v0.26.2/rerender_a_video.py

# Copyright 2023 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 sys
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import PIL.Image
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from diffusers.models.attention_processor import Attention, AttnProcessor
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
from diffusers.pipelines.controlnet.pipeline_controlnet_img2img import StableDiffusionControlNetImg2ImgPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import BaseOutput, deprecate, logging
from diffusers.utils.torch_utils import is_compiled_module, randn_tensor


gmflow_dir = "/path/to/gmflow"
sys.path.insert(0, gmflow_dir)
from gmflow.gmflow import GMFlow  # noqa: E402

from utils.utils import InputPadder  # noqa: E402


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


def coords_grid(b, h, w, homogeneous=False, device=None):
    y, x = torch.meshgrid(torch.arange(h), torch.arange(w))  # [H, W]

    stacks = [x, y]

    if homogeneous:
        ones = torch.ones_like(x)  # [H, W]
        stacks.append(ones)

    grid = torch.stack(stacks, dim=0).float()  # [2, H, W] or [3, H, W]

    grid = grid[None].repeat(b, 1, 1, 1)  # [B, 2, H, W] or [B, 3, H, W]

    if device is not None:
        grid = grid.to(device)

    return grid


def bilinear_sample(img, sample_coords, mode="bilinear", padding_mode="zeros", return_mask=False):
    # img: [B, C, H, W]
    # sample_coords: [B, 2, H, W] in image scale
    if sample_coords.size(1) != 2:  # [B, H, W, 2]
        sample_coords = sample_coords.permute(0, 3, 1, 2)

    b, _, h, w = sample_coords.shape

    # Normalize to [-1, 1]
    x_grid = 2 * sample_coords[:, 0] / (w - 1) - 1
    y_grid = 2 * sample_coords[:, 1] / (h - 1) - 1

    grid = torch.stack([x_grid, y_grid], dim=-1)  # [B, H, W, 2]

    img = F.grid_sample(img, grid, mode=mode, padding_mode=padding_mode, align_corners=True)

    if return_mask:
        mask = (x_grid >= -1) & (y_grid >= -1) & (x_grid <= 1) & (y_grid <= 1)  # [B, H, W]

        return img, mask

    return img


def flow_warp(feature, flow, mask=False, mode="bilinear", padding_mode="zeros"):
    b, c, h, w = feature.size()
    assert flow.size(1) == 2

    grid = coords_grid(b, h, w).to(flow.device) + flow  # [B, 2, H, W]
    grid = grid.to(feature.dtype)
    return bilinear_sample(feature, grid, mode=mode, padding_mode=padding_mode, return_mask=mask)


def forward_backward_consistency_check(fwd_flow, bwd_flow, alpha=0.01, beta=0.5):
    # fwd_flow, bwd_flow: [B, 2, H, W]
    # alpha and beta values are following UnFlow
    # (https://arxiv.org/abs/1711.07837)
    assert fwd_flow.dim() == 4 and bwd_flow.dim() == 4
    assert fwd_flow.size(1) == 2 and bwd_flow.size(1) == 2
    flow_mag = torch.norm(fwd_flow, dim=1) + torch.norm(bwd_flow, dim=1)  # [B, H, W]

    warped_bwd_flow = flow_warp(bwd_flow, fwd_flow)  # [B, 2, H, W]
    warped_fwd_flow = flow_warp(fwd_flow, bwd_flow)  # [B, 2, H, W]

    diff_fwd = torch.norm(fwd_flow + warped_bwd_flow, dim=1)  # [B, H, W]
    diff_bwd = torch.norm(bwd_flow + warped_fwd_flow, dim=1)

    threshold = alpha * flow_mag + beta

    fwd_occ = (diff_fwd > threshold).float()  # [B, H, W]
    bwd_occ = (diff_bwd > threshold).float()

