
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image_cond_latents: Optional[torch.Tensor] = None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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device = torch.device("cuda"),
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context_schedule: Optional[str] = None,
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context_frames: Optional[int] = None,
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context_stride: Optional[int] = None,
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context_overlap: Optional[int] = None,
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freenoise: Optional[bool] = True,
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controlnet: Optional[dict] = None,
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tora: Optional[dict] = None,
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image_cond_start_percent: float = 0.0,
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image_cond_end_percent: float = 1.0,
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):
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"""
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Function invoked when calling the pipeline for generation.
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Args:
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image. This is set to 1024 by default for the best results.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The width in pixels of the generated image. This is set to 1024 by default for the best results.
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num_frames (`int`, defaults to `48`):
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Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will
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contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where
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num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that
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needs to be satisfied is that of divisibility mentioned above.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
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in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
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passed will be used. Must be in descending order.
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guidance_scale (`float`, *optional*, defaults to 7.0):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor will ge generated by sampling using the supplied random `generator`.
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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"""
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height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial
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width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial
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self.num_frames = num_frames
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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height,
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width,
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prompt_embeds,
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negative_prompt_embeds,
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)
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self._guidance_scale = guidance_scale
|
|
self._interrupt = False
|
|
|
|
# 2. Default call parameters
|
|
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
# 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[0] > 1.0
|
|
|
|
if do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
prompt_embeds = prompt_embeds.to(self.vae_dtype)
|
|
|
|
# 4. Prepare timesteps
|
|
if sigmas is None:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
|
|
else:
|
|
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, sigmas=sigmas, device=device)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 5. Prepare latents.
|
|
latent_channels = self.vae_latent_channels
|
|
latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
|
|
|
|
# For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t
|
|
patch_size_t = getattr(self.transformer.config, "patch_size_t", None)
|
|
if patch_size_t is None:
|
|
self.transformer.config.patch_size_t = None
|
|
ofs_embed_dim = getattr(self.transformer.config, "ofs_embed_dim", None)
|
|
if ofs_embed_dim is None:
|
|
self.transformer.config.ofs_embed_dim = None
|
|
|
|
self.additional_frames = 0
|
|
if patch_size_t is not None and latent_frames % patch_size_t != 0:
|
|
self.additional_frames = patch_size_t - latent_frames % patch_size_t
|
|
num_frames += self.additional_frames * self.vae_scale_factor_temporal
|
|
|
|
latents, timesteps = self.prepare_latents(
|
|
batch_size,
|
|
latent_channels,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
device,
|
|
generator,
|
|
timesteps,
|
|
denoise_strength,
|
|
num_inference_steps,
|
|
latents,
|
|
context_size=context_frames,
|
|
context_overlap=context_overlap,
|
|
freenoise=freenoise,
|
|
)
|
|
latents = latents.to(self.vae_dtype)
|
|
|
|
if self.is_fun_inpaint and fun_mask is None: # For FUN inpaint vid2vid, we need to mask all the latents
|
|
fun_mask = torch.zeros_like(latents[:, :, :1, :, :], device=latents.device, dtype=latents.dtype)
|
|
fun_masked_video_latents = torch.zeros_like(latents, device=latents.device, dtype=latents.dtype)
