add musev

MuseV/Dockerfile

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+FROM anchorxia/musev:1.0.0
+
+#MAINTAINER 维护者信息
+LABEL MAINTAINER="anchorxia"
+LABEL Email="anchorxia@tencent.com"
+LABEL Description="musev gpu runtime image, base docker is pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel"
+ARG DEBIAN_FRONTEND=noninteractive
+
+USER root
+
+SHELL ["/bin/bash", "--login", "-c"]
+
+RUN . /opt/conda/etc/profile.d/conda.sh  \
+    && echo "source activate musev" >> ~/.bashrc \
+    && conda activate musev \
+    && conda env list \
+    && pip --no-cache-dir install cuid gradio==4.12 spaces
+USER root

MuseV/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 TMElyralab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MuseV/README-zh.md

@@ -0,0 +1,542 @@
+# MuseV [English](README.md) [中文](README-zh.md)
+
+<font size=5>MuseV：基于视觉条件并行去噪的无限长度和高保真虚拟人视频生成。
+</br>
+Zhiqiang Xia <sup>\*</sup>,
+Zhaokang Chen<sup>\*</sup>,
+Bin Wu<sup>†</sup>,
+Chao Li,
+Kwok-Wai Hung,
+Chao Zhan,
+Yingjie He, 
+Wenjiang Zhou
+(<sup>*</sup>co-first author, <sup>†</sup>Corresponding Author, benbinwu@tencent.com)
+</font>
+
+**[github](https://github.com/TMElyralab/MuseV)**    **[huggingface](https://huggingface.co/TMElyralab/MuseV)**   **[HuggingfaceSpace](https://huggingface.co/spaces/AnchorFake/MuseVDemo)**  **[project](https://tmelyralab.github.io/)**    **Technical report (comming soon)**
+
+
+我们在2023年3月相信扩散模型可以模拟世界，也开始基于扩散模型研发世界视觉模拟器。`MuseV`是在 2023 年 7 月左右实现的一个里程碑。受到 Sora 进展的启发，我们决定开源 MuseV。MuseV 站在开源的肩膀上成长，也希望能够借此反馈社区。接下来，我们将转向有前景的扩散+变换器方案。
+
+我们已经发布 <a href="https://github.com/TMElyralab/MuseTalk" style="font-size:24px; color:red;">MuseTalk</a>. `MuseTalk`是一个实时高质量的唇同步模型，可与 `MuseV` 一起构建完整的`虚拟人生成解决方案`。请保持关注！
+
+# 概述
+
+`MuseV` 是基于扩散模型的虚拟人视频生成框架，具有以下特点：
+
+1. 支持使用新颖的视觉条件并行去噪方案进行无限长度生成，不会再有误差累计的问题，尤其适用于固定相机位的场景。
+1. 提供了基于人物类型数据集训练的虚拟人视频生成预训练模型。
+1. 支持图像到视频、文本到图像到视频、视频到视频的生成。
+1. 兼容 `Stable Diffusio`n 文图生成生态系统，包括 `base_model`、`lora`、`controlnet` 等。
+1. 支持多参考图像技术，包括 `IPAdapter`、`ReferenceOnly`、`ReferenceNet`、`IPAdapterFaceID`。
+1. 我们后面也会推出训练代码。
+
+# 重要更新
+1. `musev_referencenet_pose`: `unet`, `ip_adapter` 的模型名字指定错误，请使用 `musev_referencenet_pose`而不是`musev_referencenet`，请使用最新的main分支。
+
+# 进展
+- [2024年3月27日] 发布 `MuseV` 项目和训练好的模型 `musev`、`muse_referencenet`、`muse_referencenet_pose`。
+- [03/30/2024] 在 huggingface space 上新增 [gui](https://huggingface.co/spaces/AnchorFake/MuseVDemo) 交互方式来生成视频.
+
+## 模型
+### 模型结构示意图
+![model_structure](./data/models/musev_structure.png)
+### 并行去噪算法示意图
+![parallel_denoise](./data//models/parallel_denoise.png)
+
+## 测试用例
+生成结果的所有帧直接由`MuseV`生成，没有时序超分辨、空间超分辨等任何后处理。
+<!-- # TODO: // use youtu video link? -->
+以下所有测试用例都维护在 `configs/tasks/example.yaml`，可以直接运行复现。
+**[project](https://tmelyralab.github.io/)** 有更多测试用例，包括一两分钟的长视频。
+
+### 输入文本、图像的视频生成
+#### 人类
+<table class="center">
+  <tr style="font-weight: bolder;text-align:center;">
+        <td width="50%">image</td>
+        <td width="45%">video </td>
+        <td width="5%">prompt</td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/yongen.jpeg width="400">
+    </td>
+    <td >
+     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/732cf1fd-25e7-494e-b462-969c9425d277" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/jinkesi2.jpeg width="400">
+    </td>
+    <td>
+     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/62b533d3-95f3-48db-889d-75dde1ad04b7" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/seaside4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/9b75a46c-f4e6-45ef-ad02-05729f091c8f" width="100" controls preload></video>
+    </td>   
+    <td>
+    (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/seaside_girl.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d0f3b401-09bf-4018-81c3-569ec24a4de9" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+  <!-- guitar  -->
+  <tr>
+    <td>
+      <img src=./data/images/boy_play_guitar.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/61bf955e-7161-44c8-a498-8811c4f4eb4f" width="100" controls preload></video>
+    </td>
+    <td>
+       (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/girl_play_guitar2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/40982aa7-9f6a-4e44-8ef6-3f185d284e6a" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/boy_play_guitar2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/69ea9d0c-5ed0-44b9-bca9-a4829c8d8b68" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/girl_play_guitar4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d242e8a4-08ab-474f-b4a8-b718780d2991" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <!-- famous people -->
+  <tr>
+    <td>
+      <img src=./data/images/dufu.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/28294baa-b996-420f-b1fb-046542adf87d" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3),Chinese ink painting style
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/Mona_Lisa.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/1ce11da6-14c6-4dcd-b7f9-7a5f060d71fb" width="100" controls preload></video>
+    </td>   
+    <td>
+    (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/Portrait-of-Dr.-Gachet.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/4072410a-ecea-4ee5-a9b4-735f9f462d51" width="100" controls preload></video>
+    </td>
+    <td>
+  (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3)
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/Self-Portrait-with-Cropped-Hair.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/5148beda-a1e1-44f0-ad84-2fb99ad73a11" width="100" controls preload></video>
+    </td>
+    <td>
+  (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3), animate
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/The-Laughing-Cavalier.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/df1c5943-15a3-41f5-afe7-e7497c81836d" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+</table >
+
+#### 场景
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td width="35%">image</td>
+        <td width="50%">video</td>
+        <td width="15%">prompt</td>
