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Infinite values in generated images

See original GitHub issue

For generating new images, I sample z from a zero mean and 0.6 standard deviation normal distribution and feed it to the network with reverse=True argument. But in many images, there are plenty of values greater than 1, even Inf value! How can I handle this issue? What is the problem?


Issue Analytics

  • State:open
  • Created 3 years ago
  • Comments:7 (2 by maintainers)

github_iconTop GitHub Comments

isharificommented, Sep 26, 2020

Actually, I find the part that value explosion occurs. It happens at at line 53, when it scales the input by torch.exp(logs). The Inf value often happens at layer around 80 during forward pass (reverse=True). Then the generated image with negative inf would be something like it (clamped between [0,1]): image

As a result, in backward pass, the gradient would be inf too. So the training becomes impossible.

isharificommented, May 25, 2021

There may arise a numerical issue of division by zero in

when there are zero elements in the sigmoid output. For me the following code snippet triggers the division by zero (running an unconditional generation):

glow ='cuda')

Or on cpu:

glow ='cpu')

I couldn’t reproduce it running conditional generation though. A possible fix would be elementwise adding a small value to the scale before division

Thanks. I will check if it solves the problem and let you know the result.

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