animatediff/pipelines/i2v_pipeline.py

# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
import inspect
import os.path as osp
from dataclasses import dataclass
from typing import Callable, List, Optional, Union

import numpy as np
import torch
from diffusers.configuration_utils import FrozenDict
from diffusers.loaders import IPAdapterMixin
from diffusers.models import AutoencoderKL
from diffusers.pipelines import DiffusionPipeline
from diffusers.schedulers import (DDIMScheduler, DPMSolverMultistepScheduler,
                                  EulerAncestralDiscreteScheduler,
                                  EulerDiscreteScheduler, LMSDiscreteScheduler,
                                  PNDMScheduler)
from diffusers.utils import (BaseOutput, deprecate, is_accelerate_available,
                             logging)
from diffusers.utils.import_utils import is_xformers_available
from einops import rearrange
from omegaconf import OmegaConf
from packaging import version
from safetensors import safe_open
from tqdm import tqdm
from transformers import (CLIPImageProcessor, CLIPTextModel, CLIPTokenizer,
                          CLIPVisionModelWithProjection)

from animatediff.models.resnet import InflatedConv3d
from animatediff.models.unet import UNet3DConditionModel
from animatediff.utils.convert_from_ckpt import (convert_ldm_clip_checkpoint,
                                                 convert_ldm_unet_checkpoint,
                                                 convert_ldm_vae_checkpoint)
from animatediff.utils.convert_lora_safetensor_to_diffusers import \
    convert_lora_model_level
from animatediff.utils.util import prepare_mask_coef_by_statistics

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


DEFAULT_N_PROMPT = ('wrong white balance, dark, sketches,worst quality,'
                    'low quality, deformed, distorted, disfigured, bad eyes, '
                    'wrong lips,weird mouth, bad teeth, mutated hands and fingers, '
                    'bad anatomy,wrong anatomy, amputation, extra limb, '
                    'missing limb, floating,limbs, disconnected limbs, mutation, '
                    'ugly, disgusting, bad_pictures, negative_hand-neg')


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


class I2VPipeline(DiffusionPipeline, IPAdapterMixin):
    _optional_components = []

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet3DConditionModel,
        scheduler: Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ],
        feature_extractor: CLIPImageProcessor = None,
        image_encoder: CLIPVisionModelWithProjection = None,
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file"
            )
            deprecate("steps_offset!=1", "1.0.0",
                      deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
            deprecation_message = (
                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
            )
            deprecate("clip_sample not set", "1.0.0",
                      deprecation_message, standard_warn=False)
            new_config = dict(scheduler.config)
            new_config["clip_sample"] = False
            scheduler._internal_dict = FrozenDict(new_config)

        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
            version.parse(unet.config._diffusers_version).base_version
        ) < version.parse("0.9.0.dev0")
        is_unet_sample_size_less_64 = hasattr(
            unet.config, "sample_size") and unet.config.sample_size < 64
        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
            deprecation_message = (
                "The configuration file of the unet has set the default `sample_size` to smaller than"
                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
                " in the config might lead to incorrect results in future versions. If you have downloaded this"
                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
                " the `unet/config.json` file"
            )
            deprecate("sample_size<64", "1.0.0",
                      deprecation_message, standard_warn=False)
            new_config = dict(unet.config)
            new_config["sample_size"] = 64
            unet._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            image_encoder=image_encoder,
            feature_extractor=feature_extractor,
            scheduler=scheduler,
        )
        self.vae_scale_factor = 2 ** (
            len(self.vae.config.block_out_channels) - 1)
        self.use_ip_adapter = False
        self.st_motion = None

    def set_st_motion(self, st_motion: List):
        """Set style transfer motion."""
        self.st_motion = st_motion

    @classmethod
    def build_pipeline(cls,
                       base_cfg,
                       base_model: str,
                       unet_path: str,
                       dreambooth_path: Optional[str] = None,
                       lora_path: Optional[str] = None,
                       lora_alpha: int = 0,
                       vae_path: Optional[str] = None,
                       ip_adapter_path: Optional[str] = None,
                       ip_adapter_scale: float = 0.0,
                       only_load_vae_decoder: bool = False,
                       only_load_vae_encoder: bool = False) -> 'I2VPipeline':
        """Method to build pipeline in a faster way~
        Args:
            base_cfg: The config to build model
            base_mode: The model id to initialize StableDiffusion
            unet_path: Path for i2v unet

            dreambooth_path: path for dreambooth model
            lora_path: path for lora model
            lora_alpha: value for lora scale

            only_load_vae_decoder: Only load VAE decoder from dreambooth / VAE ckpt
                and maitain encoder as original.