    return fwd_occ, bwd_occ


@torch.no_grad()
def get_warped_and_mask(flow_model, image1, image2, image3=None, pixel_consistency=False):
    if image3 is None:
        image3 = image1
    padder = InputPadder(image1.shape, padding_factor=8)
    image1, image2 = padder.pad(image1[None].cuda(), image2[None].cuda())
    results_dict = flow_model(
        image1, image2, attn_splits_list=[2], corr_radius_list=[-1], prop_radius_list=[-1], pred_bidir_flow=True
    )
    flow_pr = results_dict["flow_preds"][-1]  # [B, 2, H, W]
    fwd_flow = padder.unpad(flow_pr[0]).unsqueeze(0)  # [1, 2, H, W]
    bwd_flow = padder.unpad(flow_pr[1]).unsqueeze(0)  # [1, 2, H, W]
    fwd_occ, bwd_occ = forward_backward_consistency_check(fwd_flow, bwd_flow)  # [1, H, W] float
    if pixel_consistency:
        warped_image1 = flow_warp(image1, bwd_flow)
        bwd_occ = torch.clamp(
            bwd_occ + (abs(image2 - warped_image1).mean(dim=1) > 255 * 0.25).float(), 0, 1
        ).unsqueeze(0)
    warped_results = flow_warp(image3, bwd_flow)
    return warped_results, bwd_occ, bwd_flow


blur = T.GaussianBlur(kernel_size=(9, 9), sigma=(18, 18))


@dataclass
class TextToVideoSDPipelineOutput(BaseOutput):
    """
    Output class for text-to-video pipelines.

    Args:
        frames (`List[np.ndarray]` or `torch.FloatTensor`)
            List of denoised frames (essentially images) as NumPy arrays of shape `(height, width, num_channels)` or as
            a `torch` tensor. The length of the list denotes the video length (the number of frames).
    """

    frames: Union[List[np.ndarray], torch.FloatTensor]


@torch.no_grad()
def find_flat_region(mask):
    device = mask.device
    kernel_x = torch.Tensor([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]).unsqueeze(0).unsqueeze(0).to(device)
    kernel_y = torch.Tensor([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]).unsqueeze(0).unsqueeze(0).to(device)
    mask_ = F.pad(mask.unsqueeze(0), (1, 1, 1, 1), mode="replicate")

    grad_x = torch.nn.functional.conv2d(mask_, kernel_x)
    grad_y = torch.nn.functional.conv2d(mask_, kernel_y)
    return ((abs(grad_x) + abs(grad_y)) == 0).float()[0]


class AttnState:
    STORE = 0
    LOAD = 1
    LOAD_AND_STORE_PREV = 2

    def __init__(self):
        self.reset()

    @property
    def state(self):
        return self.__state

    @property
    def timestep(self):
        return self.__timestep

    def set_timestep(self, t):
        self.__timestep = t

    def reset(self):
        self.__state = AttnState.STORE
        self.__timestep = 0

    def to_load(self):
        self.__state = AttnState.LOAD

    def to_load_and_store_prev(self):
        self.__state = AttnState.LOAD_AND_STORE_PREV


class CrossFrameAttnProcessor(AttnProcessor):
    """
    Cross frame attention processor. Each frame attends the first frame and previous frame.

    Args:
        attn_state: Whether the model is processing the first frame or an intermediate frame
    """

    def __init__(self, attn_state: AttnState):
        super().__init__()
        self.attn_state = attn_state
        self.first_maps = {}
        self.prev_maps = {}

    def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
        # Is self attention
        if encoder_hidden_states is None:
            t = self.attn_state.timestep
            if self.attn_state.state == AttnState.STORE:
                self.first_maps[t] = hidden_states.detach()
                self.prev_maps[t] = hidden_states.detach()
                res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)
            else:
                if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
                    tmp = hidden_states.detach()
                cross_map = torch.cat((self.first_maps[t], self.prev_maps[t]), dim=1)
                res = super().__call__(attn, hidden_states, cross_map, attention_mask, temb)
                if self.attn_state.state == AttnState.LOAD_AND_STORE_PREV:
                    self.prev_maps[t] = tmp
        else:
            res = super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask, temb)

        return res


def prepare_image(image):
    if isinstance(image, torch.Tensor):
        # Batch single image
        if image.ndim == 3:
            image = image.unsqueeze(0)

        image = image.to(dtype=torch.float32)
    else:
        # preprocess image
        if isinstance(image, (PIL.Image.Image, np.ndarray)):
            image = [image]

        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
            image = [np.array(i.convert("RGB"))[None, :] for i in image]
            image = np.concatenate(image, axis=0)
        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
            image = np.concatenate([i[None, :] for i in image], axis=0)

        image = image.transpose(0, 3, 1, 2)
        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0

    return image


class RerenderAVideoPipeline(StableDiffusionControlNetImg2ImgPipeline):
    r"""
    Pipeline for video-to-video translation using Stable Diffusion with Rerender Algorithm.