+    </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/waterfall4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/852daeb6-6b58-4931-81f9-0dddfa1b4ea5" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), peaceful beautiful waterfall, an
+    endless waterfall
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/river.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d5cb2798-b5ce-497a-a058-ae63d664028e" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1), peaceful beautiful river
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/seaside2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/4a4d527a-6203-411f-afe9-31c992d26816" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+</table >
+
+### 输入视频条件的视频生成
+当前生成模式下，需要参考视频的首帧条件和参考图像的首帧条件对齐，不然会破坏首帧的信息，效果会更差。所以一般生成流程是
+1. 确定参考视频；
+2. 用参考视频的首帧走图生图、controlnet流程，可以使用`MJ`等各种平台；
+3. 拿2中的生成图、参考视频用MuseV生成视频；
+4. 
+**pose2video**
+
+`duffy` 的测试用例中，视觉条件帧的姿势与���制视频的第一帧不对齐。需要`posealign` 将解决这个问题。
+
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td width="25%">image</td>
+        <td width="65%">video</td>
+        <td width="10%">prompt</td>
+    </tr>
+  <tr>
+    <td>
+      <img src=./data/images/spark_girl.png width="200">
+      <img src=./data/images/cyber_girl.png width="200">
+    </td>
+    <td>
+        <video src="https://github.com/TMElyralab/MuseV/assets/163980830/484cc69d-c316-4464-a55b-3df929780a8e" width="400" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1) , a girl is dancing, animation
+    </td>
+  </tr>
+  <tr>   
+    <td>
+      <img src=./data/images/duffy.png width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/c44682e6-aafc-4730-8fc1-72825c1bacf2" width="400" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), is dancing, animation
+    </td>
+  </tr>
+</table >
+
+### MuseTalk
+
+`talk`的角色`孙昕荧`著名的网络大V，可以在 [抖音](https://www.douyin.com/user/MS4wLjABAAAAWDThbMPN_6Xmm_JgXexbOii1K-httbu2APdG8DvDyM8) 关注。
+
+<table class="center">
+    <tr style="font-weight: bolder;">
+        <td width="35%">name</td>
+        <td width="50%">video</td>
+    </tr>
+
+  <tr>
+    <td>
+       talk
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/951188d1-4731-4e7f-bf40-03cacba17f2f" width="100" controls preload></video>
+    </td>
+  </tr>
+    <tr>
+    <td>
+       talk
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/ba0396ab-8aba-4440-803c-18b078ae1dd9" width="100" controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+       sing
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/50b8ffab-9307-4836-99e5-947e6ce7d112" width="100" controls preload></video>
+    </td>
+  </tr>
+</table >
+
+
+# 待办事项：
+- [ ] 技术报告（即将推出）。
+- [ ] 训练代码。
+- [ ] 扩散变换生成框架。
+- [ ] `posealign` 模块。
+
+# 快速入门
+准备 Python 环境并安装额外的包，如 `diffusers`、`controlnet_aux`、`mmcm`。
+
+## 准备环境
+建议您优先使用 `docker` 来准备 Python 环境。
+
+### 准备 Python 环境
+**注意**：我们只测试了 Docker，使用 conda 或其他环境可能会遇到问题。我们将尽力解决。但依然请优先使用 `docker`。
+
+#### 方法 1：使用 Docker
+1. 拉取 Docker 镜像
+```bash
+docker pull anchorxia/musev:latest
+```
+2. 运行 Docker 容器
+```bash
+docker run --gpus all -it --entrypoint /bin/bash anchorxia/musev:latest
+```
+docker启动后默认的 conda 环境是 `musev`。
+
+#### 方法 2：使用 conda
+从 environment.yaml 创建 conda 环境
+```
+conda env create --name musev --file ./environment.yml
+```
+#### 方法 3：使用 pip requirements
+```
+pip install -r requirements.txt
+```
+#### 准备 [openmmlab](https://openmmlab.com/) 包
+如果不使用 Docker方式，还需要额外安装 mmlab 包。
+```bash
+pip install --no-cache-dir -U openmim 
+mim install mmengine 
+mim install "mmcv>=2.0.1" 
+mim install "mmdet>=3.1.0" 
+mim install "mmpose>=1.1.0" 
+```
+
+### 准备我们开发的包
+#### 下载
+```bash
+git clone --recursive https://github.com/TMElyralab/MuseV.git
+```
+#### 准备 PYTHONPATH
+```bash
+current_dir=$(pwd)
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/MMCM
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/diffusers/src
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/controlnet_aux/src
+cd MuseV
+```
+
+1. `MMCM`：多媒体、跨模态处理包。
+1. `diffusers`：基于 [diffusers](https://github.com/huggingface/diffusers) 修改的 diffusers 包。
+1. `controlnet_aux`：基于 [controlnet_aux](https://github.com/TMElyralab/controlnet_aux) 修改的包。
+
+
+## 下载模型
+```bash
+git clone https://huggingface.co/TMElyralab/MuseV ./checkpoints
+```
+- `motion`：多个版本的视频生成模型。使用小数据集 `ucf101` 和小 `webvid` 数据子集进行训练，约 60K 个视频文本对。GPU 内存消耗测试在 `resolution` $=512*512，`time_size=12`。
+    - `musev/unet`：这个版本 仅训练 `unet` 运动模块。推断 `GPU 内存消耗` $\approx 8G$。
+    - `musev_referencenet`：这个版本训练 `unet` 运动模块、`referencenet`、`IPAdapter`。推断 `GPU 内存消耗` $\approx 12G$。
+        - `unet`：`motion` 模块，具有 `Attention` 层中的 `to_k`、`to_v`，参考 `IPAdapter`。
+        - `referencenet`：类似于 `AnimateAnyone`。
+        - `ip_adapter_image_proj.bin`：图像特征变换层，参考 `IPAdapter`。
+    - `musev_referencenet_pose`：这个版本基于 `musev_referencenet`，固定 `referencenet` 和 `controlnet_pose`，训练 `unet motion` 和 `IPAdapter`。推断 `GPU 内存消耗` $\approx 12G$。
+- `t2i/sd1.5`：text2image 模型，在训练运动模块时参数被冻结。
+    - majicmixRealv6Fp16：示例，可以替换为其他 t2i 基础。从 [majicmixRealv6Fp16](https://civitai.com/models/43331/majicmix-realistic) 下载。
+- `IP-Adapter/models`：从 [IPAdapter](https://huggingface.co/h94/IP-Adapter/tree/main) 下载。
+    - `image_encoder`：视觉特征抽取模型。
+    - `ip-adapter_sd15.bin`：原始 IPAdapter 模型预训练权重。
+    - `ip-adapter-faceid_sd15.bin`：原始 IPAdapter 模型预训练权重。
+
+## 推理
+
+### 准备模型路径
+当使用示例推断命令运行示例任务时，可以跳过此步骤。
+该模块主要是在配置文件中设置模型路径和缩写，以在推断脚本中使用简单缩写而不是完整路径。
+- T2I SD：参考 `musev/configs/model/T2I_all_model.py`
+- 运动 Unet：参考 `musev/configs/model/motion_model.py`
+- 任务：参考 `musev/configs/tasks/example.yaml`
+
+### musev_referencenet
+#### 输入文本、图像的视频生成
+```bash
+python scripts/inference/text2video.py   --sd_model_name majicmixRealv6Fp16   --unet_model_name musev_referencenet --referencenet_model_name musev_referencenet --ip_adapter_model_name musev_referencenet   -test_data_path ./configs/tasks/example.yaml  --output_dir ./output  --n_batch 1  --target_datas yongen  --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder  --time_size 12 --fps 12  
+```
+**通用参数**：
+- `test_data_path`：测试用例 任务路径
+- `target_datas`：如果 `test_data_path` 中的 `name` 在 `target_datas` 中，则只运行这些子任务。`sep` 是 `,`；