        """
        # build unet
        unet = UNet3DConditionModel.from_pretrained_2d(
            base_model, subfolder="unet",
            unet_additional_kwargs=OmegaConf.to_container(
                base_cfg.unet_additional_kwargs))

        old_weights = unet.conv_in.weight
        old_bias = unet.conv_in.bias
        new_conv1 = InflatedConv3d(
            9, old_weights.shape[0],
            kernel_size=unet.conv_in.kernel_size,
            stride=unet.conv_in.stride,
            padding=unet.conv_in.padding,
            bias=True if old_bias is not None else False)
        param = torch.zeros((320, 5, 3, 3), requires_grad=True)
        new_conv1.weight = torch.nn.Parameter(
            torch.cat((old_weights, param), dim=1))
        if old_bias is not None:
            new_conv1.bias = old_bias
        unet.conv_in = new_conv1
        unet.config["in_channels"] = 9

        unet_ckpt = torch.load(unet_path, map_location='cpu')
        # filter unet ckpt, only load motion module and conv_inv
        unet_ckpt = {k: v for k, v in unet_ckpt.items()
                     if 'motion_module' in k or 'conv_in' in k}
        print(f'Unet prefix: ')
        print(set([k.split('.')[0] for k in unet_ckpt.keys()]))
        unet.load_state_dict(unet_ckpt, strict=False)

        # load vae, tokenizer, text encoder
        vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae")
        tokenizer = CLIPTokenizer.from_pretrained(
            base_model, subfolder="tokenizer")
        text_encoder = CLIPTextModel.from_pretrained(
            base_model, subfolder="text_encoder")
        noise_scheduler = DDIMScheduler(
            **OmegaConf.to_container(base_cfg.noise_scheduler_kwargs))

        if dreambooth_path and dreambooth_path.upper() != 'NONE':

            print(" >>> Begin loading DreamBooth >>>")
            base_model_state_dict = {}
            with safe_open(dreambooth_path, framework="pt", device="cpu") as f:
                for key in f.keys():
                    base_model_state_dict[key] = f.get_tensor(key)

            # load unet
            converted_unet_checkpoint = convert_ldm_unet_checkpoint(
                base_model_state_dict, unet.config)

            old_value = converted_unet_checkpoint['conv_in.weight']
            new_param = unet_ckpt['conv_in.weight'][:, 4:, :, :].clone().cpu()
            new_value = torch.nn.Parameter(
                torch.cat((old_value, new_param), dim=1))
            converted_unet_checkpoint['conv_in.weight'] = new_value
            unet.load_state_dict(converted_unet_checkpoint, strict=False)

            # load vae
            converted_vae_checkpoint = convert_ldm_vae_checkpoint(
                base_model_state_dict, vae.config,
                only_decoder=only_load_vae_decoder,
                only_encoder=only_load_vae_encoder,)
            need_strict = not (only_load_vae_decoder or only_load_vae_encoder)
            vae.load_state_dict(converted_vae_checkpoint, strict=need_strict)
            print('Prefix in loaded VAE checkpoint: ')
            print(set([k.split('.')[0]
                  for k in converted_vae_checkpoint.keys()]))

            # load text encoder
            text_encoder_checkpoint = convert_ldm_clip_checkpoint(
                base_model_state_dict)
            if text_encoder_checkpoint:
                text_encoder.load_state_dict(text_encoder_checkpoint)

            print(" <<< Loaded DreamBooth        <<<")

        if vae_path:
            print(' >>> Begin loading VAE >>>')
            vae_state_dict = {}
            if vae_path.endswith('safetensors'):
                with safe_open(vae_path, framework="pt", device="cpu") as f:
                    for key in f.keys():
                        vae_state_dict[key] = f.get_tensor(key)
            elif vae_path.endswith('ckpt') or vae_path.endswith('pt'):
                vae_state_dict = torch.load(vae_path, map_location='cpu')
            if 'state_dict' in vae_state_dict:
                vae_state_dict = vae_state_dict['state_dict']

            vae_state_dict = {
                f'first_stage_model.{k}': v for k, v in vae_state_dict.items()}

            converted_vae_checkpoint = convert_ldm_vae_checkpoint(
                vae_state_dict, vae.config,
                only_decoder=only_load_vae_decoder,
                only_encoder=only_load_vae_encoder,)
            print('Prefix in loaded VAE checkpoint: ')
            print(set([k.split('.')[0]
                  for k in converted_vae_checkpoint.keys()]))
            need_strict = not (only_load_vae_decoder or only_load_vae_encoder)
            vae.load_state_dict(converted_vae_checkpoint, strict=need_strict)
            print(" <<< Loaded VAE        <<<")

        if lora_path:

            print(" >>> Begin loading LoRA >>>")

            lora_dict = {}
            with safe_open(lora_path, framework='pt', device='cpu') as file:
                for k in file.keys():
                    lora_dict[k] = file.get_tensor(k)
            unet, text_encoder = convert_lora_model_level(
                lora_dict, unet, text_encoder, alpha=lora_alpha)

            print(" <<< Loaded LoRA        <<<")

