Local LLM To Image

Running image models locally means juggling checkpoints, Python environments, GPU diagnostics, and a usable UI. This project wraps that into one coordinated app.

• React dashboard for generation, gallery, runtime diagnostics, and image-to-image editor
• Canvas-based crop/color preprocessing plus server-side Pillow tool endpoints
• Typed API client for JSON and multipart flows with Vitest component coverage

• FastAPI routes with subprocess `diffusers` runner in a dedicated model venv
• Generation lock and CUDA OOM retry with smaller preset sizes
• Optional API key, CORS origin checks, and upload validation for local-but-exposed use
• Filesystem output storage under `outputs/generated`

• Isolate GPU inference in a subprocess/venv so the API stays fast and testable
• Allowlisted model IDs instead of arbitrary filesystem paths from the client
• Split instant browser edits from heavier Pillow tools before image-to-image runs

• Orchestrating reliable local GPU pipelines across two Python environments and a web UI
• Canvas mask retouch workflow with backend blur/median fill
• Keeping frontend types aligned with Pydantic schemas across JSON and multipart routes

• GitHub