SUPIR implements a diffusion-based approach for photo-realistic image restoration in the wild. It combines the SDXL diffusion model with LLaVA language model guidance to enhance super-resolution quality. The system integrates CLIP encoders for semantic understanding and is trained with PyTorch Lightning. The project includes both a research codebase and an online demo service.