|
|
|
|
# 5.5.
|
|
if image_cond_latents is not None:
|
|
if image_cond_latents.shape[1] == 2:
|
|
logger.info("More than one image conditioning frame received, interpolating")
|
|
padding_shape = (
|
|
batch_size,
|
|
(latents.shape[1] - 2),
|
|
self.vae_latent_channels,
|
|
height // self.vae_scale_factor_spatial,
|
|
width // self.vae_scale_factor_spatial,
|
|
)
|
|
latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
|
|
image_cond_latents = torch.cat([image_cond_latents[:, 0, :, :, :].unsqueeze(1), latent_padding, image_cond_latents[:, -1, :, :, :].unsqueeze(1)], dim=1)
|
|
if self.transformer.config.patch_size_t is not None:
|
|
first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
|
|
image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
|
|
|
|
logger.info(f"image cond latents shape: {image_cond_latents.shape}")
|
|
elif image_cond_latents.shape[1] == 1:
|
|
logger.info("Only one image conditioning frame received, img2vid")
|
|
if self.input_with_padding:
|
|
padding_shape = (
|
|
batch_size,
|
|
(latents.shape[1] - 1),
|
|
self.vae_latent_channels,
|
|
height // self.vae_scale_factor_spatial,
|
|
width // self.vae_scale_factor_spatial,
|
|
)
|
|
latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
|
|
image_cond_latents = torch.cat([image_cond_latents, latent_padding], dim=1)
|
|
# Select the first frame along the second dimension
|
|
if self.transformer.config.patch_size_t is not None:
|
|
first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
|
|
image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
|
|
else:
|
|
image_cond_latents = image_cond_latents.repeat(1, latents.shape[1], 1, 1, 1)
|
|
else:
|
|
logger.info(f"Received {image_cond_latents.shape[1]} image conditioning frames")
|
|
|
|
# 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)
|
|
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
|
|
# 7. context schedule
|
|
if context_schedule is not None:
|
|
# if image_cond_latents is not None:
|
|
# raise NotImplementedError("Context schedule not currently supported with image conditioning")
|
|
logger.info(f"Context schedule enabled: {context_frames} frames, {context_stride} stride, {context_overlap} overlap")
|
|
use_context_schedule = True
|
|
from .context import get_context_scheduler
|
|
context = get_context_scheduler(context_schedule)
|
|
#todo ofs embeds?
|
|
|
|
else:
|
|
use_context_schedule = False
|
|
logger.info("Context schedule disabled")
|
|
# 7.5. 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
|
|
)
|
|
# 7.6. Create ofs embeds if required
|
|
ofs_emb = None if self.transformer.config.ofs_embed_dim is None else latents.new_full((1,), fill_value=2.0)
|
|
|
|
if tora is not None and do_classifier_free_guidance:
|
|
video_flow_features = tora["video_flow_features"].repeat(1, 2, 1, 1, 1).contiguous()
|
|
|
|
#8. Controlnet
|
|
if controlnet is not None:
|
|
self.controlnet = controlnet["control_model"].to(device)
|
|
if self.transformer.dtype == torch.float8_e4m3fn:
|
|
for name, param in self.controlnet.named_parameters():
|
|
if "patch_embed" not in name and param.data.dtype != torch.float8_e4m3fn:
|
|
param.data = param.data.to(torch.float8_e4m3fn)
|
|
else:
|
|
self.controlnet.to(self.transformer.dtype)
|
|
|
|
if getattr(self.transformer, 'fp8_matmul_enabled', False):
|
|
from .fp8_optimization import convert_fp8_linear
|
|
if not hasattr(self.controlnet, 'fp8_matmul_enabled') or not self.controlnet.fp8_matmul_enabled:
|
|
convert_fp8_linear(self.controlnet, torch.float16)
|
|
setattr(self.controlnet, "fp8_matmul_enabled", True)
|
|
|
|
control_frames = controlnet["control_frames"].to(device).to(self.controlnet.dtype).contiguous()
|
|
control_frames = torch.cat([control_frames] * 2) if do_classifier_free_guidance else control_frames
|
|
control_weights = controlnet["control_weights"]
|
|
logger.info(f"Controlnet enabled with weights: {control_weights}")
