Comments (1)
Since we did not change the generator from ESRGAN but only the training procedure, you can simply use the original ESRGAN or BasicSR repository for testing. You just need to adapt the path of the model checkpoint such that it points to your desired pre-trained model. You can find some of the models we trained here
from real-world-sr.
Related Issues (20)
- LR image generation HOT 2
- issue in DSGAN part about the UNsupervied training HOT 2
- didn't match because some of the arguments have invalid types: (list, keepdim=bool)
- _pickle.PicklingError: Can't pickle <function <lambda> at 0x7f308810e2f0>: attribute lookup <lambda> on __main__ failed
- WIll I need to retrain DSGAN for 2x SR? HOT 1
- There is no if __name__ == '__main__': in train.py
- RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 256, 1, 1]] is at version 2; expected version 1 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck! HOT 15
- ModuleNotFoundError: No module named 'PerceptualSimilarity' HOT 1
- Where is the source images Z for Discriminator?
- As for PerceptualSimilarity
- Pre-trained Models
- Files for DSGAN SDSR pretrained model broken HOT 1
- cannot import name 'models' from 'PerceptualSimilarity'
- no attribute PerceptualLoss HOT 8
- Loss Curves for Training HOT 2
- Trouble training ESRGAN with jpeg artifact images. HOT 2
- About test.yml HOT 5
- Why the dsgan's pretrained models can't uncompress? HOT 3
- Could I use the dsgan with my own image? HOT 4
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from real-world-sr.