I$^2$SB: Image-to-Image Schrödinger Bridge
Authors: Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos Theodorou, Weili Nie, Anima Anandkumar
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate I2SB in solving various image restoration tasks, including inpainting, super-resolution, deblurring, and JPEG restoration on Image Net 256 256 and show that I2SB surpasses standard conditional diffusion models with more interpretable generative processes. |
| Researcher Affiliation | Collaboration | 1Georgia Institute of Technology 2NVIDIA 3California Institute of Technology. |
| Pseudocode | Yes | Algorithm 1 Training |
| Open Source Code | Yes | Project page and codes: https://i2sb.github.io/. |
| Open Datasets | Yes | on Image Net 256 256 (Deng et al., 2009) |
| Dataset Splits | Yes | Following the baselines (Saharia et al., 2022; Song et al., 2022), we report super-resolution results on the full Image Net validation set and report the remaining results on a 10k validation subset. |
| Hardware Specification | No | The inference time is measured on a V100 16G. (This is mentioned only for inference time in a specific table, not as the general hardware used for training or all experiments, so it's not a full specification.) |
| Software Dependencies | No | Official Pytorch implementation of our I2SB can be found in https://github.com/NVlabs/I2SB. (This refers to the implementation language/framework but doesn't give specific version numbers for dependencies). |
| Experiment Setup | Yes | By default, we use 1000 sampling time steps for all tasks with quadratic discretization (Song et al., 2020a). |