3 papers
cs.CV2025
Textualize Visual Prompt for Image Editing via Diffusion Bridge
Pengcheng Xu, Qingnan Fan, Fei Kou +5
Visual prompt, a pair of before-and-after edited images, can convey indescribable imagery transformations and prosper in image editing. However, current visual prompt methods rely…
cs.CV2024
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution
Jiangang Wang, Qingnan Fan, Jinwei Chen +3
Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing di…
cs.CV2024
CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion Models
Gaoyang Zhang, Bingtao Fu, Qingnan Fan +5
Text-to-image (T2I) diffusion models excel at generating photorealistic images but often fail to render accurate spatial relationships. We identify two core issues underlying this…