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stable-diffusion
Discontinued This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI] (by lstein)
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Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
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Discontinued Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
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diffusers
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
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For those of you following the lstein fork (especially the development branch), it has been making great progress. I'm not getting black images anymore and the speed has gone up significantly.
Not one click install by a long shot, but the documentation is pretty clear to follow. Anyone with a bit of CLI experience can do it and if you don't have that, this is a great way to kind of stumble your way towards something working and learn in the process...
https://github.com/lstein/stable-diffusion/tree/development/...
I don't understand why all these crazy forks don't switch to using the HuggingFace codebase[1]. It's much better code and easier to add features to.
It's true you have to use the code from git rather than a release, but that's not hard.
https://github.com/nlothian/m1_huggingface_diffusers_demo is my clean demo repo with a notebook showing the usage. The standard HuggingFace examples (eg for img2img[2]) port across with no trouble too.
[1] https://github.com/huggingface/diffusers
[2] https://github.com/huggingface/diffusers#image-to-image-text...
This really is a one-click installer, which is excellent. As far as I know it's the first and only one for M1. It's quite a polished UI, but there are a lot of features missing. The first ones that come to mind are:
- No way to specify a seed. This is an important part of SD workflows, letting you redo an image with a slightly tweaked a prompt.
- No way to specify a custom model. Alternative models (such as Waifu Diffusion) are fun to play with too.
- No way to generate batches of images.
Other GUIs such as https://github.com/sd-webui/stable-diffusion-webui have many more features - but not all will be trivial to port to M1.
P.S.: For the people asking - yes, it can do NSFW images. I checked.
for those with stable diffusion running, an acquaintance and I have been working on another GUI https://github.com/breadthe/sd-buddy/
which offers custom seed and batches of images (we are also working on parametric prompting https://github.com/breadthe/sd-buddy/discussions/12#discussi... )
i'd love to get to img2img and alt models next.
i've been putting together a guide to prompts here https://github.com/sw-yx/prompt-eng/blob/main/PROMPTS.md
I don't understand why all these crazy forks don't switch to using the HuggingFace codebase[1]. It's much better code and easier to add features to.
It's true you have to use the code from git rather than a release, but that's not hard.
https://github.com/nlothian/m1_huggingface_diffusers_demo is my clean demo repo with a notebook showing the usage. The standard HuggingFace examples (eg for img2img[2]) port across with no trouble too.
[1] https://github.com/huggingface/diffusers
[2] https://github.com/huggingface/diffusers#image-to-image-text...
Inertia, mostly. The official press release on August 10th linked to https://github.com/CompVis/stable-diffusion and diffusers didn't add support for Stable Diffusion until 5782e0393d on August 14th. There has been a ton of work on adding features on top of the CompVis Github release and backporting that work to Diffusers just isn't as interesting as adding new features to the existing fork. There has been some adoption of Diffusers though.
Just done a run on my 3080 under Windows using https://github.com/bfirsh/stable-diffusion.git and it's about 8 iterations/sec when nothing else is using CPU or GPU.
Which would be less of an issue if I didn't have to fix a conda bug while installing StableDiffusion.
https://github.com/conda/conda/pull/11818
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