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Fix the final weights for perceptual losses
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* cleaner configs * visualizer genearalize * visualizer genearalize * lama-celeba configs * optimizers config and celeba readme upd * noop vis in predict.py * default optimizers config * default opt fixed Co-authored-by: Aleksei Silvestrov <a.silvestrov@samsung.net>
Huggingface spaces web demo
Seems to fix the data generation script for me. Is this correct?
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Dear Author, I would like to kindly ask you some questions regarding training on the CelebA dataset. Specifically, based on LaMa-Fourier, what small modifications or fine-tuning strategies could be applied to slightly improve the final performance metrics? I am currently working on my undergraduate graduation thesis, and these suggestions would be extremely helpful to my research. I would sincerely appreciate it if you could share some advice or insights. Thank you very much for your time and help. @senya-ashukha @andreas128 @elimohl @cohimame @AK391 |
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Seems to fix the data generation script for me. Is this correct?