Open Source Mac AI Image Generators
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AI Image Generators for Mac
View 48 business solutionsBrowse free open source AI Image Generators and projects for Mac below. Use the toggles on the left to filter open source AI Image Generators by OS, license, language, programming language, and project status.
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AUTOMATIC1111 Stable Diffusion web UI
Stable Diffusion web UI
AUTOMATIC1111's stable-diffusion-webui is a powerful, user-friendly web interface built on the Gradio library that allows users to easily interact with Stable Diffusion models for AI-powered image generation. Supporting both text-to-image (txt2img) and image-to-image (img2img) generation, this open-source UI offers a rich feature set including inpainting, outpainting, attention control, and multiple advanced upscaling options. With a flexible installation process across Windows, Linux, and Apple Silicon, plus support for GPUs and CPUs, it caters to a wide range of users—from hobbyists to professionals. The interface also supports prompt editing, batch processing, custom scripts, and many community extensions, making it a highly customizable and continually evolving platform for creative AI art generation. -
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Fooocus
Focus on prompting and generating
Fooocus is an open-source image generation software that simplifies the process of creating images from text prompts. Built on Gradio and leveraging Stable Diffusion XL, Fooocus eliminates the need for manual parameter tweaking, allowing users to focus solely on crafting prompts. It offers a user-friendly interface with minimal setup, making advanced image synthesis accessible to a broader audience. -
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ComfyUI
The most powerful and modular diffusion model GUI, api and backend
The most powerful and modular diffusion model is GUI and backend. This UI will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart-based interface. We are a team dedicated to iterating and improving ComfyUI, supporting the ComfyUI ecosystem with tools like node manager, node registry, cli, automated testing, and public documentation. Open source AI models will win in the long run against closed models and we are only at the beginning. Our core mission is to advance and democratize AI tooling. We believe that the future of AI tooling is open-source and community-driven. -
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Z-Image
Image generation model with single-stream diffusion transformer
Z-Image is an efficient, open-source image generation foundation model built to make high-quality image synthesis more accessible. With just 6 billion parameters — far fewer than many large-scale models — it uses a novel “single-stream diffusion Transformer” architecture to deliver photorealistic image generation, demonstrating that excellence does not always require extremely large model sizes. The project includes several variants: Z-Image-Turbo, a distilled version optimized for speed and low resource consumption; Z-Image-Base, the full-capacity foundation model; and Z-Image-Edit, fine-tuned for image editing tasks. Despite its compact size, Z-Image produces outputs that closely rival those from much larger models — including strong rendering of bilingual (English and Chinese) text inside images, accurate prompt adherence, and good layout and composition. -
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FastSD CPU
Fast stable diffusion on CPU and AI PC
FastSD CPU is an optimized fork of Stable Diffusion designed to run efficiently on CPUs and devices without dedicated GPUs by leveraging Latent Consistency Models and Adversarial Diffusion Distillation techniques that accelerate inference. It focuses on bringing fast text-to-image generation to mainstream hardware like desktop CPUs, lower-end laptops, or edge devices without requiring high-end graphics processors. The repository contains multiple interfaces including a desktop GUI for simple generation, an advanced web-based UI with support for extensions like LoRA and ControlNet, and a command-line interface for scripted usage or server deployments. With support for performance-oriented libraries such as OpenVINO and hardware acceleration on platforms like Intel AI PCs, FastSD CPU aims to shrink generation times dramatically compared with naive CPU implementations. -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels), which allows for photography-quality images, detailed product shots, infographics or UI mockups rather than just low-resolution drafts. FLUX.2 is built with a modern architecture (a flow-matching transformer + a revamped VAE + a strong vision-language encoder), enabling strong prompt adherence, correct rendering of text/typography in images, reliable lighting, layout, and physical realism, and consistent style/character/product identity across multiple generations or edits. -
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Stable Diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
Stable Diffusion Version 2. The Stable Diffusion project, developed by Stability AI, is a cutting-edge image synthesis model that utilizes latent diffusion techniques for high-resolution image generation. It offers an advanced method of generating images based on text input, making it highly flexible for various creative applications. The repository contains pretrained models, various checkpoints, and tools to facilitate image generation tasks, such as fine-tuning and modifying the models. Stability AI's approach to image synthesis has contributed to creating detailed, scalable images while maintaining efficiency. -
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Easy Diffusion
An easy 1-click way to create beautiful artwork on your PC using AI
Easy Diffusion is a widely used community-driven repository offering a simple, one-click way to install and use Stable Diffusion-based generative AI on a personal computer without advanced technical skills or prior setup. It provides a browser-based user interface that runs locally, allowing users to type text prompts and immediately generate images directly within their web browser, democratizing access to powerful text-to-image models for artists and hobbyists alike. The project abstracts away environment setup, dependencies, and model installation — tasks that can be daunting to beginners — and instead lets users focus on creative experimentation with prompt phrasing, model parameters, and image output settings. Because it’s designed to be easy to install and use, EasyDiffusion’s interface includes options for queuing multiple jobs, applying modifiers like upscaling or face correction, and adjusting generation parameters like guidance scale and resolution. -
