Create bckp.py

mimicmotion/pipelines/bckp.py

@@ -0,0 +1,618 @@
+import inspect
+from dataclasses import dataclass
+from typing import Callable, Dict, List, Optional, Union
+
+import PIL.Image
+import einops
+import numpy as np
+import torch
+from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
+from diffusers.models import AutoencoderKLTemporalDecoder, UNetSpatioTemporalConditionModel
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import retrieve_timesteps
+from diffusers.pipelines.stable_video_diffusion.pipeline_stable_video_diffusion \
+    import _resize_with_antialiasing, _append_dims
+from diffusers.schedulers import EulerDiscreteScheduler
+from diffusers.utils import BaseOutput, logging
+from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from ..modules.pose_net import PoseNet
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def _append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
+def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+    batch_size, channels, num_frames, height, width = video.shape
+    outputs = []
+    for batch_idx in range(batch_size):
+        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
+        batch_output = processor.postprocess(batch_vid, output_type)
+
+        outputs.append(batch_output)
+
+    if output_type == "np":
+        outputs = np.stack(outputs)
+
+    elif output_type == "pt":
+        outputs = torch.stack(outputs)
+
+    elif not output_type == "pil":
+        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
+
+    return outputs
+
+
+@dataclass
+class MimicMotionPipelineOutput(BaseOutput):
+    r"""
+    Output class for mimicmotion pipeline.
+    Args:
+        frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):
+            List of denoised PIL images of length `batch_size` or numpy array or torch tensor of shape `(batch_size,
+            num_frames, height, width, num_channels)`.
+    """
+
+    frames: Union[List[List[PIL.Image.Image]], np.ndarray, torch.Tensor]
+
+
+class MimicMotionPipeline(DiffusionPipeline):
+    r"""
+    Pipeline to generate video from an input image using Stable Video Diffusion.
+    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 ([`AutoencoderKLTemporalDecoder`]):
+            Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
+        image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
+            Frozen CLIP image-encoder ([laion/CLIP-ViT-H-14-laion2B-s32B-b79K]
+            (https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K)).
+        unet ([`UNetSpatioTemporalConditionModel`]):
+            A `UNetSpatioTemporalConditionModel` to denoise the encoded image latents.
+        scheduler ([`EulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images.
+        pose_net ([`PoseNet`]):
+            A `` to inject pose signals into unet.
+    """
+
+    model_cpu_offload_seq = "image_encoder->unet->vae"
+    _callback_tensor_inputs = ["latents"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKLTemporalDecoder,
+        image_encoder: CLIPVisionModelWithProjection,
+        unet: UNetSpatioTemporalConditionModel,
+        scheduler: EulerDiscreteScheduler,
+        feature_extractor: CLIPImageProcessor,
+        pose_net: PoseNet,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            image_encoder=image_encoder,
+            unet=unet,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            pose_net=pose_net,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def _encode_image(
+        self, 
+        image: PipelineImageInput, 
+        device: Union[str, torch.device], 
+        num_videos_per_prompt: int, 
+        do_classifier_free_guidance: bool):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.image_processor.pil_to_numpy(image)
+            image = self.image_processor.numpy_to_pt(image)
+
+            # We normalize the image before resizing to match with the original implementation.
+            # Then we unnormalize it after resizing.
+            image = image * 2.0 - 1.0
+            image = _resize_with_antialiasing(image, (224, 224))
+            image = (image + 1.0) / 2.0
+
+            # Normalize the image with for CLIP input
+            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 do_classifier_free_guidance:
+            negative_image_embeddings = torch.zeros_like(image_embeddings)
+
+            # 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
+            image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
+
+        return image_embeddings
+
+    def _encode_vae_image(
+        self,
+        image: torch.Tensor,
+        device: Union[str, torch.device],
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
+    ):
+        image = image.to(device=device, dtype=self.vae.dtype)
+        image_latents = self.vae.encode(image).latent_dist.mode()
+
+        if do_classifier_free_guidance:
+            negative_image_latents = torch.zeros_like(image_latents)
+
+            # 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
+            image_latents = torch.cat([negative_image_latents, image_latents])
+
+        # duplicate image_latents for each generation per prompt, using mps friendly method
+        image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1)
+
+        return image_latents
+
+    def _get_add_time_ids(
+        self,
+        fps: int,
+        motion_bucket_id: int,
+        noise_aug_strength: float,
+        dtype: torch.dtype,
+        batch_size: int,
+        num_videos_per_prompt: int,
+        do_classifier_free_guidance: bool,
+    ):
+        add_time_ids = [fps, motion_bucket_id, noise_aug_strength]
+
+        passed_add_embed_dim = self.unet.config.addition_time_embed_dim * len(add_time_ids)
+        expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+        if expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, " \
+                f"but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. " \
