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LICENSE

+Tencent is pleased to support the open source community by making MimicMotion available. 
+
+Copyright (C) 2023 THL A29 Limited, a Tencent company.  All rights reserved. The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+MimicMotion is licensed under the Apache License Version 2.0 except for the third-party components listed below.
+
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+Other dependencies and licenses:
+
+
+Open Source Software Licensed under the Apache License Version 2.0:
+The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2023 THL A29 Limited.
+--------------------------------------------------------------------
+1. diffusers
+Copyright (c) diffusers original author and authors
+
+2. DWPose
+Copyright 2018-2020 Open-MMLab.
+Please note this software has been modified by Tencent in this distribution.
+
+3. transformers
+Copyright (c) transformers original author and authors
+
+4. decord
+Copyright (c) DWPoseoriginal author and authors
+
+
+A copy of Apache 2.0 has been included in this file.
+
+
+
+Open Source Software Licensed under the BSD 3-Clause License:
+--------------------------------------------------------------------
+1. torch
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+2. omegaconf
+Copyright (c) 2018, Omry Yadan
+All rights reserved.
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+Copyright (c) Soumith Chintala 2016, 
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+Copyright © 2010-2023 by Jeffrey A. Clark (Alex) and contributors.
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+
+    Copyright © 1997-2011 by Secret Labs AB
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+Open Source Software Licensed under the Matplotlib License and Other Licenses of the Third-Party Components therein:
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+License agreement for matplotlib versions 1.3.0 and later
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\ No newline at end of file

README.md

+# WORK IN PROGRESS
+# MimicMotion wrapper for ComfyUI
+
+needs SVD 1.1 to ComfyUI/models/diffusers https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1/tree/main
+the whole folder, but subfolders only need one of the .safetensors + config.json
\ No newline at end of file

__init__.py

+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
\ No newline at end of file

configs/test.yaml

+# base svd model path
+base_model_path: models/SVD/stable-video-diffusion-img2vid-xt-1-1
+
+# checkpoint path
+ckpt_path: models/MimicMotion.pth
+
+test_case:
+  - ref_video_path: assets/example_data/videos/pose1.mp4
+    ref_image_path: assets/example_data/images/demo1.jpg
+    num_frames: 16
+    resolution: 576
+    frames_overlap: 6
+    num_inference_steps: 25
+    noise_aug_strength: 0
+    guidance_scale: 2.0
+    sample_stride: 2
+    fps: 15
+    seed: 42
+
+

constants.py

+# w/h apsect ratio
+ASPECT_RATIO = 9 / 16

environment.yaml

+name: mimicmotion
+channels:
+  - pytorch
+  - nvidia
+dependencies:
+  - python=3.11
+  - pytorch=2.0.1
+  - torchvision=0.15.2
+  - pytorch-cuda=11.7
+  - pip
+  - pip:
+    - diffusers==0.27.0
+    - transformers==4.32.1
+    - decord==0.6.0
+    - einops
+    - omegaconf

