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TMElyralab / MuseV

MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising
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diffusion human-video-generation image2video infinite-length musev video-generation

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MuseV English 中文

MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising
Zhiqiang Xia *, Zhaokang Chen*, Bin Wu†, Chao Li, Kwok-Wai Hung, Chao Zhan, Yingjie He, Wenjiang Zhou (*co-first author, †Corresponding Author, benbinwu@tencent.com)

Lyra Lab, Tencent Music Entertainment

github huggingface HuggingfaceSpace project 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:

  1. We have released MuseTalk, a real-time high quality lip sync model, which can be applied with MuseV as a complete virtual human generation solution.
  2. :new: We are thrilled to announce that MusePose has been released. MusePose is an image-to-video generation framework for virtual human under control signal like pose. Together with MuseV and MuseTalk, we hope the community can join us and march towards the vision where a virtual human can be generated end2end with native ability of full body movement and interaction.

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

Model

Overview of model structure

model_structure

Parallel denoising

parallel_denoise

Cases

All frames were generated directly from text2video model, without any post process. MoreCase is in project, including 1-2 minute video.

Examples bellow can be accessed at configs/tasks/example.yaml

Text/Image2Video

Human

image video prompt
(masterpiece, best quality, highres:1),(1boy, solo:1),(eye blinks:1.8),(head wave:1.3)
(masterpiece, best quality, highres:1), peaceful beautiful sea scene
(masterpiece, best quality, highres:1), peaceful beautiful sea scene
(masterpiece, best quality, highres:1), playing guitar
(masterpiece, best quality, highres:1), playing guitar
(masterpiece, best quality, highres:1),(1man, solo:1),(eye blinks:1.8),(head wave:1.3),Chinese ink painting style
(masterpiece, best quality, highres:1),(1girl, solo:1),(beautiful face, soft skin, costume:1),(eye blinks:{eye_blinks_factor}),(head wave:1.3)
#### Scene
image video prompt
(masterpiece, best quality, highres:1), peaceful beautiful waterfall, an endless waterfall
(masterpiece, best quality, highres:1), peaceful beautiful sea scene
### 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.
image video prompt
(masterpiece, best quality, highres:1) , a girl is dancing, animation
(masterpiece, best quality, highres:1), is dancing, animation
### 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).
name video
talk
sing
# 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 ```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. # 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.