i2v_enhance/pipeline_i2vgen_xl.py

# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection

from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.models import AutoencoderKL
from diffusers.models.unets.unet_i2vgen_xl import I2VGenXLUNet
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import (
    BaseOutput,
    logging,
    replace_example_docstring,
)
from diffusers.utils.torch_utils import randn_tensor
from diffusers.video_processor import VideoProcessor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
import random


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

EXAMPLE_DOC_STRING = """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import I2VGenXLPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> pipeline = I2VGenXLPipeline.from_pretrained(
        ...     "ali-vilab/i2vgen-xl", torch_dtype=torch.float16, variant="fp16"
        ... )
        >>> pipeline.enable_model_cpu_offload()

        >>> image_url = (
        ...     "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/i2vgen_xl_images/img_0009.png"
        ... )
        >>> image = load_image(image_url).convert("RGB")

        >>> prompt = "Papers were floating in the air on a table in the library"
        >>> negative_prompt = "Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms"
        >>> generator = torch.manual_seed(8888)

        >>> frames = pipeline(
        ...     prompt=prompt,
        ...     image=image,
        ...     num_inference_steps=50,
        ...     negative_prompt=negative_prompt,
        ...     guidance_scale=9.0,
        ...     generator=generator,
        ... ).frames[0]
        >>> video_path = export_to_gif(frames, "i2v.gif")
        ```
"""


@dataclass
class I2VGenXLPipelineOutput(BaseOutput):
    r"""
     Output class for image-to-video pipeline.

     Args:
         frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
             List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
             denoised
     PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
    `(batch_size, num_frames, channels, height, width)`
    """

    frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]

# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents


def retrieve_latents(
    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
        return encoder_output.latent_dist.sample(generator)
    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
        return encoder_output.latent_dist.mode()
    elif hasattr(encoder_output, "latents"):
        return encoder_output.latents
    else:
        raise AttributeError(
            "Could not access latents of provided encoder_output")


class I2VGenXLPipeline(
    DiffusionPipeline,
    StableDiffusionMixin,
):
    r"""
    Pipeline for image-to-video generation as proposed in [I2VGenXL](https://i2vgen-xl.github.io/).

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, 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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
        tokenizer (`CLIPTokenizer`):
            A [`~transformers.CLIPTokenizer`] to tokenize text.
        unet ([`I2VGenXLUNet`]):
            A [`I2VGenXLUNet`] to denoise the encoded video latents.
        scheduler ([`DDIMScheduler`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
    """

    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        image_encoder: CLIPVisionModelWithProjection,
        feature_extractor: CLIPImageProcessor,
        unet: I2VGenXLUNet,
        scheduler: DDIMScheduler,
    ):
        super().__init__()

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            image_encoder=image_encoder,
            feature_extractor=feature_extractor,
            unet=unet,
            scheduler=scheduler,
        )
        self.vae_scale_factor = 2 ** (
            len(self.vae.config.block_out_channels) - 1)
        # `do_resize=False` as we do custom resizing.
        self.video_processor = VideoProcessor(
            vae_scale_factor=self.vae_scale_factor, do_resize=False)

    @property
    def guidance_scale(self):
        return self._guidance_scale

    # 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.
    @property
    def do_classifier_free_guidance(self):
        return self._guidance_scale > 1

    def encode_prompt(
        self,
        prompt,
        device,
        num_videos_per_prompt,
        negative_prompt=None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        clip_skip: Optional[int] = 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
                less than `1`).
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.
        """
        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

            if clip_skip is None:
                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device), attention_mask=attention_mask)
                prompt_embeds = prompt_embeds[0]
            else:
                prompt_embeds = self.text_encoder(
                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                prompt_embeds = self.text_encoder.text_model.final_layer_norm(
                    prompt_embeds)

        if self.text_encoder is not None:
            prompt_embeds_dtype = self.text_encoder.dtype
        elif self.unet is not None:
            prompt_embeds_dtype = self.unet.dtype
        else:
            prompt_embeds_dtype = prompt_embeds.dtype

        prompt_embeds = prompt_embeds.to(
            dtype=prompt_embeds_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 self.do_classifier_free_guidance and negative_prompt_embeds is None:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif 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

