Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[TIP2026] Official codes of CCSRv2 and CCSRv1: Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
| Date | Stars |
|---|---|
| 2026-07-31 | 607 |
| 2026-08-06 | 607 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center">
<img src="figs/logo.png" width="400">
</p>
<div align="center">
<h2>Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution</h2>
<a href='https://arxiv.org/pdf/2401.00877'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
[Lingchen Sun](https://scholar.google.com/citations?hl=zh-CN&tzom=-480&user=ZCDjTn8AAAAJ)<sup>1,2</sup>
| [Rongyuan Wu](https://scholar.google.com/citations?user=A-U8zE8AAAAJ&hl=zh-CN)<sup>1,2</sup> |
[Jie Liang](https://scholar.google.com.sg/citations?user=REWxLZsAAAAJ&hl)<sup>2</sup> |
[Zhengqiang Zhang](https://scholar.google.com/citations?hl=zh-CN&user=UX26wSMAAAAJ&view_op=list_works&sortby=pubdate)<sup>1,2</sup> |
[Hongwei Yong](https://scholar.google.com.hk/citations?user=Xii74qQAAAAJ&hl=zh-CN)<sup>1</sup> |
[Lei Zhang](https://www4.comp.polyu.edu.hk/~cslzhang)<sup>1,2</sup>
<sup>1</sup>The Hong Kong Polytechnic University, <sup>2</sup>OPPO Research Institute
</div>
:star: If CCSR is helpful to your images or projects, please help star this repo. Thanks! :hugs:
## 🧡ྀི What's New in CCSR-v2?
We have implemented the CCSR-v2 code based on the [Diffusers](https://github.com/huggingface/diffusers). Compared to CCSR-v1, CCSR-v2 brings a host of upgrades:
- 🛠️**Step Flexibility**: Offers flexibility in diffusion step selection, **allowing users to freely adjust the number of steps to suit their specific requirements**. This adaptability **requires no additional re-training**, ensuring seamless integration into diverse workflows.
- ⚡**Efficiency**: Supports highly efficient inference with **as few as 2 or even 1 diffusion step**, drastically reducing computation time without compromising quality.
- 📈**Enhanced Clarity**: With upgraded algorithms, CCSR-v2 restores images with crisper details while maintaining fidelity.
- ⚖️**Results stability**: CCSR-v2 exhibits significantly improved stability in synthesizing fine image details, ensuring higher-quality outputs.
- 🔄**Stage 2 Refinement**: In CCSR-v2, the output $\hat{x}_{0 \gets T}$ from Stage 1 is now directly fed into Stage 2, streamlining the restoration process into an efficient one-step diffusion workflow. This strategy boosts both speed and performance.
Visual comparisons between the SR outputs with the same input low-quality image but two different noise samples by different DM-based
methods. `S` denotes diffusion sampling timesteps. Existing DM-based methods, including StableSR, PASD, SeeSR, SUPIR and AddSR, **show noticeable instability with the different noise samples**. OSEDiff directly takes low-quality image as input without
noise sampling. It is deterministic and stable, but **cannot perform multi-step diffusion** for high generative capacity. In contrast, **our proposed CCSR method
is flexible for both multi-step diffusion and single-step diffusion, while producing stable results with high fidelity and visual quality**.
## ⏰ Update
- **2024.12.12**: Code and models for CCSR-v2 are released. 👀 Please refer to this [branch](https://github.com/csslc/CCSR/tree/CCSR-v2.0).
- **2024.9.25**: ⭐[CCSR-v2](https://arxiv.org/pdf/2401.00877) is released, offering reduced step requirements and supporting flexible diffusion step selection (2 or even 1 step) during the inference stage without the need for re-training.
- **2023.12.23**: Code and models for [CCSR-v1](https://arxiv.org/pdf/2401.00877v1) are released. Please refer to this [branch](https://github.com/csslc/CCSR/tree/CCSR-v1.0).
## 🌟 Overview Framework
## 😍 Visual Results
### Demo on Real-world SR
[<img src="figs/compare_1.png" height="213px"/>](https://imgsli.com/MzI2MTg5) [<img src="figs/compare_2.png" height="213px"/>](https://imgsli.com/MzI2MTky/1/3) [<img src="figs/compare_3.png" height="213px"/>](https://imgsli.com/MzI2MTk0/0/2) [<img src="figs/compare_4.png" height="213px"/>](https://imgsli.com/MzI2MTk1/0/2)
52
Chenxi
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:17ce21bd893f4a14, desc:super resolution