ai-notes
m1_huggingface_diffusers_demo
| ai-notes | m1_huggingface_diffusers_demo | |
|---|---|---|
| 15 | 5 | |
| 4,554 | 15 | |
| - | - | |
| 9.8 | 10.0 | |
| 8 days ago | over 1 year ago | |
| HTML | Jupyter Notebook | |
| MIT License | MIT License |
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ai-notes
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Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch
the field just moves fast. I have curated a list of non-hypey writers and youtubers who explain these things for a typical SWE audience if you are interested. https://github.com/swyxio/ai-notes/blob/main/Resources/Good%...
- SDXL Turbo: A Real-Time Text-to-Image Generation Model
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DeepEval – Unit Testing for LLMs
added to my notes! https://github.com/swyxio/ai-notes/
- ChatGPT Code Interpreter Capabilities
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Google just released a 100% free learning path on Generative AI with 9 Courses
and here are mine, organized by beginner/intermediate/advanced
https://github.com/swyxio/ai-notes/blob/main/README.md#top-a...
and then you can go into the individual modality specific notes for more reading
- Show HN: Self-host Whisper As a Service with GUI and queueing
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Show HN: YouTube Summaries Using GPT
there's https://learnprompting.org/
i've also been keeping a popular series of notes https://github.com/sw-yx/ai-notes/blob/main/TEXT_PROMPTS.md
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Show HN: I reverse prompt engineered every Notion AI feature
Direct link to the source prompts are here: https://github.com/sw-yx/ai-notes/blob/main/Resources/Notion...
- GitHub - sw-yx/prompt-eng: notes for prompt engineering
- My hand-curated list of major distros and forks of Stable Diffusion. Please suggest anything I missed!
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?
text2image-gui - Somewhat modular text2image GUI, initially just for Stable Diffusion
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.
sd-buddy - Companion desktop app for the self-hosted M1 Mac version of Stable Diffusion
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]
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
perceiver-pytorch - Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
stable-diffusion - A latent text-to-image diffusion model
rocm-build - build scripts for ROCm
jupyter-collaboration - A Jupyter Server Extension Providing Support for Y Documents