+- `sd_model_cfg_path`：T2I sd 模型路径，模型配置路径或模型路径。
+- `sd_model_name`：sd 模型名称，用于在 `sd_model_cfg_path` 中选择完整模型路径。使用 `,` 分隔的多个模型名称，或 `all`。
+- `unet_model_cfg_path`：运动 unet 模型配置路径或模型路径。
+- `unet_model_name`：unet 模型名称，用于获取 `unet_model_cfg_path` 中的模型路径，并在 `musev/models/unet_loader.py` 中初始化 unet 类实例。使用 `,` 分隔的多个模型名称，或 `all`。如果 `unet_model_cfg_path` 是模型路径，则 `unet_name` 必须在 `musev/models/unet_loader.py` 中支持。
+- `time_size`：扩散模型每次生成一个片段，这里是一个片段的帧数。默认为 `12`。
+- `n_batch`：首尾相连方式生成总片段数，$total\_frames=n\_batch * time\_size + n\_viscond$，默认为 `1`。
+- `context_frames`： 并行去噪子窗口一次生成的帧数。如果 `time_size` > `context_frame`，则会启动并行去噪逻辑， `time_size` 窗口会分成多个子窗口进行并行去噪。默认为 `12`。
+
+生成长视频，有两种方法，可以共同使用：
+1. `视觉条件并行去噪`：设置 `n_batch=1`，`time_size` = 想要的所有帧。
+2. `传统的首尾相连方式`：设置 `time_size` = `context_frames` = 一次片段的帧数 (`12`)，`context_overlap` = 0。会首尾相连方式生成`n_batch`片段数，首尾相连存在误差累计，当`n_batch`越大，最后的结果越差。
+
+
+**模型参数**：
+支持 `referencenet`、`IPAdapter`、`IPAdapterFaceID`、`Facein`。
+- `referencenet_model_name`：`referencenet` 模型名称。
+- `ImageClipVisionFeatureExtractor`：`ImageEmbExtractor` 名称，在 `IPAdapter` 中提取视觉特征。
+- `vision_clip_model_path`：`ImageClipVisionFeatureExtractor` 模型路径。
+- `ip_adapter_model_name`：来自 `IPAdapter` 的，它是 `ImagePromptEmbProj`，与 `ImageEmbExtractor` 一起使用。
+- `ip_adapter_face_model_name`：`IPAdapterFaceID`，来自 `IPAdapter`，应该设置 `face_image_path`。
+
+**一些影响运动范围和生成结果的参数**：
+- `video_guidance_scale`：类似于 text2image，控制 cond 和 uncond 之间的影响，影响较大，默认为 `3.5`。
+- `guidance_scale`：在第一帧图像中 cond 和 uncond 之间的参数比例，，影响不大，默认为 `3.5`。
+- `use_condition_image`：是否使用给定的第一帧进行视频生成。
+- `redraw_condition_image`：是否重新绘制给定的第一帧图像。
+- `video_negative_prompt`：配置文件中全 `negative_prompt` 的缩写。默认为 `V2`。
+
+
+#### 输入视频的视频生成
+```bash
+python scripts/inference/video2video.py --sd_model_name majicmixRealv6Fp16  --unet_model_name musev_referencenet --referencenet_model_name   musev_referencenet --ip_adapter_model_name musev_referencenet    -test_data_path ./configs/tasks/example.yaml    --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder      --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas dance1 --fps 12 --time_size 12
+```
+**一些重要参数**
+
+大多数参数与 `musev_text2video` 相同。`video2video` 的特殊参数有：
+1. 需要在 `test_data` 中设置 `video_path`��现在支持 `rgb video` 和 `controlnet_middle_video`。
+- `which2video`： 参与引导视频视频的参考视频部分。 如果是 `video_middle`，则只使用类似`pose`、`depth`的 `video_middle`，如果是 `video`， 视频本身会参与视频噪声初始化。等价于`img2imge`。
+- `controlnet_name`：是否使用 `controlnet condition`，例如 `dwpose,depth`， pose的话 优先建议使用`dwpose_body_hand`。
+- `video_is_middle`：`video_path` 是 `rgb video` 还是 `controlnet_middle_video`。可以为 `test_data_path` 中的每个 `test_data` 设置。
+- `video_has_condition`：condtion_images 是否与 video_path 的第一帧对齐。如果不是，则首先生成 `condition_images`，然后与拼接对齐。设置在 `test_data` 中。
+
+所有 `controlnet_names` 维护在 [mmcm](https://github.com/TMElyralab/MMCM/blob/main/mmcm/vision/feature_extractor/controlnet.py#L513)
+```python
+['pose', 'pose_body', 'pose_hand', 'pose_face', 'pose_hand_body', 'pose_hand_face', 'dwpose', 'dwpose_face', 'dwpose_hand', 'dwpose_body', 'dwpose_body_hand', 'canny', 'tile', 'hed', 'hed_scribble', 'depth', 'pidi', 'normal_bae', 'lineart', 'lineart_anime', 'zoe', 'sam', 'mobile_sam', 'leres', 'content', 'face_detector']
+```
+
+### musev_referencenet_pose
+仅用于 `pose2video`
+基于 `musev_referencenet` 训练，固定 `referencenet`、`pose-controlnet` 和 `T2I`，训练 `motion` 模块和 `IPAdapter`。
+```bash
+python scripts/inference/video2video.py --sd_model_name majicmixRealv6Fp16  --unet_model_name musev_referencenet_pose --referencenet_model_name   musev_referencenet --ip_adapter_model_name musev_referencenet_pose    -test_data_path ./configs/tasks/example.yaml    --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder      --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas  dance1   --fps 12 --time_size 12
+```
+
+### musev
+仅有动作模块，没有 referencenet，需要更少的 GPU 内存。
+#### 文本到视频
+```bash
+python scripts/inference/text2video.py   --sd_model_name majicmixRealv6Fp16   --unet_model_name musev   -test_data_path ./configs/tasks/example.yaml  --output_dir ./output  --n_batch 1  --target_datas yongen  --time_size 12 --fps 12
+```
+#### 视频到视频
+```bash
+python scripts/inference/video2video.py --sd_model_name majicmixRealv6Fp16  --unet_model_name musev    -test_data_path ./configs/tasks/example.yaml --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas  dance1   --fps 12 --time_size 12
+```
+
+### Gradio 演示
+MuseV 提供 gradio 脚本，可在本地机器上生成 GUI，方便生成视频。
+
+```bash
+cd scripts/gradio
+python app.py
+```
+
+# 致谢
+1. MuseV 开发过程中参考学习了很多开源工作 [TuneAVideo](https://github.com/showlab/Tune-A-Video)、[diffusers](https://github.com/huggingface/diffusers)、[Moore-AnimateAnyone](https://github.com/MooreThreads/Moore-AnimateAnyone/tree/master/src/pipelines)、[animatediff](https://github.com/guoyww/AnimateDiff)、[IP-Adapter](https://github.com/tencent-ailab/IP-Adapter)、[AnimateAnyone](https://arxiv.org/abs/2311.17117)、[VideoFusion](https://arxiv.org/abs/2303.08320) 和 [insightface](https://github.com/deepinsight/insightface)。
+2. MuseV 基于 `ucf101` 和 `webvid` 数据集构建。
+
+感谢开源社区的贡献！
+
+# 限制
+
+`MuseV` 仍然存在很多待优化项，包括：
+
+1. 缺乏泛化能力。对视觉条件帧敏感，有些视觉条件图像表现良好，有些表现不佳。有些预训练的 t2i 模型表现良好，有些表现不佳。
+1. 有限的视频生成类型和有限的动作范围，部分原因是训练数据类型有限。发布的 `MuseV` 已经在大约 6 万对分辨率为 `512*320` 的人类文本视频对上进行了训练。`MuseV` 在较低分辨率下具有更大的动作范围，但视频质量较低。`MuseV` 在高分辨率下画质很好、但动作范围较小。在更大、更高分辨率、更高质量的文本视频数据集上进行训练可能会使 `MuseV` 更好。
+1. 因为使用 `webvid` 训练会有水印问题。使用没有水印的、更干净的数据集可能会解决这个问题。
+1. 有限类型的长视频生成。视觉条件并行去噪可以解决视频生成的累积误差，但当前的方法只适用于相对固定的摄像机场景。
+1. referencenet 和 IP-Adapter 训练不足，因为时间有限和资源有限。
+1. 代码结构不够完善。`MuseV` 支持丰富而动态的功能，但代码复杂且未经过重构。熟悉需要时间。
+   
+
+<!-- # Contribution 暂时不需要组织开源共建 -->
+# 引用
+```bib
+@article{musev,
+  title={MuseV: 基于视觉条件的并行去噪的无限长度和高保真虚拟人视频生成},
+  author={Xia, Zhiqiang and Chen, Zhaokang and Wu, Bin and Li, Chao and Hung, Kwok-Wai and Zhan, Chao and He, Yingjie and Zhou, Wenjiang},
+  journal={arxiv},
+  year={2024}
+}
+```
+# 免责声明/许可
+1. `代码`：`MuseV` 的代码采用 `MIT` 许可证发布，学术用途和商业用途都可以。
+1. `模型`：训练好的模型仅供非商业研究目的使用。
+1. `其他开源模型`：使用的其他开源模型必须遵守他们的许可证，如 `insightface`、`IP-Adapter`、`ft-mse-vae` 等。
+1. 测试数据收集自互联网，仅供非商业研究目的使用。
+1. `AIGC`：本项目旨在积极影响基于人工智能的视频生成领域。用户被授予使用此工具创建视频的自由，但他们应该遵守当地法律，并负责任地使用。开发人员不对用户可能的不当使用承担任何责任。

