#1111th of 42 in Image and videoFor image, video and design, image and design
mochidiffusion
Native macOS app for generating images offline with Stable Diffusion and FLUX.2 Klein
- Stars
- 8k
- License
- GPL-3.0
- Last commit
- Sep 2026
- Last release
- Sep 2026
- Language
- Swift
7,799 on 3 Apr 20267,974 on 30 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Mochi Diffusion is a SwiftUI macOS app that generates images locally using Apple's Core ML Stable Diffusion, with optional ControlNet and image-to-image, and the Iris pipeline for FLUX.2 Klein models on Metal. Output is saved as PNG with generation settings embedded, and a built-in gallery handles import, save and sync. It collects no telemetry and sends nothing to the cloud.
Who it is for: Mac users who want local image generation in a native app
Strengths
- Runs fully offline; README states no data or telemetry is collected
- Uses Neural Engine via Core ML for low memory use on Apple Silicon
- Supports FLUX.2 Klein 4B and 9B without model conversion, with up to 4 input images
- PNG output embeds settings readable by AUTOMATIC1111-compatible tools and Civitai
Weaknesses
- macOS only: requires Apple Silicon and macOS 15.6 or later
- Core ML Stable Diffusion models have a fixed image size set at conversion
- Models must be downloaded or converted and placed manually in a model folder
- First use of a model can take up to 2 minutes to compile for the Neural Engine
What it needs
- GPU required
- Needs Apple Core ML Stable Diffusion, iris.c, Xcode 26.0 or later (to build)
- Models: Core ML Stable Diffusion (split_einsum, original), ControlNet (Core ML), FLUX.2 Klein 4B, FLUX.2 Klein 9B
Quick answers
Is mochidiffusion open source?
Yes. Its code is on GitHub under the GPL-3.0 license.
Does mochidiffusion need a GPU?
Yes, it needs a GPU.
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