base on ViViD: Video Virtual Try-on using Diffusion Models # ViViD ViViD: Video Virtual Try-on using Diffusion Models [](https://arxiv.org/abs/2405.11794) [](https://alibaba-yuanjing-aigclab.github.io/ViViD) [](https://huggingface.co/alibaba-yuanjing-aigclab/ViViD) ## Dataset Dataset released: [ViViD](https://huggingface.co/datasets/alibaba-yuanjing-aigclab/ViViD) ## Installation ``` git clone https://github.com/alibaba-yuanjing-aigclab/ViViD cd ViViD ``` ### Environment ``` conda create -n vivid python=3.10 conda activate vivid pip install -r requirements.txt ``` ### Weights You can place the weights anywhere you like, for example, ```./ckpts```. If you put them somewhere else, you just need to update the path in ```./configs/prompts/*.yaml```. #### Stable Diffusion Image Variations ``` cd ckpts git lfs install git clone https://huggingface.co/lambdalabs/sd-image-variations-diffusers ``` #### SD-VAE-ft-mse ``` git lfs install git clone https://huggingface.co/stabilityai/sd-vae-ft-mse ``` #### Motion Module Download [mm_sd_v15_v2](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt) #### ViViD ``` git lfs install git clone https://huggingface.co/alibaba-yuanjing-aigclab/ViViD ``` ## Inference We provide two demos in ```./configs/prompts/```, run the following commands to have a try😼. ``` python vivid.py --config ./configs/prompts/upper1.yaml python vivid.py --config ./configs/prompts/lower1.yaml ``` ## Data As illustrated in ```./data```, the following data should be provided. ```text ./data/ |-- agnostic | |-- video1.mp4 | |-- video2.mp4 | ... |-- agnostic_mask | |-- video1.mp4 | |-- video2.mp4 | ... |-- cloth | |-- cloth1.jpg | |-- cloth2.jpg | ... |-- cloth_mask | |-- cloth1.jpg | |-- cloth2.jpg | ... |-- densepose | |-- video1.mp4 | |-- video2.mp4 | ... |-- videos | |-- video1.mp4 | |-- video2.mp4 | ... ``` ### Agnostic and agnostic_mask video This part is a bit complex, you can obtain them through any of the following three ways: 1. Follow [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) to extract them frame-by-frame.(recommended) 2. Use [SAM](https://github.com/facebookresearch/segment-anything) + Gaussian Blur.(see ```./tools/sam_agnostic.py``` for an example) 3. Mask editor tools. Note that the shape and size of the agnostic area may affect the try-on results. ### Densepose video See [vid2densepose](https://github.com/Flode-Labs/vid2densepose).(Thanks) ### Cloth mask Any detection tool is ok for obtaining the mask, like [SAM](https://github.com/facebookresearch/segment-anything). ## BibTeX ```text @misc{fang2024vivid, title={ViViD: Video Virtual Try-on using Diffusion Models}, author={Zixun Fang and Wei Zhai and Aimin Su and Hongliang Song and Kai Zhu and Mao Wang and Yu Chen and Zhiheng Liu and Yang Cao and Zheng-Jun Zha}, year={2024}, eprint={2405.11794}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Contact Us **Zixun Fang**: [[email protected]](mailto:[email protected]) **Yu Chen**: [[email protected]](mailto:[email protected]) ", Assign "at most 3 tags" to the expected json: {"id":"10516","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"