Comments (2)
Sure. What kind of dataset and codebase you use? Actually, the StyleGAN defines Wasserstein distance in its loss.py. You may modify it accordingly.
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I am a GAN beginner in SR, so I try to implement your FQ-part in my discriminator network, behind block module, code framework is based on ProGAN and dataset is CelebA-HQ. I find that training process seems stable from the observation of reconstructed image, when I set the weight of WGAN adversarial loss to 0.05, but I am not sure if it's caused by this operation, because at the beginning of training, the loss value is norm, and just suddenly collapse when weight of adversarial loss item sets 1. Is the influence of hyperparameter so great?
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Related Issues (13)
- Request - Trained weights HOT 5
- use quantized or not? HOT 1
- compute_kid error in python3 HOT 1
- weird result HOT 15
- A better Colab? HOT 6
- FQ-U-GAT-IT pytorch HOT 2
- Cant run launch_I128_bs256x4.sh
- NaNs for C10 HOT 1
- How do I generate images after training the FQ-BIGAN model? I have successfully trained it on cifar10 and cifar100 datasets. HOT 3
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- metric of FQ-U-get-it
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