Comments (3)
@albusdemens I just pushed a new version of the library. From now on, the network settings during training are stored and on generation it will auto instantiate the GAN correctly. You may retroactively fix your problem if you know the image size and network capacity you trained with, and continue training a bit further until the next checkpoint, in which case the problem will correct itself.
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@albusdemens Hi! When you generate, you have to supply the same network settings as you did when you trained. So the command you would execute would be along the lines of stylegan2_pytorch --image-size 512 --network-capacity {whatever you used} --generate
. I'll make this more seamless later today by storing these settings in a dot file
from stylegan2-pytorch.
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Related Issues (20)
- Parameters for generating faces HOT 1
- Performance on MNIST
- Out of memory after exactly 5024 iterations? HOT 1
- Web application for generating content while reloading.
- Tesla V100 GPU - 2600 Image - Too Slow Training HOT 1
- Uneven GPU utilization.
- Trainning on images with one (single) channel HOT 1
- Bug: random_hflip function HOT 1
- where can i download train data?
- Bug: gradient_accumulate_contexts function HOT 1
- Generate full resolution images 1024x1024 HOT 1
- generate all seeds of latent space
- ability to calculate Perpectual Path Length (PPL)? HOT 1
- Save Interval Flag
- /torch_utils/custom_ops.py - _find_compiler_bindir: incorrect Visual Studio Path
- Inconsistent evaluation of self.av HOT 2
- How to Train on a Single Image
- Examples on save_every and evaluate_every in README section needed.
- 3D adaptation for medical imaging HOT 1
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