MuseV/README.md

@@ -0,0 +1,545 @@
+# MuseV [English](README.md) [中文](README-zh.md)
+
+<font size=5>MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned  Parallel Denoising
+</br>
+Zhiqiang Xia <sup>\*</sup>,
+Zhaokang Chen<sup>\*</sup>,
+Bin Wu<sup>†</sup>,
+Chao Li,
+Kwok-Wai Hung,
+Chao Zhan,
+Yingjie He, 
+Wenjiang Zhou
+(<sup>*</sup>co-first author, <sup>†</sup>Corresponding Author, benbinwu@tencent.com)
+</font>
+
+**[github](https://github.com/TMElyralab/MuseV)**    **[huggingface](https://huggingface.co/TMElyralab/MuseV)**  **[HuggingfaceSpace](https://huggingface.co/spaces/AnchorFake/MuseVDemo)**  **[project](https://tmelyralab.github.io/)**    **Technical report (comming soon)**
+
+
+We have setup **the world simulator vision since March 2023, believing diffusion models can simulate the world**. `MuseV` was a milestone achieved around **July 2023**. Amazed by the progress of Sora, we decided to opensource `MuseV`, hopefully it will benefit the community. Next we will move on to the promising diffusion+transformer scheme.
+
+
+Update: We have released <a href="https://github.com/TMElyralab/MuseTalk" style="font-size:24px; color:red;">MuseTalk</a>, a real-time high quality lip sync model, which can be applied with MuseV as a complete virtual human generation solution.
+
+# Overview
+`MuseV` is a diffusion-based virtual human video generation framework, which 
+1. supports **infinite length** generation using a novel **Visual Conditioned Parallel Denoising scheme**.
+2. checkpoint available for virtual human video generation trained on human dataset.
+3. supports Image2Video, Text2Image2Video, Video2Video.
+4. compatible with the **Stable Diffusion ecosystem**, including `base_model`, `lora`, `controlnet`, etc. 
+5. supports multi reference image technology, including `IPAdapter`, `ReferenceOnly`, `ReferenceNet`, `IPAdapterFaceID`.
+6. training codes (comming very soon).
+
+# Important bug fixes
+1. `musev_referencenet_pose`: model_name of `unet`, `ip_adapter` of Command is not correct, please use `musev_referencenet_pose` instead of `musev_referencenet`.
+
+# News
+- [03/27/2024] release `MuseV` project and trained model `musev`, `muse_referencenet`.
+- [03/30/2024] add huggingface space gradio to generate video in gui
+
+## Model
+### Overview of model structure
+![model_structure](./data/models/musev_structure.png)
+### Parallel denoising
+![parallel_denoise](./data//models/parallel_denoise.png)
+
+## Cases
+All frames were generated directly from text2video model, without any post process.
+<!-- # TODO: // use youtu video link? -->
+Examples bellow can be accessed at `configs/tasks/example.yaml`
+MoreCase is in **[project](https://tmelyralab.github.io/)** 
+
+### Text/Image2Video
+
+#### Human
+
+<table class="center">
+  <tr style="font-weight: bolder;text-align:center;">
+        <td width="50%">image</td>
+        <td width="45%">video </td>
+        <td width="5%">prompt</td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/yongen.jpeg width="400">
+    </td>
+    <td >
+     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/732cf1fd-25e7-494e-b462-969c9425d277" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/jinkesi2.jpeg width="400">
+    </td>
+    <td>
+     <video src="https://github.com/TMElyralab/MuseV/assets/163980830/62b533d3-95f3-48db-889d-75dde1ad04b7" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/seaside4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/9b75a46c-f4e6-45ef-ad02-05729f091c8f" width="100" controls preload></video>
+    </td>   
+    <td>
+    (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/seaside_girl.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d0f3b401-09bf-4018-81c3-569ec24a4de9" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+  <!-- guitar  -->
+  <tr>
+    <td>
+      <img src=./data/images/boy_play_guitar.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/61bf955e-7161-44c8-a498-8811c4f4eb4f" width="100" controls preload></video>
+    </td>
+    <td>
+       (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/girl_play_guitar2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/40982aa7-9f6a-4e44-8ef6-3f185d284e6a" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/boy_play_guitar2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/69ea9d0c-5ed0-44b9-bca9-a4829c8d8b68" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/girl_play_guitar4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d242e8a4-08ab-474f-b4a8-b718780d2991" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1), playing guitar
+    </td>
+  </tr>
+  <!-- famous people -->
+  <tr>
+    <td>
+      <img src=./data/images/dufu.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/28294baa-b996-420f-b1fb-046542adf87d" width="100" controls preload></video>
+    </td>
+    <td>
+    (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3),Chinese ink painting style
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/Mona_Lisa.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/1ce11da6-14c6-4dcd-b7f9-7a5f060d71fb" width="100" controls preload></video>
+    </td>   
+    <td>
+    (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/Portrait-of-Dr.-Gachet.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/4072410a-ecea-4ee5-a9b4-735f9f462d51" width="100" controls preload></video>
+    </td>
+    <td>
+  (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3)
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/Self-Portrait-with-Cropped-Hair.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/5148beda-a1e1-44f0-ad84-2fb99ad73a11" width="100" controls preload></video>
+    </td>
+    <td>
+  (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3), animate
+    </td>
+  </tr>
+  <tr>
+    <td>
+      <img src=./data/images/The-Laughing-Cavalier.jpg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/df1c5943-15a3-41f5-afe7-e7497c81836d" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+    </td>
+  </tr>
+</table >
+
+#### Scene
+
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td width="35%">image</td>
+        <td width="50%">video</td>
+        <td width="15%">prompt</td>
+    </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/waterfall4.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/852daeb6-6b58-4931-81f9-0dddfa1b4ea5" width="100" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), peaceful beautiful waterfall, an
+    endless waterfall
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/river.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/d5cb2798-b5ce-497a-a058-ae63d664028e" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1), peaceful beautiful river
+    </td>
+  </tr>
+
+  <tr>
+    <td>
+      <img src=./data/images/seaside2.jpeg width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/4a4d527a-6203-411f-afe9-31c992d26816" width="100" controls preload></video>
+    </td>
+    <td>(masterpiece, best quality, highres:1), peaceful beautiful sea scene
+    </td>
+  </tr>
+</table >
+
+### VideoMiddle2Video
+
+**pose2video**
+In `duffy` mode, pose of the vision condition frame is not aligned with the first frame of control video. `posealign` will solve the problem.
+
+<table class="center">
+    <tr style="font-weight: bolder;text-align:center;">
+        <td width="25%">image</td>
+        <td width="65%">video</td>
+        <td width="10%">prompt</td>
+    </tr>
+  <tr>
+    <td>