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Qwen-Image
Qwen-Image is a powerful image generation foundation model
Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence. The model excels not only in text rendering but also in a wide range of artistic styles, including photorealistic, impressionist, anime, and minimalist aesthetics. Qwen-Image supports sophisticated editing tasks such as style transfer, object insertion and removal, detail enhancement, and even human pose manipulation, making it suitable for both professional and casual users. It also includes advanced image understanding capabilities like object detection, semantic segmentation, depth and edge estimation, and novel view synthesis. -
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ArtCraft
Crafting engine for artists, designers, and filmmakers
ArtCraft is an open-source desktop creative environment designed as an IDE for interactive AI-driven image and video creation, with the goal of transforming traditional prompting into a more hands-on crafting workflow. The project positions itself as an intentional “crafting engine” for artists, designers, and filmmakers who want deeper control over generative media pipelines. Rather than relying purely on text prompts, ArtCraft emphasizes visual manipulation, compositional control, and iterative refinement so creators can treat AI output more like a malleable creative medium. The application is built with performance and responsiveness in mind, enabling users to move between different creative canvases and asset workflows within a unified interface. It aims to support complex multimedia generation workflows including image, video, and potentially 3D content creation, making it useful for experimental filmmaking and advanced visual design. -
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Dream Textures
Stable Diffusion built-in to Blender
Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what you're looking for. Texture entire models and scenes with depth to image. Inpaint to fix up images and convert existing textures into seamless ones automatically. Outpaint to increase the size of an image by extending it in any direction. Perform style transfer and create novel animations with Stable Diffusion as a post processing step. Dream Textures has been tested with CUDA and Apple Silicon GPUs. Over 4GB of VRAM is recommended. -
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
InvokeAI is an implementation of Stable Diffusion, the open source text-to-image and image-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images. -
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stable-diffusion.cpp
Diffusion model(SD,Flux,Wan,Qwen Image,Z-Image,...) inference
stable-diffusion.cpp is a lightweight, high-performance implementation of Stable Diffusion and related generative models written entirely in portable C/C++, designed to run on virtually any device without heavy dependencies. It enables text-to-image and image-to-image generation, supports a growing set of models like SD1.x, SD2.x, SDXL, SD-Turbo, Qwen Image, and more, and is continually updated with support for cutting-edge model variants including video and image editing models. The project is built on the ggml backend, which allows efficient execution on CPUs and GPUs via backends like CUDA, Vulkan, Metal, OpenCL, and SYCL, making it suitable for everything from desktops to mobile devices. It includes options for ControlNet, LoRA models, upscaling via ESRGAN, and advanced sampling techniques, giving developers and users a rich toolkit for creative workflows. -
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Diffusion Bee
Diffusion Bee is the easiest way to run Stable Diffusion locally
Diffusion Bee is a user-friendly local application designed to make running the Stable Diffusion text-to-image generative model as simple as possible on macOS machines, including both Intel and Apple Silicon. It wraps Stable Diffusion and its dependencies into a one-click installer so users don’t need to manually install Python, drivers, or machine-learning frameworks to generate images. The app runs entirely on the local machine so images are created offline and no user data is sent to external servers unless explicitly chosen, preserving privacy. Users can generate images from text prompts, perform image-to-image transformations, and apply additional features like inpainting, outpainting, and model-based upscaling directly within a clean graphical interface. It’s optimized for Apple hardware performance and can automatically manage features like ControlNet, LoRA models, and advanced prompt options without exposing complexity to the user. -
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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Stable Diffusion v 2.1 web UI
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img, in paint and upscale4x. Gradio app for Stable Diffusion 2 by Stability AI. It uses Hugging Face Diffusers implementation. Currently supported pipelines are text-to-image, image-to-image, inpainting, upscaling and depth-to-image. -
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FLUX.1
Official inference repo for FLUX.1 models
FLUX.1 repository contains inference code and tooling for the FLUX.1 text-to-image diffusion models, enabling developers and researchers to generate and edit images from natural-language prompts using open-weight versions of the model on their own hardware or within custom applications. The project is part of a larger family of FLUX models developed by Black Forest Labs, designed to produce high-quality, detailed visuals from text descriptions with competitive prompt adherence and artistic fidelity. This repo focuses on running the open-source model variants efficiently, providing scripts, model loading logic, and examples for local installations, and supports integration with Python toolchains like PyTorch and popular generative pipelines. Users can launch CLI tools to generate images, experiment with different FLUX variants, and extend the base code for research-oriented applications. -
