I already use GFPGANCleanv1-NoCE-C2.pth and is working great. Please help me with this problem...
Thanks.
python inference_gfpgan.py --upscale 2 --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs --save_root results
inference_gfpgan.py:37: UserWarning: The unoptimized RealESRGAN is very slow on CPU. We do not use it. If you really want to use it, please modify the corresponding codes.
warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
Traceback (most recent call last):
File "inference_gfpgan.py", line 98, in <module>
main()
File "inference_gfpgan.py", line 52, in main
restorer = GFPGANer(
File "C:\Users\Zeus\Downloads\GFPGAN\gfpgan\utils.py", line 65, in __init__
self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
File "C:\Users\Zeus\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 1406, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for GFPGANv1Clean:
Missing key(s) in state_dict: "conv_body_first.weight", "conv_body_first.bias", "conv_body_down.0.conv1.weight", "conv_body_down.0.conv1.bias", "conv_body_down.0.conv2.weight", "conv_body_down.0.conv2.bias", "conv_body_down.0.skip.weight", "conv_body_down.1.conv1.weight", "conv_body_down.1.conv1.bias", "conv_body_down.1.conv2.weight", "conv_body_down.1.conv2.bias", "conv_body_down.1.skip.weight", "conv_body_down.2.conv1.weight", "conv_body_down.2.conv1.bias", "conv_body_down.2.conv2.weight", "conv_body_down.2.conv2.bias", "conv_body_down.2.skip.weight", "conv_body_down.3.conv1.weight", "conv_body_down.3.conv1.bias", "conv_body_down.3.conv2.weight", "conv_body_down.3.conv2.bias", "conv_body_down.3.skip.weight", "conv_body_down.4.conv1.weight", "conv_body_down.4.conv1.bias", "conv_body_down.4.conv2.weight", "conv_body_down.4.conv2.bias", "conv_body_down.4.skip.weight", "conv_body_down.5.conv1.weight", "conv_body_down.5.conv1.bias", "conv_body_down.5.conv2.weight", "conv_body_down.5.conv2.bias", "conv_body_down.5.skip.weight", "conv_body_down.6.conv1.weight", "conv_body_down.6.conv1.bias", "conv_body_down.6.conv2.weight", "conv_body_down.6.conv2.bias", "conv_body_down.6.skip.weight", "final_conv.weight", "final_conv.bias", "conv_body_up.0.conv1.weight", "conv_body_up.0.conv1.bias", "conv_body_up.0.conv2.bias", "conv_body_up.1.conv1.weight", "conv_body_up.1.conv1.bias", "conv_body_up.1.conv2.bias", "conv_body_up.2.conv1.weight", "conv_body_up.2.conv1.bias", "conv_body_up.2.conv2.bias", "conv_body_up.3.conv1.weight", "conv_body_up.3.conv1.bias", "conv_body_up.3.conv2.bias", "conv_body_up.4.conv1.weight", "conv_body_up.4.conv1.bias", "conv_body_up.4.conv2.bias", "conv_body_up.5.conv1.weight", "conv_body_up.5.conv1.bias", "conv_body_up.5.conv2.bias", "conv_body_up.6.conv1.weight", "conv_body_up.6.conv1.bias", "conv_body_up.6.conv2.bias", "stylegan_decoder.style_mlp.9.weight", "stylegan_decoder.style_mlp.9.bias", "stylegan_decoder.style_mlp.11.weight", "stylegan_decoder.style_mlp.11.bias", "stylegan_decoder.style_mlp.13.weight", "stylegan_decoder.style_mlp.13.bias", "stylegan_decoder.style_mlp.15.weight", "stylegan_decoder.style_mlp.15.bias", "stylegan_decoder.style_conv1.bias", "stylegan_decoder.style_convs.0.bias", "stylegan_decoder.style_convs.1.bias", "stylegan_decoder.style_convs.2.bias", "stylegan_decoder.style_convs.3.bias", "stylegan_decoder.style_convs.4.bias", "stylegan_decoder.style_convs.5.bias", "stylegan_decoder.style_convs.6.bias", "stylegan_decoder.style_convs.7.bias", "stylegan_decoder.style_convs.8.bias", "stylegan_decoder.style_convs.9.bias", "stylegan_decoder.style_convs.10.bias", "stylegan_decoder.style_convs.11.bias", "stylegan_decoder.style_convs.12.bias", "stylegan_decoder.style_convs.13.bias".
