Thank you for the suggestion. At first glance it does seem overwhelming, I will check it out. The problem im solving has to be rapidly tested, i will run out of time implementing this.

Im unable to load full res image into the model and train it even with batch size 1 and all sorts of optimizations. My idea is to add two small modules to my network. One at the front which downscales the image and one at the back which upscales the image.

The problem here is the upscaling, it will need to be some sort of super resolution model.

Hello, does anyone have suggestions on how to do guided image upsacling?
Basically I have 6000x6000 image which im unable to load in network because of GPU memory. I had this idea of resizing the image to something like 1500x1500 and then upscaling it back to 6000x6000. But I have to do it without losing details and dont want to use super resolution models (im ofraid they will hallucinate and inpaint). If I already have the ground truth resolution, how can I use it to guide the upscaling?

Hello, does anyone have experience with vision transformers?

I get wierd grid artifacts, especially on white / bright, textureless walls or sky.

Here is how it looks like: https://imgur.com/a/dwF69Z3
Im using maxim architecture: https://github.com/vztu/maxim-pytorch

My general task is image enchancement (make image prettier)
I have also tried simple GAN methods https://github.com/eezkni/UEGAN which doesnt have such issues

I have researched a bit but im unable to formualte this problem properly. I have found that guided filters might help here but havent tested them yet. Thanks