Stability Matrix is a cross-platform desktop application (Windows, macOS, Linux) that serves as a unified package manager, model browser, and inference UI for AI image generation tools like Stable Diffusion, ComfyUI, Fooocus, and Forge. The codebase is built with .NET 9 using Avalonia UI (with legacy WPF remnants) and follows the MVVM pattern with CommunityToolkit.Mvvm and DI via Injectio. Key entry points include the MainWindow, PackageManagerPage, InferencePage (text-to-image, image-to-image, video, upscale), and CheckpointBrowserPage (CivitAI, HuggingFace, OpenModelDB). The system manages Python virtual environments, installs packages via pip/uv, and orchestrates inference through ComfyUI's API or built-in Gemini providers. External dependencies include Refit for API clients, LiteDB for local caching, and SkiaSharp for image processing. The architecture is a modular monolith with clear separation of concerns via service interfaces, view models, and custom controls.
The system is a comprehensive toolkit for AI-driven image and video generation, primarily leveraging diffusion models such as Stable Diffusion, Stable Diffusion XL, and Stable Video Diffusion. It provides both a command-line interface (CLI) and an HTTP API server for generating images from text prompts, performing image-to-image translation, inpainting, ControlNet-guided generation, upscaling, face restoration, and video frame interpolation. The architecture is modular, separating core model implementations, enhancements, processors, and utilities, with a strong emphasis on extensibility through configurable backends and pluggable components. Key features include support for multiple sampling algorithms (DDIM, PLMS, K-diffusion solvers), integration with ControlNet for conditional generation via edge, depth, pose, and other maps, and post-processing pipelines for upscaling (using RealESRGAN or similar) and face enhancement (using CodeFormer). The system also incorporates multimodal capabilities through integrated models like BLIP for image captioning and visual question answering, and segment anything model (SAM) for segmentation tasks. Model weight management is handled through a centralized system that resolves and converts weights from various sources (Hugging Face Hub, local cache) and formats (CompVis, Diffusers, Refiners). The CLI offers commands like `imagine` for text-to-image generation, `aimg` as a multi-command interface for imagine, upscale, edit, server, and videogen operations, and `run-server` to launch the HTTP API with a web interface (StableStudio). The HTTP API provides REST endpoints for programmatic access, complete with OpenAPI documentation and GPU-aware concurrency control to handle multiple requests safely. Overall, the system is designed to be user-friendly yet powerful, automating complex workflows like model downloads and caching while providing low-level control for advanced users.
This repository contains the public-facing source code for Draw Things, a production-grade AI image and video generation application for macOS/iOS/Linux. The codebase implements a comprehensive suite of diffusion models (Stable Diffusion, FLUX, HiDream, Qwen, MiniMax, etc.), samplers, LoRA training, model conversion, and a gRPC-based distributed inference server. It is written primarily in Swift, using Bazel for builds, and includes extensive use of FlatBuffers for data serialization, SQLite for persistence, and a custom neural network compiler (NNC) for on-device inference. The architecture is modular, with separate libraries for tokenization (CLIP, Tiktoken, SentencePiece), model zoos (downloadable model specifications), image generation pipelines, and cloud authentication. The repository also includes CLI tools (draw-things-cli, gRPCServerCLI, model converters) and server management scripts for deploying GPU-backed inference services with Tailscale, Envoy, and Docker. While the core application is a consumer-facing app, the codebase also contains research-grade components such as instruction count estimators for performance profiling, formal verification tools (Z3, Lean), and web search integration with browser fallback. The code is well-structured with many reusable patterns, though it carries some maintenance overhead from vendored dependencies and generated code.