+      <img src=./data/images/spark_girl.png width="200">
+      <img src=./data/images/cyber_girl.png width="200">
+    </td>
+    <td>
+        <video src="https://github.com/TMElyralab/MuseV/assets/163980830/484cc69d-c316-4464-a55b-3df929780a8e" width="400" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1) , a girl is dancing, animation
+    </td>
+  </tr>
+  <tr>   
+    <td>
+      <img src=./data/images/duffy.png width="400">
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/c44682e6-aafc-4730-8fc1-72825c1bacf2" width="400" controls preload></video>
+    </td>
+    <td>
+      (masterpiece, best quality, highres:1), is dancing, animation
+    </td>
+  </tr>
+</table >
+
+### MuseTalk
+The character of talk, `Sun Xinying` is a supermodel KOL. You can follow her on [douyin](https://www.douyin.com/user/MS4wLjABAAAAWDThbMPN_6Xmm_JgXexbOii1K-httbu2APdG8DvDyM8).
+
+<table class="center">
+    <tr style="font-weight: bolder;">
+        <td width="35%">name</td>
+        <td width="50%">video</td>
+    </tr>
+
+  <tr>
+    <td>
+       talk
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/951188d1-4731-4e7f-bf40-03cacba17f2f" width="100" controls preload></video>
+    </td>
+  </tr>
+    <tr>
+    <td>
+       talk
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/ba0396ab-8aba-4440-803c-18b078ae1dd9" width="100" controls preload></video>
+    </td>
+  </tr>
+  <tr>
+    <td>
+       sing
+    </td>
+    <td>
+      <video src="https://github.com/TMElyralab/MuseV/assets/163980830/50b8ffab-9307-4836-99e5-947e6ce7d112" width="100" controls preload></video>
+    </td>
+  </tr>
+</table >
+
+
+# TODO:
+- [ ] technical report (comming soon).
+- [ ] training codes.
+- [ ] release pretrained unet model, which is trained with controlnet、referencenet、IPAdapter, which is better on pose2video.
+- [ ] support diffusion transformer generation framework.
+- [ ] release `posealign` module
+
+# Quickstart
+Prepare python environment and install extra package like `diffusers`, `controlnet_aux`, `mmcm`.
+
+
+## Prepare environment
+You are recommended to use `docker` primarily to prepare python environment.
+### prepare python env
+**Attention**: we only test with docker, there are maybe trouble with conda, or requirement. We will try to fix it. Use `docker` Please.
+
+#### Method 1: docker
+1. pull docker image
+```bash
+docker pull anchorxia/musev:latest
+```
+2. run docker
+```bash
+docker run --gpus all -it --entrypoint /bin/bash anchorxia/musev:latest
+```
+The default conda env is `musev`.
+
+#### Method 2: conda 
+create conda environment from environment.yaml
+```
+conda env create --name musev --file ./environment.yml
+```
+#### Method 3: pip requirements
+```
+pip install -r requirements.txt
+```
+#### Prepare mmlab package
+if not use docker, should install mmlab package additionally.
+```bash
+pip install --no-cache-dir -U openmim 
+mim install mmengine 
+mim install "mmcv>=2.0.1" 
+mim install "mmdet>=3.1.0" 
+mim install "mmpose>=1.1.0" 
+```
+
+### Prepare custom package / modified package
+#### clone
+```bash
+git clone --recursive https://github.com/TMElyralab/MuseV.git
+```
+#### prepare PYTHONPATH
+```bash
+current_dir=$(pwd)
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/MMCM
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/diffusers/src
+export PYTHONPATH=${PYTHONPATH}:${current_dir}/MuseV/controlnet_aux/src
+cd MuseV
+```
+
+1. `MMCM`: multi media, cross modal process package。
+1. `diffusers`: modified diffusers package based on [diffusers](https://github.com/huggingface/diffusers)
+1. `controlnet_aux`: modified based on [controlnet_aux](https://github.com/TMElyralab/controlnet_aux)
+
+
+## Download models
+```bash
+git clone https://huggingface.co/TMElyralab/MuseV ./checkpoints
+```
+- `motion`: text2video model, trained on tiny `ucf101` and tiny `webvid` dataset, approximately 60K videos text pairs. GPU memory consumption  testing on `resolution`$=512*512$, `time_size=12`.
+    - `musev/unet`: only has and train `unet` motion module. `GPU memory consumption` $\approx 8G$. 
+    - `musev_referencenet`: train `unet` module, `referencenet`, `IPAdapter`. `GPU memory consumption` $\approx 12G$. 
+        - `unet`: `motion` module, which has `to_k`, `to_v` in `Attention` layer refer to `IPAdapter`
+        - `referencenet`: similar to `AnimateAnyone`
+        - `ip_adapter_image_proj.bin`: images clip emb project layer, refer to `IPAdapter`
+    - `musev_referencenet_pose`: based on `musev_referencenet`, fix `referencenet`and `controlnet_pose`, train `unet motion` and `IPAdapter`. `GPU memory consumption` $\approx 12G$
+- `t2i/sd1.5`: text2image model, parameter are frozen when training motion module. Different `t2i` base_model has a significant impact.
+    - `majicmixRealv6Fp16`: example, could be replaced with other t2i base. download from [majicmixRealv6Fp16](https://civitai.com/models/43331?modelVersionId=94640)
+    - `fantasticmix_v10`: example, could be replaced with other t2i base. download from [fantasticmix_v10](https://civitai.com/models/22402?modelVersionId=26744)
+- `IP-Adapter/models`: download from [IPAdapter](https://huggingface.co/h94/IP-Adapter/tree/main)
+    - `image_encoder`: vision clip model.
+    - `ip-adapter_sd15.bin`: original IPAdapter model checkpoint.
+    - `ip-adapter-faceid_sd15.bin`: original IPAdapter model checkpoint.
+
+## Inference
+
+### Prepare model_path
+Skip this step when run example task with example inference command.
+Set model path and abbreviation in config, to use abbreviation in inference script.
+- T2I SD：ref to `musev/configs/model/T2I_all_model.py`
+- Motion Unet: refer to `musev/configs/model/motion_model.py`
+- Task: refer to `musev/configs/tasks/example.yaml`
+
+### musev_referencenet
+#### text2video
+```bash
+python scripts/inference/text2video.py   --sd_model_name majicmixRealv6Fp16   --unet_model_name musev_referencenet --referencenet_model_name musev_referencenet --ip_adapter_model_name musev_referencenet   -test_data_path ./configs/tasks/example.yaml  --output_dir ./output  --n_batch 1  --target_datas yongen  --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder  --time_size 12 --fps 12  
+```
+**common parameters**:
+- `test_data_path`: task_path in yaml extention
+- `target_datas`: sep is `,`, sample subtasks if `name` in `test_data_path` is in `target_datas`.
+- `sd_model_cfg_path`: T2I sd models path, model config path or model path.
+- `sd_model_name`: sd model name, which use to choose full model path in sd_model_cfg_path. multi model names with sep =`,`, or `all`
+- `unet_model_cfg_path`: motion unet model config path or model path。
+- `unet_model_name`: unet model name, use to get model path in `unet_model_cfg_path`, and init unet class instance in `musev/models/unet_loader.py`. multi model names with sep=`,`, or `all`. If `unet_model_cfg_path` is model path, `unet_name` must be supported in `musev/models/unet_loader.py`
+- `time_size`: num_frames per diffusion denoise generation。default=`12`.
+- `n_batch`: generation numbers of shot, $total\_frames=n\_batch * time\_size + n\_viscond$, default=`1`。
+- `context_frames`: context_frames num. If `time_size` > `context_frame`，`time_size` window is split into many sub-windows for parallel denoising"。 default=`12`。
+
+To generate long videos, there two ways:
+1. `visual conditioned parallel denoise`: set `n_batch=1`, `time_size` = all frames you want.
