MochiDiffusion is a local client for Stable Diffusion that functions as an AI image generation studio. It provides a workspace for performing text-to-image, image-to-image, and inpainting tasks, enabling the production of high-resolution images offline using local hardware and neural engine acceleration.
The project includes a local model manager for importing, organizing, and converting machine learning models into compatible formats for offline execution. It features a ControlNet integration tool to guide structural composition and spatial layout, alongside a dedicated image upscaler that uses super-resolution algorithms to increase image dimensions and fine detail.
The application covers a broad capability surface including image refinement through multi-stage processing, metadata embedding for persisting prompts in EXIF fields, and generative media asset management via a searchable gallery. It also incorporates a safety-checker for content filtering and a background task queue to manage compute-heavy generation requests.
