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diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
★ 34.6k スターApache-2.0
ガイドを見る →Fitness-AQAは、ParitoshParmarが公開しているオープンソースの簡単インストールプロジェクトです（GitHub: ParitoshParmar/Fitness-AQA）。GitHubスターは113、フォークは43で、NOASSERTIONライセンスの下で公開されています。GitHubでの最終更新は2026年2月10日です。
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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LibreAIカタログに掲載された簡単インストールプロジェクトの中で最もスターの多いもの — Fitness-AQAが合わなかった場合に検討したい、現実的な候補リストです。
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カタログの中でFitness-AQAとスター数が近い簡単インストールプロジェクト — 前後の棚もそのまま見て回れます。
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