            # Apply clip_skip to negative prompt embeds
            if clip_skip is None:
                negative_prompt_embeds = self.text_encoder(
                    uncond_input.input_ids.to(device),
                    attention_mask=attention_mask,
                )
                negative_prompt_embeds = negative_prompt_embeds[0]
            else:
                negative_prompt_embeds = self.text_encoder(
                    uncond_input.input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
                )
                # Access the `hidden_states` first, that contains a tuple of
                # all the hidden states from the encoder layers. Then index into
                # the tuple to access the hidden states from the desired layer.
                negative_prompt_embeds = negative_prompt_embeds[-1][-(
                    clip_skip + 1)]
                # We also need to apply the final LayerNorm here to not mess with the
                # representations. The `last_hidden_states` that we typically use for
                # obtaining the final prompt representations passes through the LayerNorm
                # layer.
                negative_prompt_embeds = self.text_encoder.text_model.final_layer_norm(
                    negative_prompt_embeds)

        if self.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=prompt_embeds_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)

        return prompt_embeds, negative_prompt_embeds

    def _encode_image(self, image, device, num_videos_per_prompt):
        dtype = next(self.image_encoder.parameters()).dtype

        if not isinstance(image, torch.Tensor):
            image = self.video_processor.pil_to_numpy(image)
            image = self.video_processor.numpy_to_pt(image)

            # Normalize the image with CLIP training stats.
            image = self.feature_extractor(
                images=image,
                do_normalize=True,
                do_center_crop=False,
                do_resize=False,
                do_rescale=False,
                return_tensors="pt",
            ).pixel_values

        image = image.to(device=device, dtype=dtype)
        image_embeddings = self.image_encoder(image).image_embeds
        image_embeddings = image_embeddings.unsqueeze(1)

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

        if self.do_classifier_free_guidance:
            negative_image_embeddings = torch.zeros_like(image_embeddings)
            image_embeddings = torch.cat(
                [negative_image_embeddings, image_embeddings])

        return image_embeddings

    def decode_latents(self, latents, decode_chunk_size=None):
        latents = 1 / self.vae.config.scaling_factor * latents

        batch_size, channels, num_frames, height, width = latents.shape
        latents = latents.permute(0, 2, 1, 3, 4).reshape(
            batch_size * num_frames, channels, height, width)

        if decode_chunk_size is not None:
            frames = []
            for i in range(0, latents.shape[0], decode_chunk_size):
                frame = self.vae.decode(
                    latents[i: i + decode_chunk_size]).sample
                frames.append(frame)
            image = torch.cat(frames, dim=0)
        else:
            image = self.vae.decode(latents).sample

        decode_shape = (batch_size, num_frames, -1) + image.shape[2:]
        video = image[None, :].reshape(decode_shape).permute(0, 2, 1, 3, 4)

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        video = video.float()
        return video

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta):
        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]

        accepts_eta = "eta" in set(inspect.signature(
            self.scheduler.step).parameters.keys())
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(
            inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = generator
        return extra_step_kwargs

    def check_inputs(
        self,
        prompt,
        image,
        height,
        width,
        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 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}."
                )

        if (
            not isinstance(image, torch.Tensor)
            and not isinstance(image, PIL.Image.Image)
            and not isinstance(image, list)
        ):
            raise ValueError(
                "`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
                f" {type(image)}"
            )

    def prepare_image_latents(
        self,
        image,
        device,
        num_frames,
        num_videos_per_prompt,
    ):
        image = image.to(device=device)
        image_latents = self.vae.encode(image).latent_dist.sample()
        image_latents = image_latents * self.vae.config.scaling_factor

        # Add frames dimension to image latents
        image_latents = image_latents.unsqueeze(2)

        # Append a position mask for each subsequent frame
        # after the intial image latent frame
        frame_position_mask = []
        for frame_idx in range(num_frames - 1):
            scale = (frame_idx + 1) / (num_frames - 1)
            frame_position_mask.append(
                torch.ones_like(image_latents[:, :, :1]) * scale)
        if frame_position_mask:
            frame_position_mask = torch.cat(frame_position_mask, dim=2)
            image_latents = torch.cat(
                [image_latents, frame_position_mask], dim=2)

        # duplicate image_latents for each generation per prompt, using mps friendly method
        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1, 1)

        if self.do_classifier_free_guidance:
            image_latents = torch.cat([image_latents] * 2)

        return image_latents

    # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
    def prepare_latents(
        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
    ):
        shape = (
            batch_size,
            num_channels_latents,
            num_frames,
            height // self.vae_scale_factor,
            width // self.vae_scale_factor,
        )
        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 latents is None:
            latents = randn_tensor(
                shape, generator=generator, device=device, dtype=dtype)
        else:
            latents = latents.to(device)

