[CVPR2024 Oral] Image Processing GNN: Breaking Rigidity in Super-Resolution

Paper | Project Page |

This is the official repo of our CVPR'24 paper **Image Processing GNN: Breaking Rigidity in Super-Resolution**. In the paper, we propose IPG: a Graph-based SR model that achieves outstanding performance on various SR benchmarks.

News

7/6/2024: We opensourced the code & weights of IPG!

6/19/2024: Our work got the Best Student Runner-up Award of CVPR'24!🎉🎉

6/2/2024: We open-sourced U-DiT, an efficient U-Net-style DiT variant.

Weights & Visual Results

Model Scale Urban100 Weights Visual Results
IPG 2x 34.48 🤗Link 🤗Link
IPG 3x 30.36 🤗Link 🤗Link
IPG 4x 28.13 🤗Link 🤗Link
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.