+1. `traditional end-to-end`: set `time_size` = `context_frames` = frames of a shot (`12`), `context_overlap` = 0；
+
+
+**model parameters**：
+supports `referencenet`, `IPAdapter`, `IPAdapterFaceID`, `Facein`.
+- referencenet_model_name: `referencenet` model name.
+- ImageClipVisionFeatureExtractor: `ImageEmbExtractor` name, extractor vision clip emb used in `IPAdapter`.
+- vision_clip_model_path: `ImageClipVisionFeatureExtractor` model path.
+- ip_adapter_model_name: from `IPAdapter`, it's `ImagePromptEmbProj`, used with `ImageEmbExtractor`。
+- ip_adapter_face_model_name: `IPAdapterFaceID`, from `IPAdapter` to keep faceid，should set `face_image_path`。
+
+**Some parameters that affect the motion range and generation results**：
+- `video_guidance_scale`: Similar to text2image, control influence between cond and uncond，default=`3.5`
+- `guidance_scale`:  The parameter ratio in the first frame image between cond and uncond, default=`3.5`
+- `use_condition_image`:  Whether to use the given first frame for video generation.
+- `redraw_condition_image`: Whether to redraw the given first frame image.
+- `video_negative_prompt`: Abbreviation of full `negative_prompt` in config path. default=`V2`.
+
+
+#### video2video
+`t2i` base_model has a significant impact. In this case, `fantasticmix_v10` performs better than `majicmixRealv6Fp16`.
+```bash
+python scripts/inference/video2video.py --sd_model_name fantasticmix_v10  --unet_model_name musev_referencenet --referencenet_model_name   musev_referencenet --ip_adapter_model_name musev_referencenet    -test_data_path ./configs/tasks/example.yaml    --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder      --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas dance1 --fps 12 --time_size 12
+```
+**import parameters**
+
+Most of the parameters are same as `musev_text2video`. Special parameters of `video2video` are:
+1. need to set `video_path` in `test_data`. Now supports `rgb video` and `controlnet_middle_video`。
+- `which2video`: whether `rgb` video influences initial noise, more strongly than controlnet condition. If `True`, then redraw video.
+- `controlnet_name`：whether to use `controlnet condition`, such as `dwpose,depth`.
+- `video_is_middle`: `video_path` is `rgb video` or  `controlnet_middle_video`. Can be set for every `test_data` in test_data_path.
+- `video_has_condition`: whether condtion_images is aligned with the first frame of video_path. If Not, firstly generate `condition_images` and then align with concatation. set in `test_data`。
+
+all controlnet_names refer to [mmcm](https://github.com/TMElyralab/MMCM/blob/main/mmcm/vision/feature_extractor/controlnet.py#L513)
+```python
+['pose', 'pose_body', 'pose_hand', 'pose_face', 'pose_hand_body', 'pose_hand_face', 'dwpose', 'dwpose_face', 'dwpose_hand', 'dwpose_body', 'dwpose_body_hand', 'canny', 'tile', 'hed', 'hed_scribble', 'depth', 'pidi', 'normal_bae', 'lineart', 'lineart_anime', 'zoe', 'sam', 'mobile_sam', 'leres', 'content', 'face_detector']
+```
+
+### musev_referencenet_pose
+Only used for `pose2video`
+train based on `musev_referencenet`, fix `referencenet`, `pose-controlnet`, and `T2I`, train `motion` module and `IPAdapter`.
+
+`t2i` base_model has a significant impact. In this case, `fantasticmix_v10` performs better than `majicmixRealv6Fp16`.
+
+```bash
+python scripts/inference/video2video.py --sd_model_name fantasticmix_v10  --unet_model_name musev_referencenet_pose --referencenet_model_name   musev_referencenet --ip_adapter_model_name musev_referencenet_pose    -test_data_path ./configs/tasks/example.yaml    --vision_clip_extractor_class_name ImageClipVisionFeatureExtractor --vision_clip_model_path ./checkpoints/IP-Adapter/models/image_encoder      --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas  dance1   --fps 12 --time_size 12
+```
+
+### musev
+Only has motion module, no referencenet, requiring less gpu memory.
+#### text2video
+```bash
+python scripts/inference/text2video.py   --sd_model_name majicmixRealv6Fp16   --unet_model_name musev   -test_data_path ./configs/tasks/example.yaml  --output_dir ./output  --n_batch 1  --target_datas yongen  --time_size 12 --fps 12
+```
+#### video2video
+```bash
+python scripts/inference/video2video.py --sd_model_name fantasticmix_v10  --unet_model_name musev    -test_data_path ./configs/tasks/example.yaml --output_dir ./output  --n_batch 1 --controlnet_name dwpose_body_hand  --which2video "video_middle"  --target_datas  dance1   --fps 12 --time_size 12
+```
+
+### Gradio demo
+MuseV provides gradio script to generate a GUI in a local machine to generate video conveniently. 
+
+```bash
+cd scripts/gradio
+python app.py
+```
+
+
+# Acknowledgements
+
+1. MuseV has referred much to [TuneAVideo](https://github.com/showlab/Tune-A-Video), [diffusers](https://github.com/huggingface/diffusers), [Moore-AnimateAnyone](https://github.com/MooreThreads/Moore-AnimateAnyone/tree/master/src/pipelines), [animatediff](https://github.com/guoyww/AnimateDiff), [IP-Adapter](https://github.com/tencent-ailab/IP-Adapter), [AnimateAnyone](https://arxiv.org/abs/2311.17117), [VideoFusion](https://arxiv.org/abs/2303.08320), [insightface](https://github.com/deepinsight/insightface). 
+2. MuseV has been built on `ucf101` and `webvid` datasets.
+
+Thanks for open-sourcing!
+
+# Limitation
+There are still many limitations, including
+
+1. Lack of generalization ability. Some visual condition image perform well, some perform bad. Some t2i pretraied model perform well, some perform bad.
+1. Limited types of video generation and limited motion range, partly because of limited types of training data. The released `MuseV` has been trained on approximately 60K human text-video pairs with resolution `512*320`. `MuseV` has greater motion range while lower video quality at lower resolution. `MuseV` tends to generate less motion range with high video quality. Trained on larger, higher resolution, higher quality text-video dataset may make `MuseV` better.
+1. Watermarks may appear because of `webvid`. A cleaner dataset withour watermarks may solve this issue.
+1. Limited types of long video generation. Visual Conditioned Parallel Denoise can solve accumulated error of video generation, but the current method is only suitable for relatively fixed camera scenes.
+1. Undertrained referencenet and IP-Adapter, beacause of limited time and limited resources.
+1. Understructured code. `MuseV`  supports rich and dynamic features, but with complex and unrefacted codes. It takes time to familiarize.
+   
+
+<!-- # Contribution 暂时不需要组织开源共建 -->
+# Citation
+```bib
+@article{musev,
+  title={MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising},
+  author={Xia, Zhiqiang and Chen, Zhaokang and Wu, Bin and Li, Chao and Hung, Kwok-Wai and Zhan, Chao and He, Yingjie and Zhou, Wenjiang},
+  journal={arxiv},
+  year={2024}
+}
+```
+# Disclaimer/License
+1. `code`: The code of MuseV is released under the MIT License. There is no limitation for both academic and commercial usage.
+1. `model`: The trained model are available for non-commercial research purposes only.
+1. `other opensource model`: Other open-source models used must comply with their license, such as `insightface`, `IP-Adapter`, `ft-mse-vae`, etc.
+1. The testdata are collected from internet, which are available for non-commercial research purposes only.
+1. `AIGC`: This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.