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HunyuanImage-3.0
A Powerful Native Multimodal Model for Image Generation
HunyuanImage-3.0 is a powerful, native multimodal text-to-image generation model released by Tencent’s Hunyuan team. It unifies multimodal understanding and generation in a single autoregressive framework, combining text and image modalities seamlessly rather than relying on separate image-only diffusion components. It uses a Mixture-of-Experts (MoE) architecture with many expert subnetworks to scale efficiently, deploying only a subset of experts per token, which allows large parameter counts without linear inference cost explosion. The model is intended to be competitive with closed-source image generation systems, aiming for high fidelity, prompt adherence, fine detail, and even “world knowledge” reasoning (i.e. leveraging context, semantics, or common sense in generation). The GitHub repo includes code, scripts, model loading instructions, inference utilities, prompt handling, and integration with standard ML tooling (e.g. Hugging Face / Transformers). -
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ImageReward
[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences
ImageReward is the first general-purpose human preference reward model (RM) designed for evaluating text-to-image generation, introduced alongside the NeurIPS 2023 paper ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. Trained on 137k expert-annotated image pairs, ImageReward significantly outperforms existing scoring methods like CLIP, Aesthetic, and BLIP in capturing human visual preferences. It is provided as a Python package (image-reward) that enables quick scoring of generated images against textual prompts, with APIs for ranking, scoring, and filtering outputs. Beyond evaluation, ImageReward supports Reward Feedback Learning (ReFL), a method for directly fine-tuning diffusion models such as Stable Diffusion using human-preference feedback, leading to demonstrable improvements in image quality. -
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Mochi Diffusion
Run Stable Diffusion on Mac natively
Run Stable Diffusion on Mac natively. This app uses Apple's Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. Extremely fast and memory efficient (~150MB with Neural Engine) Runs well on all Apple Silicon Macs by fully utilizing Neural Engine. Generate images locally and completely offline. Generate images based on an existing image (commonly known as Image2Image) Generated images are saved with prompt info inside EXIF metadata (view in Finder's Get Info window) Convert generated images to high resolution (using RealESRGAN) Autosave & restore images. Use custom Stable Diffusion Core ML models. No worries about pickled models. macOS native app using SwiftUI. -
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MochiDiffusion
Run Stable Diffusion on Mac natively
MochiDiffusion is a native macOS application that allows users to run Stable Diffusion models locally, leveraging Apple Silicon GPU acceleration via Core ML. It offers users GUI controls for prompts and model configuration without needing Python or Docker, enabling offline image generation. -
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stable-diffusion-webui-forge
A Fork from Github repository of Illyasviel's Forge
This is for use by the StableProjectorz https://stableprojectorz.com Kept here, in case the file changes URL in his repo. The URL must remain the same, so that StableProjectorz installer can always download it. -
23
Auto-Photoshop-StableDiffusion-Plugin
Plug-in that makes it easy to generate stable diffusion images
Auto-Photoshop-StableDiffusion-Plugin is an open-source extension that seamlessly integrates Stable Diffusion image generation directly into Adobe Photoshop, allowing artists and designers to generate, edit, and refine AI-generated imagery without leaving the Photoshop environment. It bridges Photoshop with popular diffusion backends like AUTOMATIC1111 or ComfyUI, effectively embedding powerful generative tools into a familiar creative workflow so users can apply AI creation to layers, selections, and masks while retaining Photoshop’s full editing capabilities. With this plugin, users can generate new visuals from text prompts, use selectable areas for inpainting or outpainting, and adjust images using AI features while still leveraging traditional design tools such as brushes, filters, and adjustment layers. The integration dramatically reduces context switching between standalone AI tools and professional design software, empowering more efficient experimentation and iteration. -
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Deep Exemplar-based Video Colorization
The source code of CVPR 2019 paper "Deep Exemplar-based Colorization"
The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization". End-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively. In order to colorize your own video, it requires to extract the video frames, and provide a reference image as an example. -
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AI Logo Generator
A free + OSS logo generator powered by Flux on Together AI
AI Logo Generator is an open-source AI logo generator that lets you create professional-looking logos in seconds from a simple text prompt. It uses the Flux Pro 1.1 model hosted on Together AI to generate logos, so the heavy lifting is done by a state-of-the-art image model while the app focuses on UX and workflow. The project is built with Next.js and TypeScript, and it uses shadcn/ui plus Tailwind CSS for a modern, responsive interface that feels like a polished SaaS product rather than a demo. It integrates Clerk for authentication so users can sign in, save their logo history (planned via a dashboard), and potentially manage usage tied to their own API key. Upstash Redis is used for rate limiting, which is important for controlling API usage and preventing abuse when generating many images. Analytics and observability are baked in with Plausible and Helicone, so developers can monitor usage patterns and model behavior over time.
