HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
apple-aiml-research
ml-stable-diffusion
Python2022–2025
apple-aiml-research / ml-stable-diffusion

ml-stable-diffusion

Stable Diffusion with Core ML on Apple Silicon

17Distinct people
18Mentions
0Explicit recommenders
18KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Stable Diffusion with Core ML on Apple Silicon

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:02:05 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year18 dated mentions

Observed mentions: 2022-12-01 20:12:50 UTC to 2025-09-10 01:58:42 UTC.

0369122022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 11 mentions; 4 submissions, 7 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC1147
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC404
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC211
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

2 sources
arctanx·May 26, 2024Submission

Show HN: Offline sketch to image geneartor in a whiteboard

I'm excited to share my indie product, https://drawing.pics In this post, I'll share all the technical details behind it. So, what exactly is DrawingPics? It's an image-to-image and sketch-to-image generator that runs in a whiteboard, allowing you to draw your draft directly on the canvas and easily generate the desired image. How does it work from a technical perspective? TLDR: Miniconda + Diffusers + Electron + Excalidraw One of the most important aspects of creating this tool was finding a stable diffusion locally inference solution. There are many solutions available, such as https://github.com/leejet/stable-diffusion.cpp and https://github.com/apple/ml-stable-diffusion. I tested the C++ version, but the inference speed was very slow, and Metal GPU support still had issues (you can find relevant issues in their repo). Ultimately, I de
Read the rest
cided to use python to run it because PyTorch is mature and MPS support is well-established. And I chose Miniconda, because it can create a small, isolated Python environment to run the program. The AI model should run in the background so that we can continuously produce images while you draw. We need to find an RPC method to enable communication between the Python process and Electron's Node.js process. The easiest way is to run a Python HTTP server, but the memory usage is too high. We need a more lightweight solution, so I used xmlrpc for memory efficiency, although there might be better alternatives that I'm unaware of. The AI inference part is handled by diffusers, which is great, but I had to apply some custom patches to make it work in this situation. This can be a bit challenging if you're not familiar with Python. For the frontend, I initially used a low-level canvas library and tried to implement a drawing pad from scratch. However, it had too many details, so I chose a more mature option: Excalidraw. It's fantastic, with the only shortcoming being limited API support. Finally, I combined all these technologies in Electron, ensuring they work smoothly on both the main process and the renderer process. Ok, Is DrawingPics free to use? 80% of the project is free to use. I only charge a lifetime license for the effort I've put in, as I've spent 4 months on it and made numerous improvements based on user feedback. Currently, only Mac is supported. Windows support will be added later.
87 points · Open discussion
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 - [2] https://github.com/huggingface/swift-coreml-diffusers
In Show HN: Run Stable Diffusion Directly on iPhone

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.