Comments (1)
Hi there,
Thanks for you interest in our work. Sorry to make the confusion there.
- The answer is that they are both correct. In paper, our narative is based on the original GAN, whose discriminator outputs the probability that the given image is real, so 0.5 is the middle point. However, in practice, people use different loss functions and the probablity might not be actually trained. Here, we built our code based on the StyleGAN2 and the
Discriminator(real_images)
will output the logits not the probability so you don't need- 0.5
here. The middle point of the logits for GAN is 0.0. - We set
ada_kimg=100
for all experiments, which is also the default value forada_kimg
. - Yes, the code is suitable for multi-gpu training by just including
--gpus=4
in the command. Yes, 'Diffusion' gets different updates on different ranks.batch_size
is thebatch_size
used for training. You could see details in thediffusion-stylegan2/train.py
dictionaycfg_specs
, which is inherited from StyleGAN2 paper.
from diffusion-gan.
Related Issues (20)
- Conditioning signal in Diffusion ProjectedGAN HOT 2
- diffusion.py HOT 3
- No module named 'upfirdn2d_plugin' HOT 5
- AssertionError: Default process group is not initialized HOT 1
- RuntimeError: Both events must be recorded before calculating elapsed time. HOT 3
- ninja: build stopped: subcommand failed. HOT 5
- Error while running train.py HOT 3
- 'tuple' object is not callable HOT 1
- how to lower fid? HOT 2
- Use Diffusion-GAN in Other GAN Architecture HOT 2
- How to use Diffusion-GAN in GP-UNIT
- A tiny question in diffusion.py HOT 2
- FID discrepancy HOT 3
- Application HOT 1
- about the value of target HOT 1
- regarding the setting of the hyperparameter "ada_kimg" HOT 4
- grid_sampler_2d_backward() is missing value for argument 'output_mask'.
- How to modify discriminator to incorporate diffusion timestep t?
- Questions about adaptive diffusion. HOT 2
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from diffusion-gan.