models/pipeline_flatten.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 inspect
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from dataclasses import dataclass

import numpy as np
import PIL.Image
import torch
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL
from diffusers import ModelMixin
from diffusers.schedulers import DDIMScheduler, DDIMInverseScheduler
from diffusers.utils import (
    PIL_INTERPOLATION,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    BaseOutput
)
from diffusers.pipeline_utils import DiffusionPipeline
from einops import rearrange
from .unet import UNet3DConditionModel

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


@dataclass
class FlattenPipelineOutput(BaseOutput):
    videos: Union[torch.Tensor, np.ndarray]

class FlattenPipeline(DiffusionPipeline):
    r"""
    pipeline for FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing.

    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 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 ([`UNet3DConditionModel`]): Conditional U-Net architecture to denoise the encoded video latents.
        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`].
        inverse_scheduler ([`SchedulerMixin`]):
            DDIM inversion scheduler .
    """
    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet3DConditionModel,
        scheduler: DDIMScheduler,
        inverse_scheduler: DDIMInverseScheduler
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            inverse_scheduler=inverse_scheduler
        )
        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
    def enable_vae_slicing(self):
        r"""
        Enable sliced VAE decoding.

        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
        steps. This is useful to save some memory and allow larger batch sizes.
        """
        self.vae.enable_slicing()

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
    def disable_vae_slicing(self):
        r"""
        Disable sliced VAE decoding. If `enable_vae_slicing` was previously invoked, this method will go back to
        computing decoding in one step.
        """
        self.vae.disable_slicing()

    def enable_sequential_cpu_offload(self, gpu_id=0):
        r"""
        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
        text_encoder, vae, and safety checker have their state dicts saved to CPU and then are moved to a
        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
        Note that offloading happens on a submodule basis. Memory savings are higher than with
        `enable_model_cpu_offload`, but performance is lower.
        """
        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError("Please install accelerate via `pip install accelerate`")

        device = torch.device(f"cuda:{gpu_id}")

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
            cpu_offload(cpu_offloaded_model, device)

        if self.safety_checker is not None:
            cpu_offload(self.safety_checker, execution_device=device, offload_buffers=True)

    def enable_model_cpu_offload(self, gpu_id=0):
        r"""
        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
        """
        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

        device = torch.device(f"cuda:{gpu_id}")

        hook = None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.vae]:
            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)

        if self.safety_checker is not None:
            # the safety checker can offload the vae again
            _, hook = cpu_offload_with_hook(self.safety_checker, device, prev_module_hook=hook)

        # We'll offload the last model manually.
        self.final_offload_hook = hook

    @property
    def _execution_device(self):
        r"""
        Returns the device on which the pipeline's models will be executed. After calling
        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
        hooks.
        """
        if not hasattr(self.unet, "_hf_hook"):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(module, "_hf_hook")
                and hasattr(module._hf_hook, "execution_device")
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device)
        return self.device

    def _encode_prompt(
        self,
        prompt,
        device,
        num_videos_per_prompt,
        do_classifier_free_guidance,
        negative_prompt=None,
        prompt_embeds: Optional[torch.FloatTensor] = None,
        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
             prompt (`str` or `List[str]`, *optional*):
                prompt to be encoded
            device: (`torch.device`):
                torch device
            num_videos_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            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. 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`).
            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.
        """
        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]

        if prompt_embeds is None:
            text_inputs = self.tokenizer(
                prompt,
                padding="max_length",
                max_length=self.tokenizer.model_max_length,
                truncation=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[:, self.tokenizer.model_max_length - 1 : -1]
                )
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
                )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = text_inputs.attention_mask.to(device)
            else:
                attention_mask = None

            prompt_embeds = self.text_encoder(
                text_input_ids.to(device),
                attention_mask=attention_mask,
            )
            prompt_embeds = prompt_embeds[0]

        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

        bs_embed, seq_len, _ = prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
        prompt_embeds = prompt_embeds.view(bs_embed * num_videos_per_prompt, seq_len, -1)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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 isinstance(negative_prompt, str):
                uncond_tokens = [negative_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`."
                )
            else:
                uncond_tokens = negative_prompt

            max_length = prompt_embeds.shape[1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="pt",
            )

