Comments (9)
For each block half of outputs splitted into z and appended to zs.
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If you need to reconstruct then you only need a output z lists. Random zs is used for sampling from the model.
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Thanks for your answer!
Why do we need a z list rather than a single z?
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Thanks a lot! I think I have more understanding of the glow model.
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Hi, It is me again!
I can't understand the self.scale = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
and "out = out * torch.exp(self.scale * 3)" in the ZeroConv2d module. Could you please tell me what role this plays? I appreciate your answer!
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`if args.n_bits < 8:
image = torch.floor(image / 2 ** (8 - args.n_bits))
image = image / n_bins - 0.5`
Hi, a more question. Does this code mean image=image/256-0.5? What is the good of this way?
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loss = -log(n_bins) * n_pixel
Hi~ Why do we need this constant loss? I appreciate your answer! Thanks a lot!
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Hi, do you train the face for image_size 256*256? What is the setup of hyperparameters? I set the batch=2 and keep the other, the results are so bad.
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The parallel train can't work. And if I change the n_block from 4 to 6 as suggested by the paper, the loss becomes Nan.
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Related Issues (20)
- Something wrong with affine coupling? HOT 4
- Question: Does this work with larger resolutions? HOT 2
- coupling.py is error:The size of tensor a (2) must match the size of tensor b (62) at non-singleton dimension 1
- Conditional Gaussian prior parameters produce unnormalized likelihoods HOT 2
- Change image_size to 256 HOT 7
- File Checkpoint HOT 1
- a question about dataset
- Loss value HOT 2
- a question about the sigmoid function in the affine coupling layer HOT 2
- loss NAN HOT 1
- Maybe something wrong with affine paramter in argparse? HOT 1
- Act Norm Output issue HOT 1
- Why my sample pictures are black? HOT 2
- 如果对图像生成,glow感兴趣,或者需要帮助,可以联系我
- any pretrained models?
- too smalll value of logP
- Flow not perfectly invertible HOT 4
- why with torch.no_grad() when i == 0: HOT 1
- what's the difference between the " reconstruct=True" and " reconstruct=False"
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