inference.py

+import os
+import argparse
+import logging
+import math
+from omegaconf import OmegaConf
+from datetime import datetime
+from pathlib import Path
+
+import numpy as np
+import torch.jit
+from torchvision.datasets.folder import pil_loader
+from torchvision.transforms.functional import pil_to_tensor, resize, center_crop
+from torchvision.transforms.functional import to_pil_image
+
+
+from constants import ASPECT_RATIO
+
+from mimicmotion.pipelines.pipeline_mimicmotion import MimicMotionPipeline
+from mimicmotion.utils.loader import create_pipeline
+from mimicmotion.utils.utils import save_to_mp4
+from mimicmotion.dwpose.preprocess import get_video_pose, get_image_pose
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s: [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def preprocess(video_path, image_path, resolution=576, sample_stride=2):
+    """preprocess ref image pose and video pose
+
+    Args:
+        video_path (str): input video pose path
+        image_path (str): reference image path
+        resolution (int, optional):  Defaults to 576.
+        sample_stride (int, optional): Defaults to 2.
+    """
+    image_pixels = pil_loader(image_path)
+    image_pixels = pil_to_tensor(image_pixels) # (c, h, w)
+    h, w = image_pixels.shape[-2:]
+    ############################ compute target h/w according to original aspect ratio ###############################
+    if h>w:
+        w_target, h_target = resolution, int(resolution / ASPECT_RATIO // 64) * 64
+    else:
+        w_target, h_target = int(resolution / ASPECT_RATIO // 64) * 64, resolution
+    h_w_ratio = float(h) / float(w)
+    if h_w_ratio < h_target / w_target:
+        h_resize, w_resize = h_target, math.ceil(h_target / h_w_ratio)
+    else:
+        h_resize, w_resize = math.ceil(w_target * h_w_ratio), w_target
+    image_pixels = resize(image_pixels, [h_resize, w_resize], antialias=None)
+    image_pixels = center_crop(image_pixels, [h_target, w_target])
+    image_pixels = image_pixels.permute((1, 2, 0)).numpy()
+    ##################################### get image&video pose value #################################################
+    image_pose = get_image_pose(image_pixels)
+    video_pose = get_video_pose(video_path, image_pixels, sample_stride=sample_stride)
+    pose_pixels = np.concatenate([np.expand_dims(image_pose, 0), video_pose])
+    image_pixels = np.transpose(np.expand_dims(image_pixels, 0), (0, 3, 1, 2))
+    return torch.from_numpy(pose_pixels.copy()) / 127.5 - 1, torch.from_numpy(image_pixels) / 127.5 - 1
+
+
+def run_pipeline(pipeline: MimicMotionPipeline, image_pixels, pose_pixels, device, task_config):
+    image_pixels = [to_pil_image(img.to(torch.uint8)) for img in (image_pixels + 1.0) * 127.5]
+    pose_pixels = pose_pixels.unsqueeze(0).to(device)
+    generator = torch.Generator(device=device)
+    generator.manual_seed(task_config.seed)
+    frames = pipeline(
+        image_pixels, image_pose=pose_pixels, num_frames=pose_pixels.size(1),
+        tile_size=task_config.num_frames, tile_overlap=task_config.frames_overlap,
+        height=pose_pixels.shape[-2], width=pose_pixels.shape[-1], fps=7,
+        noise_aug_strength=task_config.noise_aug_strength, num_inference_steps=task_config.num_inference_steps,
+        generator=generator, min_guidance_scale=task_config.guidance_scale, 
+        max_guidance_scale=task_config.guidance_scale, decode_chunk_size=8, output_type="pt", device=device
+    ).frames.cpu()
+    video_frames = (frames * 255.0).to(torch.uint8)
+
+    for vid_idx in range(video_frames.shape[0]):
+        # deprecated first frame because of ref image
+        _video_frames = video_frames[vid_idx, 1:]
+
+    return _video_frames
+
+
+@torch.no_grad()
+def main(args):
+    if not args.no_use_float16 :
+        torch.set_default_dtype(torch.float16)
+
+    infer_config = OmegaConf.load(args.inference_config)
+    pipeline = create_pipeline(infer_config, device)
+
+    for task in infer_config.test_case:
+        ############################################## Pre-process data ##############################################
+        pose_pixels, image_pixels = preprocess(
+            task.ref_video_path, task.ref_image_path, 
+            resolution=task.resolution, sample_stride=task.sample_stride
+        )
+        ########################################### Run MimicMotion pipeline ###########################################
+        _video_frames = run_pipeline(
+            pipeline, 
+            image_pixels, pose_pixels, 
+            device, task
+        )
+        ################################### save results to output folder. ###########################################
+        save_to_mp4(
+            _video_frames, 
+            f"{args.output_dir}/{os.path.basename(task.ref_video_path).split('.')[0]}" \
+            f"_{datetime.now().strftime('%Y%m%d%H%M%S')}.mp4",
+            fps=task.fps,
+        )
+
+def set_logger(log_file=None, log_level=logging.INFO):
+    log_handler = logging.FileHandler(log_file, "w")
+    log_handler.setFormatter(
+        logging.Formatter("[%(asctime)s][%(name)s][%(levelname)s]: %(message)s")
+    )
+    log_handler.setLevel(log_level)
+    logger.addHandler(log_handler)
+
+
+if __name__ == "__main__":    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--log_file", type=str, default=None)
+    parser.add_argument("--inference_config", type=str, default="configs/test.yaml") #ToDo
+    parser.add_argument("--output_dir", type=str, default="outputs/", help="path to output")
+    parser.add_argument("--no_use_float16",
+                        action="store_true",
+                        help="Whether use float16 to speed up inference",
+    )
+    args = parser.parse_args()
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    set_logger(args.log_file \
+               if args.log_file is not None else f"{args.output_dir}/{datetime.now().strftime('%Y%m%d%H%M%S')}.log")
+    main(args)
+    logger.info(f"--- Finished ---")
+

