Add complete implementation

Dockerfile

@@ -1,49 +1,69 @@
 FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04
 
 ENV DEBIAN_FRONTEND=noninteractive
+ENV HF_HUB_ENABLE_HF_TRANSFER=0
+ENV HUGGINGFACE_HUB_CACHE=/data/huggingface
 
-# Add retry logic for apt-get
-RUN echo 'Acquire::Retries "3";' > /etc/apt/apt.conf.d/80-retries
-
-# Clean and update package lists
-RUN rm -rf /var/lib/apt/lists/* && \
-    apt-get clean && \
-    apt-get update --fix-missing
-
-# Install system dependencies with retry logic
-RUN apt-get install -y --no-install-recommends --fix-missing \
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
     git \
     python3-pip \
     python3-dev \
     ffmpeg \
     libsm6 \
     libxext6 \
+    wget \
     && rm -rf /var/lib/apt/lists/*
 
-# Set up a new user named "user" with user ID 1000
+# Set up a new user
 RUN useradd -m -u 1000 user
+
+# Create directories and set permissions
+RUN mkdir -p /data/huggingface /data/models && \
+    chown -R user:user /data
+
+# Switch to user
 USER user
 
-# Set home to the user's home directory
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
+# Set environment variables
+ENV HOME=/home/user
+ENV PATH=/home/user/.local/bin:$PATH
 
-# Set the working directory
+# Set up workspace
 WORKDIR $HOME/app
 
-# Install dependencies
+# Install Python packages
+COPY --chown=user:user requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy the application code
-COPY --chown=user . .
+# Install additional dependencies
+RUN pip install --no-cache-dir openmim && \
+    mim install mmengine && \
+    mim install "mmcv>=2.0.1" && \
+    mim install "mmdet>=3.1.0" && \
+    mim install "mmpose>=1.1.0"
 
-# Create data directory with correct permissions
-USER root
-RUN mkdir -p /data && chown -R user:user /data
-USER user
+# Clone and set up MuseV
+RUN git clone https://github.com/TMElyralab/MuseV.git && \
+    cd MuseV && \
+    git clone https://github.com/huggingface/diffusers.git && \
+    git clone https://github.com/patrickvonplaten/controlnet_aux.git && \
+    cd diffusers && pip install -e . && \
+    cd ../controlnet_aux && pip install -e .
+
+# Set up Python path
+ENV PYTHONPATH="${HOME}/app:${HOME}/app/MuseV:${HOME}/app/MuseV/MMCM:${HOME}/app/MuseV/diffusers/src:${HOME}/app/MuseV/controlnet_aux/src:${HOME}/app/MuseV/scripts/gradio"
+
+# Copy application code
+COPY --chown=user:user . .
+
+# Debug information
+RUN echo "Current directory:" && pwd && \
+    echo "Directory contents:" && ls -la && \
+    echo "MuseV directory contents:" && ls -la MuseV && \
+    echo "Gradio scripts directory contents:" && ls -la MuseV/scripts/gradio && \
+    echo "Python path:" && echo $PYTHONPATH
 
-# Expose the port
 EXPOSE 7860
 
-# Start the Gradio app
-CMD ["python", "app.py"] 
+CMD ["python", "-u", "app.py"] 

README.md

@@ -1,496 +1,14 @@
 ---
-title: MuseV
-emoji: 🎨
+title: MuseV Demo
+emoji: 🎬
 colorFrom: blue
-colorTo: purple
-sdk: docker
-app_port: 7860
+colorTo: indigo
+sdk: gradio
+sdk_version: "4.16.0"
+app_file: app.py
 pinned: false
 ---
 
-# MuseV [English](README.md) [中文](README-zh.md)
+# MuseV: Infinite-length and High Fidelity Virtual Human Video Generation
 
-<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, [email protected])
-</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/MuseV_Page/)**    **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.
-MoreCase is in **[project](https://tmelyralab.github.io/MuseV_Page/)**, including **1-2 minute video**.
-
-<!-- # TODO: // use youtu video link? -->
-Examples bellow can be accessed at `configs/tasks/example.yaml`
-
-
-### 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/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>
-  <!-- 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>
-</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/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>
-    <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`.
-
-## Third party integration
-Thanks for the third-party integration, which makes installation and use more convenient for everyone.
-We also hope you note that we have not verified, maintained, or updated third-party. Please refer to this project for specific results.
-
-### [ComfyUI](https://github.com/chaojie/ComfyUI-MuseV)
-### [One click integration package in windows](https://www.bilibili.com/video/BV1ux4y1v7pF/?vd_source=fe03b064abab17b79e22a692551405c3)
-netdisk:https://www.123pan.com/s/Pf5Yjv-Bb9W3.html
-
-code: glut
-
-## 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.could be replaced with other t2i base.
-    - `majicmixRealv6Fp16`: example, download from [majicmixRealv6Fp16](https://civitai.com/models/43331?modelVersionId=94640)
-    - `fantasticmix_v10`: example, 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`
-- `use_condition_image`:  Whether to use the given first frame for video generation, if not generate vision condition frames first. Default=`True`.
-- `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` as reference video in `test_data`. Now reference video supports `rgb video` and `controlnet_middle_video`。
-- `which2video`: whether `rgb` video influences initial noise, influence of `rgb` is stronger than of  controlnet condition.
-- `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, exrtact condition of `condition_images` firstly generate, 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
-##### pose align
-```bash
-python ./pose_align/pose_align.py --max_frame 200 --vidfn ./data/source_video/dance.mp4 --imgfn_refer ./data/images/man.jpg --outfn_ref_img_pose ./data/pose_align_results/ref_img_pose.jpg --outfn_align_pose_video ./data/pose_align_results/align_pose_video.mp4 --outfn ./data/pose_align_results/align_demo.mp4
-```
-- `max_frame`: how many frames to align (count from the first frame)
-- `vidfn`：real dance video in rgb
-- `imgfn_refer`: refer image path
-- `outfn_ref_img_pose`: output path of the pose of the refer img
-- `outfn_align_pose_video`: output path of the aligned video of the refer img
-- `outfn`: output path of the alignment visualization
-
-
-
-https://github.com/TMElyralab/MuseV/assets/47803475/787d7193-ec69-43f4-a0e5-73986a808f51
-
-
-
-
-then you can use the aligned pose `outfn_align_pose_video` for pose guided generation. You may need to modify the example in the config file `./configs/tasks/example.yaml`
-##### generation
-```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 without 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.
+A Hugging Face Space for the MuseV project, which enables infinite-length and high fidelity virtual human video generation with visual conditioned parallel denoising.