            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
                attention_mask = uncond_input.attention_mask.to(device)
            else:
                attention_mask = None

            negative_prompt_embeds = self.text_encoder(
                uncond_input.input_ids.to(device),
                attention_mask=attention_mask,
            )
            negative_prompt_embeds = negative_prompt_embeds[0]

        if do_classifier_free_guidance:
            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = negative_prompt_embeds.shape[1]

            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)

            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_videos_per_prompt, 1)
            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        return prompt_embeds

    def decode_latents(self, latents, return_tensor=False):
        video_length = latents.shape[2]
        latents = 1 / 0.18215 * latents
        latents = rearrange(latents, "b c f h w -> (b f) c h w")
        video = self.vae.decode(latents).sample
        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
        video = (video / 2 + 0.5).clamp(0, 1)
        if return_tensor:
            return video
        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
        video = video.cpu().float().numpy()
        return video

    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

    def check_inputs(
        self,
        prompt,
        # image,
        height,
        width,
        callback_steps,
        negative_prompt=None,
        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_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}."
                )


    def check_image(self, image, prompt, prompt_embeds):
        image_is_pil = isinstance(image, PIL.Image.Image)
        image_is_tensor = isinstance(image, torch.Tensor)
        image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
        image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)

        if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list:
            raise TypeError(
                "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors"
            )

        if image_is_pil:
            image_batch_size = 1
        elif image_is_tensor:
            image_batch_size = image.shape[0]
        elif image_is_pil_list:
            image_batch_size = len(image)
        elif image_is_tensor_list:
            image_batch_size = len(image)

        if prompt is not None and isinstance(prompt, str):
            prompt_batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            prompt_batch_size = len(prompt)
        elif prompt_embeds is not None:
            prompt_batch_size = prompt_embeds.shape[0]

        if image_batch_size != 1 and image_batch_size != prompt_batch_size:
            raise ValueError(
                f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
            )

    def prepare_image(
        self, image, width, height, batch_size, num_videos_per_prompt, device, dtype, do_classifier_free_guidance
    ):
        if not isinstance(image, torch.Tensor):
            if isinstance(image, PIL.Image.Image):
                image = [image]

            if isinstance(image[0], PIL.Image.Image):
                images = []

                for image_ in image:
                    image_ = image_.convert("RGB")
                    image_ = image_.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
                    image_ = np.array(image_)
                    image_ = image_[None, :]
                    images.append(image_)

                image = images

                image = np.concatenate(image, axis=0)
                image = np.array(image).astype(np.float32) / 255.0
                image = image.transpose(0, 3, 1, 2)
                image = 2.0 * image - 1.0
                image = torch.from_numpy(image)
            elif isinstance(image[0], torch.Tensor):
                image = torch.cat(image, dim=0)

        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_videos_per_prompt

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

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

        return image

    def prepare_video_latents(self, frames, batch_size, dtype, device, generator=None):
        if not isinstance(frames, (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)}"
            )

        frames = frames[0].to(device=device, dtype=dtype)
        frames = rearrange(frames, "c f h w -> f c h w" )

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

        latents = self.vae.config.scaling_factor * latents

        latents = rearrange(latents, "f c h w ->c f h w" )

        return latents[None]

    def _default_height_width(self, height, width, image):
        # NOTE: It is possible that a list of images have different
        # dimensions for each image, so just checking the first image
        # is not _exactly_ correct, but it is simple.
        while isinstance(image, list):
            image = image[0]

        if height is None:
            if isinstance(image, PIL.Image.Image):
                height = image.height
            elif isinstance(image, torch.Tensor):
                height = image.shape[3]

            height = (height // 8) * 8  # round down to nearest multiple of 8

        if width is None:
            if isinstance(image, PIL.Image.Image):
                width = image.width
            elif isinstance(image, torch.Tensor):
                width = image.shape[2]

            width = (width // 8) * 8  # round down to nearest multiple of 8

        return height, width

    def get_alpha_prev(self, timestep):
        prev_timestep = timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
        alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
        return alpha_prod_t_prev

    def get_slide_window_indices(self, video_length, window_size):
        assert window_size >=3
        key_frame_indices = np.arange(0, video_length, window_size-1).tolist()