        # move model to device
        device = torch.device('cuda')
        unet_dtype = torch.float16
        tenc_dtype = torch.float16
        vae_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32

        unet = unet.to(device=device, dtype=unet_dtype)
        text_encoder = text_encoder.to(device=device, dtype=tenc_dtype)
        vae = vae.to(device=device, dtype=vae_dtype)
        print(f'Set Unet to {unet_dtype}')
        print(f'Set text encoder to {tenc_dtype}')
        print(f'Set vae to {vae_dtype}')

        if is_xformers_available():
            unet.enable_xformers_memory_efficient_attention()

        pipeline = cls(unet=unet,
                       vae=vae,
                       tokenizer=tokenizer,
                       text_encoder=text_encoder,
                       scheduler=noise_scheduler)

        # ip_adapter_path = 'h94/IP-Adapter'
        if ip_adapter_path and ip_adapter_scale > 0:
            ip_adapter_name = 'ip-adapter_sd15.bin'
            # only online repo need subfolder
            if not osp.isdir(ip_adapter_path):
                subfolder = 'models'
            else:
                subfolder = ''
            pipeline.load_ip_adapter(
                ip_adapter_path, subfolder, ip_adapter_name)
            pipeline.set_ip_adapter_scale(ip_adapter_scale)
            pipeline.use_ip_adapter = True
            print(f'Load IP-Adapter, scale: {ip_adapter_scale}')

        # text_inversion_path = './models/TextualInversion/easynegative.safetensors'
        # if text_inversion_path:
        #     pipeline.load_textual_inversion(text_inversion_path, 'easynegative')

        return pipeline

    def enable_vae_slicing(self):
        self.vae.enable_slicing()

    def disable_vae_slicing(self):
        self.vae.disable_slicing()

    def enable_sequential_cpu_offload(self, gpu_id=0):
        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]:
            if cpu_offloaded_model is not None:
                cpu_offload(cpu_offloaded_model, device)

    @property
    def _execution_device(self):
        if self.device != torch.device("meta") or 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):
        batch_size = len(prompt) if isinstance(prompt, list) else 1

        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

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

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

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            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 = text_input_ids.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

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

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            seq_len = uncond_embeddings.shape[1]
            uncond_embeddings = uncond_embeddings.repeat(
                1, num_videos_per_prompt, 1)
            uncond_embeddings = uncond_embeddings.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
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        return text_embeddings

    def decode_latents(self, latents):
        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 = []
        for frame_idx in tqdm(range(latents.shape[0])):
            video.append(self.vae.decode(
                latents[frame_idx:frame_idx+1]).sample)
        video = torch.cat(video)
        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
        video = (video / 2 + 0.5).clamp(0, 1)
        # 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, height, width, callback_steps):
        if 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 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)}."
            )

    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:]

        return timesteps, num_inference_steps - t_start

    def prepare_latents(self, add_noise_time_step, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
        shape = (batch_size, num_channels_latents, video_length, 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:
            rand_device = "cpu" if device.type == "mps" else device

            if isinstance(generator, list):
                shape = shape
                # shape = (1,) + shape[1:]
                latents = [
                    torch.randn(
                        shape, generator=generator[i], device=rand_device, dtype=dtype)
                    for i in range(batch_size)
                ]
                latents = torch.cat(latents, dim=0).to(device)
            else:
                latents = torch.randn(
                    shape, generator=generator, device=rand_device, dtype=dtype).to(device)
        else:
            if latents.shape != shape:
                raise ValueError(
                    f"Unexpected latents shape, got {latents.shape}, expected {shape}")
            latents = latents.to(device)

        return latents

    def encode_image(self, image, device, num_images_per_prompt):
        """Encode image for ip-adapter. Copied from
        https://github.com/huggingface/diffusers/blob/f9487783228cd500a21555da3346db40e8f05992/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L492-L514  # noqa
        """
        dtype = next(self.image_encoder.parameters()).dtype

        if not isinstance(image, torch.Tensor):
            image = self.feature_extractor(
                image, return_tensors="pt").pixel_values

        image = image.to(device=device, dtype=dtype)
        image_embeds = self.image_encoder(image).image_embeds
        image_embeds = image_embeds.repeat_interleave(
            num_images_per_prompt, dim=0)

        uncond_image_embeds = torch.zeros_like(image_embeds)
        return image_embeds, uncond_image_embeds

    @torch.no_grad()
    def __call__(
        self,
        image: np.ndarray,
        prompt: Union[str, List[str]],
        video_length: Optional[int],
        height: Optional[int] = None,
        width: Optional[int] = None,
        global_inf_num: int = 0,
        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,
        output_type: Optional[str] = "tensor",
        return_dict: bool = True,
        callback: Optional[Callable[[
            int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,

        cond_frame: int = 0,
        mask_sim_template_idx: int = 0,
        ip_adapter_scale: float = 0,
        strength: float = 1,
        is_real_img: bool = False,
        progress_fn=None,
        **kwargs,
    ):
        # 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

        assert strength > 0 and strength <= 1, (
            f'"strength" for img2vid must in (0, 1]. But receive {strength}.')

