Comments (3)
- You can continue your training from the pretrained models.
- The FFHQ component locations are for FFHQ. For your own dataset, I think i) You can first remove the component discriminators. or ii) generate the locations with the provided scripts https://github.com/TencentARC/GFPGAN/blob/master/scripts/parse_landmark.py
- The current training process only requires High-Resolution images, and the data loader will generate low-resolution ones on the fly.
- You can finetune from the pre-trained GFPGAN.
- Or use the provided StyleGAN. (I think you may not need to train the StyleGAN on your own dataset.)
from gfpgan.
Also does the model downsample the images while training, or will I need to provide my own version of low resolution faces?
from gfpgan.
I trained on my own dataset without pretrained Stylegan. Results, that I got, so-so...I didn't understand how to use my own pretrained Stylegan
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Related Issues (20)
- 10 HOT 1
- 28 HOT 3
- Jvkvfxj HOT 2
- 'torchvision.transforms.functional_tensor' has been depreciated to 'torchvision.transforms.functional' HOT 2
- Anaconda Prompt Displays Error Code When Running Pip Command
- Huggingface scaling issue
- Training Data and configurations for GFPGAN v1.4
- ls: cannot access 'results/cmp': No such file or directory HOT 4
- Would love to have you all as part of our company
- Issues with PyTorch Distributed Training on Google Colab HOT 12
- Please help me
- DirectML on Windows with AMD GPUs
- Aaa
- 关于数据集的问题 HOT 2
- TEST GFPGAN
- Photo upload problem
- 请问推理的时候如何能进行batch操作?
- Reconstruir esta foto
- Зураг сэргээх
- Missing Modules, Colab doesn't work anymore HOT 3
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from gfpgan.