netflix
/

void-model

@@ -1,1244 +0,0 @@
-# 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)