+                f"Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        add_time_ids = add_time_ids.repeat(batch_size * num_videos_per_prompt, 1)
+
+        if do_classifier_free_guidance:
+            add_time_ids = torch.cat([add_time_ids, add_time_ids])
+
+        return add_time_ids
+
+    def decode_latents(
+        self, 
+        latents: torch.Tensor, 
+        num_frames: int, 
+        decode_chunk_size: int = 8):
+        # [batch, frames, channels, height, width] -> [batch*frames, channels, height, width]
+        latents = latents.flatten(0, 1)
+
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        forward_vae_fn = self.vae._orig_mod.forward if is_compiled_module(self.vae) else self.vae.forward
+        accepts_num_frames = "num_frames" in set(inspect.signature(forward_vae_fn).parameters.keys())
+
+        # decode decode_chunk_size frames at a time to avoid OOM
+        frames = []
+        for i in range(0, latents.shape[0], decode_chunk_size):
+            num_frames_in = latents[i: i + decode_chunk_size].shape[0]
+            decode_kwargs = {}
+            if accepts_num_frames:
+                # we only pass num_frames_in if it's expected
+                decode_kwargs["num_frames"] = num_frames_in
+
+            frame = self.vae.decode(latents[i: i + decode_chunk_size], **decode_kwargs).sample
+            frames.append(frame.cpu())
+        frames = torch.cat(frames, dim=0)
+
+        # [batch*frames, channels, height, width] -> [batch, channels, frames, height, width]
+        frames = frames.reshape(-1, num_frames, *frames.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        frames = frames.float()
+        return frames
+
+    def check_inputs(self, image, height, width):
+        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.FloatTensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
+                f" {type(image)}"
+            )
+
+        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}.")
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_frames: int,
+        num_channels_latents: int,
+        height: int,
+        width: int,
+        dtype: torch.dtype,
+        device: Union[str, torch.device],
+        generator: torch.Generator,
+        latents: Optional[torch.Tensor] = None,
+    ):
+        shape = (
+            batch_size,
+            num_frames,
+            num_channels_latents // 2,
+            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
+
+    @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):
+        if isinstance(self.guidance_scale, (int, float)):
+            return self.guidance_scale > 1
+        return self.guidance_scale.max() > 1
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    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
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.FloatTensor],
+        image_pose: Union[torch.FloatTensor],
+        height: int = 576,
+        width: int = 1024,
+        num_frames: Optional[int] = None,
+        tile_size: Optional[int] = 16,
+        tile_overlap: Optional[int] = 4,
+        num_inference_steps: int = 25,
+        min_guidance_scale: float = 1.0,
+        max_guidance_scale: float = 3.0,
+        fps: int = 7,
+        motion_bucket_id: int = 127,
+        noise_aug_strength: float = 0.02,
+        image_only_indicator: bool = False,
+        decode_chunk_size: Optional[int] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        return_dict: bool = True,
+        device: Union[str, torch.device] =None,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+        Args:
+            image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.FloatTensor`):
+                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).
+            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_frames (`int`, *optional*):
+                The number of video frames to generate. Defaults to 14 for `stable-video-diffusion-img2vid` 
+                and to 25 for `stable-video-diffusion-img2vid-xt`
+            num_inference_steps (`int`, *optional*, defaults to 25):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference. This parameter is modulated by `strength`.
+            min_guidance_scale (`float`, *optional*, defaults to 1.0):
+                The minimum guidance scale. Used for the classifier free guidance with first frame.
+            max_guidance_scale (`float`, *optional*, defaults to 3.0):
+                The maximum guidance scale. Used for the classifier free guidance with last frame.
+            fps (`int`, *optional*, defaults to 7):
+                Frames per second.The rate at which the generated images shall be exported to a video after generation.
+                Note that Stable Diffusion Video's UNet was micro-conditioned on fps-1 during training.
+            motion_bucket_id (`int`, *optional*, defaults to 127):
+                The motion bucket ID. Used as conditioning for the generation. 
+                The higher the number the more motion will be in the video.
+            noise_aug_strength (`float`, *optional*, defaults to 0.02):
+                The amount of noise added to the init image, 
+                the higher it is the less the video will look like the init image. Increase it for more motion.
+            image_only_indicator (`bool`, *optional*, defaults to False):
+                Whether to treat the inputs as batch of images instead of videos.
+            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.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            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.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 is generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            device:
+                On which device the pipeline runs on.
+        Returns:
+            [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, 
+                [`~pipelines.stable_diffusion.StableVideoDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list of list with the generated frames.
