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diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
★ 34,6 mil estrelasApache-2.0
Ver o guia →Fitness-AQA é um projeto de instalação fácil de código aberto de ParitoshParmar (ParitoshParmar/Fitness-AQA no GitHub). Tem 113 estrelas no GitHub e 43 forks, é publicado sob a licença NOASSERTION e foi atualizado pela última vez no GitHub em 10 de fevereiro de 2026.
Run fitness-aqa locally: fitness action quality assessment or your ai-fitness co.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around action quality assessment for a focused workflow.
Built around action recognition for a focused workflow.
Built around action understanding for a focused workflow.
Built around disentangled representations for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/ParitoshParmar/Fitness-AQA.gitSet up Python dependencies.
cd Fitness-AQA && pip install -r requirements.txtLaunch the application.
cd Fitness-AQA && python main.pyIt fitness Action Quality Assessment or your AI-Fitness Coach [ECCV 2022], giving you a working starting point you can run and adapt.
Yes — the code is publicly available, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
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