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GitHub Repository Setup

Repository Description (Short)

Full-stack AI image generation web app optimized for Apple Silicon. RealisticVision v5.1 model running on Core ML + ANE. Swift/Vapor backend + Next.js frontend.

Repository Description (Long)

HeyIm is a production-ready web application for AI image generation, optimized specifically for Apple Silicon (M1/M2/M3). It features a Swift/Vapor backend that leverages Core ML and the Apple Neural Engine for blazing-fast inference, paired with a modern Next.js frontend.

Key highlights:

  • ⚡ 8-10 seconds per image on Mac Mini M2
  • 🎨 RealisticVision v5.1 model (portrait specialist)
  • 🖼️ Image-to-image with strength control
  • 🧠 ANE optimization (80-100% utilization)
  • 🌐 REST API + Web UI
  • 🚀 Production deployment with launchd

Perfect for developers wanting to run Stable Diffusion locally without cloud APIs or Python dependencies.

Topics (GitHub Tags)

  • stable-diffusion
  • core-ml
  • apple-silicon
  • swift
  • vapor
  • nextjs
  • typescript
  • ai
  • image-generation
  • neural-engine
  • macos
  • m1
  • m2
  • machine-learning
  • webui

README Badges

Add these to the top of README.md:

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Swift 5.8+](https://img.shields.io/badge/Swift-5.8+-orange.svg)](https://swift.org)
[![Next.js 16](https://img.shields.io/badge/Next.js-16-black)](https://nextjs.org)
[![Apple Silicon](https://img.shields.io/badge/Apple%20Silicon-M1%2FM2%2FM3-blue)](https://www.apple.com/mac/)

Social Media Announcement

Twitter/X

🚀 Just open-sourced HeyIm - a full-stack Stable Diffusion web app for Apple Silicon!

⚡ 8-10s per image on M2 🧠 ANE-optimized Core ML 🖼️ Image-to-image support 🌐 Swift backend + Next.js frontend

Perfect for running SD locally without Python!

https://github.com/phucdhh/HeyIm

#StableDiffusion #AppleSilicon #Swift #MachineLearning

Reddit (r/StableDiffusion, r/MachineLearning)

Title: [P] HeyIm - Full-stack Stable Diffusion web app optimized for Apple Silicon (M1/M2/M3)

I built a production-ready web application for running Stable Diffusion locally on Mac, leveraging Core ML and the Apple Neural Engine for maximum performance.

Key Features:

  • ⚡ 8-10 seconds per image (30 steps) on Mac Mini M2 base
  • 🧠 ANE utilization: 80-100% (UNet runs entirely on Neural Engine)
  • 🎨 RealisticVision v5.1 model (portrait specialist)
  • 🖼️ Image-to-image with PNDM scheduler for face preservation
  • 🌐 REST API + modern web UI (Next.js)
  • 🚀 Production deployment scripts included

Tech Stack:

  • Backend: Swift 5.8+ with Vapor framework
  • Frontend: Next.js 16 + TypeScript + Tailwind
  • ML: Core ML with SPLIT_EINSUM attention
  • Deployment: launchd + Cloudflare Tunnel

Why this vs Python?

  • No Python/conda environment needed
  • Native performance on Apple Silicon
  • Lower memory footprint
  • Built-in macOS integration

GitHub: https://github.com/phucdhh/HeyIm

Happy to answer questions! This is my first open-source ML project.

Hacker News

Title: Show HN: HeyIm – Stable Diffusion web app for Apple Silicon (Swift + Core ML)

Description: A full-stack web application for running Stable Diffusion locally on Mac using Swift, Core ML, and the Apple Neural Engine. 8-10 second generation time on M2, with image-to-image support and modern web UI.

GitHub About Section

Website: (leave empty or add your demo URL) Topics: stable-diffusion, core-ml, apple-silicon, swift, vapor, nextjs, ai, image-generation

Initial Issue Templates

Create these in .github/ISSUES/

Bug Report

**Describe the bug**
A clear description of what the bug is.

**Environment**
- macOS version:
- Hardware (Mac Mini/MacBook/Mac Studio):
- RAM:
- Model being used:

**Steps to reproduce**
1. 
2. 
3. 

**Expected behavior**
What you expected to happen.

**Actual behavior**
What actually happened.

**Logs**
Paste relevant logs here.

Feature Request

**Feature description**
Clear description of the feature you'd like.

**Use case**
Why is this feature needed? What problem does it solve?

**Proposed implementation**
(Optional) Ideas on how this could be implemented.

**Alternatives considered**
(Optional) Other solutions you've thought about.

Pull Request Template

## Description
Brief description of what this PR does.

## Changes
- [ ] Backend changes
- [ ] Frontend changes
- [ ] Documentation updates
- [ ] New dependencies

## Testing
- [ ] Tested locally
- [ ] All existing tests pass
- [ ] Added new tests (if applicable)

## Screenshots
(If UI changes)

## Checklist
- [ ] Code follows project style
- [ ] Documentation updated
- [ ] No sensitive data included
- [ ] Ready for review

License Notice for README

Add to bottom of README:

## License & Attribution

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

### Model License
The RealisticVision v5.1 model has its own license terms. Please ensure compliance with the model's license when using this software commercially.

### Acknowledgments
- **Apple** - [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion) for Core ML conversion tools
- **StabilityAI** - Original Stable Diffusion architecture
- **RealisticVision** - Model fine-tuning

### Disclaimer
This project is for educational and research purposes. Always respect model licenses and usage terms.

SEO & Discoverability

GitHub Search Keywords (include in README):

  • "stable diffusion macos"
  • "core ml image generation"
  • "apple silicon ai"
  • "swift machine learning"
  • "local ai image generation"
  • "m1 m2 stable diffusion"

Make sure these appear naturally in:

  • README.md introduction
  • Project description
  • Documentation headers

Community Engagement Plan

  1. Week 1: Share on r/StableDiffusion, r/MachineLearning
  2. Week 2: Post on Hacker News "Show HN"
  3. Week 3: Tweet with relevant hashtags
  4. Week 4: Write blog post with tutorial

Engagement Tips:

  • Respond to issues within 24 hours
  • Be open to contributions
  • Acknowledge contributors
  • Share progress updates
  • Create roadmap for future features