Comments (2)
mchong6
commented on May 11, 2024
2
You can save a model by running
torch.save({"g": generator.state_dict()}, f"checkpoint.pt")
and then when you want to resume the training load it
ckpt = torch.load('model.pt', map_location=lambda storage, loc: storage) generator.load_state_dict(ckpt["g"], strict=False)
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osushilover
commented on May 11, 2024
Yes, I could!
Thank you for your reply, sir.
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Related Issues (20)
- Could you release training codes? HOT 1
- i
- change the encoder to restyle HOT 3
- finetuning question HOT 1
- Ampere incompatibility? HOT 3
- about HOT 4
- Style modulation layers, style parameters and controlling low-level features. HOT 2
- Using another pretrained StyleGAN2 HOT 2
- How I can convert into CoreML?
- Different results with automatic alignment and manual crop HOT 1
- Does not working on cuda:1 HOT 3
- How to train with many references image without running out of memory? HOT 1
- How to choose the swapping layers?
- input must be a CUDA tensor
- Other than faces?
- e4e_ffhq_encode.pt can not be downloaded HOT 2
- replicate.exceptions.ModelError: name 'latent_dim' is not defined HOT 1
- Wonder if you have ever tried to decrease the number of style modulation layers
- supergirl.pt and supergirl_preserve_color.pt not find?
- ModelError
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