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diffusers
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
★ 34,6 k étoilesApache-2.0
Voir le guide →VAE-BlessUp est un projet de image open source, créé par saltacc (saltacc/VAE-BlessUp sur GitHub). Il compte 84 étoiles sur GitHub et 2 forks, il est publié sous la licence Custom et a été mis à jour pour la dernière fois sur GitHub le 7 septembre 2026.
Easily modify and tune a Stable Diffusion VAE
Get the source and its Python setup.
Point it at the VAE you want to modify.
Use the tool's modification workflow.
Export the tuned weights.
Modify Stable Diffusion VAEs easily.
One job, done simply.
Hackable, readable code.
Works with the SD ecosystem.
For pipeline-level tweakers.
Read LICENSE before commercial use.
Download the source and move into the project folder.
git clone https://github.com/saltacc/VAE-BlessUp.git
cd VAE-BlessUpEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at saltacc/VAE-BlessUp with 162 forks. Check the repo's contributors and recent commits to see how active maintenance is.
It ships under a custom license — read the repo's LICENSE file before commercial use.
The autoencoder that encodes/decodes images in Stable Diffusion.
Check the README for compatibility details.
Basic familiarity with SD models helps a lot.
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.
FLUX, Stable Diffusion, SDXL, SD3, LoRA, Fine Tuning, DreamBooth, Training, Automatic1111, Forge WebUI, SwarmUI, DeepFake, TTS, Animation, Text To Video, Tut…
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.