Unexpected key(s) in state_dict: "conv_body_first.0.weight", "conv_body_first.1.bias", "conv_body_down.0.conv1.0.weight", "conv_body_down.0.conv1.1.bias", "conv_body_down.0.conv2.1.weight", "conv_body_down.0.conv2.2.bias", "conv_body_down.0.skip.1.weight", "conv_body_down.1.conv1.0.weight", "conv_body_down.1.conv1.1.bias", "conv_body_down.1.conv2.1.weight", "conv_body_down.1.conv2.2.bias", "conv_body_down.1.skip.1.weight", "conv_body_down.2.conv1.0.weight", "conv_body_down.2.conv1.1.bias", "conv_body_down.2.conv2.1.weight", "conv_body_down.2.conv2.2.bias", "conv_body_down.2.skip.1.weight", "conv_body_down.3.conv1.0.weight", "conv_body_down.3.conv1.1.bias", "conv_body_down.3.conv2.1.weight", "conv_body_down.3.conv2.2.bias", "conv_body_down.3.skip.1.weight", "conv_body_down.4.conv1.0.weight", "conv_body_down.4.conv1.1.bias", "conv_body_down.4.conv2.1.weight", "conv_body_down.4.conv2.2.bias", "conv_body_down.4.skip.1.weight", "conv_body_down.5.conv1.0.weight", "conv_body_down.5.conv1.1.bias", "conv_body_down.5.conv2.1.weight", "conv_body_down.5.conv2.2.bias", "conv_body_down.5.skip.1.weight", "conv_body_down.6.conv1.0.weight", "conv_body_down.6.conv1.1.bias", "conv_body_down.6.conv2.1.weight", "conv_body_down.6.conv2.2.bias", "conv_body_down.6.skip.1.weight", "final_conv.0.weight", "final_conv.1.bias", "conv_body_up.0.conv1.0.weight", "conv_body_up.0.conv1.1.bias", "conv_body_up.0.conv2.activation.bias", "conv_body_up.1.conv1.0.weight", "conv_body_up.1.conv1.1.bias", "conv_body_up.1.conv2.activation.bias", "conv_body_up.2.conv1.0.weight", "conv_body_up.2.conv1.1.bias", "conv_body_up.2.conv2.activation.bias", "conv_body_up.3.conv1.0.weight", "conv_body_up.3.conv1.1.bias", "conv_body_up.3.conv2.activation.bias", "conv_body_up.4.conv1.0.weight", "conv_body_up.4.conv1.1.bias", "conv_body_up.4.conv2.activation.bias", "conv_body_up.5.conv1.0.weight", "conv_body_up.5.conv1.1.bias", "conv_body_up.5.conv2.activation.bias", "conv_body_up.6.conv1.0.weight", "conv_body_up.6.conv1.1.bias", "conv_body_up.6.conv2.activation.bias", "stylegan_decoder.style_mlp.2.weight", "stylegan_decoder.style_mlp.2.bias", "stylegan_decoder.style_mlp.4.weight", "stylegan_decoder.style_mlp.4.bias", "stylegan_decoder.style_mlp.6.weight", "stylegan_decoder.style_mlp.6.bias", "stylegan_decoder.style_mlp.8.weight", "stylegan_decoder.style_mlp.8.bias", "stylegan_decoder.style_conv1.activate.bias", "stylegan_decoder.style_convs.0.activate.bias", "stylegan_decoder.style_convs.1.activate.bias", "stylegan_decoder.style_convs.2.activate.bias", "stylegan_decoder.style_convs.3.activate.bias", "stylegan_decoder.style_convs.4.activate.bias", "stylegan_decoder.style_convs.5.activate.bias", "stylegan_decoder.style_convs.6.activate.bias", "stylegan_decoder.style_convs.7.activate.bias", "stylegan_decoder.style_convs.8.activate.bias", "stylegan_decoder.style_convs.9.activate.bias", "stylegan_decoder.style_convs.10.activate.bias", "stylegan_decoder.style_convs.11.activate.bias", "stylegan_decoder.style_convs.12.activate.bias", "stylegan_decoder.style_convs.13.activate.bias".
