yuanzhi-zhu/OFTSR
[ICLR2026] "OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs"
This project helps image editors and graphic designers enhance the resolution of low-quality images to produce sharper, more detailed versions. It takes a blurry or pixelated image and outputs a high-resolution image, offering control over how realistic or faithful to the original the enhanced image appears. It's designed for professionals who need to quickly upgrade image quality for various visual applications.
Use this if you need to upscale images with a focus on balancing crisp details and a natural look, especially when dealing with varied image sources like portraits or general photography.
Not ideal if you primarily need to perform object detection or other image analysis tasks, as this tool focuses solely on image quality enhancement.
Stars
39
Forks
—
Language
Python
License
—
Category
Last pushed
Feb 07, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/yuanzhi-zhu/OFTSR"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
devzhk/InverseBench
InverseBench (ICLR 2025 spotlight)
guyyariv/DyPE
Official implementation for "DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion".
wyhuai/DDNM
[ICLR 2023 Oral] Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
bahjat-kawar/ddrm
[NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository
yuanzhi-zhu/DiffPIR
"Denoising Diffusion Models for Plug-and-Play Image Restoration", Yuanzhi Zhu, Kai Zhang,...