        # Append last index
        if key_frame_indices[-1] != (video_length-1):
            key_frame_indices.append(video_length-1)

        slices = np.split(np.arange(video_length), key_frame_indices)
        inter_frame_list = []
        for s in slices:
            if len(s) < 2:
                continue
            inter_frame_list.append(s[1:].tolist())
        return key_frame_indices, inter_frame_list

    def get_inverse_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)

        # safety for t_start overflow to prevent empty timsteps slice
        if t_start == 0:
            return self.inverse_scheduler.timesteps, num_inference_steps
        timesteps = self.inverse_scheduler.timesteps[:-t_start]

        return timesteps, num_inference_steps - t_start

    def clean_features(self):
        self.unet.up_blocks[1].resnets[0].out_layers_inject_features = None
        self.unet.up_blocks[1].resnets[1].out_layers_inject_features = None
        self.unet.up_blocks[2].resnets[0].out_layers_inject_features = None
        self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.inject_k = None
        self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.inject_k = None
        self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.inject_k = None
        self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.inject_k = None
        self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.inject_k = None
        self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.inject_q = None
        self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.inject_k = None

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        video_length: Optional[int] = 1,
        frames: Union[List[torch.FloatTensor], List[PIL.Image.Image], List[List[torch.FloatTensor]], List[List[PIL.Image.Image]]] = None,
        height: Optional[int] = None,
        width: Optional[int] = None,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_videos_per_prompt: Optional[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: Optional[str] = "tensor",
        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,
        **kwargs,
    ):
        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[torch.FloatTensor]`, `List[PIL.Image.Image]`,
                    `List[List[torch.FloatTensor]]`, or `List[List[PIL.Image.Image]]`):
                The original video frames to be edited.
            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                The width in pixels of the generated image.
            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. 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`).
            num_videos_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
        """
        height, width = self._default_height_width(height, width, frames)

        self.check_inputs(
            prompt,
            height,
            width,
            callback_steps,
            negative_prompt,
            prompt_embeds,
            negative_prompt_embeds,
        )

        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

        # encode empty prompt
        prompt_embeds = self._encode_prompt(
            "",
            device,
            num_videos_per_prompt,
            do_classifier_free_guidance=do_classifier_free_guidance,
            negative_prompt=None,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        images = []
        for i_img in frames:
            i_img = self.prepare_image(
                image=i_img,
                width=width,
                height=height,
                batch_size=batch_size * num_videos_per_prompt,
                num_videos_per_prompt=num_videos_per_prompt,
                device=device,
                dtype=self.unet.dtype,
                do_classifier_free_guidance=do_classifier_free_guidance,
            )
            images.append(i_img)
        frames = torch.stack(images, dim=2)  # b x c x f x h x w

        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        latents = self.prepare_video_latents(frames, batch_size, self.unet.dtype, device, generator=generator)

        saved_features0 = []
        saved_features1 = []
        saved_features2 = []
        saved_q4 = []
        saved_k4 = []
        saved_q5 = []
        saved_k5 = []
        saved_q6 = []
        saved_k6 = []
        saved_q7 = []
        saved_k7 = []
        saved_q8 = []
        saved_k8 = []
        saved_q9 = []
        saved_k9 = []

        # ddim inverse
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        num_inverse_steps = 100
        self.inverse_scheduler.set_timesteps(num_inverse_steps, device=device)
        inverse_timesteps, num_inverse_steps = self.get_inverse_timesteps(num_inverse_steps, 1, device)
        num_warmup_steps = len(inverse_timesteps) - num_inverse_steps * self.inverse_scheduler.order

        with self.progress_bar(total=num_inverse_steps-1) as progress_bar:
            for i, t in enumerate(inverse_timesteps[1:]):
                # 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.inverse_scheduler.scale_model_input(latent_model_input, t)