size mismatch for conv_body_up.3.conv2.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for conv_body_up.3.skip.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 256, 1, 1]).
size mismatch for conv_body_up.4.conv2.weight: copying a param with shape torch.Size([64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
size mismatch for conv_body_up.4.skip.weight: copying a param with shape torch.Size([64, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
size mismatch for conv_body_up.5.conv2.weight: copying a param with shape torch.Size([32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 128, 3, 3]).
size mismatch for conv_body_up.5.skip.weight: copying a param with shape torch.Size([32, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 128, 1, 1]).
size mismatch for conv_body_up.6.conv2.weight: copying a param with shape torch.Size([16, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 64, 3, 3]).
size mismatch for conv_body_up.6.skip.weight: copying a param with shape torch.Size([16, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 64, 1, 1]).
size mismatch for toRGB.3.weight: copying a param with shape torch.Size([3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 256, 1, 1]).
size mismatch for toRGB.4.weight: copying a param with shape torch.Size([3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 128, 1, 1]).
size mismatch for toRGB.5.weight: copying a param with shape torch.Size([3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 64, 1, 1]).
size mismatch for toRGB.6.weight: copying a param with shape torch.Size([3, 16, 1, 1]) from checkpoint, the shape in current model is torch.Size([3, 32, 1, 1]).
size mismatch for stylegan_decoder.style_convs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.weight: copying a param with shape torch.Size([1, 256, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 512, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.7.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 512, 3, 3]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.style_convs.8.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.weight: copying a param with shape torch.Size([1, 128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 256, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.9.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 256, 3, 3]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.style_convs.10.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.weight: copying a param with shape torch.Size([1, 64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 128, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.11.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 128, 3, 3]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.style_convs.12.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.weight: copying a param with shape torch.Size([1, 32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([1, 64, 64, 3, 3]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.style_convs.13.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 512, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([512, 512]).
size mismatch for stylegan_decoder.to_rgbs.3.modulated_conv.modulation.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 256, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for stylegan_decoder.to_rgbs.4.modulated_conv.modulation.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 128, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.weight: copying a param with shape torch.Size([64, 512]) from checkpoint, the shape in current model is torch.Size([128, 512]).
size mismatch for stylegan_decoder.to_rgbs.5.modulated_conv.modulation.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.weight: copying a param with shape torch.Size([1, 3, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 3, 64, 1, 1]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.weight: copying a param with shape torch.Size([32, 512]) from checkpoint, the shape in current model is torch.Size([64, 512]).
size mismatch for stylegan_decoder.to_rgbs.6.modulated_conv.modulation.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_scale.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_scale.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_scale.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_scale.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_scale.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_scale.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_scale.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_scale.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.3.0.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.0.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.3.2.weight: copying a param with shape torch.Size([128, 128, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 256, 3, 3]).
size mismatch for condition_shift.3.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for condition_shift.4.0.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.0.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.4.2.weight: copying a param with shape torch.Size([64, 64, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 128, 3, 3]).
size mismatch for condition_shift.4.2.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for condition_shift.5.0.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.0.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.5.2.weight: copying a param with shape torch.Size([32, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 64, 3, 3]).
size mismatch for condition_shift.5.2.bias: copying a param with shape torch.Size([32]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for condition_shift.6.0.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.0.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for condition_shift.6.2.weight: copying a param with shape torch.Size([16, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).
size mismatch for condition_shift.6.2.bias: copying a param with shape torch.Size([16]) from checkpoint, the shape in current model is torch.Size([32]).