ml-stable-diffusion
psat_exercise_code
| ml-stable-diffusion | psat_exercise_code | |
|---|---|---|
| 45 | 1 | |
| 16,111 | 5 | |
| 0.7% | - | |
| 7.4 | 0.0 | |
| 26 days ago | 7 months ago | |
| Python | Java | |
| GNU General Public License v3.0 or later | - |
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ml-stable-diffusion
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Show HN: Run Stable Diffusion Directly on iPhone
Not sure how that got in here. Apple released CoreML Stable Diffusion library a little over a year ago [1]. Hugging Face released their version of the example app for the CoreML Stable Diffusion library [2].
The app should be able to run on iPhone 14 Pro, I believe the requirements is about 6-8Gb of RAM. And I was not able to run it on iPhone 13 Mini, because it has only 4Gb of RAM.
- [1] https://github.com/apple/ml-stable-diffusion
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Apple releases MLX; has working Stable Diffusion example
Where are you seeing a Stable Diffusion example? I'm familiar with Apple's CoreML Implementation of StableDiffusion, but is there something else in the SD world available for download now as part of MLX?
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Stable Diffusion XL on iPhone with Core ML
Other features and improvements to the repo https://github.com/apple/ml-stable-diffusion
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FLaNK Stack Weekly for 20 June 2023
M1! https://github.com/apple/ml-stable-diffusion
- Apple Introduces M2 Ultra with up to 192GB Unified Memory - LLM powerhouse?
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Need help choosing between two laptops
M2 MBA can run Stable Diffusion and LLaMa comfortably, which means generating your potential game/image asset locally. They're pretty much impractical in 7340.
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Speed Is All You Need: On-Device Acceleration of Large Diffusion Models
Interestingly these are OpenCL kernels so in theory some of the optimizations might run out-of-the-box on CPUs.
It would be instructive to compare their speedups on the iPhone to the Apple CoreML implementation: https://github.com/apple/ml-stable-diffusion
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Is it worth buying a used M1 Mac for stable diffusion when you have iPad M1 but Intel Mac
Stable Diffusion runs great on my M1 Macs. The Draw Things app makes it really easy to run too. You also can’t disregard that Apple’s M chips actually have dedicated neural processing for ML/AI. This actual makes a Mac more affordable in this category because you don’t need to purchase a beefy graphics card. Not to mention that Apple has even optimized their software specifically for Stable Diffusion (related GitHub). Draw Things can take advantage of this. There’s a few guides to running the web UI on M1 too. I prefer the Draw Things app because of how easy it is to use, but the web UI is also nice because of all of the plugins and workflows that the community has built over time.
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Stable diffusion for Apple silicon
LINKS: ml-stable-diffusion: https://github.com/apple/ml-stable-diffusion Diffusers (HuggingFace Mac App): https://apps.apple.com/app/diffusers/id1666309574?mt=12
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Apple: Transformer architecture optimized for Apple Silicon
So, is Stable Diffusion working finally on TPU or not? DiffusionBee uses GPU and running this https://github.com/apple/ml-stable-diffusion with CPU_AND_NE just segfaults
psat_exercise_code
What are some alternatives?
MochiDiffusion - Run Stable Diffusion on Mac natively
socketify.py - Bringing Http/Https and WebSockets High Performance servers for PyPy3 and Python3
ml-ane-transformers - Reference implementation of the Transformer architecture optimized for Apple Neural Engine (ANE)
ml-image-classification-example-for-openmv - Build a TensorFlow Lite based computer vision emoji input device with OpenMV 📷 → ✋ 👎 👍 👊
modelscope - ModelScope: bring the notion of Model-as-a-Service to life.
pulsar-recipes - A StreamNative library containing a collection of recipes that are implemented on top of the Pulsar client to provide higher-level functionality closer to the application domain.
devrel-mastodon - List of DevRel folk on Mastodon
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
serverless-java-masterclass - Labs exercises for the masterclass "Serverless Java with Spring Boot"
stable-diffusion-webui - Stable Diffusion web UI
jsonhero-web - JSON Hero is an open-source, beautiful JSON explorer for the web that lets you browse, search and navigate your JSON files at speed. 🚀. Built with 💜 by the Trigger.dev team.