|
|
control_start = controlnet["control_start"]
|
|
control_end = controlnet["control_end"]
|
|
else:
|
|
controlnet_states = None
|
|
control_weights= None
|
|
|
|
if tora is not None:
|
|
trajectory_length = tora["video_flow_features"].shape[1]
|
|
logger.info(f"Tora trajectory length: {trajectory_length}")
|
|
#if trajectory_length != latents.shape[1]:
|
|
# raise ValueError(f"Tora trajectory length {trajectory_length} does not match inpaint_latents count {latents.shape[2]}")
|
|
for module in self.transformer.fuser_list:
|
|
for param in module.parameters():
|
|
param.data = param.data.to(self.vae_dtype).to(device)
|
|
|
|
logger.info(f"Sampling {num_frames} frames in {latent_frames} latent frames at {width}x{height} with {num_inference_steps} inference steps")
|
|
|
|
from .latent_preview import prepare_callback
|
|
callback = prepare_callback(self.transformer, num_inference_steps)
|
|
|
|
# 9. Denoising loop
|
|
comfy_pbar = ProgressBar(len(timesteps))
|
|
with self.progress_bar(total=len(timesteps)) as progress_bar:
|
|
old_pred_original_sample = None # for DPM-solver++
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
# region context schedule sampling
|
|
if use_context_schedule:
|
|
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)
|
|
counter = torch.zeros_like(latent_model_input)
|
|
noise_pred = torch.zeros_like(latent_model_input)
|
|
|
|
if image_cond_latents is not None:
|
|
latent_image_input = torch.cat([image_cond_latents] * 2) if do_classifier_free_guidance else image_cond_latents
|
|
latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
|
|
current_step_percentage = i / num_inference_steps
|
|
|
|
# use same rotary embeddings for all context windows
|
|
image_rotary_emb = (
|
|
self._prepare_rotary_positional_embeddings(height, width, context_frames, device)
|
|
if self.transformer.config.use_rotary_positional_embeddings
|
|
else None
|
|
)
|
|
|
|
context_queue = list(context(
|
|
i, num_inference_steps, latents.shape[1], context_frames, context_stride, context_overlap,
|
|
))
|
|
|
|
if controlnet is not None:
|
|
# controlnet frames are not temporally compressed, so try to match the context frames that are
|
|
control_context_queue = list(context(
|
|
i,
|
|
num_inference_steps,
|
|
control_frames.shape[1],
|
|
context_frames * self.vae_scale_factor_temporal,
|
|
context_stride * self.vae_scale_factor_temporal,
|
|
context_overlap * self.vae_scale_factor_temporal,
|
|
))
|
|
|
|
for c, control_c in zip(context_queue, control_context_queue):
|
|
partial_latent_model_input = latent_model_input[:, c, :, :, :]
|
|
partial_control_frames = control_frames[:, control_c, :, :, :]
|
|
|
|
controlnet_states = None
|
|
|
|
if (control_start <= current_step_percentage <= control_end):
|
|
# extract controlnet hidden state
|
|
controlnet_states = self.controlnet(
|
|
hidden_states=partial_latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
image_rotary_emb=image_rotary_emb,
|
|
controlnet_states=partial_control_frames,
|
|
timestep=timestep,
|
|
return_dict=False,
|
|
)[0]
|
|
if isinstance(controlnet_states, (tuple, list)):
|
|
controlnet_states = [x.to(dtype=self.controlnet.dtype) for x in controlnet_states]
|
|
else:
|
|
controlnet_states = controlnet_states.to(dtype=self.controlnet.dtype)
|
|
|
|
# predict noise model_output
|
|
noise_pred[:, c, :, :, :] += self.transformer(
|
|
hidden_states=partial_latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timestep,
|
|
image_rotary_emb=image_rotary_emb,
|
|
return_dict=False,
|
|
controlnet_states=controlnet_states,
|
|
controlnet_weights=control_weights,
|
|
)[0]
|
|
|
|
counter[:, c, :, :, :] += 1
|
|
noise_pred = noise_pred.float()
|
|
else:
|
|
for c in context_queue:
|
|
partial_latent_model_input = latent_model_input[:, c, :, :, :]
|
|
if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]):
|
|
if do_classifier_free_guidance:
|
|
