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When using diffusion for image super-resolution, having too many inference steps can cause the details to deviate more from the real image.
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I have created an API for Real-ESRGAN using FastAPI, and it is working properly for multiple user requests. However, when I am initially loading the models (Real-ESRGAN and GFPGAN) using lru_cache (fu…
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**Short Description**
Single-Image Super-Resolution describes the domain of enhancing image resolution for single images (as opposed to groups of images of a scene, for example). Solutions in this do…
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## STORM Image Reconstruction Feature
### Objective
The objective of this issue is to develop a robust STORM image reconstruction feature within our existing image preprocessing GUI. This feature …
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## environment
- Windows11
- python3.10.9
- torch 2.3.1
## Problem description
I encountered the following problem
```
Traceback (most recent call last):
File "...\BasicSTISR\main.py", li…
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Dear author,
first and foremost thank you very much for your great work and your great effort towards improving burst super-resolution.
I am currently myself training and testing your model on print…
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Hello!
I've also emailed you the same question but you seem miss it.
I've read your paper One-Step Effective Diffusion Network for Real-World Image Super-Resolution, I found it very interesting and…
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### Search before asking
- [X] I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussions) and fou…
tppqt updated
1 month ago
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### Search before asking
- [X] I have searched the YOLOv8 [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### YOLOv8 Component
_No response_
### Bug
…
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This thread should be reserved for requesting the addition of new papers and repos to be added in the repository, since we are entering the roaring 20s.