Stable Diffusion

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founded 3 years ago
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This is a copy of /r/stablediffusion wiki to help people who need access to that information


Howdy and welcome to r/stablediffusion! I'm u/Sandcheeze and I have collected these resources and links to help enjoy Stable Diffusion whether you are here for the first time or looking to add more customization to your image generations.

If you'd like to show support, feel free to send us kind words or check out our Discord. Donations are appreciated, but not necessary as you being a great part of the community is all we ask for.

Note: The community resources provided here are not endorsed, vetted, nor provided by Stability AI.

#Stable Diffusion

Local Installation

Active Community Repos/Forks to install on your PC and keep it local.

Online Websites

Websites with usable Stable Diffusion right in your browser. No need to install anything.

Mobile Apps

Stable Diffusion on your mobile device.

Tutorials

Learn how to improve your skills in using Stable Diffusion even if a beginner or expert.

Dream Booth

How-to train a custom model and resources on doing so.

Models

Specially trained towards certain subjects and/or styles.

Embeddings

Tokens trained on specific subjects and/or styles.

Bots

Either bots you can self-host, or bots you can use directly on various websites and services such as Discord, Reddit etc

3rd Party Plugins

SD plugins for programs such as Discord, Photoshop, Krita, Blender, Gimp, etc.

Other useful tools

#Community

Games

  • PictionAIry : (Video|2-6 Players) - The image guessing game where AI does the drawing!

Podcasts

Databases or Lists

Still updating this with more links as I collect them all here.

FAQ

How do I use Stable Diffusion?

  • Check out our guides section above!

Will it run on my machine?

  • Stable Diffusion requires a 4GB+ VRAM GPU to run locally. However, much beefier graphics cards (10, 20, 30 Series Nvidia Cards) will be necessary to generate high resolution or high step images. However, anyone can run it online through DreamStudio or hosting it on their own GPU compute cloud server.
  • Only Nvidia cards are officially supported.
  • AMD support is available here unofficially.
  • Apple M1 Chip support is available here unofficially.
  • Intel based Macs currently do not work with Stable Diffusion.

How do I get a website or resource added here?

*If you have a suggestion for a website or a project to add to our list, or if you would like to contribute to the wiki, please don't hesitate to reach out to us via modmail or message me.

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Abstract

Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction should change. We introduce DiffusionOPSD as an on-policy self-distillation framework that converts image-level reward guidance into explicit targets for clean-output predictions at sampled queries. At each outer iteration, a frozen behavior policy generates trajectories and supplies query states and anchors. Reward gradients construct bounded positive and negative targets around each anchor. The trainable policy fits these targets as detached supervision through finite fitting before an exponential moving average update refreshes the behavior policy. This setup lets us measure target construction and finite realization separately. Controlled same-query experiments show that larger target-construction gains do not necessarily translate into larger realized gains after a single fitting update. Across SD 3.5-M and the step-distilled Z-Image-Turbo, our approach achieves the best final held-out scores in 19 of 20 reward-matched settings across two backbones and ten evaluators. It outperforms the strongest competing method by up to 44.0% and reduces training GPU-hours relative to DiffusionNFT by 40% on SD 3.5-M and 63% on Z-Image-Turbo. These results support on-policy self-distillation as an efficient and analyzable approach to diffusion post-training by converting image-level reward guidance into explicit and continually refreshed intermediate supervision, thereby opening a path toward more efficient and diagnosable alignment.

Paper: https://arxiv.org/pdf/2608.24646

Code: https://github.com/worldbench/DiffusionOPSD

LoRAs: https://huggingface.co/WeiChow/DiffusionOPSD/tree/main

Project Page: https://diffusionopsd.github.io/#overview

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