        # scale the initial noise by the standard deviation required by the scheduler
        latents = latents * self.scheduler.init_noise_sigma
        return latents

    # 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:]
        if hasattr(self.scheduler, "set_begin_index"):
            self.scheduler.set_begin_index(t_start * self.scheduler.order)

        return timesteps, num_inference_steps - t_start

    # Similar to image, we need to prepare the latents for the video.
    def prepare_video_latents(
        self, video, timestep, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
    ):
        video = video.to(device=device, dtype=dtype)
        is_long = video.shape[2] > 16

        # change from (b, c, f, h, w) -> (b * f, c, w, h)
        bsz, channel, frames, width, height = video.shape
        video = video.permute(0, 2, 1, 3, 4).reshape(
            bsz * frames, channel, width, height)

        if video.shape[1] == 4:
            init_latents = video
        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 = [
                    retrieve_latents(self.vae.encode(
                        video[i: i + 1]), generator=generator[i])
                    for i in range(batch_size)
                ]
                init_latents = torch.cat(init_latents, dim=0)
            else:
                if not is_long:
                    # 1 step encoding
                    init_latents = retrieve_latents(
                        self.vae.encode(video), generator=generator)
                else:
                    # chunk by chunk encoding. for low-memory consumption.
                    video_list = torch.chunk(
                        video, video.shape[0] // 16, dim=0)
                    with torch.no_grad():
                        init_latents = []
                        for video_chunk in video_list:
                            video_chunk = retrieve_latents(
                                self.vae.encode(video_chunk), generator=generator)
                            init_latents.append(video_chunk)
                        init_latents = torch.cat(init_latents, dim=0)
                    # torch.cuda.empty_cache()

            init_latents = self.vae.config.scaling_factor * init_latents

        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `video` 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)

        latents = self.scheduler.add_noise(init_latents, noise, timestep)
        latents = latents[None, :].reshape(
            (bsz, frames, latents.shape[1]) + latents.shape[2:]).permute(0, 2, 1, 3, 4)

        return latents

    @torch.no_grad()
    @replace_example_docstring(EXAMPLE_DOC_STRING)
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        # Now image can be either a single image or a list of images (when randomized blending is enalbled).
        image: Union[List[PipelineImageInput], PipelineImageInput] = None,
        video: Union[List[np.ndarray], torch.Tensor] = None,
        strength: float = 0.97,
        overlap_size: int = 0,
        chunk_size: int = 38,
        height: Optional[int] = 720,
        width: Optional[int] = 1280,
        target_fps: Optional[int] = 38,
        num_frames: int = 38,
        num_inference_steps: int = 50,
        guidance_scale: float = 9.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        eta: float = 0.0,
        num_videos_per_prompt: Optional[int] = 1,
        decode_chunk_size: Optional[int] = 1,
        generator: Optional[Union[torch.Generator,
                                  List[torch.Generator]]] = None,
        latents: Optional[torch.Tensor] = None,
        prompt_embeds: Optional[torch.Tensor] = None,
        negative_prompt_embeds: Optional[torch.Tensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        clip_skip: Optional[int] = 1,
    ):
        r"""
        The call function to the pipeline for image-to-video generation with [`I2VGenXLPipeline`].

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
                Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
                [`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
            video (`List[np.ndarray]` or `torch.Tensor`):
                Video to guide video enhancement.
            strength (`float`, *optional*, defaults to 0.97):
                Indicates extent to transform the reference `video`. Must be between 0 and 1. `image` is used as a
                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
                essentially ignores `image`.
            overlap_size (`int`, *optional*, defaults to 0):
                This parameter is used in randomized blending, when it is enabled.
                It defines the size of overlap between neighbouring chunks. 
            chunk_size (`int`, *optional*, defaults to 38):
                This parameter is used in randomized blending, when it is enabled.
                It defines the number of frames we will enhance during each chunk of randomized blending.
            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.
            target_fps (`int`, *optional*):
                Frames per second. The rate at which the generated images shall be exported to a video after
                generation. This is also used as a "micro-condition" while generation.
            num_frames (`int`, *optional*):
                The number of video frames to generate.
            num_inference_steps (`int`, *optional*):
                The number of denoising steps.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                A higher guidance scale value encourages the model to generate images closely linked to the text
                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
            eta (`float`, *optional*):
                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
            num_videos_per_prompt (`int`, *optional*):
                The number of images to generate per prompt.
            decode_chunk_size (`int`, *optional*):
                The number of frames to decode at a time. The higher the chunk size, the higher the temporal
                consistency between frames, but also the higher the memory consumption. By default, the decoder will
                decode all frames at once for maximal quality. Reduce `decode_chunk_size` to reduce memory usage.
            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            latents (`torch.Tensor`, *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 is generated by sampling using the supplied random `generator`.
            prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
                provided, text embeddings are generated from the `prompt` input argument.
            negative_prompt_embeds (`torch.Tensor`, *optional*):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.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.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            clip_skip (`int`, *optional*):
                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
                the output of the pre-final layer will be used for computing the prompt embeddings.