mimicmotion/modules/attention.py

+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention import BasicTransformerBlock, TemporalBasicTransformerBlock
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.resnet import AlphaBlender
+from diffusers.utils import BaseOutput
+from torch import nn
+
+
+@dataclass
+class TransformerTemporalModelOutput(BaseOutput):
+    """
+    The output of [`TransformerTemporalModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input.
+    """
+
+    sample: torch.FloatTensor
+
+
+class TransformerTemporalModel(ModelMixin, ConfigMixin):
+    """
+    A Transformer model for video-like data.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        attention_bias (`bool`, *optional*):
+            Configure if the `TransformerBlock` attention should contain a bias parameter.
+        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
+            This is fixed during training since it is used to learn a number of position embeddings.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`):
+            Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported
+            activation functions.
+        norm_elementwise_affine (`bool`, *optional*):
+            Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization.
+        double_self_attention (`bool`, *optional*):
+            Configure if each `TransformerBlock` should contain two self-attention layers.
+        positional_embeddings: (`str`, *optional*):
+            The type of positional embeddings to apply to the sequence input before passing use.
+        num_positional_embeddings: (`int`, *optional*):
+            The maximum length of the sequence over which to apply positional embeddings.
+    """
+
+    @register_to_config
+    def __init__(
+            self,
+            num_attention_heads: int = 16,
+            attention_head_dim: int = 88,
+            in_channels: Optional[int] = None,
+            out_channels: Optional[int] = None,
+            num_layers: int = 1,
+            dropout: float = 0.0,
+            norm_num_groups: int = 32,
+            cross_attention_dim: Optional[int] = None,
+            attention_bias: bool = False,
+            sample_size: Optional[int] = None,
+            activation_fn: str = "geglu",
+            norm_elementwise_affine: bool = True,
+            double_self_attention: bool = True,
+            positional_embeddings: Optional[str] = None,
+            num_positional_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    double_self_attention=double_self_attention,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    positional_embeddings=positional_embeddings,
+                    num_positional_embeddings=num_positional_embeddings,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+    def forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            encoder_hidden_states: Optional[torch.LongTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            class_labels: torch.LongTensor = None,
+            num_frames: int = 1,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            return_dict: bool = True,
+    ) -> TransformerTemporalModelOutput:
+        """
+        The [`TransformerTemporal`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, 
+                `torch.FloatTensor` of shape `(batch size, channel, height, width)`if continuous): Input hidden_states.
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            timestep ( `torch.LongTensor`, *optional*):
+                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
+            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
+                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
+                `AdaLayerZeroNorm`.
+            num_frames (`int`, *optional*, defaults to 1):
+                The number of frames to be processed per batch. This is used to reshape the hidden states.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in [diffusers.models.attention_processor](
+                https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
+                returned, otherwise a `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+        batch_frames, channel, height, width = hidden_states.shape
+        batch_size = batch_frames // num_frames
+
+        residual = hidden_states
+
+        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width)
+        hidden_states = hidden_states.permute(0, 2, 1, 3, 4)
+
+        hidden_states = self.norm(hidden_states)
+        hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel)
+
+        hidden_states = self.proj_in(hidden_states)
+
+        # 2. Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                timestep=timestep,
+                cross_attention_kwargs=cross_attention_kwargs,
+                class_labels=class_labels,
+            )
+
+        # 3. Output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = (
+            hidden_states[None, None, :]
+            .reshape(batch_size, height, width, num_frames, channel)
+            .permute(0, 3, 4, 1, 2)
+            .contiguous()
+        )
+        hidden_states = hidden_states.reshape(batch_frames, channel, height, width)
+
+        output = hidden_states + residual
+
+        if not return_dict:
+            return (output,)
+
+        return TransformerTemporalModelOutput(sample=output)
+
+
+class TransformerSpatioTemporalModel(nn.Module):
+    """
+    A Transformer model for video-like data.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        out_channels (`int`, *optional*):
+            The number of channels in the output (specify if the input is **continuous**).
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+    """
+
+    def __init__(
+            self,
+            num_attention_heads: int = 16,
+            attention_head_dim: int = 88,
+            in_channels: int = 320,
+            out_channels: Optional[int] = None,
+            num_layers: int = 1,
+            cross_attention_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+
+        inner_dim = num_attention_heads * attention_head_dim
+        self.inner_dim = inner_dim
+
+        # 2. Define input layers
+        self.in_channels = in_channels
+        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        time_mix_inner_dim = inner_dim
+        self.temporal_transformer_blocks = nn.ModuleList(
+            [
+                TemporalBasicTransformerBlock(
+                    inner_dim,
+                    time_mix_inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        time_embed_dim = in_channels * 4
+        self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
+        self.time_proj = Timesteps(in_channels, True, 0)
+        self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
+
+        # 4. Define output layers
+        self.out_channels = in_channels if out_channels is None else out_channels
+        # TODO: should use out_channels for continuous projections
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            encoder_hidden_states: Optional[torch.Tensor] = None,
+            image_only_indicator: Optional[torch.Tensor] = None,
+            return_dict: bool = True,
+    ):
+        """
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input hidden_states.
+            num_frames (`int`):
+                The number of frames to be processed per batch. This is used to reshape the hidden states.
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
+                A tensor indicating whether the input contains only images. 1 indicates that the input contains only
+                images, 0 indicates that the input contains video frames.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] 
+                instead of a plain tuple.
+
+        Returns:
+            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
+                returned, otherwise a `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+        batch_frames, _, height, width = hidden_states.shape
+        num_frames = image_only_indicator.shape[-1]
+        batch_size = batch_frames // num_frames
+
+        time_context = encoder_hidden_states
+        time_context_first_timestep = time_context[None, :].reshape(
+            batch_size, num_frames, -1, time_context.shape[-1]
+        )[:, 0]
+        time_context = time_context_first_timestep[None, :].broadcast_to(
+            height * width, batch_size, 1, time_context.shape[-1]
+        )
+        time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1])
+
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)
+        hidden_states = torch.utils.checkpoint.checkpoint(self.proj_in, hidden_states)
+
+        num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
+        num_frames_emb = num_frames_emb.repeat(batch_size, 1)
+        num_frames_emb = num_frames_emb.reshape(-1)
+        t_emb = self.time_proj(num_frames_emb)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+
+        emb = self.time_pos_embed(t_emb)
+        emb = emb[:, None, :]
+
+        # 2. Blocks
+        for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
+            if self.gradient_checkpointing:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    block,
+                    hidden_states,
+                    None,
+                    encoder_hidden_states,
+                    None,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+            hidden_states_mix = hidden_states
+            hidden_states_mix = hidden_states_mix + emb
+
+            if self.gradient_checkpointing:
+                hidden_states_mix = torch.utils.checkpoint.checkpoint(
+                    temporal_block,
+                    hidden_states_mix,
+                    num_frames,
+                    time_context,
+                )
+                hidden_states = self.time_mixer(
+                    x_spatial=hidden_states,
+                    x_temporal=hidden_states_mix,
+                    image_only_indicator=image_only_indicator,
+                )
+            else:
+                hidden_states_mix = temporal_block(
+                    hidden_states_mix,
+                    num_frames=num_frames,
+                    encoder_hidden_states=time_context,
+                )
+                hidden_states = self.time_mixer(
+                    x_spatial=hidden_states,
+                    x_temporal=hidden_states_mix,
+                    image_only_indicator=image_only_indicator,
+                )
+
+        # 3. Output
+        hidden_states = torch.utils.checkpoint.checkpoint(self.proj_out, hidden_states)
+        hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        output = hidden_states + residual
+
+        if not return_dict:
+            return (output,)
+
+        return TransformerTemporalModelOutput(sample=output)

