diffusers/pipeline_cogvideox_fun_inpaint.py

# 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 Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
from diffusers.image_processor import VaeImageProcessor
from diffusers.models.embeddings import get_1d_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 einops import rearrange

from transformers import T5EncoderModel, T5Tokenizer
from cogvideox_transformer3d import CogVideoXTransformer3DModel
from cogvideox_vae import AutoencoderKLCogVideoX

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


EXAMPLE_DOC_STRING = """
    Examples:
        ```python
        pass
        ```
"""

# Copied from diffusers.models.embeddings.get_3d_rotary_pos_embed
def get_3d_rotary_pos_embed(
    embed_dim,
    crops_coords,
    grid_size,
    temporal_size,
    theta: int = 10000,
    use_real: bool = True,
    grid_type: str = "linspace",
    max_size: Optional[Tuple[int, int]] = None,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
    """
    RoPE for video tokens with 3D structure.

    Args:
    embed_dim: (`int`):
        The embedding dimension size, corresponding to hidden_size_head.
    crops_coords (`Tuple[int]`):
        The top-left and bottom-right coordinates of the crop.
    grid_size (`Tuple[int]`):
        The grid size of the spatial positional embedding (height, width).
    temporal_size (`int`):
        The size of the temporal dimension.
    theta (`float`):
        Scaling factor for frequency computation.
    grid_type (`str`):
        Whether to use "linspace" or "slice" to compute grids.

    Returns:
        `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
    """
    if use_real is not True:
        raise ValueError(" `use_real = False` is not currently supported for get_3d_rotary_pos_embed")

    if grid_type == "linspace":
        start, stop = crops_coords
        grid_size_h, grid_size_w = grid_size
        grid_h = np.linspace(start[0], stop[0], grid_size_h, endpoint=False, dtype=np.float32)
        grid_w = np.linspace(start[1], stop[1], grid_size_w, endpoint=False, dtype=np.float32)
        grid_t = np.arange(temporal_size, dtype=np.float32)
        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
    elif grid_type == "slice":
        max_h, max_w = max_size
        grid_size_h, grid_size_w = grid_size
        grid_h = np.arange(max_h, dtype=np.float32)
        grid_w = np.arange(max_w, dtype=np.float32)
        grid_t = np.arange(temporal_size, dtype=np.float32)
    else:
        raise ValueError("Invalid value passed for `grid_type`.")

    # Compute dimensions for each axis
    dim_t = embed_dim // 4
    dim_h = embed_dim // 8 * 3
    dim_w = embed_dim // 8 * 3

    # Temporal frequencies
    freqs_t = get_1d_rotary_pos_embed(dim_t, grid_t, use_real=True)
    # Spatial frequencies for height and width
    freqs_h = get_1d_rotary_pos_embed(dim_h, grid_h, use_real=True)
    freqs_w = get_1d_rotary_pos_embed(dim_w, grid_w, use_real=True)

    # BroadCast and concatenate temporal and spaial frequencie (height and width) into a 3d tensor
    def combine_time_height_width(freqs_t, freqs_h, freqs_w):
        freqs_t = freqs_t[:, None, None, :].expand(
            -1, grid_size_h, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_w, dim_t
        freqs_h = freqs_h[None, :, None, :].expand(
            temporal_size, -1, grid_size_w, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_h
        freqs_w = freqs_w[None, None, :, :].expand(
            temporal_size, grid_size_h, -1, -1
        )  # temporal_size, grid_size_h, grid_size_2, dim_w

        freqs = torch.cat(
            [freqs_t, freqs_h, freqs_w], dim=-1
        )  # temporal_size, grid_size_h, grid_size_w, (dim_t + dim_h + dim_w)
        freqs = freqs.view(
            temporal_size * grid_size_h * grid_size_w, -1
        )  # (temporal_size * grid_size_h * grid_size_w), (dim_t + dim_h + dim_w)
        return freqs

    t_cos, t_sin = freqs_t  # both t_cos and t_sin has shape: temporal_size, dim_t
    h_cos, h_sin = freqs_h  # both h_cos and h_sin has shape: grid_size_h, dim_h
    w_cos, w_sin = freqs_w  # both w_cos and w_sin has shape: grid_size_w, dim_w

    if grid_type == "slice":
        t_cos, t_sin = t_cos[:temporal_size], t_sin[:temporal_size]
        h_cos, h_sin = h_cos[:grid_size_h], h_sin[:grid_size_h]
        w_cos, w_sin = w_cos[:grid_size_w], w_sin[:grid_size_w]

    cos = combine_time_height_width(t_cos, h_cos, w_cos)
    sin = combine_time_height_width(t_sin, h_sin, w_sin)
    return cos, sin