    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.)

    In addition the pipeline inherits the following loading methods:
        - *Textual-Inversion*: [`loaders.TextualInversionLoaderMixin.load_textual_inversion`]

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
            Provides additional conditioning to the unet during the denoising process. If you set multiple ControlNets
            as a list, the outputs from each ControlNet are added together to create one combined additional
            conditioning.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPImageProcessor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPImageProcessor,
        image_encoder=None,
        requires_safety_checker: bool = True,
    ):
        super().__init__(
            vae,
            text_encoder,
            tokenizer,
            unet,
            controlnet,
            scheduler,
            safety_checker,
            feature_extractor,
            image_encoder,
            requires_safety_checker,
        )

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        if isinstance(controlnet, (list, tuple)):
            controlnet = MultiControlNetModel(controlnet)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            controlnet=controlnet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
        self.control_image_processor = VaeImageProcessor(
            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
        )
        self.register_to_config(requires_safety_checker=requires_safety_checker)
        self.attn_state = AttnState()
        attn_processor_dict = {}
        for k in unet.attn_processors.keys():
            if k.startswith("up"):
                attn_processor_dict[k] = CrossFrameAttnProcessor(self.attn_state)
            else:
                attn_processor_dict[k] = AttnProcessor()

        self.unet.set_attn_processor(attn_processor_dict)

        flow_model = GMFlow(
            feature_channels=128,
            num_scales=1,
            upsample_factor=8,
            num_head=1,
            attention_type="swin",
            ffn_dim_expansion=4,
            num_transformer_layers=6,
        ).to("cuda")

        checkpoint = torch.utils.model_zoo.load_url(
            "https://huggingface.co/Anonymous-sub/Rerender/resolve/main/models/gmflow_sintel-0c07dcb3.pth",
            map_location=lambda storage, loc: storage,
        )
        weights = checkpoint["model"] if "model" in checkpoint else checkpoint
        flow_model.load_state_dict(weights, strict=False)
        flow_model.eval()
        self.flow_model = flow_model

    # Modified from src/diffusers/pipelines/controlnet/pipeline_controlnet.StableDiffusionControlNetImg2ImgPipeline.check_inputs
    def check_inputs(
        self,
        prompt,
        callback_steps,
        negative_prompt=None,
        prompt_embeds=None,
        negative_prompt_embeds=None,
        controlnet_conditioning_scale=1.0,
        control_guidance_start=0.0,
        control_guidance_end=1.0,
    ):
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        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 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}."
                )

        # `prompt` needs more sophisticated handling when there are multiple
        # conditionings.
        if isinstance(self.controlnet, MultiControlNetModel):
            if isinstance(prompt, list):
                logger.warning(
                    f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
                    " prompts. The conditionings will be fixed across the prompts."
                )

        is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
            self.controlnet, torch._dynamo.eval_frame.OptimizedModule
        )

        # Check `controlnet_conditioning_scale`
        if (
            isinstance(self.controlnet, ControlNetModel)
            or is_compiled
            and isinstance(self.controlnet._orig_mod, ControlNetModel)
        ):
            if not isinstance(controlnet_conditioning_scale, float):
                raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
        elif (
            isinstance(self.controlnet, MultiControlNetModel)
            or is_compiled
            and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
        ):
            if isinstance(controlnet_conditioning_scale, list):
                if any(isinstance(i, list) for i in controlnet_conditioning_scale):
                    raise ValueError("A single batch of multiple conditionings are supported at the moment.")
            elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
                self.controlnet.nets
            ):
                raise ValueError(
                    "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
                    " the same length as the number of controlnets"
                )
        else:
            assert False

        if len(control_guidance_start) != len(control_guidance_end):
            raise ValueError(
                f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
            )

        if isinstance(self.controlnet, MultiControlNetModel):
            if len(control_guidance_start) != len(self.controlnet.nets):
                raise ValueError(
                    f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
                )

        for start, end in zip(control_guidance_start, control_guidance_end):
            if start >= end:
                raise ValueError(
                    f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
                )
            if start < 0.0:
                raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
            if end > 1.0:
                raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")

    # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
    def prepare_control_image(
        self,
        image,
        width,
        height,
        batch_size,
        num_images_per_prompt,
        device,
        dtype,
        do_classifier_free_guidance=False,
        guess_mode=False,
    ):
        image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32)
        image_batch_size = image.shape[0]

        if image_batch_size == 1:
            repeat_by = batch_size
        else:
            # image batch size is the same as prompt batch size
            repeat_by = num_images_per_prompt

        image = image.repeat_interleave(repeat_by, dim=0)

        image = image.to(device=device, dtype=dtype)

        if do_classifier_free_guidance and not guess_mode:
            image = torch.cat([image] * 2)

        return image

    # 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

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
            raise ValueError(
                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
            )

        image = image.to(device=device, dtype=dtype)

        batch_size = batch_size * num_images_per_prompt

        if image.shape[1] == 4:
            init_latents = image

        else:
            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."
                )

            elif isinstance(generator, list):
                init_latents = [
                    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
                ]
                init_latents = torch.cat(init_latents, dim=0)
            else:
                init_latents = self.vae.encode(image).latent_dist.sample(generator)

            init_latents = self.vae.config.scaling_factor * init_latents

        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
            # expand init_latents for batch_size
            deprecation_message = (
                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
                " your script to pass as many initial images as text prompts to suppress this warning."
            )
            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
            additional_image_per_prompt = batch_size // init_latents.shape[0]
            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            init_latents = torch.cat([init_latents], dim=0)

        shape = init_latents.shape
        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)

        # get latents
        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = init_latents

        return latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        frames: Union[List[np.ndarray], torch.FloatTensor] = None,
        control_frames: Union[List[np.ndarray], torch.FloatTensor] = None,
        strength: float = 0.8,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        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: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
        guess_mode: bool = False,
        control_guidance_start: Union[float, List[float]] = 0.0,
        control_guidance_end: Union[float, List[float]] = 1.0,
        warp_start: Union[float, List[float]] = 0.0,
        warp_end: Union[float, List[float]] = 0.3,
        mask_start: Union[float, List[float]] = 0.5,
        mask_end: Union[float, List[float]] = 0.8,
        smooth_boundary: bool = True,
        mask_strength: Union[float, List[float]] = 0.5,
        inner_strength: Union[float, List[float]] = 0.9,
    ):
        r"""
        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.
            frames (`List[np.ndarray]` or `torch.FloatTensor`): The input images to be used as the starting point for the image generation process.
            control_frames (`List[np.ndarray]` or `torch.FloatTensor`): The ControlNet input images condition to provide guidance to the `unet` for generation.
            strength ('float'): SDEdit strength.
            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.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                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.
            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`).
            eta (`float`, *optional*, defaults to 0.0):
                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
                [`schedulers.DDIMScheduler`], will be ignored for others.
            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.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
                corresponding scale as a list. Note that by default, we use a smaller conditioning scale for inpainting
                than for [`~StableDiffusionControlNetPipeline.__call__`].
            guess_mode (`bool`, *optional*, defaults to `False`):
                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
                The percentage of total steps at which the controlnet starts applying.
            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
                The percentage of total steps at which the controlnet stops applying.
            warp_start (`float`): Shape-aware fusion start timestep.
            warp_end (`float`): Shape-aware fusion end timestep.
            mask_start (`float`): Pixel-aware fusion start timestep.
            mask_end (`float`):Pixel-aware fusion end timestep.
            smooth_boundary (`bool`): Smooth fusion boundary. Set `True` to prevent artifacts at boundary.
            mask_strength (`float`): Pixel-aware fusion strength.
            inner_strength (`float`): Pixel-aware fusion detail level.

        Examples:

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet

        # align format for control guidance
        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
            control_guidance_start, control_guidance_end = (
                mult * [control_guidance_start],
                mult * [control_guidance_end],
            )

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(
            prompt,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
            controlnet_conditioning_scale,
            control_guidance_start,
            control_guidance_end,
        )

        # 2. Define call parameters
        # Currently we only support 1 prompt
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            assert False
        else:
            assert False
        num_images_per_prompt = 1

        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

        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)

        global_pool_conditions = (
            controlnet.config.global_pool_conditions
            if isinstance(controlnet, ControlNetModel)
            else controlnet.nets[0].config.global_pool_conditions
        )
        guess_mode = guess_mode or global_pool_conditions

        # 3. Encode input prompt
        text_encoder_lora_scale = (
            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
        )
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            lora_scale=text_encoder_lora_scale,
        )

        # 4. Process the first frame
        height, width = None, None
        output_frames = []
        self.attn_state.reset()