        Examples:

        Returns:
            [`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] is
                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
        """
        # 0. Default height and width to unet
        height = height or self.unet.config.sample_size * self.vae_scale_factor
        width = width or self.unet.config.sample_size * self.vae_scale_factor

        # 1. Check inputs. Raise error if not correct
        self.check_inputs(prompt, image, height, width,
                          negative_prompt, prompt_embeds, negative_prompt_embeds)

        # 2. Define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        self._guidance_scale = guidance_scale

        # 3.1 Encode input text prompt
        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
            prompt,
            device,
            num_videos_per_prompt,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            clip_skip=clip_skip,
        )
        # 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
        if self.do_classifier_free_guidance:
            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])

        # 3.2 Encode image prompt
        # 3.2.1 Image encodings.
        # https://github.com/ali-vilab/i2vgen-xl/blob/2539c9262ff8a2a22fa9daecbfd13f0a2dbc32d0/tools/inferences/inference_i2vgen_entrance.py#L114
        # As now we can have a list of images (when randomized blending), we encode each image separately as before.
        image_embeddings_list = []
        for img in image:
            cropped_image = _center_crop_wide(img, (width, width))
            cropped_image = _resize_bilinear(
                cropped_image, (self.feature_extractor.crop_size["width"],
                                self.feature_extractor.crop_size["height"])
            )
            image_embeddings = self._encode_image(
                cropped_image, device, num_videos_per_prompt)
            image_embeddings_list.append(image_embeddings)

        # 3.2.2 Image latents.
        # As now we can have a list of images (when randomized blending), we encode each image separately as before.
        image_latents_list = []
        for img in image:
            resized_image = _center_crop_wide(img, (width, height))
            img = self.video_processor.preprocess(resized_image).to(
                device=device, dtype=image_embeddings_list[0].dtype)
            image_latents = self.prepare_image_latents(
                img,
                device=device,
                num_frames=num_frames,
                num_videos_per_prompt=num_videos_per_prompt,
            )
            image_latents_list.append(image_latents)

        # 3.3 Prepare additional conditions for the UNet.
        if self.do_classifier_free_guidance:
            fps_tensor = torch.tensor([target_fps, target_fps]).to(device)
        else:
            fps_tensor = torch.tensor([target_fps]).to(device)
        fps_tensor = fps_tensor.repeat(
            batch_size * num_videos_per_prompt, 1).ravel()

        # 3.4 Preprocess video, similar to images.
        video = self.video_processor.preprocess_video(video).to(
            device=device, dtype=image_embeddings_list[0].dtype)
        num_images_per_prompt = 1

        # 4. Prepare timesteps. This will be used for modified SDEdit approach.
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(
            num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(
            batch_size * num_images_per_prompt)

        # 5. Prepare latent variables. Now we get latents for input video.
        num_channels_latents = self.unet.config.in_channels
        latents = self.prepare_video_latents(
            video,
            latent_timestep,
            batch_size * num_videos_per_prompt,
            num_channels_latents,
            num_frames,
            height,
            width,
            prompt_embeds.dtype,
            device,
            generator,
            latents,
        )

        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 7. Denoising loop
        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):
                latents_denoised = torch.empty_like(latents)

                CHUNK_START = 0
                # Each chunk must have a corresponding 1st frame
                for idx in range(len(image_latents_list)):
                    latents_chunk = latents[:, :,
                                            CHUNK_START:CHUNK_START + chunk_size]

                    # expand the latents if we are doing classifier free guidance
                    latent_model_input = torch.cat(
                        [latents_chunk] * 2) if self.do_classifier_free_guidance else latents_chunk
                    latent_model_input = self.scheduler.scale_model_input(
                        latent_model_input, t)

