sd-buddy
m1_huggingface_diffusers_demo
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| sd-buddy | m1_huggingface_diffusers_demo | |
|---|---|---|
| 1 | 5 | |
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| over 1 year ago | over 1 year ago | |
| Svelte | Jupyter Notebook | |
| - | MIT License |
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sd-buddy
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
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.
m1_huggingface_diffusers_demo
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JupyterLab 4.0
The trick is that you have to deactivate the virtual environment and then resource it after adding Jupyter to that virtual environment.
Most shells cache executable paths, so the path for jupyter will be the global path, not the one for your virtual environment. This is unfortunately not at all obvious and leads to very hard to track down bugs that seem to disappear and reappear if you aren't familiar with the issue.
I have a recipe here which always works: https://github.com/nlothian/m1_huggingface_diffusers_demo#se...
If you don't have requirements.txt then do this: `pip3 install jupyter` for that line, then `deactivate` and `source ./venv/bin/activate`.
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Bunny AI
This is how I did it on an M1 in September: https://github.com/nlothian/m1_huggingface_diffusers_demo
I think it probably needs updating now, but it should give you something to start with.
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
On my M1 MAx with 32 GB I'm getting 1.5 iterations/second (ie, ~30 seconds for the standard 50 iterations) using this example: https://github.com/nlothian/m1_huggingface_diffusers_demo
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Nvidia Hopper Sweeps AI Inference Benchmarks in MLPerf Debut
Out of interest I've been running a bunch of the huggingface version of StableDiffusion using the M1 accelerated branch on my M1 Max[1]. I'm getting 1.54 it/s compared to 2.0 it/s for a Nvidia T4 Tesla on Google Collab.
T4 Tesla gets 21,691 queries/second for for ResNet, compared to 81,292 q/s for the new H100, 41,893 q/s for the A100 and 6164 q/s for the new Jetson.
So you can expect maybe 15,000 q/s on a M1 Max. But some tests seem to indicate a lot less[2] - not sure what is happening there.
[1] Setup like this: https://github.com/nlothian/m1_huggingface_diffusers_demo
[2] https://tlkh.dev/benchmarking-the-apple-m1-max#heading-resne...
What are some alternatives?
ai-notes - notes for software engineers getting up to speed on new AI developments. Serves as datastore for https://latent.space writing, and product brainstorming, but has cleaned up canonical references under the /Resources folder.
stable-diffusion-webui - Stable Diffusion web UI
diffusionbee-stable-diffusion-ui - 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.
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
stable-diffusion
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion-ui - 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]
jupyter-collaboration - A Jupyter Server Extension Providing Support for Y Documents