                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    **kwargs,
                ).sample

                if t in timesteps:
                    saved_features0.append(self.unet.up_blocks[1].resnets[0].out_layers_features.cpu())
                    saved_features1.append(self.unet.up_blocks[1].resnets[1].out_layers_features.cpu())
                    saved_features2.append(self.unet.up_blocks[2].resnets[0].out_layers_features.cpu())
                    saved_q4.append(self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.q.cpu())
                    saved_k4.append(self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.k.cpu())
                    saved_q5.append(self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.q.cpu())
                    saved_k5.append(self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.k.cpu())
                    saved_q6.append(self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.q.cpu())
                    saved_k6.append(self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.k.cpu())
                    saved_q7.append(self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.q.cpu())
                    saved_k7.append(self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.k.cpu())
                    saved_q8.append(self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.q.cpu())
                    saved_k8.append(self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.k.cpu())
                    saved_q9.append(self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.q.cpu())
                    saved_k9.append(self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.k.cpu())


                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + 1 * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.inverse_scheduler.step(noise_pred, t, latents).prev_sample
                if i == len(inverse_timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.inverse_scheduler.order == 0):
                    progress_bar.update()

        saved_features0.reverse()
        saved_features1.reverse()
        saved_features2.reverse()
        saved_q4.reverse()
        saved_k4.reverse()
        saved_q5.reverse()
        saved_k5.reverse()
        saved_q6.reverse()
        saved_k6.reverse()
        saved_q7.reverse()
        saved_k7.reverse()
        saved_q8.reverse()
        saved_k8.reverse()
        saved_q9.reverse()
        saved_k9.reverse()

        # video sampling
        prompt_embeds = self._encode_prompt(
            prompt,
            device,
            num_videos_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=None,
            negative_prompt_embeds=negative_prompt_embeds,
        )

        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                torch.cuda.empty_cache()

                # 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)

                # inject features
                if i < kwargs["inject_step"]:
                    self.unet.up_blocks[1].resnets[0].out_layers_inject_features = saved_features0[i].to(device)
                    self.unet.up_blocks[1].resnets[1].out_layers_inject_features = saved_features1[i].to(device)
                    self.unet.up_blocks[2].resnets[0].out_layers_inject_features = saved_features2[i].to(device)
                    self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.inject_q = saved_q4[i].to(device)
                    self.unet.up_blocks[1].attentions[1].transformer_blocks[0].attn1.inject_k = saved_k4[i].to(device)
                    self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.inject_q = saved_q5[i].to(device)
                    self.unet.up_blocks[1].attentions[2].transformer_blocks[0].attn1.inject_k = saved_k5[i].to(device)
                    self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.inject_q = saved_q6[i].to(device)
                    self.unet.up_blocks[2].attentions[0].transformer_blocks[0].attn1.inject_k = saved_k6[i].to(device)
                    self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.inject_q = saved_q7[i].to(device)
                    self.unet.up_blocks[2].attentions[1].transformer_blocks[0].attn1.inject_k = saved_k7[i].to(device)
                    self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.inject_q = saved_q8[i].to(device)
                    self.unet.up_blocks[2].attentions[2].transformer_blocks[0].attn1.inject_k = saved_k8[i].to(device)
                    self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.inject_q = saved_q9[i].to(device)
                    self.unet.up_blocks[3].attentions[0].transformer_blocks[0].attn1.inject_k = saved_k9[i].to(device)
                else:
                    self.clean_features()

                noise_pred = self.unet(
                    latent_model_input,
                    t,
                    encoder_hidden_states=prompt_embeds,
                    cross_attention_kwargs=cross_attention_kwargs,
                    **kwargs,
                ).sample

                self.clean_features()

                # 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)

                # compute the previous noisy sample x_t -> x_t-1
                step_dict = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
                latents = step_dict.prev_sample

                # 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 we do sequential model offloading, let's offload unet
        # manually for max memory savings
        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.unet.to("cpu")
            torch.cuda.empty_cache()
        # Post-processing
        video = self.decode_latents(latents)

        # Convert to tensor
        if output_type == "tensor":
            video = torch.from_numpy(video)

        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
            self.final_offload_hook.offload()

        if not return_dict:
            return video

        return FlattenPipelineOutput(videos=video)