        # 4.1 prepare frames
        image = self.image_processor.preprocess(frames[0]).to(dtype=torch.float32)
        first_image = image[0]  # C, H, W

        # 4.2 Prepare controlnet_conditioning_image
        # Currently we only support single control
        if isinstance(controlnet, ControlNetModel):
            control_image = self.prepare_control_image(
                image=control_frames[0],
                width=width,
                height=height,
                batch_size=batch_size,
                num_images_per_prompt=1,
                device=device,
                dtype=controlnet.dtype,
                do_classifier_free_guidance=do_classifier_free_guidance,
                guess_mode=guess_mode,
            )
        else:
            assert False

        # 4.3 Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size)

        # 4.4 Prepare latent variables
        latents = self.prepare_latents(
            image,
            latent_timestep,
            batch_size,
            num_images_per_prompt,
            prompt_embeds.dtype,
            device,
            generator,
        )

        # 4.5 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)

        # 4.6 Create tensor stating which controlnets to keep
        controlnet_keep = []
        for i in range(len(timesteps)):
            keeps = [
                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                for s, e in zip(control_guidance_start, control_guidance_end)
            ]
            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

        first_x0_list = []

        # 4.7 Denoising loop
        num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order
        with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                self.attn_state.set_timestep(t.item())

                # expand the latents if we are doing classifier free guidance
                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)

                # controlnet(s) inference
                if guess_mode and do_classifier_free_guidance:
                    # Infer ControlNet only for the conditional batch.
                    control_model_input = latents
                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                else:
                    control_model_input = latent_model_input
                    controlnet_prompt_embeds = prompt_embeds

                if isinstance(controlnet_keep[i], list):
                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                else:
                    controlnet_cond_scale = controlnet_conditioning_scale
                    if isinstance(controlnet_cond_scale, list):
                        controlnet_cond_scale = controlnet_cond_scale[0]
                    cond_scale = controlnet_cond_scale * controlnet_keep[i]

                down_block_res_samples, mid_block_res_sample = self.controlnet(
                    control_model_input,
                    t,
                    encoder_hidden_states=controlnet_prompt_embeds,
                    controlnet_cond=control_image,
                    conditioning_scale=cond_scale,
                    guess_mode=guess_mode,
                    return_dict=False,
                )

                if guess_mode and do_classifier_free_guidance:
                    # Infered ControlNet only for the conditional batch.
                    # To apply the output of ControlNet to both the unconditional and conditional batches,
                    # add 0 to the unconditional batch to keep it unchanged.
                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])

                # predict the noise residual
                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    down_block_additional_residuals=down_block_res_samples,
                    mid_block_additional_residual=mid_block_res_sample,
                    return_dict=False,
                )[0]

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                alpha_prod_t = self.scheduler.alphas_cumprod[t]
                beta_prod_t = 1 - alpha_prod_t
                pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
                first_x0 = pred_x0.detach()
                first_x0_list.append(first_x0)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        if not output_type == "latent":
            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
        else:
            image = latents

        first_result = image
        prev_result = image
        do_denormalize = [True] * image.shape[0]
        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

        output_frames.append(image[0])

        # 5. Process each frame
        for idx in range(1, len(frames)):
            image = frames[idx]
            prev_image = frames[idx - 1]
            control_image = control_frames[idx]
            # 5.1 prepare frames
            image = self.image_processor.preprocess(image).to(dtype=torch.float32)
            prev_image = self.image_processor.preprocess(prev_image).to(dtype=torch.float32)

            warped_0, bwd_occ_0, bwd_flow_0 = get_warped_and_mask(
                self.flow_model, first_image, image[0], first_result, False
            )
            blend_mask_0 = blur(F.max_pool2d(bwd_occ_0, kernel_size=9, stride=1, padding=4))
            blend_mask_0 = torch.clamp(blend_mask_0 + bwd_occ_0, 0, 1)

            warped_pre, bwd_occ_pre, bwd_flow_pre = get_warped_and_mask(
                self.flow_model, prev_image[0], image[0], prev_result, False
            )
            blend_mask_pre = blur(F.max_pool2d(bwd_occ_pre, kernel_size=9, stride=1, padding=4))
            blend_mask_pre = torch.clamp(blend_mask_pre + bwd_occ_pre, 0, 1)

            warp_mask = 1 - F.max_pool2d(blend_mask_0, kernel_size=8)
            warp_flow = F.interpolate(bwd_flow_0 / 8.0, scale_factor=1.0 / 8, mode="bilinear")