mimicmotion/modules/pose_net.py

+from pathlib import Path
+
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+
+
+class PoseNet(nn.Module):
+    """a tiny conv network for introducing pose sequence as the condition
+    """
+    def __init__(self, noise_latent_channels=320, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # multiple convolution layers
+        self.conv_layers = nn.Sequential(
+            nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=4, stride=2, padding=1),
+            nn.SiLU(),
+
+            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=4, stride=2, padding=1),
+            nn.SiLU(),
+
+            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2, padding=1),
+            nn.SiLU(),
+
+            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1),
+            nn.SiLU(),
+            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
+            nn.SiLU()
+        )
+
+        # Final projection layer
+        self.final_proj = nn.Conv2d(in_channels=128, out_channels=noise_latent_channels, kernel_size=1)
+
+        # Initialize layers
+        self._initialize_weights()
+
+        self.scale = nn.Parameter(torch.ones(1) * 2)
+
+    def _initialize_weights(self):
+        """Initialize weights with He. initialization and zero out the biases
+        """
+        for m in self.conv_layers:
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
+                init.normal_(m.weight, mean=0.0, std=np.sqrt(2. / n))
+                if m.bias is not None:
+                    init.zeros_(m.bias)
+        init.zeros_(self.final_proj.weight)
+        if self.final_proj.bias is not None:
+            init.zeros_(self.final_proj.bias)
+
+    def forward(self, x):
+        if x.ndim == 5:
+            x = einops.rearrange(x, "b f c h w -> (b f) c h w")
+        x = self.conv_layers(x)
+        x = self.final_proj(x)
+
+        return x * self.scale
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_path):
+        """load pretrained pose-net weights
+        """
+        if not Path(pretrained_model_path).exists():
+            print(f"There is no model file in {pretrained_model_path}")
+        print(f"loaded PoseNet's pretrained weights from {pretrained_model_path}.")
+
+        state_dict = torch.load(pretrained_model_path, map_location="cpu")
+        model = PoseNet(noise_latent_channels=320)
+
+        model.load_state_dict(state_dict, strict=True)
+
+        return model