+        Examples:
+        ```py
+        from diffusers import StableVideoDiffusionPipeline
+        from diffusers.utils import load_image, export_to_video
+        pipe = StableVideoDiffusionPipeline.from_pretrained(
+            "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16")
+        pipe.to("cuda")
+        image = load_image(
+        "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200")
+        image = image.resize((1024, 576))
+        frames = pipe(image, num_frames=25, decode_chunk_size=8).frames[0]
+        export_to_video(frames, "generated.mp4", fps=7)
+        ```
+        """
+        # 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
+
+        num_frames = num_frames if num_frames is not None else self.unet.config.num_frames
+        decode_chunk_size = decode_chunk_size if decode_chunk_size is not None else num_frames
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(image, height, width)
+
+        # 2. Define call parameters
+        if isinstance(image, PIL.Image.Image):
+            batch_size = 1
+        elif isinstance(image, list):
+            batch_size = len(image)
+        else:
+            batch_size = image.shape[0]
+        device = device if device is not None else 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 = max_guidance_scale
+
+        # 3. Encode input image
+        self.image_encoder.to(device)
+        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
+        self.image_encoder.cpu()
+
+        # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
+        # is why it is reduced here.
+        fps = fps - 1
+
+        # 4. Encode input image using VAE
+        image = self.image_processor.preprocess(image, height=height, width=width).to(device)
+        noise = randn_tensor(image.shape, generator=generator, device=device, dtype=image.dtype)
+        image = image + noise_aug_strength * noise
+
+        self.vae.to(device)
+        image_latents = self._encode_vae_image(
+            image,
+            device=device,
+            num_videos_per_prompt=num_videos_per_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+        )
+        image_latents = image_latents.to(image_embeddings.dtype)
+        self.vae.cpu()
+
+        # Repeat the image latents for each frame so we can concatenate them with the noise
+        # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
+        image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
+
+        # 5. Get Added Time IDs
+        added_time_ids = self._get_add_time_ids(
+            fps,
+            motion_bucket_id,
+            noise_aug_strength,
+            image_embeddings.dtype,
+            batch_size,
+            num_videos_per_prompt,
+            self.do_classifier_free_guidance,
+        )
+        added_time_ids = added_time_ids.to(device)
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, None)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            tile_size,
+            num_channels_latents,
+            height,
+            width,
+            image_embeddings.dtype,
+            device,
+            generator,
+            latents,
+        )
+        latents = latents.repeat(1, num_frames // tile_size + 1, 1, 1, 1)[:, :num_frames]
+
+        # 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, 0.0)
+
+        # 7. Prepare guidance scale
+        guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
+        guidance_scale = guidance_scale.to(device, latents.dtype)
+        guidance_scale = guidance_scale.repeat(batch_size * num_videos_per_prompt, 1)
+        guidance_scale = _append_dims(guidance_scale, latents.ndim)
+
+        self._guidance_scale = guidance_scale
+
+        # 8. Denoising loop
+        self._num_timesteps = len(timesteps)
+        indices = [[0, *range(i + 1, min(i + tile_size, num_frames))] for i in
+                   range(0, num_frames - tile_size + 1, tile_size - tile_overlap)]
+        if indices[-1][-1] < num_frames - 1:
+            indices.append([0, *range(num_frames - tile_size + 1, num_frames)])
+
+        self.pose_net.to(device)
+        self.unet.to(device)
+
+        with torch.cuda.device(device):
+            torch.cuda.empty_cache()
+
+        with self.progress_bar(total=len(timesteps) * len(indices)) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # Concatenate image_latents over channels dimension
+                latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
+
+                # predict the noise residual
+                noise_pred = torch.zeros_like(image_latents)
+                noise_pred_cnt = image_latents.new_zeros((num_frames,))
+                weight = (torch.arange(tile_size, device=device) + 0.5) * 2. / tile_size
+                weight = torch.minimum(weight, 2 - weight)
+                for idx in indices:
+
+                    # classification-free inference
+                    pose_latents = self.pose_net(image_pose[idx].to(device))
+                    _noise_pred = self.unet(
+                        latent_model_input[:1, idx],
+                        t,
+                        encoder_hidden_states=image_embeddings[:1],
+                        added_time_ids=added_time_ids[:1],
+                        pose_latents=None,
+                        image_only_indicator=image_only_indicator,
+                        return_dict=False,
+                    )[0]
+                    noise_pred[:1, idx] += _noise_pred * weight[:, None, None, None]
+
+                    # normal inference
+                    _noise_pred = self.unet(
+                        latent_model_input[1:, idx],
+                        t,
+                        encoder_hidden_states=image_embeddings[1:],
+                        added_time_ids=added_time_ids[1:],
+                        pose_latents=pose_latents,
+                        image_only_indicator=image_only_indicator,
+                        return_dict=False,
+                    )[0]
+                    noise_pred[1:, idx] += _noise_pred * weight[:, None, None, None]
+
+                    noise_pred_cnt[idx] += weight
+                    progress_bar.update()
+                noise_pred.div_(noise_pred_cnt[:, None, None, None])
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+
+        self.pose_net.cpu()
+        self.unet.cpu()
+
+        if not output_type == "latent":
+            self.vae.decoder.to(device)
+            frames = self.decode_latents(latents, num_frames, decode_chunk_size)
+            frames = tensor2vid(frames, self.image_processor, output_type=output_type)
+        else:
+            frames = latents
+
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return frames
+
+        return MimicMotionPipelineOutput(frames=frames)