MuseV/configs/model/T2I_all_model.py

@@ -0,0 +1,15 @@
+import os
+
+
+T2IDir = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "t2i"
+)
+
+MODEL_CFG = {
+    "majicmixRealv6Fp16": {
+        "sd": os.path.join(T2IDir, "sd1.5/majicmixRealv6Fp16"),
+    },
+    "fantasticmix_v10": {
+        "sd": os.path.join(T2IDir, "sd1.5/fantasticmix_v10"),
+    },
+}

MuseV/configs/model/ip_adapter.py

@@ -0,0 +1,66 @@
+import os
+
+IPAdapterModelDir = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "IP-Adapter"
+)
+
+
+MotionDir = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "motion"
+)
+
+
+MODEL_CFG = {
+    "IPAdapter": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "models/image_encoder"),
+        "ip_ckpt": os.path.join(IPAdapterModelDir, "ip-adapter_sd15.bin"),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 4,
+        "clip_embeddings_dim": 1024,
+        "desp": "",
+    },
+    "IPAdapterPlus": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "image_encoder"),
+        "ip_ckpt": os.path.join(IPAdapterModelDir, "ip-adapter-plus_sd15.bin"),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 16,
+        "clip_embeddings_dim": 1024,
+        "desp": "",
+    },
+    "IPAdapterPlus-face": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "image_encoder"),
+        "ip_ckpt": os.path.join(IPAdapterModelDir, "ip-adapter-plus-face_sd15.bin"),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 16,
+        "clip_embeddings_dim": 1024,
+        "desp": "",
+    },
+    "IPAdapterFaceID": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "image_encoder"),
+        "ip_ckpt": os.path.join(IPAdapterModelDir, "ip-adapter-faceid_sd15.bin"),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 4,
+        "clip_embeddings_dim": 512,
+        "desp": "",
+    },
+    "musev_referencenet": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "image_encoder"),
+        "ip_ckpt": os.path.join(
+            MotionDir, "musev_referencenet/ip_adapter_image_proj.bin"
+        ),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 4,
+        "clip_embeddings_dim": 1024,
+        "desp": "",
+    },
+    "musev_referencenet_pose": {
+        "ip_image_encoder": os.path.join(IPAdapterModelDir, "image_encoder"),
+        "ip_ckpt": os.path.join(
+            MotionDir, "musev_referencenet_pose/ip_adapter_image_proj.bin"
+        ),
+        "ip_scale": 1.0,
+        "clip_extra_context_tokens": 4,
+        "clip_embeddings_dim": 1024,
+        "desp": "",
+    },
+}

MuseV/configs/model/lcm_model.py

@@ -0,0 +1,17 @@
+import os
+
+
+LCMDir = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "lcm"
+)
+
+
+MODEL_CFG = {
+    "lcm": {
+        os.path.join(LCMDir, "lcm-lora-sdv1-5/pytorch_lora_weights.safetensors"): {
+            "strength": 1.0,
+            "lora_block_weight": "ALL",
+            "strength_offset": 0,
+        },
+    },
+}

MuseV/configs/model/motion_model.py

@@ -0,0 +1,22 @@
+import os
+
+
+MotionDIr = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "motion"
+)
+
+
+MODEL_CFG = {
+    "musev": {
+        "unet": os.path.join(MotionDIr, "musev"),
+        "desp": "only train unet motion module, fix t2i",
+    },
+    "musev_referencenet": {
+        "unet": os.path.join(MotionDIr, "musev_referencenet"),
+        "desp": "train referencenet, IPAdapter and unet motion module, fix t2i",
+    },
+    "musev_referencenet_pose": {
+        "unet": os.path.join(MotionDIr, "musev_referencenet_pose"),
+        "desp": "train  unet motion module and IPAdapter, fix t2i and referencenet",
+    },
+}

MuseV/configs/model/negative_prompt.py

@@ -0,0 +1,32 @@
+Negative_Prompt_CFG = {
+    "Empty": {
+        "base_model": "",
+        "prompt": "",
+        "refer": "",
+    },
+    "V1": {
+        "base_model": "",
+        "prompt": "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, tail, watermarks",
+        "refer": "",
+    },
+    "V2": {
+        "base_model": "",
+        "prompt": "badhandv4, ng_deepnegative_v1_75t, (((multiple heads))), (((bad body))), (((two people))), ((extra arms)), ((deformed body)), (((sexy))), paintings,(((two heads))), ((big head)),sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (((nsfw))), nipples, extra fingers, (extra legs), (long neck), mutated hands, (fused fingers), (too many fingers)",
+        "refer": "Weiban",
+    },
+    "V3": {
+        "base_model": "",
+        "prompt": "badhandv4, ng_deepnegative_v1_75t, bad quality",
+        "refer": "",
+    },
+    "V4": {
+        "base_model": "",
+        "prompt": "badhandv4,ng_deepnegative_v1_75t,EasyNegativeV2,bad_prompt_version2-neg,bad quality",
+        "refer": "",
+    },
+    "V5": {
+        "base_model": "",
+        "prompt": "(((multiple heads))), (((bad body))), (((two people))), ((extra arms)), ((deformed body)), (((sexy))), paintings,(((two heads))), ((big head)),sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, (((nsfw))), nipples, extra fingers, (extra legs), (long neck), mutated hands, (fused fingers), (too many fingers)",
+        "refer": "Weiban",
+    },
+}

MuseV/configs/model/referencenet.py

@@ -0,0 +1,14 @@
+import os
+
+
+MotionDIr = os.path.join(
+    os.path.dirname(os.path.abspath(__file__)), "../../checkpoints", "motion"
+)
+
+
+MODEL_CFG = {
+    "musev_referencenet": {
+        "net": os.path.join(MotionDIr, "musev_referencenet"),
+        "desp": "",
+    },
+}