mimicmotion/modules/unet.py

+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.models.attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput, logging
+
+from diffusers.models.unets.unet_3d_blocks import get_down_block, get_up_block, UNetMidBlockSpatioTemporal
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNetSpatioTemporalConditionOutput(BaseOutput):
+    """
+    The output of [`UNetSpatioTemporalConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+
+class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state,
+    and a timestep and returns a sample shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 8): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlockSpatioTemporal", 
+            "CrossAttnDownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", "DownBlockSpatioTemporal")`):
+            The tuple of downsample blocks to use.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockSpatioTemporal", 
+            "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal")`):
+            The tuple of upsample blocks to use.
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        addition_time_embed_dim: (`int`, defaults to 256):
+            Dimension to to encode the additional time ids.
+        projection_class_embeddings_input_dim (`int`, defaults to 768):
+            The dimension of the projection of encoded `added_time_ids`.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_3d_blocks.CrossAttnDownBlockSpatioTemporal`], 
+            [`~models.unet_3d_blocks.CrossAttnUpBlockSpatioTemporal`],
+            [`~models.unet_3d_blocks.UNetMidBlockSpatioTemporal`].
+        num_attention_heads (`int`, `Tuple[int]`, defaults to `(5, 10, 10, 20)`):
+            The number of attention heads.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+            self,
+            sample_size: Optional[int] = None,
+            in_channels: int = 8,
+            out_channels: int = 4,
+            down_block_types: Tuple[str] = (
+                    "CrossAttnDownBlockSpatioTemporal",
+                    "CrossAttnDownBlockSpatioTemporal",
+                    "CrossAttnDownBlockSpatioTemporal",
+                    "DownBlockSpatioTemporal",
+            ),
+            up_block_types: Tuple[str] = (
+                    "UpBlockSpatioTemporal",
+                    "CrossAttnUpBlockSpatioTemporal",
+                    "CrossAttnUpBlockSpatioTemporal",
+                    "CrossAttnUpBlockSpatioTemporal",
+            ),
+            block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+            addition_time_embed_dim: int = 256,
+            projection_class_embeddings_input_dim: int = 768,
+            layers_per_block: Union[int, Tuple[int]] = 2,
+            cross_attention_dim: Union[int, Tuple[int]] = 1024,
+            transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+            num_attention_heads: Union[int, Tuple[int]] = (5, 10, 10, 20),
+            num_frames: int = 25,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. " \
+                f"`down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. " \
+                f"`block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. " \
+                f"`num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. " \
+                f"`cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. " \
+                f"`layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        self.conv_in = nn.Conv2d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=3,
+            padding=1,
+        )
+
+        # time
+        time_embed_dim = block_out_channels[0] * 4
+
+        self.time_proj = Timesteps(block_out_channels[0], True, downscale_freq_shift=0)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        self.add_time_proj = Timesteps(addition_time_embed_dim, True, downscale_freq_shift=0)
+        self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=1e-5,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                resnet_act_fn="silu",
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlockSpatioTemporal(
+            block_out_channels[-1],
+            temb_channels=blocks_time_embed_dim,
+            transformer_layers_per_block=transformer_layers_per_block[-1],
+            cross_attention_dim=cross_attention_dim[-1],
+            num_attention_heads=num_attention_heads[-1],
+        )
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=1e-5,
+                resolution_idx=i,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                resnet_act_fn="silu",
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-5)
+        self.conv_act = nn.SiLU()
+
+        self.conv_out = nn.Conv2d(
+            block_out_channels[0],
+            out_channels,
+            kernel_size=3,
+            padding=1,
+        )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+                name: str,
+                module: torch.nn.Module,
+                processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` " \
+                f"when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
+    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
+        """
+        Sets the attention processor to use [feed forward
+        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
+
+        Parameters:
+            chunk_size (`int`, *optional*):
+                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
+                over each tensor of dim=`dim`.
+            dim (`int`, *optional*, defaults to `0`):
+                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
+                or dim=1 (sequence length).
+        """
+        if dim not in [0, 1]:
+            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
+
+        # By default chunk size is 1
+        chunk_size = chunk_size or 1
+
+        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
+            if hasattr(module, "set_chunk_feed_forward"):
+                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
+
+            for child in module.children():
+                fn_recursive_feed_forward(child, chunk_size, dim)
+
+        for module in self.children():
+            fn_recursive_feed_forward(module, chunk_size, dim)
+
+    def forward(
+            self,
+            sample: torch.FloatTensor,
+            timestep: Union[torch.Tensor, float, int],
+            encoder_hidden_states: torch.Tensor,
+            added_time_ids: torch.Tensor,
+            pose_latents: torch.Tensor = None,
+            image_only_indicator: bool = False,
+            return_dict: bool = True,
+    ) -> Union[UNetSpatioTemporalConditionOutput, Tuple]:
+        r"""
+        The [`UNetSpatioTemporalConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, num_frames, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, cross_attention_dim)`.
+            added_time_ids: (`torch.FloatTensor`):
+                The additional time ids with shape `(batch, num_additional_ids)`. These are encoded with sinusoidal
+                embeddings and added to the time embeddings.
+            pose_latents: (`torch.FloatTensor`):
+                The additional latents for pose sequences.
+            image_only_indicator (`bool`, *optional*, defaults to `False`):
+                Whether or not training with all images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] 
+                instead of a plain tuple.
+        Returns:
+            [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] or `tuple`:
+                If `return_dict` is True, 
+                an [`~models.unet_slatio_temporal.UNetSpatioTemporalConditionOutput`] is returned, 
+                otherwise a `tuple` is returned where the first element is the sample tensor.
+        """
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        batch_size, num_frames = sample.shape[:2]
+        timesteps = timesteps.expand(batch_size)
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb)
+
+        time_embeds = self.add_time_proj(added_time_ids.flatten())
+        time_embeds = time_embeds.reshape((batch_size, -1))
+        time_embeds = time_embeds.to(emb.dtype)
+        aug_emb = self.add_embedding(time_embeds)
+        emb = emb + aug_emb
+
+        # Flatten the batch and frames dimensions
+        # sample: [batch, frames, channels, height, width] -> [batch * frames, channels, height, width]
+        sample = sample.flatten(0, 1)
+        # Repeat the embeddings num_video_frames times
+        # emb: [batch, channels] -> [batch * frames, channels]
+        emb = emb.repeat_interleave(num_frames, dim=0)
+        # encoder_hidden_states: [batch, 1, channels] -> [batch * frames, 1, channels]
+        encoder_hidden_states = encoder_hidden_states.repeat_interleave(num_frames, dim=0)
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+        if pose_latents is not None:
+            sample = sample + pose_latents
+
+        image_only_indicator = torch.ones(batch_size, num_frames, dtype=sample.dtype, device=sample.device) \
+            if image_only_indicator else torch.zeros(batch_size, num_frames, dtype=sample.dtype, device=sample.device)
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                )
+            else:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    image_only_indicator=image_only_indicator,
+                )
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        sample = self.mid_block(
+            hidden_states=sample,
+            temb=emb,
+            encoder_hidden_states=encoder_hidden_states,
+            image_only_indicator=image_only_indicator,
+        )
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    image_only_indicator=image_only_indicator,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    image_only_indicator=image_only_indicator,
+                )
+
+        # 6. post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        # 7. Reshape back to original shape
+        sample = sample.reshape(batch_size, num_frames, *sample.shape[1:])
+
+        if not return_dict:
+            return (sample,)
+
+        return UNetSpatioTemporalConditionOutput(sample=sample)

