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Messages - OmniStrife

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Hey guys, I've stumbled upon a thread in Resetera where a new method of improving old textures is discussed,
The results are amazing!



Apparently anyone can use the method.

Here's the thread title:
Quote
Credit to Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi/Arxiv on their paper about Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. I suggest you give it a read if you're interested in this kind of thing.

https://arxiv.org/pdf/1609.04802.pdf

Credit to Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, Xiaoou Tang for their paper on ESRGAN and for providing the ESRGAN repo with pretrained models

https://arxiv.org/pdf/1809.00219.pdf
https://github.com/xinntao/ESRGAN

Guide if ya wanna try it yourself:
Credit to kingdomakrillic their amazing work!

https://kingdomakrillic.tumblr.com/post/178254875891/i-figured-out-how-to-get-esrgan-and-sftgan

(also you don't need an NVIDIA card for this just go into test.py and change “device = torch.device(‘cuda’)“ to “ device = torch.device(‘cpu’)”. No AMD/Intel though)
(if you want to try this, make sure you have the image at its native resolution!)
(for the best results, don't use compressed strawberries off the internet)

NVIDIA has their own Generative Adverserial Network but you have to sign up to use it as it is still in beta: https://developer.nvidia.com/gwmt

Please read through the whole thread! I won't be updating this OP with newer images and instead'll be posting newer stuff within the thread! There's some cool stuff below!

Explanation:

Long story short, Enhanced Super Resolution Generative Adverserial Network, or ESRGAN, is an upscaling method that is capable of generating realistic textures during single image super-resolution. Basically it's a machine learning technique that uses a generative adverserial network to upres smaller images. By doing it over several passes, it will usually produce an image with more fidelity than methods such as SRCNN and SRGAN. In fact, ESRGAN is based off SRGAN. The difference between the two is that ESRGAN improves on SRGAN's network architecture, adversarial loss and perceptual loss. Furthermore ESRGAN

source (with more examples): https://www.resetera.com/threads/ai-neural-networks-being-used-to-generate-hq-textures-for-older-games-you-can-do-it-yourself.88272/

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SOLVED!!
How?? what kind of a post is this? I have the same error.  :oops:

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