# 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


def add_noise_to_reference_video(image, ratio=None):
    if ratio is None:
        sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device)
        sigma = torch.exp(sigma).to(image.dtype)
    else:
        sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio
    
    image_noise = torch.randn_like(image) * sigma[:, None, None, None, None]
    image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise)
    image = image + image_noise
    return image


@dataclass
class CogVideoXFunPipelineOutput(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 CogVideoXFunInpaintPipeline(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->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=False, 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, noise_aug_strength
    ):
        # 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:
            if self.transformer.config.add_noise_in_inpaint_model:
                masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength)
            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)

        p = self.transformer.config.patch_size
        p_t = self.transformer.config.patch_size_t

        base_size_width = self.transformer.config.sample_width // p
        base_size_height = self.transformer.config.sample_height // p

        if p_t is None:
            # CogVideoX 1.0
            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,
            )
        else:
            # CogVideoX 1.5
            base_num_frames = (num_frames + p_t - 1) // p_t

            freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
                embed_dim=self.transformer.config.attention_head_dim,
                crops_coords=None,
                grid_size=(grid_height, grid_width),
                temporal_size=base_num_frames,
                grid_type="slice",
                max_size=(base_size_height, base_size_width),
            )

        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 attention_kwargs(self):
        return self._attention_kwargs

    @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,
        attention_kwargs: Optional[Dict[str, Any]] = None,
        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
        max_sequence_length: int = 226,
        strength: float = 1,
        noise_aug_strength: float = 0.0563,
        comfyui_progressbar: bool = False,
        temporal_multidiffusion_stride: int = 16,
        use_trimask: bool = False,
        zero_out_mask_region: bool = False,
        binarize_mask: bool = False,
        skip_unet: bool = False,
        use_vae_mask: bool = False,
        stack_mask: bool = False,
    ) -> Union[CogVideoXFunPipelineOutput, 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.CogVideoXFunPipelineOutput`] or `tuple`:
            [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXFunPipelineOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is a list with the generated images.
        """

        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs

        height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial
        width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial
        num_frames = num_frames or self.transformer.config.sample_frames

        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._attention_kwargs = attention_kwargs
        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
        logger.info(f'Use cfg: {do_classifier_free_guidance}, guidance_scale={guidance_scale}')

        # 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:
            video_length = num_frames
            init_video = None

        # Magvae needs the number of frames to be 4n + 1.
        local_latent_length = (num_frames - 1) // self.vae_scale_factor_temporal + 1
        # For CogVideoX 1.5, the latent frames should be clipped to make it divisible by patch_size_t
        patch_size_t = self.transformer.config.patch_size_t
        additional_frames = 0
        if patch_size_t is not None and local_latent_length % patch_size_t != 0:
            additional_frames = local_latent_length % patch_size_t
            num_frames -= additional_frames * self.vae_scale_factor_temporal
        if num_frames <= 0:
            num_frames = 1

        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) 
                if use_trimask:
                    mask_condition = torch.where(mask_condition > 0.75, 1., mask_condition)
                    mask_condition = torch.where((mask_condition <= 0.75) * (mask_condition >= 0.25), 127. / 255., mask_condition)
                    mask_condition = torch.where(mask_condition < 0.25, 0., mask_condition)
                else:
                    mask_condition = torch.where(mask_condition > 0.5, 1., 0.)
                    
                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:
                        if zero_out_mask_region:
                            masked_video = init_video * (mask_condition_tile < 0.75) + torch.ones_like(init_video) * (mask_condition_tile > 0.75) * -1
                        else:
                            masked_video = init_video
                    else:
                        masked_video = masked_video_latents

                    mask_encoded, masked_video_latents = self.prepare_mask_latents(
                        1 - mask_condition_tile if use_vae_mask else None,
                        masked_video,
                        batch_size,
                        height,
                        width,
                        prompt_embeds.dtype,
                        device,
                        generator,
                        do_classifier_free_guidance,
                        noise_aug_strength=noise_aug_strength,
                    )
                    if not use_vae_mask and not stack_mask:
                        mask_latents = resize_mask(1 - mask_condition, masked_video_latents)
                        if binarize_mask:
                            if use_trimask:
                                mask_latents = torch.where(mask_latents > 0.75, 1., mask_latents)
                                mask_latents = torch.where((mask_latents <= 0.75) * (mask_latents >= 0.25), 0.5, mask_latents)
                                mask_latents = torch.where(mask_latents < 0.25, 0., mask_latents)
                            else:
                                mask_latents = torch.where(mask_latents < 0.9, 0., 1.).to(mask_latents.dtype)