                    # predict the noise residual
                    noise_pred = self.unet(
                        latent_model_input,
                        t,
                        encoder_hidden_states=prompt_embeds,
                        fps=fps_tensor,
                        image_latents=image_latents_list[idx],
                        image_embeddings=image_embeddings_list[idx],
                        cross_attention_kwargs=cross_attention_kwargs,
                        return_dict=False,
                    )[0]

                    # perform guidance
                    if self.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)

                    # reshape latents_chunk
                    batch_size, channel, frames, width, height = latents_chunk.shape
                    latents_chunk = latents_chunk.permute(0, 2, 1, 3, 4).reshape(
                        batch_size * frames, channel, width, height)
                    noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(
                        batch_size * frames, channel, width, height)

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

                    # reshape latents back
                    latents_chunk = latents_chunk[None, :].reshape(
                        batch_size, frames, channel, width, height).permute(0, 2, 1, 3, 4)

                    # Make sure random_offset is set correctly.
                    if CHUNK_START == 0:
                        random_offset = 0
                    else:
                        if overlap_size != 0:
                            random_offset = random.randint(0, overlap_size - 1)
                        else:
                            random_offset = 0

                    # Apply Randomized Blending.
                    latents_denoised[:, :, CHUNK_START + random_offset:CHUNK_START +
                                     chunk_size] = latents_chunk[:, :, random_offset:]
                    CHUNK_START += chunk_size - overlap_size

                latents = latents_denoised

                if CHUNK_START + overlap_size > latents_denoised.shape[2]:
                    raise NotImplementedError(f"Video of size={latents_denoised.shape[2]} is not dividable into chunks "
                                              f"with size={chunk_size} and overlap={overlap_size}")

                # 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()

        # 8. Post processing
        if output_type == "latent":
            video = latents
        else:
            video_tensor = self.decode_latents(
                latents, decode_chunk_size=decode_chunk_size)
            video = self.video_processor.postprocess_video(
                video=video_tensor, output_type=output_type)

        # 9. Offload all models
        self.maybe_free_model_hooks()

        if not return_dict:
            return (video,)

        return I2VGenXLPipelineOutput(frames=video)


# The following utilities are taken and adapted from
# https://github.com/ali-vilab/i2vgen-xl/blob/main/utils/transforms.py.


def _convert_pt_to_pil(image: Union[torch.Tensor, List[torch.Tensor]]):
    if isinstance(image, list) and isinstance(image[0], torch.Tensor):
        image = torch.cat(image, 0)

    if isinstance(image, torch.Tensor):
        if image.ndim == 3:
            image = image.unsqueeze(0)

        image_numpy = VaeImageProcessor.pt_to_numpy(image)
        image_pil = VaeImageProcessor.numpy_to_pil(image_numpy)
        image = image_pil

    return image


def _resize_bilinear(
    image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]], resolution: Tuple[int, int]
):
    # First convert the images to PIL in case they are float tensors (only relevant for tests now).
    image = _convert_pt_to_pil(image)

    if isinstance(image, list):
        image = [u.resize(resolution, PIL.Image.BILINEAR) for u in image]
    else:
        image = image.resize(resolution, PIL.Image.BILINEAR)
    return image


def _center_crop_wide(
    image: Union[torch.Tensor, List[torch.Tensor], PIL.Image.Image, List[PIL.Image.Image]], resolution: Tuple[int, int]
):
    # First convert the images to PIL in case they are float tensors (only relevant for tests now).
    image = _convert_pt_to_pil(image)

    if isinstance(image, list):
        scale = min(image[0].size[0] / resolution[0],
                    image[0].size[1] / resolution[1])
        image = [u.resize((round(u.width // scale), round(u.height //
                          scale)), resample=PIL.Image.BOX) for u in image]

        # center crop
        x1 = (image[0].width - resolution[0]) // 2
        y1 = (image[0].height - resolution[1]) // 2
        image = [u.crop((x1, y1, x1 + resolution[0], y1 + resolution[1]))
                 for u in image]
        return image
    else:
        scale = min(image.size[0] / resolution[0],
                    image.size[1] / resolution[1])
        image = image.resize((round(image.width // scale),
                             round(image.height // scale)), resample=PIL.Image.BOX)
        x1 = (image.width - resolution[0]) // 2
        y1 = (image.height - resolution[1]) // 2
        image = image.crop((x1, y1, x1 + resolution[0], y1 + resolution[1]))
        return image