partial_video_flow_features = tora["video_flow_features"][:, c, :, :, :].repeat(1, 2, 1, 1, 1).contiguous()
|
|
else:
|
|
partial_video_flow_features = tora["video_flow_features"][:, c, :, :, :]
|
|
else:
|
|
partial_video_flow_features = None
|
|
|
|
# predict noise model_output
|
|
noise_pred[:, c, :, :, :] += self.transformer(
|
|
hidden_states=partial_latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timestep,
|
|
image_rotary_emb=image_rotary_emb,
|
|
video_flow_features=partial_video_flow_features,
|
|
return_dict=False
|
|
)[0]
|
|
|
|
counter[:, c, :, :, :] += 1
|
|
noise_pred = noise_pred.float()
|
|
|
|
noise_pred /= counter
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + self._guidance_scale[i] * (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)
|
|
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
comfy_pbar.update(1)
|
|
|
|
# region sampling
|
|
else:
|
|
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)
|
|
|
|
current_step_percentage = i / num_inference_steps
|
|
|
|
if image_cond_latents is not None:
|
|
if not image_cond_start_percent <= current_step_percentage <= image_cond_end_percent:
|
|
latent_image_input = torch.zeros_like(latent_model_input)
|
|
else:
|
|
latent_image_input = torch.cat([image_cond_latents] * 2) if do_classifier_free_guidance else image_cond_latents
|
|
if fun_mask is not None: #for fun img2vid and interpolation
|
|
fun_inpaint_mask = torch.cat([fun_mask] * 2) if do_classifier_free_guidance else fun_mask
|
|
masks_input = torch.cat([fun_inpaint_mask, latent_image_input], dim=2)
|
|
latent_model_input = torch.cat([latent_model_input, masks_input], dim=2)
|
|
else:
|
|
latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=2)
|
|
else: # for Fun inpaint vid2vid
|
|
if fun_mask is not None:
|
|
fun_inpaint_mask = torch.cat([fun_mask] * 2) if do_classifier_free_guidance else fun_mask
|
|
fun_inpaint_masked_video_latents = torch.cat([fun_masked_video_latents] * 2) if do_classifier_free_guidance else fun_masked_video_latents
|
|
fun_inpaint_latents = torch.cat([fun_inpaint_mask, fun_inpaint_masked_video_latents], dim=2).to(latents.dtype)
|
|
latent_model_input = torch.cat([latent_model_input, fun_inpaint_latents], dim=2)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
|
|
if controlnet is not None:
|
|
controlnet_states = None
|
|
if (control_start <= current_step_percentage <= control_end):
|
|
# extract controlnet hidden state
|
|
controlnet_states = self.controlnet(
|
|
hidden_states=latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
image_rotary_emb=image_rotary_emb,
|
|
controlnet_states=control_frames,
|
|
timestep=timestep,
|
|
return_dict=False,
|
|
)[0]
|
|
if isinstance(controlnet_states, (tuple, list)):
|
|
controlnet_states = [x.to(dtype=self.vae_dtype) for x in controlnet_states]
|
|
else:
|
|
controlnet_states = controlnet_states.to(dtype=self.vae_dtype)
|
|
|
|
# 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,
|
|
ofs=ofs_emb,
|
|
return_dict=False,
|
|
controlnet_states=controlnet_states,
|
|
controlnet_weights=control_weights,
|
|
video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
|
|
)[0]
|
|
noise_pred = noise_pred.float()
|
|
if isinstance(self.scheduler, CogVideoXDPMScheduler):
|
|
self._guidance_scale[i] = 1 + guidance_scale[i] * (
|
|
(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[i] * (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.to(self.vae_dtype), **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.to(self.vae_dtype),
|
|
**extra_step_kwargs,
|
|
return_dict=False,
|
|
)
|
|
latents = latents.to(prompt_embeds.dtype)
|
|
|
|
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
|
progress_bar.update()
|
|
if callback is not None:
|
|
callback(i, latents.detach()[-1], None, num_inference_steps)
|
|
else:
|
|
comfy_pbar.update(1)
|
|
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
return latents |