Comments (3)
Welcome! Feel free to ask any further questions if you'll face any issues.
Oh, I forgot to add that in fact our model does not need 32GB of memory when trained on 4 GPUs.
In fact, it consumes <10GB of memory on each GPU.
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Hi! We trained all the 256x256 models on 4 V100s with 32 GB each for ~2 days. It is very similar in training time to StyleGAN2-ADA. When training on a custom dataset, please keep in mind that the model.generator.time_enc.min_period_len
parameter affects the motion quality a lot: for example, we used model.generator.time_enc.min_period_len=16
for Face Forensics and model.generator.time_enc.min_period_len=256
for SkyTimelapse. In some sense, it reflects the speed of motion — the smaller it is, the more high-frequency the motions would be
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Thanks a lot!!!
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Related Issues (20)
- About FaceForensics Dataset HOT 2
- Linearly spaced periods stated in Appendix B.3 is not correct HOT 1
- Low IS on UCF101 HOT 16
- Face Forensics dataset preprocessing HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: 'src\\experiment_config.yaml'
- Run stylegan-v Conditionally
- Question about FVD calculation HOT 2
- Question about the release of Pre-trained checkpoints HOT 2
- I tried to generate fake video samples with your pretrained weights but....
- ImportError: cannot import name 'CoordFuser' from 'src.training.layers' HOT 4
- TypeError: Descriptors cannot be created directly
- Projection of real video with multiple frames
- RainbowJelly dataset
- Problem regarding true data statics computation for FVD HOT 4
- Rainbow jelly isn't available
- MOCOGAN: training isn't runing
- Error: batch_size should be a positive integer value, but got batch_size=0 HOT 1
- FVD Metrics HOT 1
- FaceForensics download
- The frame sequence is out of order when evaluating fvd
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