MuseV/configs/tasks/example.yaml

@@ -0,0 +1,210 @@
+# - name: task_name
+#   condition_images: vision condition images path
+#   video_path: str, default null, used for video2video 
+#   prompt: text to guide image generation
+#   ipadapter_image: image_path for IP-Apdater
+#   refer_image: image_path for referencenet, generally speaking, same as ipadapter_image
+#   height: int # The shorter the image size, the larger the motion amplitude, and the lower video quality.
+#   width: int #  The longer the W&H, the smaller the motion amplitude, and the higher video quality.
+#   img_length_ratio: float, generation video size is (height, width) * img_length_ratio
+
+# text/image2video
+- condition_images: ./data/images/yongen.jpeg
+  eye_blinks_factor: 1.8
+  height: 1308
+  img_length_ratio: 0.957
+  ipadapter_image: ${.condition_images}
+  name: yongen
+  prompt: (masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 736
+- condition_images: ./data/images/jinkesi2.jpeg
+  eye_blinks_factor: 1.8
+  height: 714
+  img_length_ratio: 1.25
+  ipadapter_image: ${.condition_images}
+  name: jinkesi2
+  prompt: (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 563
+- condition_images: ./data/images/seaside4.jpeg
+  eye_blinks_factor: 1.8
+  height: 317
+  img_length_ratio: 2.221
+  ipadapter_image: ${.condition_images}
+  name: seaside4
+  prompt: (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 564
+- condition_images: ./data/images/seaside_girl.jpeg
+  eye_blinks_factor: 1.8
+  height: 736
+  img_length_ratio: 0.957
+  ipadapter_image: ${.condition_images}
+  name: seaside_girl
+  prompt: (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 736
+- condition_images: ./data/images/boy_play_guitar.jpeg
+  eye_blinks_factor: 1.8
+  height: 846
+  img_length_ratio: 1.248
+  ipadapter_image: ${.condition_images}
+  name: boy_play_guitar
+  prompt: (masterpiece, best quality, highres:1), playing guitar
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 564
+- condition_images: ./data/images/girl_play_guitar2.jpeg
+  eye_blinks_factor: 1.8
+  height: 1002
+  img_length_ratio: 1.248
+  ipadapter_image: ${.condition_images}
+  name: girl_play_guitar2
+  prompt: (masterpiece, best quality, highres:1), playing guitar
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 564
+- condition_images: ./data/images/boy_play_guitar2.jpeg
+  eye_blinks_factor: 1.8
+  height: 630
+  img_length_ratio: 1.676
+  ipadapter_image: ${.condition_images}
+  name: boy_play_guitar2
+  prompt: (masterpiece, best quality, highres:1), playing guitar
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 420
+- condition_images: ./data/images/girl_play_guitar4.jpeg
+  eye_blinks_factor: 1.8
+  height: 846
+  img_length_ratio: 1.248
+  ipadapter_image: ${.condition_images}
+  name: girl_play_guitar4
+  prompt: (masterpiece, best quality, highres:1), playing guitar
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 564
+- condition_images: ./data/images/dufu.jpeg
+  eye_blinks_factor: 1.8
+  height: 500
+  img_length_ratio: 1.495
+  ipadapter_image: ${.condition_images}
+  name: dufu
+  prompt: (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3),Chinese ink painting style
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 471
+- condition_images: ./data/images/Mona_Lisa..jpg
+  eye_blinks_factor: 1.8
+  height: 894
+  img_length_ratio: 1.173
+  ipadapter_image: ${.condition_images}
+  name: Mona_Lisa.
+  prompt: (masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face,
+    soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 600
+- condition_images: ./data/images/Portrait-of-Dr.-Gachet.jpg
+  eye_blinks_factor: 1.8
+  height: 985
+  img_length_ratio: 0.88
+  ipadapter_image: ${.condition_images}
+  name: Portrait-of-Dr.-Gachet
+  prompt: (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3)
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 800
+- condition_images: ./data/images/Self-Portrait-with-Cropped-Hair.jpg
+  eye_blinks_factor: 1.8
+  height: 565
+  img_length_ratio: 1.246
+  ipadapter_image: ${.condition_images}
+  name: Self-Portrait-with-Cropped-Hair
+  prompt: (masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3), animate
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 848
+- condition_images: ./data/images/The-Laughing-Cavalier.jpg
+  eye_blinks_factor: 1.8
+  height: 1462
+  img_length_ratio: 0.587
+  ipadapter_image: ${.condition_images}
+  name: The-Laughing-Cavalier
+  prompt: (masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3)
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 1200
+
+# scene
+- condition_images: ./data/images/waterfall4.jpeg
+  eye_blinks_factor: 1.8
+  height: 846
+  img_length_ratio: 1.248
+  ipadapter_image: ${.condition_images}
+  name: waterfall4
+  prompt: (masterpiece, best quality, highres:1), peaceful beautiful waterfall, an
+    endless waterfall
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 564
+- condition_images: ./data/images/river.jpeg
+  eye_blinks_factor: 1.8
+  height: 736
+  img_length_ratio: 0.957
+  ipadapter_image: ${.condition_images}
+  name: river
+  prompt: (masterpiece, best quality, highres:1), peaceful beautiful river
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 736
+- condition_images: ./data/images/seaside2.jpeg
+  eye_blinks_factor: 1.8
+  height: 1313
+  img_length_ratio: 0.957
+  ipadapter_image: ${.condition_images}
+  name: seaside2
+  prompt: (masterpiece, best quality, highres:1), peaceful beautiful sea scene
+  refer_image: ${.condition_images}
+  video_path: null
+  width: 736
+
+# video2video
+- name: "dance1"
+  prompt: "(masterpiece, best quality, highres:1) , a girl is dancing, wearing a dress made of stars, animation"
+  video_path:  ./data/source_video/video1_girl_poseseq.mp4 
+  condition_images: ./data/images/spark_girl.png
+  refer_image: ${.condition_images}
+  ipadapter_image: ${.condition_images}
+  height: 960
+  width: 512
+  img_length_ratio: 1.0
+  video_is_middle: True # if true, means video_path is controlnet condition, not natural rgb video
+
+- name: "dance2"
+  prompt: "(best quality), ((masterpiece)), (highres), illustration, original, extremely detailed wallpaper"
+  video_path:  ./data/source_video/video1_girl_poseseq.mp4 
+  condition_images: ./data/images/cyber_girl.png
+  refer_image: ${.condition_images}
+  ipadapter_image: ${.condition_images}
+  height: 960
+  width: 512
+  img_length_ratio: 1.0
+  video_is_middle: True # if true, means video_path is controlnet condition, not natural rgb video
+
+- name: "duffy"
+  prompt: "(best quality), ((masterpiece)), (highres), illustration, original, extremely detailed wallpaper"
+  video_path: ./data/source_video/pose-for-Duffy-4.mp4
+  condition_images: ./data/images/duffy.png
+  refer_image: ${.condition_images}
+  ipadapter_image: ${.condition_images}
+  height: 1280
+  width: 704
+  img_length_ratio: 1.0
+  video_is_middle: True  # if true, means video_path is controlnet condition, not natural rgb video

MuseV/data/demo/video1.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad8eb17005da389731d2a04d61a39166b753270a893e04ab3801b798fe04441d
+size 5411952

MuseV/data/result_video/Mona_Lisa,_by_Leonardo_da_Vinci,_from_C2RMF_retouched.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70511e47ce0b9a6a900d7fbc73fb0858093892d50e54b55fff01cf1a64761a76
+size 22006433

MuseV/data/result_video/Portrait-of-Dr.-Gachet.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeefb3376d0673e73f089eb823940148e3bce7c6a0a4d70ff718214a182575fe
+size 10461495

MuseV/data/result_video/Self-Portrait-with-Cropped-Hair.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e0815b390d0ea8c054d686bd8f853e35dfbc1d0e8b56d322f821157dd848e9d
+size 7903114

MuseV/data/result_video/The-Laughing-Cavalier.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d33672c0568171515e023b0a8957f6f8156162f103a852a52683f50ebed013d
+size 15858438

MuseV/data/result_video/boy_play_guitar.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7904b17270333b3c79a646f780e2a4ae9dee694666bfc2b5f2e3068cccdd2fd2
+size 5287268

MuseV/data/result_video/boy_play_guitar2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b20142a81dda07575033e03efa83ff3de68ca70e25012f94dc1e685f13a286bb
+size 10422247

MuseV/data/result_video/dufu.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52cbdc476dfa1fd397c32322eae2a2d4ee49ed81c199c009f5b471e05bef9b57
+size 6989110

MuseV/data/result_video/girl_play_guitar2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8af8364bdd1dae773d141002dc2f8cd7984d601ba11c40a9c51c52b1c1df2098
+size 12359908

MuseV/data/result_video/girl_play_guitar4.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25779e28678634af3e581b4c1d27600f135c4a47d8bbb9c4d33516e33d07ab10
+size 12515713

MuseV/data/result_video/jinkesi2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32df5fc00c1aafd3e632b63746710e062a762b1e60f4ed65c341a2c62b0a28ad
+size 10238715

MuseV/data/result_video/river.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f858724157ba77866ea0d0f84360c39902ab7ae5550ff84039ef65461b483a87
+size 8106770

MuseV/data/result_video/seaside2.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1315c3e56692d90b3dd1054571eee4836e0216ba62de7b88cf93315e57602144
+size 22850669

MuseV/data/result_video/seaside4.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8355786c16e0599fd8d9ddde1af5a8ab22387681ba46d35389a6e1edb76584d
+size 18601524

MuseV/data/result_video/seaside_girl.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f574477019a97675656a8f252af68d5b69a719e8535e5fdf9da40c7259e80ced
+size 9171386

MuseV/data/result_video/waterfall4.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4328f4dcbc1cfec39e466cdc8f9364d126a852146d60c45be8e764cae40b3ae
+size 16799648

MuseV/data/result_video/yongen.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04ffa16f7d5bd6be644f7146236825f4c3df30200d992912203fcb4b292d9341
+size 15588944