            # 5.2 Prepare controlnet_conditioning_image
            # Currently we only support single control
            if isinstance(controlnet, ControlNetModel):
                control_image = self.prepare_control_image(
                    image=control_image,
                    width=width,
                    height=height,
                    batch_size=batch_size,
                    num_images_per_prompt=1,
                    device=device,
                    dtype=controlnet.dtype,
                    do_classifier_free_guidance=do_classifier_free_guidance,
                    guess_mode=guess_mode,
                )
            else:
                assert False

            # 5.3 Prepare timesteps
            self.scheduler.set_timesteps(num_inference_steps, device=device)
            timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
            latent_timestep = timesteps[:1].repeat(batch_size)

            skip_t = int(num_inference_steps * (1 - strength))
            warp_start_t = int(warp_start * num_inference_steps)
            warp_end_t = int(warp_end * num_inference_steps)
            mask_start_t = int(mask_start * num_inference_steps)
            mask_end_t = int(mask_end * num_inference_steps)

            # 5.4 Prepare latent variables
            init_latents = self.prepare_latents(
                image,
                latent_timestep,
                batch_size,
                num_images_per_prompt,
                prompt_embeds.dtype,
                device,
                generator,
            )

            # 5.5 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)

            # 5.6 Create tensor stating which controlnets to keep
            controlnet_keep = []
            for i in range(len(timesteps)):
                keeps = [
                    1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
                    for s, e in zip(control_guidance_start, control_guidance_end)
                ]
                controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)

            # 5.7 Denoising loop
            num_warmup_steps = len(timesteps) - cur_num_inference_steps * self.scheduler.order

            def denoising_loop(latents, mask=None, xtrg=None, noise_rescale=None):
                dir_xt = 0
                latents_dtype = latents.dtype
                with self.progress_bar(total=cur_num_inference_steps) as progress_bar:
                    for i, t in enumerate(timesteps):
                        self.attn_state.set_timestep(t.item())
                        if i + skip_t >= mask_start_t and i + skip_t <= mask_end_t and xtrg is not None:
                            rescale = torch.maximum(1.0 - mask, (1 - mask**2) ** 0.5 * inner_strength)
                            if noise_rescale is not None:
                                rescale = (1.0 - mask) * (1 - noise_rescale) + rescale * noise_rescale
                            noise = randn_tensor(xtrg.shape, generator=generator, device=device, dtype=xtrg.dtype)
                            latents_ref = self.scheduler.add_noise(xtrg, noise, t)
                            latents = latents_ref * mask + (1.0 - mask) * (latents - dir_xt) + rescale * dir_xt
                            latents = latents.to(latents_dtype)

                        # expand the latents if we are doing classifier free guidance
                        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)

                        # controlnet(s) inference
                        if guess_mode and do_classifier_free_guidance:
                            # Infer ControlNet only for the conditional batch.
                            control_model_input = latents
                            control_model_input = self.scheduler.scale_model_input(control_model_input, t)
                            controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
                        else:
                            control_model_input = latent_model_input
                            controlnet_prompt_embeds = prompt_embeds

                        if isinstance(controlnet_keep[i], list):
                            cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
                        else:
                            controlnet_cond_scale = controlnet_conditioning_scale
                            if isinstance(controlnet_cond_scale, list):
                                controlnet_cond_scale = controlnet_cond_scale[0]
                            cond_scale = controlnet_cond_scale * controlnet_keep[i]
                        down_block_res_samples, mid_block_res_sample = self.controlnet(
                            control_model_input,
                            t,
                            encoder_hidden_states=controlnet_prompt_embeds,
                            controlnet_cond=control_image,
                            conditioning_scale=cond_scale,
                            guess_mode=guess_mode,
                            return_dict=False,
                        )

                        if guess_mode and do_classifier_free_guidance:
                            # Infered ControlNet only for the conditional batch.
                            # To apply the output of ControlNet to both the unconditional and conditional batches,
                            # add 0 to the unconditional batch to keep it unchanged.
                            down_block_res_samples = [
                                torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples
                            ]
                            mid_block_res_sample = torch.cat(
                                [torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
                            )