app.py

@@ -1,24 +1,96 @@
+from __future__ import annotations
+
+import os
+import time
+import sys
+import PIL.Image
+import numpy as np
 import gradio as gr
+import spaces
+import cuid
+
+from huggingface_hub import snapshot_download
+
+# Set up paths
+ProjectDir = os.path.dirname(os.path.abspath(__file__))
+CheckpointsDir = os.path.join(ProjectDir, "checkpoints")
+MuseVDir = os.path.join(ProjectDir, "MuseV")
+GradioScriptsDir = os.path.join(MuseVDir, "scripts", "gradio")
+
+# Add the MuseV paths to sys.path
+paths_to_add = [
+    GradioScriptsDir,
+    MuseVDir,
+    os.path.join(MuseVDir, "MMCM"),
+    os.path.join(MuseVDir, "diffusers", "src"),
+    os.path.join(MuseVDir, "controlnet_aux", "src"),
+    os.path.dirname(os.path.abspath(__file__))
+]
+
+for path in paths_to_add:
+    if os.path.exists(path) and path not in sys.path:
+        sys.path.insert(0, path)
+        print(f"Added {path} to PYTHONPATH")
+
+def download_model():
+    if not os.path.exists(CheckpointsDir):
+        print("Checkpoint Not Downloaded, start downloading...")
+        tic = time.time()
+        snapshot_download(
+            repo_id="TMElyralab/MuseV",
+            local_dir=CheckpointsDir,
+            max_workers=8,
+        )
+        toc = time.time()
+        print(f"download cost {toc-tic} seconds")
+    else:
+        print("Already download the model.")
+
+print("Starting model download...")
+download_model()
+print("Model download complete.")
+
+print("Setting up paths...")
+for path in sys.path:
+    print(f"Path: {path}")
+
+print("Attempting to import gradio modules...")
+try:
+    from gradio_video2video import online_v2v_inference
+    print("Successfully imported video2video")
+except Exception as e:
+    print(f"Error importing video2video: {str(e)}")
+    print(f"Current directory: {os.getcwd()}")
+    print(f"Directory contents: {os.listdir('.')}")
+    if os.path.exists(GradioScriptsDir):
+        print(f"Gradio scripts directory contents: {os.listdir(GradioScriptsDir)}")
+
+try:
+    from gradio_text2video import online_t2v_inference
+    print("Successfully imported text2video")
+except Exception as e:
+    print(f"Error importing text2video: {str(e)}")
+
+ignore_video2video = False
+max_image_edge = 1280
 
-def process_video(video_path):
-    # Initialize the pipeline
-    # pipeline = MuseVPipeline()
-    
-    # Process the video
-    # result = pipeline.process(video_path)
-    
-    # For now, just return the input video
-    return video_path
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=process_video,
-    inputs=gr.Video(label="Upload Video"),
-    outputs=gr.Video(label="Processed Video"),
-    title="MuseV - Video Processing",
-    description="Upload a video to process with MuseV"
+print("Setting up Gradio interface...")
+demo = gr.Interface(
+    fn=online_t2v_inference,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Image(label="Reference Image"),
+        gr.Number(label="Seed", value=-1),
+        gr.Number(label="FPS", value=6),
+        gr.Number(label="Width", value=-1),
+        gr.Number(label="Height", value=-1),
+        gr.Number(label="Video Length", value=12),
+        gr.Number(label="Image Edge Ratio", value=1.0),
+    ],
+    outputs=gr.Video(),
+    title="MuseV Demo",
+    description="Generate videos from text and reference images"
 )
 
-# Launch the interface
-if __name__ == "__main__":
-    iface.launch(server_name="0.0.0.0", server_port=7860) 
+print("Launching Gradio interface...")
+demo.queue().launch(server_name="0.0.0.0", server_port=7860) 

requirements.txt

@@ -1,4 +1,19 @@
-gradio==4.12
-spaces
+gradio>=4.16.0
+numpy
 cuid
-huggingface_hub
+spaces
+huggingface_hub
+openmim
+mmengine
+mmcv>=2.0.1
+mmdet>=3.1.0
+mmpose>=1.1.0
+Pillow
+torch
+torchvision
+diffusers
+transformers
+accelerate
+safetensors
+opencv-python-headless
+moviepy