I am trying to derive latent encodings for cutom faces, as done in https://github.com/Puzer/stylegan-encoder.

Here are the details after porting the same to pytorch:

from models.stylegan_generator import StyleGANGenerator

#load the pre-trained synthesis network
m_synth = StyleGANGenerator("stylegan_ffhq").model.synthesis.cuda().eval()

#process the output of the synthesis module
class PostProcAfterSynth(nn.Module):
    def __init__(self):
        super(PostProcAfterSynth, self).__init__()
    def forward(self, gen_img):
        #remap to [0,1]
        return (gen_img+1)/2
    
post_proc_layer = PostProcAfterSynth()

#preprocess the generated image before feeding into perceptual model    
class PreProcBeforePerception(nn.Module):
    def __init__(self, img_size):
        super(PreProcBeforePerception, self).__init__()
        self.img_size = img_size
        self.mean = torch.tensor([0.485, 0.456, 0.406], device=device).view(-1, 1, 1)
        self.std = torch.tensor([0.229, 0.224, 0.225], device=device).view(-1, 1, 1)
    def forward(self, gen_img):
        #resize input image
        gen_img = F.adaptive_avg_pool2d(gen_img, self.img_size)
        #normalize
        gen_img = (gen_img - self.mean) / self.std
        return gen_img
    
pre_proc_layer = PreProcBeforePerception(img_size=256)

#use pre-trained vgg model for feature extraction
m_vgg = models.vgg16(pretrained=True).features[:16].to(device).eval()

#set up the model
model = nn.Sequential(m_synth)
model.add_module(str(1), post_proc_layer)
model.add_module(str(2), pre_proc_layer)
model.add_module(str(3), m_vgg)

for param in model.parameters():
    param.requires_grad_(False)

print(m_vgg)

Sequential(
  (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (1): ReLU(inplace)
  (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (3): ReLU(inplace)
  (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (6): ReLU(inplace)
  (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (8): ReLU(inplace)
  (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (11): ReLU(inplace)
  (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (13): ReLU(inplace)
  (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (15): ReLU(inplace)
)

As done by Puzer, I select the [conv->conv->pool->conv->conv->pool->conv->conv->conv] section of the vgg network for feature extraction.

Pre-computing the features for the reference image:

ref_img_path = "."
ref_img = np.array(Image.open(ref_img_path))
ref_img = ref_img.astype(np.float32)/255.
ref_img = np.array([np.transpose(ref_img, (2,0,1))])
ref_img = torch.tensor(ref_img, device=device)
ref_img = pre_proc_layer(ref_img)
ref_img_features = m_vgg(ref_img).detach()

Optimization:

trainable_latent = torch.randn((1,18,512), device=device).requires_grad_(True)
loss_func = torch.nn.MSELoss()

optimizer = optim.SGD([trainable_latent], lr=0.5)

losses = []
for i in tqdm(range(1000)):
    optimizer.zero_grad()
    gen_img_features = model(trainable_latent)
    loss = loss_func(gen_img_features, ref_img_features)
    loss_val = loss.data.cpu()
    losses.append(loss_val)
    loss.backward()
    optimizer.step()

The latent encoding and subsequent generated images are of a poor quality. The results are nowhere near as crisp as that by Puzer.

What I have tried:

1. Learning Z space latent instead of WP+
2. Variety of optimizers, learning rate, iterations combos

What could be wrong:

1. There might be issues with my pipeline above (new to pytorch)
2. There might be some difference in pre-trained vgg networks for pytorch and keras, that I might have failed to take into account.
3. The perceptual model used is not complex enough. (but it does work for Puzer)

Any help with the above would be much appreciated.

Hi, the age demo in the paper makes an adult into a child, could you please tell me what will if I set the age attribute as an extreme value?

Can it make an adult into a one-year-old baby？Can it keep the generator output a normal human face?

