Super-resolution Model Adaptation Based on Diffusion Model

The Stable-Diffusion model currently holds a dominant position in image generation technology. However, images generated by this model are influenced by training and can only produce images of a specific resolution. To enhance image resolution generated by the Stable-Diffusion model, this study investigates the combined impact of various mainstream Super-Resolution models with the Stable-Diffusion model. The experiment aims to enhance both the quality and speed of image generation. It evaluates the performance of different Super-Resolution models in boosting the resolution of images inferred by the Stable-Diffusion model while preserving image details. Research findings demonstrate that certain Super-Resolution models can significantly enhance image resolution while upholding high-quality images, offering an effective approach to achieve efficient image generation with limited computational resources.