                        # predict the noise residual
                        noise_pred = self.unet(
                            latent_model_input,
                            t,
                            encoder_hidden_states=prompt_embeds,
                            cross_attention_kwargs=cross_attention_kwargs,
                            down_block_additional_residuals=down_block_res_samples,
                            mid_block_additional_residual=mid_block_res_sample,
                            return_dict=False,
                        )[0]

                        # perform guidance
                        if do_classifier_free_guidance:
                            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                        # Get pred_x0 from scheduler
                        alpha_prod_t = self.scheduler.alphas_cumprod[t]
                        beta_prod_t = 1 - alpha_prod_t
                        pred_x0 = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

                        if i + skip_t >= warp_start_t and i + skip_t <= warp_end_t:
                            # warp x_0
                            pred_x0 = (
                                flow_warp(first_x0_list[i], warp_flow, mode="nearest") * warp_mask
                                + (1 - warp_mask) * pred_x0
                            )

                            # get x_t from x_0
                            latents = self.scheduler.add_noise(pred_x0, noise_pred, t).to(latents_dtype)

                        prev_t = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                        if i == len(timesteps) - 1:
                            alpha_t_prev = 1.0
                        else:
                            alpha_t_prev = self.scheduler.alphas_cumprod[prev_t]

                        dir_xt = (1.0 - alpha_t_prev) ** 0.5 * noise_pred

                        # compute the previous noisy sample x_t -> x_t-1
                        latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[
                            0
                        ]

                        # call the callback, if provided
                        if i == len(timesteps) - 1 or (
                            (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
                        ):
                            progress_bar.update()
                            if callback is not None and i % callback_steps == 0:
                                callback(i, t, latents)

                    return latents

            if mask_start_t <= mask_end_t:
                self.attn_state.to_load()
            else:
                self.attn_state.to_load_and_store_prev()
            latents = denoising_loop(init_latents)

            if mask_start_t <= mask_end_t:
                direct_result = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]

                blend_results = (1 - blend_mask_pre) * warped_pre + blend_mask_pre * direct_result
                blend_results = (1 - blend_mask_0) * warped_0 + blend_mask_0 * blend_results

                bwd_occ = 1 - torch.clamp(1 - bwd_occ_pre + 1 - bwd_occ_0, 0, 1)
                blend_mask = blur(F.max_pool2d(bwd_occ, kernel_size=9, stride=1, padding=4))
                blend_mask = 1 - torch.clamp(blend_mask + bwd_occ, 0, 1)

                blend_results = blend_results.to(latents.dtype)
                xtrg = self.vae.encode(blend_results).latent_dist.sample(generator)
                xtrg = self.vae.config.scaling_factor * xtrg
                blend_results_rec = self.vae.decode(xtrg / self.vae.config.scaling_factor, return_dict=False)[0]
                xtrg_rec = self.vae.encode(blend_results_rec).latent_dist.sample(generator)
                xtrg_rec = self.vae.config.scaling_factor * xtrg_rec
                xtrg_ = xtrg + (xtrg - xtrg_rec)
                blend_results_rec_new = self.vae.decode(xtrg_ / self.vae.config.scaling_factor, return_dict=False)[0]
                tmp = (abs(blend_results_rec_new - blend_results).mean(dim=1, keepdims=True) > 0.25).float()

                mask_x = F.max_pool2d(
                    (F.interpolate(tmp, scale_factor=1 / 8.0, mode="bilinear") > 0).float(),
                    kernel_size=3,
                    stride=1,
                    padding=1,
                )

                mask = 1 - F.max_pool2d(1 - blend_mask, kernel_size=8)  # * (1-mask_x)

                if smooth_boundary:
                    noise_rescale = find_flat_region(mask)
                else:
                    noise_rescale = torch.ones_like(mask)

                xtrg = (xtrg + (1 - mask_x) * (xtrg - xtrg_rec)) * mask
                xtrg = xtrg.to(latents.dtype)

                self.scheduler.set_timesteps(num_inference_steps, device=device)
                timesteps, cur_num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)

                self.attn_state.to_load_and_store_prev()
                latents = denoising_loop(init_latents, mask * mask_strength, xtrg, noise_rescale)

            if not output_type == "latent":
                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
            else:
                image = latents

            prev_result = image

            do_denormalize = [True] * image.shape[0]
            image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)

            output_frames.append(image[0])

        # Offload last model to CPU
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return output_frames

        return TextToVideoSDPipelineOutput(frames=output_frames)