mimicmotion/pipelines/pipeline_mimicmotion.py

+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
+
+from comfy.utils import ProgressBar
+
+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_pose_image(
+        self, 
+        pose_image: torch.Tensor, 
+        do_classifier_free_guidance: bool,
+    ):
+        # Get latents_pose
+        pose_latents = self.pose_net(pose_image)
+
+        if do_classifier_free_guidance:
+            negative_pose_latents = torch.zeros_like(pose_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
+            pose_latents = torch.cat([negative_pose_latents, pose_latents])
+
+        return pose_latents
+
+    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)
+        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):
+        return True # TODO
+        if isinstance(self.guidance_scale, (int, float)):
+            return self.guidance_scale
+        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,
+        first_n_frames=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
+        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
+
+        # 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
+
+        # needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+        # if needs_upcasting:
+        #     self.vae.to(dtype=torch.float32)
+
+        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)
+
+        ref_latent = first_n_frames[:, 0] if first_n_frames is not None else None
+        pose_latents = self._encode_pose_image(
+            image_pose, do_classifier_free_guidance=self.do_classifier_free_guidance,
+        )
+
+        # cast back to fp16 if needed
+        # if needs_upcasting:
+        #     self.vae.to(dtype=torch.float16)
+
+        # 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)
+        pose_latents = einops.rearrange(pose_latents, '(b f) c h w -> b f c h w', f=num_frames)
+        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)])
+        comfy_pbar = ProgressBar(len(timesteps))
+        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,))
+                # image_pose = pixel_values_pose[:, frame_start:frame_start + self.num_frames, ...]
+                weight = (torch.arange(tile_size, device=device) + 0.5) * 2. / tile_size
+                weight = torch.minimum(weight, 2 - weight)
+                for idx in indices:
+                    _noise_pred = self.unet(
+                        latent_model_input[:, idx],
+                        t,
+                        encoder_hidden_states=image_embeddings,
+                        added_time_ids=added_time_ids,
+                        pose_latents=pose_latents[:, idx].flatten(0, 1),
+                        image_only_indicator=image_only_indicator,
+                        return_dict=False,
+                    )[0]
+                    noise_pred[:, idx] += _noise_pred * weight[:, None, None, None]
+                    noise_pred_cnt[idx] += weight
+                    progress_bar.update()
+                    comfy_pbar.update(1)
+                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)
+
+                if first_n_frames is not None:
+                    sigma = self.scheduler.sigmas[self.scheduler.step_index]
+                    _latents = latents[:, 1:1 + first_n_frames.size(1)]
+                    tmp = (first_n_frames - _latents / (sigma ** 2 + 1)) / (-sigma / ((sigma ** 2 + 1) ** 0.5))
+                    noise_pred[:, 1:1 + first_n_frames.size(1)] = tmp
+
+                # 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)
+
+        if not output_type == "latent":
+            # cast back to fp16 if needed
+            # if needs_upcasting:
+            #     self.vae.to(dtype=torch.float16)
+            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)