        # Check inputs. Raise error if not correct
        self.check_inputs(prompt, height, width, callback_steps)

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

        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 input prompt
        prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size

        if negative_prompt is None:
            negative_prompt = DEFAULT_N_PROMPT
        negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [
            negative_prompt] * batch_size
        text_embeddings = self._encode_prompt(
            prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
        )

        # Prepare timesteps
        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)

        # Prepare latent variables
        num_channels_latents = self.unet.in_channels
        latents = self.prepare_latents(
            latent_timestep,
            batch_size * num_videos_per_prompt,
            4,
            video_length,
            height,
            width,
            text_embeddings.dtype,
            device,
            generator,
            latents,
        )

        shape = (batch_size, num_channels_latents, video_length, height //
                 self.vae_scale_factor, width // self.vae_scale_factor)

        raw_image = image.copy()
        image = torch.from_numpy(image)[None, ...].permute(0, 3, 1, 2)
        image = image / 255  # [0, 1]
        image = image * 2 - 1   # [-1, 1]
        image = image.to(device=device, dtype=self.vae.dtype)

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

        image_latent = image_latent.to(device=device, dtype=self.unet.dtype)
        image_latent = torch.nn.functional.interpolate(
            image_latent, size=[shape[-2], shape[-1]])
        image_latent_padding = image_latent.clone() * 0.18215
        mask = torch.zeros((shape[0], 1, shape[2], shape[3], shape[4])).to(
            device=device, dtype=self.unet.dtype)

        # prepare mask
        # NOTE: pass specific st_motion for real image style transfer
        if mask_sim_template_idx == -1 and is_real_img:
            mask_coef = prepare_mask_coef_by_statistics(
                video_length, cond_frame, mask_sim_template_idx, self.st_motion)
        else:
            mask_coef = prepare_mask_coef_by_statistics(
                video_length, cond_frame, mask_sim_template_idx)

        masked_image = torch.zeros(shape[0], 4, shape[2], shape[3], shape[4]).to(
            device=device, dtype=self.unet.dtype)
        for f in range(video_length):
            mask[:, :, f, :, :] = mask_coef[f]
            masked_image[:, :, f, :, :] = image_latent_padding.clone()

        # Prepare extra step kwargs.
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
        masked_image = torch.cat(
            [masked_image] * 2) if do_classifier_free_guidance else masked_image
        # Denoising loop
        num_warmup_steps = len(timesteps) - \
                               num_inference_steps * self.scheduler.order

        # prepare for ip-adapter
        if self.use_ip_adapter:
            image_embeds, neg_image_embeds = self.encode_image(
                raw_image, device, num_videos_per_prompt)
            image_embeds = torch.cat([neg_image_embeds, image_embeds])
            image_embeds = image_embeds.to(device, self.unet.dtype)

            self.set_ip_adapter_scale(ip_adapter_scale)
            print(f'Set IP-Adapter Scale as {ip_adapter_scale}')

        else:
            image_embeds = None

        # prepare for latents if strength < 1, add convert gaussian latent to masked_img and add noise
        if strength < 1:
            noise = torch.randn_like(latents)
            latents = self.scheduler.add_noise(
                masked_image[0], noise, timesteps[0])

        if progress_fn is None:
            progress_bar = tqdm(timesteps)
            terminal_pbar = None
        else:
            progress_bar = progress_fn.tqdm(timesteps)
            terminal_pbar = tqdm(total=len(timesteps))

        for i, t in enumerate(progress_bar):
            # 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)

            # predict the noise residual
            noise_pred = self.unet(
                latent_model_input,
                mask,
                masked_image,
                t,
                encoder_hidden_states=text_embeddings,
                image_embeds=image_embeds
            )['sample']

            # 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
            latents = self.scheduler.step(
                noise_pred, t, latents, **extra_step_kwargs).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):
                if callback is not None and i % callback_steps == 0:
                    callback(i, t, latents)

            if terminal_pbar is not None:
                terminal_pbar.update(1)

        # Post-processing
        video = self.decode_latents(latents.to(device, dtype=self.vae.dtype))

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

        if not return_dict:
            return video

        return AnimationPipelineOutput(videos=video)