AuraSR is a 600M parameter upsampler model derived from the GigaGAN paper. It works super fast and uses a very limited VRAM below 5 GB. It is deterministic upscaler. It works perfect in some images but fails in some images so it is worth to give it a shot.
GitHub official repo : https://github.com/fal-ai/aura-sr
I have developed 1-click installers and a batch upscaler App.
You can download installers and advanced batch App from below link:
https://www.patreon.com/posts/110060645
Check the screenshots and examples below
Windows Requirements
Python 3.10, FFmpeg, Cuda 11.8, C++ tools and Git
If it doesn't work make sure to below tutorial and install everything exactly as shown in this below tutorial
How to Install and Use on Windows
Extract the attached GigaGAN_Upscaler_v1.zip into a folder like c:/giga_upscale
Then double click and install with Windows_Install.bat file
It will generate an isolated virtual environment venv folder and install requirements
Then double click and start the Gradio App with Windows_Start_App.bat file
When first time running it will download models into your Hugging Face cache folder
Hugging Face cache folder setup explained below
https://www.patreon.com/posts/108419878
All upscaled images will be saved into outputs folder automatically with same name and plus numbering if necessary
You can also batch upscale a folder
How to Install and Use on Cloud
Follow Massed Compute and RunPod instructions
Usage is same as on Windows
For Kaggle start a Kaggle notebook, import our Kaggle notebook and follow the instructions
App Screenshots
Examples