mimicmotion/utils/loader.py

+import logging
+
+import torch
+import torch.utils.checkpoint
+from diffusers.models import AutoencoderKLTemporalDecoder
+from diffusers.schedulers import EulerDiscreteScheduler
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from ..modules.unet import UNetSpatioTemporalConditionModel
+from ..modules.pose_net import PoseNet
+from ..pipelines.pipeline_mimicmotion import MimicMotionPipeline
+
+logger = logging.getLogger(__name__)
+
+class MimicMotionModel(torch.nn.Module):
+    def __init__(self, base_model_path):
+        """construnct base model components and load pretrained svd model except pose-net
+        Args:
+            base_model_path (str): pretrained svd model path
+        """
+        super().__init__()
+        self.unet = UNetSpatioTemporalConditionModel.from_config(
+            UNetSpatioTemporalConditionModel.load_config(base_model_path, subfolder="unet"))
+        self.vae = AutoencoderKLTemporalDecoder.from_pretrained(
+            base_model_path, subfolder="vae").half()
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            base_model_path, subfolder="image_encoder")
+        self.noise_scheduler = EulerDiscreteScheduler.from_pretrained(
+            base_model_path, subfolder="scheduler")
+        self.feature_extractor = CLIPImageProcessor.from_pretrained(
+            base_model_path, subfolder="feature_extractor")
+        # pose_net
+        self.pose_net = PoseNet(noise_latent_channels=self.unet.config.block_out_channels[0])
+
+def create_pipeline(infer_config, device):
+    """create mimicmotion pipeline and load pretrained weight
+
+    Args:
+        infer_config (str): 
+        device (str or torch.device): "cpu" or "cuda:{device_id}"
+    """
+    mimicmotion_models = MimicMotionModel(infer_config.base_model_path).to(device=device).eval()
+    mimicmotion_models.load_state_dict(torch.load(infer_config.ckpt_path, map_location=device), strict=False)
+    pipeline = MimicMotionPipeline(
+        vae=mimicmotion_models.vae, 
+        image_encoder=mimicmotion_models.image_encoder, 
+        unet=mimicmotion_models.unet, 
+        scheduler=mimicmotion_models.noise_scheduler,
+        feature_extractor=mimicmotion_models.feature_extractor, 
+        pose_net=mimicmotion_models.pose_net
+    )
+    return pipeline
+

mimicmotion/utils/utils.py

+import logging
+from pathlib import Path
+
+from torchvision.io import write_video
+
+logger = logging.getLogger(__name__)
+
+def save_to_mp4(frames, save_path, fps=7):
+    frames = frames.permute((0, 2, 3, 1))  # (f, c, h, w) to (f, h, w, c)
+    Path(save_path).parent.mkdir(parents=True, exist_ok=True)
+    write_video(save_path, frames, fps=fps)
+

