Infinite values in generated images

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?

Thanks.

[isharifi]

Actually, I find the part that value explosion occurs. It happens at module.py 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]):

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

[isharifi]

There may arise a numerical issue of division by zero in

https://github.com/chaiyujin/glow-pytorch/blob/487a6b149295f4ec4b36e408f63604c593ff2031/glow/models.py#L93

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

torch.cuda.manual_seed_all(16)
glow = glow.to('cuda')
glow(reverse=True)

Or on cpu:

torch.manual_seed(37)
glow = glow.to('cpu')
glow(reverse=True)

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.