                        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
                        mask = rearrange(mask, "b c f h w -> b f c h w")
                    elif stack_mask:
                        mask_latents = torch.cat([
                            torch.repeat_interleave(mask_condition[:, :, 0:1], repeats=4, dim=2),
                            mask_condition[:, :, 1:],
                        ], dim=2)
                        mask_latents = mask_latents.view(
                            mask_latents.shape[0],
                            mask_latents.shape[2] // 4,
                            4,
                            mask_latents.shape[3],
                            mask_latents.shape[4],
                        )
                        mask_latents = mask_latents.transpose(1, 2)
                        mask_latents = resize_mask(1 - mask_latents, masked_video_latents).to(latents.device, latents.dtype)
                        mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
                    else:
                        mask_input = (
                            torch.cat([mask_encoded] * 2) if do_classifier_free_guidance else mask_encoded
                        )

                    masked_video_latents_input = (
                        torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
                    )

                    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")

                    # concat(binary mask, encode(mask * video))
                    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)
        logger.debug(f'Pipeline mask {mask_condition.shape} {mask_condition.dtype} {mask_condition.min()} {mask_condition.max()}')
        # 8. Denoising loop
        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
        latent_temporal_window_size = (num_frames - 1) // 4 + 1
        if latents.size(1) > latent_temporal_window_size:
            logger.info(f'Adopt temporal multidiffusion for the latents {latents.shape} {latents.dtype}')

        # VAE experiment
        if skip_unet:
            masked_video_latents = rearrange(masked_video_latents, "b c f h w -> b f c h w")
            if output_type == "numpy":
                video = self.decode_latents(masked_video_latents)
            elif not output_type == "latent":
                video = self.decode_latents(masked_video_latents)
                video = self.video_processor.postprocess_video(video=video, output_type=output_type)
            else:
                video = masked_video_latents

            # Offload all models
            self.maybe_free_model_hooks()

            if not return_dict:
                video = torch.from_numpy(video)

            return CogVideoXFunPipelineOutput(videos=video)

        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

                def _sample(_latents, _inpaint_latents):
                    # 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
                    )

                    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)
                    return _latents

                if latents.size(1) <= latent_temporal_window_size:
                    latents = _sample(latents, inpaint_latents)
                else:
                    # adopt temporal multidiffusion
                    latents_canvas = torch.zeros_like(latents).float()
                    weights_canvas = torch.zeros(1, latents.size(1), 1, 1, 1).to(latents.device).float()
                    temporal_stride = temporal_multidiffusion_stride // 4
                    assert latent_temporal_window_size > temporal_stride

                    time_beg = 0
                    while time_beg < latents.size(1):
                        time_end = min(time_beg + latent_temporal_window_size, latents.size(1))
                        
                        latents_i = latents[:, time_beg:time_end]
                        if inpaint_latents is not None:
                            inpaint_latents_i = inpaint_latents[:, time_beg:time_end]
                        else:
                            inpaint_latents_i = None
                        
                        latents_i = _sample(latents_i, inpaint_latents_i)

                        weights_i = torch.ones(1, time_end - time_beg, 1, 1, 1).to(latents.device).to(latents.dtype)
                        if time_beg > 0 and temporal_stride > 0:
                            weights_i[:, :temporal_stride] = (torch.linspace(0., 1., temporal_stride + 2)[1:-1]
                                                                .to(latents.device)
                                                                .to(latents.dtype)
                                                                .reshape(1, temporal_stride, 1, 1, 1))
                        if time_end < latents.size(1) and temporal_stride > 0:
                            weights_i[:, -temporal_stride:] = (torch.linspace(1., 0., temporal_stride + 2)[1:-1]
                                                                .to(latents.device)
                                                                .to(latents.dtype)
                                                                .reshape(1, temporal_stride, 1, 1, 1))

                        latents_canvas[:, time_beg:time_end] += latents_i * weights_i
                        weights_canvas[:, time_beg:time_end] += weights_i

                        time_beg = time_end - temporal_stride
                        if time_end >= latents.size(1):
                            break
                    latents = (latents_canvas / weights_canvas).to(latents.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 CogVideoXFunPipelineOutput(videos=video)