nodes.py

+import os
+from omegaconf import OmegaConf
+import torch
+import torch.nn.functional as F
+import sys
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(script_directory)
+
+from einops import repeat
+import folder_paths
+import comfy.model_management as mm
+import comfy.utils
+
+from contextlib import nullcontext
+try:
+    from accelerate import init_empty_weights
+    is_accelerate_available = True
+except:
+    pass
+
+from mimicmotion.pipelines.pipeline_mimicmotion import MimicMotionPipeline
+
+from diffusers.models import AutoencoderKLTemporalDecoder
+from diffusers.schedulers import EulerDiscreteScheduler
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from mimicmotion.modules.unet import UNetSpatioTemporalConditionModel
+from mimicmotion.modules.pose_net import PoseNet
+from mimicmotion.pipelines.pipeline_mimicmotion import MimicMotionPipeline
+
+class MimicMotionModel(torch.nn.Module):
+    def __init__(self, base_model_path):
+        """construnct base model components and load pretrained svd model except pose-net
+        Args:
+            base_model_path (str): pretrained svd model path
+        """
+        super().__init__()
+        self.unet = UNetSpatioTemporalConditionModel.from_config(
+            UNetSpatioTemporalConditionModel.load_config(base_model_path, subfolder="unet", variant="fp16"))
+        self.vae = AutoencoderKLTemporalDecoder.from_pretrained(
+            base_model_path, subfolder="vae", variant="fp16")
+        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+            base_model_path, subfolder="image_encoder", variant="fp16")
+        self.noise_scheduler = EulerDiscreteScheduler.from_pretrained(
+            base_model_path, subfolder="scheduler")
+        self.feature_extractor = CLIPImageProcessor.from_pretrained(
+            base_model_path, subfolder="feature_extractor")
+        # pose_net
+        self.pose_net = PoseNet(noise_latent_channels=self.unet.config.block_out_channels[0])
+
+class DownloadAndLoadMimicMotionModel:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model": (
+                    [   'MimicMotion-fp16.safetensors',
+                    ],
+                    ),
+            "precision": (
+                    [
+                        'fp32',
+                        'fp16',
+                        'bf16',
+                    ], {
+                        "default": 'fp16'
+                    }),
+            },
+        }
+
+    RETURN_TYPES = ("MIMICPIPE",)
+    RETURN_NAMES = ("mimic_pipeline",)
+    FUNCTION = "loadmodel"
+    CATEGORY = "MimicMotionWrapper"
+
+    def loadmodel(self, precision, model):
+        device = mm.get_torch_device()
+        mm.soft_empty_cache()
+        dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
+        
+        download_path = os.path.join(folder_paths.models_dir, "mimicmotion")
+        model_path = os.path.join(download_path, model)
+        
+        if not os.path.exists(model_path):
+            print(f"Downloading model to: {model_path}")
+            from huggingface_hub import snapshot_download
+            snapshot_download(repo_id="Kijai/MimicMotion_pruned", 
+                                allow_patterns=[f"*{model}*"],
+                                local_dir=download_path, 
+                                local_dir_use_symlinks=False)
+
+        ckpt_base_name = os.path.basename(model_path)
+        print(f"Loading model from: {model_path}")
+
+        svd_path = os.path.join(folder_paths.models_dir, "diffusers", "stable-video-diffusion-img2vid-xt-1-1")
+
+        if not os.path.exists(svd_path):
+            raise ValueError(f"Please download stable-video-diffusion-img2vid-xt-1-1 to {svd_path}")
+
+        mimicmotion_models = MimicMotionModel(svd_path).to(device=device).eval()
+        mimicmotion_models.load_state_dict(comfy.utils.load_torch_file(model_path), strict=False)
+
+        pipeline = MimicMotionPipeline(
+            vae=mimicmotion_models.vae, 
+            image_encoder=mimicmotion_models.image_encoder, 
+            unet=mimicmotion_models.unet, 
+            scheduler=mimicmotion_models.noise_scheduler,
+            feature_extractor=mimicmotion_models.feature_extractor, 
+            pose_net=mimicmotion_models.pose_net,
+        )
+        pipeline.unet.to(dtype)
+        pipeline.pose_net.to(dtype)
+        pipeline.vae.to(dtype)
+        pipeline.image_encoder.to(dtype)
+        pipeline.pose_net.to(dtype)        
+
+        mimic_model = {
+            'pipeline': pipeline,
+            'dtype': dtype
+        }
+        return (mimic_model,)
+    
+class MimicMotionSampler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "mimic_pipeline": ("MIMICPIPE",),
+            "ref_image": ("IMAGE",),
+            "pose_images": ("IMAGE",),
+            "steps": ("INT", {"default": 25, "min": 1, "max": 200, "step": 1}),
+            "cfg_min": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 20.0, "step": 0.01}),
+            "cfg_max": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 20.0, "step": 0.01}),
+            "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+            "fps": ("INT", {"default": 15, "min": 2, "max": 100, "step": 1}),
+            "noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            "keep_model_loaded": ("BOOLEAN", {"default": True}),            
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "process"
+    CATEGORY = "MimicMotionWrapper"
+
+    def process(self, mimic_pipeline, ref_image, pose_images, cfg_min, cfg_max, steps, seed, noise_aug_strength, fps, keep_model_loaded):
+        device = mm.get_torch_device()
+        offload_device = mm.unet_offload_device()
+        mm.unload_all_models()
+        mm.soft_empty_cache()
+        dtype = mimic_pipeline['dtype']
+        pipeline = mimic_pipeline['pipeline']
+
+        B, H, W, C = pose_images.shape
+        ref_image = ref_image.permute(0, 3, 1, 2).to(device).to(dtype)
+        pose_images = pose_images.permute(0, 3, 1, 2).to(device).to(dtype)
+        ref_image = ref_image * 2 - 1
+        pose_images = pose_images * 2 - 1
+
+        generator = torch.Generator(device=device)
+        generator.manual_seed(seed)
+
+        frames = pipeline(
+            ref_image, 
+            image_pose=pose_images, 
+            num_frames=B,
+            tile_size = 16, 
+            tile_overlap= 6,
+            height=H,
+            width=W, 
+            fps=fps,
+            noise_aug_strength=noise_aug_strength, 
+            num_inference_steps=steps,
+            generator=generator,
+            min_guidance_scale=cfg_min, 
+            max_guidance_scale=cfg_max, 
+            decode_chunk_size=8, 
+            output_type="pt", 
+            device=device
+        ).frames
+        frames = frames.squeeze(0).permute(0, 2, 3, 1).cpu().float()
+        print(frames.shape)
+
+        return frames,
+    
+
+NODE_CLASS_MAPPINGS = {
+    "DownloadAndLoadMimicMotionModel": DownloadAndLoadMimicMotionModel,
+    "MimicMotionSampler": MimicMotionSampler,
+
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DownloadAndLoadMimicMotionModel": "DownloadAndLoadMimicMotionModel",
+    "MimicMotionSampler": "MimicMotionSampler",
+}

requirements.txt

+diffusers>=0.27.0
+transformers>=4.32.1
\ No newline at end of file