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SinGAN is a very impressive work. But in the SR mode, I do not understand the reason why SinGAN is able to generate the correct SR image. I mean, not only the correct image size but the position of ob…
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Thanks for your work.
As shown in Sec. 3.1, three steps are included in the event data super-resolution (1. event representation, 2. representation super-resolution, and 3. event stream recovering).…
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Hi, thanks for sharing your code. Have u tried to transform from low resolution to high resolution image, like super resolution? Do you have any suggestions if I apply it to SR problem. Thanks.
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Thanks for sharing your great work. You mentioned that this work was inspired by a super-resolution technique in the paper.
I am reversely trying to modify the code to train 3D super resolution task.…
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[ResSHIFT](https://github.com/zsyOAOA/ResShift) method, which as far as I can tell is among SOTA for Image Super-Resolution. This code base is imo easier to understand and cleaner so would be practica…
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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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Hello, thank you for your great work on high-resolution image-to-3D generation!
I noticed that you utilized a ControlNet-Tile based on SD1.5 to achieve the first stage of super-resolution. I am curio…
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Hello. For the image super-resolution task, in your code, you just save the low-resolution image and the inversion results without using Eq.(7) for optimization. Could you please tell me how Eq.(7) is…
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I want to super-resolution the image from 512 to 1024, Should I train vqgan separately? or can i use exist vqgan?
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I test ddnm on arbitrary imagenet image but did not get as good results as the demo.
The command I use is :
CUDA_VISIBLE_DEVICES=3 python main.py --resize_y --config confs/inet256.yml --path_y ./ILS…