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Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Python 90.23% C++ 1.30% Cuda 8.48%

generative-adversarial-network stylegan2 transformer attention-mechanism

style-transformer's People

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repo-collection peterzhousz zhigangjiang chenchy jackzhousz akulutka dl-vit zivzone peterzs summatic jaewoong1 sevenbruce hellog2n

style-transformer's Issues

Hello, could you provide me with the code for calculating SWD and FID in your paper?

Thanks a lot!

how to get inference_edit pretrained model?

i want to test inference_edit.py
i downloaded style-transformer-ffhq.pt and run.
but i got error message.

how can i fix it?

TypeError: forward() missing 1 required positional argument: 'styles'

models\stylegan2\op\fused_act.py

About Model Size

Thank you for your excellent work.
In section 5.1 Implementation Details, you mentioned that your model is based on pSp encoder. So why is your model lighter than pSp (as shown in Table 1)?

invalid syntax (init.py, line 1)

Code for quantitative comparison

Hi,
Your work is very interesting !
Could you have any plan to release code for quantitative evaluation (AD metric) ?
Thanks!

about pretrained model

hello, thanks your work! Could you send me a pretrained model? look forward to your reply as soon as possible! thans~

TypeError: upfirdn2d(): incompatible function arguments. The following argument types are supported:

CUDA_VISIBLE_DEVICES=3 python scripts/train.py --dataset_type=ffhq_encode --exp_dir=results/debug --batch_size=2 --test_batch_size=2 --val_interval=2500 --save_interval=5000 --stylegan_weights=pretrained_models/stylegan2-ffhq-config-f.pt
{'batch_size': 2,
'board_interval': 50,
'checkpoint_path': None,
'dataset_type': 'ffhq_encode',
'exp_dir': 'results/debug',
'id_lambda': 0.1,
'image_interval': 5000,
'input_nc': 3,
'l2_lambda': 1.0,
'l2_ref_lambda': 1.0,
'l2_src_lambda': 1.0,
'label_nc': 0,
'learn_in_w': False,
'learning_rate': 0.0001,
'lpips_lambda': 0.8,
'max_steps': 600000,
'moco_lambda': 0,
'optim_name': 'ranger',
'output_size': 1024,
'resize_factors': None,
'save_interval': 5000,
'start_from_latent_avg': True,
'stylegan_weights': 'pretrained_models/stylegan2-ffhq-config-f.pt',
'test_batch_size': 2,
'test_workers': 0,
'train_decoder': False,
'val_interval': 2500,
'workers': 0}
Loading encoders weights from irse50!
Loading decoder weights from pretrained!
Loading ResNet ArcFace
Loading dataset for ffhq_encode
Number of training samples: 70000
Number of test samples: 30000
Traceback (most recent call last):
File "scripts/train.py", line 35, in
main()
File "scripts/train.py", line 31, in main
coach.train()
File "/home/hba/xurz/style-transformer-backup/./training/coach_invert.py", line 82, in train
y_hat, latent = self.net.forward(x, return_latents=True)
File "/home/hba/xurz/style-transformer-backup/./models/style_transformer.py", line 73, in forward
images, result_latent = self.decoder([codes],
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/hba/xurz/style-transformer-backup/./models/stylegan2/model.py", line 530, in forward
out = conv1(out, latent[:, i], noise=noise1)
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/hba/xurz/style-transformer-backup/./models/stylegan2/model.py", line 333, in forward
out = self.conv(input, style)
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/hba/xurz/style-transformer-backup/./models/stylegan2/model.py", line 258, in forward
out = self.blur(out)
File "/home/hba/miniconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, **kwargs)
File "/home/hba/xurz/style-transformer-backup/./models/stylegan2/model.py", line 85, in forward
out = upfirdn2d(input, self.kernel, pad=self.pad)
TypeError: upfirdn2d(): incompatible function arguments. The following argument types are supported:
1. (arg0: at::Tensor, arg1: at::Tensor, arg2: int, arg3: int, arg4: int, arg5: int, arg6: int, arg7: int, arg8: int, arg9: int) -> at::Tensor

Invoked with: tensor([[[[-7.0863e-02, -1.9267e-02, 1.1953e-01, ..., 3.6038e-02,
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      ...,
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        2.4659e-01, -8.3309e-03],
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        1.6582e-01, -5.9815e-02],
      [ 8.7558e-02,  2.2698e-01, -3.9275e-01,  ...,  8.2376e-01,
        2.9159e-01, -5.6248e-02]]]], device='cuda:0',
   grad_fn=<ViewBackward0>), tensor([[0.0625, 0.1875, 0.1875, 0.0625],
    [0.1875, 0.5625, 0.5625, 0.1875],
    [0.1875, 0.5625, 0.5625, 0.1875],
    [0.0625, 0.1875, 0.1875, 0.0625]], device='cuda:0'); kwargs: pad=(1, 1)

style_transformer.py, AttributeError: 'NoneType' object has no attribute 'repeat'

		if self.opts.start_from_latent_avg:
			if self.opts.learn_in_w:
				codes = codes + self.latent_avg.repeat(codes.shape[0], 1)
			else:
				codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)

AttributeError: 'NoneType' object has no attribute 'repeat', How can I fix it? Does it mean that I need another model? so self.latent_avg isn't None.

Why did you delete some code?

and I downloaded fused_bias_act.cpp, fused_bias_act_kernel.cu, upfirdn2d.cpp, upfirdn2d_kernel.cu and w_norm.py from https://github.com/omertov/encoder4editing.

u should release ur final code

How to use the repo to edit the face attribtues like the figure presents?

Hi, This is a good work. and I didn't see any command or options in the inference code for editing attributes. DO I miss something? Could you explain it?

Thanks.

Can you provide your latent_classifier model?

I am very interesting in your program.

Can you provide your latent_classifier model?

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