(base) PS E:\darshan\pytorch_stylegan_encoder-master\InterFaceGAN> python generate_data.py -m stylegan_ffhq -o data/pggan_celebahq -n 10000 [2020-01-20 03:53:48,282][INFO] Initializing generator. [2020-01-20 03:53:48,521][WARNING] No pre-trained model will be loaded! Traceback (most recent call last): File "generate_data.py", line 111, in main() File "generate_data.py", line 64, in main model = StyleGANGenerator(args.model_name, logger) File "E:\darshan\pytorch_stylegan_encoder-master\InterFaceGAN\models\stylegan_generator.py", line 42, in init super().init(model_name, logger) File "E:\darshan\pytorch_stylegan_encoder-master\InterFaceGAN\models\base_generator.py", line 103, in init self.model.eval().to(self.run_device) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 426, in to return self._apply(convert) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 202, in _apply module._apply(fn) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 202, in _apply module._apply(fn) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 202, in _apply module._apply(fn) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 224, in apply param_applied = fn(param) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 424, in convert return t.to(device, dtype if t.is_floating_point() else None, non_blocking) File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\cuda_init.py", line 192, in _lazy_init check_driver() File "C:\Users\HpZ8\Anaconda3\lib\site-packages\torch\cuda_init.py", line 95, in _check_driver raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enabled

Hi guys,

Thank you for your great work. I have a question.

I found that generate_data.py works very well with my style-gan models, but when I trained PGGAN with the same dataset of images and then ran generate_data.py for generating 10k images, all images look like this:

I tried another checkpoint but it gave similar results:

I guess it is caused by some misconfiguration between my PGGAN and the inference code of InterFaceGAN, but haven't found it yet. Can you give me some advice please?

Hi Yujun,

In the paper you claimed that it must use GAN inversion method to map real images to latent codes, and StyleGAN inversion methods are much better, are there documents introducing how to do the inversion? Any comments are appreciated! Best Regards.

It seems that for pggan, state dictionaries for the model trained by paper authors (file karras2018iclr-celebahq-1024x1024.pkl) and custom models that are trained by their released code are different. You can check the differences here: https://www.diffchecker.com/0hFYlK82.

Unfortunately I was unable to tweak your code so that it would convert my trained model correctly.

Did you try converting custom trained model using pggan code? If so, could you please provide a model you trained that worked for you other than the one provided by pggan authors?

Dear author, after checking this repository, I have found that there isn't included a encoder-decoder model as the paper tests in Figure 11. WILL this release in the near future?

Hi Shenyujun, Thanks so much for awesome work! I saw you have pose direction in repo, but there is no roll or pitch rotation for pose in that direction. Do you think it's possible to train with both pitch, roll and yaw? BTW, any attribute predictor or data could share regarding the pose generation? That will be really helpful, not found in original stylegan repo. Thanks

Hi, thanks for the paper and the results are impressive!

I tested the code with "stylegan_ffhq" model and "stylegan_ffhq_pose_boundary.npy or stylegan_ffhq_pose_w_boundary.npy", with the default settings, but the results are not very good.

The person identity, age, even gender changed simultaneously with the pose. Regarding to the "stylegan_ffhq_pose_w_boundary.npy", the degree of pose changes are more or less ignorable.

python edit.py -m stylegan_ffhq -o results/stylegan_ffhq_pose_w_boundary -b ./boundaries/stylegan_ffhq_pose_w_boundary.npy -n 10

Is there anything that I have to adjust?

I have a question on your implementation of the truncation module. Why do the first 9 channels of the W+ code are same? It looks like you separate W+ code into just 2 blocks but 18 blocks? This is strange for that in the official code each channel(I mean 18, not 512) of W+ code is different.

Hi! Thanks for your great research! Is there any chance you can provide attributes predictor that you used for finding boundaries? I didn't really get about scores that it should give to attributes on the pictures. Also for example for binary attributes like glasses/no glasses is it possible to manually put labels of say 1 for glasses, 0 for no glasses and feed this data to train a new boundary?

Hi there!

Thank you so much for such wonderful repo, appreciate your efforts!

Can you guys tell me, How can provide my own input images for experimentation, instead of some celeb data? @ShenYujun @younesbelkada @clementapa

In order to do it, What are the changes I need to make in colab file? I am using this file: https://colab.research.google.com/github/genforce/interfacegan/blob/master/docs/InterFaceGAN.ipynb#scrollTo=ccONBF60mVir

Basically, I want to use this code to check it on my set of images.

Any help be appreciated, Thanks in advance!!

I am looking for training module in your provided code files to train the model on boats data. Can you please guide me where I can find required files to train your model on my data? Or is there any other way to fine tune existing models?

Hello, I am trying to implement your code but I keep getting this error despite changing all 'tf.' functions to 'tf.compat.v1.' I solved couple of no module error but I cannot find this one anywhere in the .py files. Is it something happens upon loading the model? Thank you in advance for your help :)

I can get latent code by using encoder network to invert real image, and then put it into pre-train StyleGAN2, I can get reconstruction result. It works well. But I save this latent code as .npy and use : python edit.py -m stylegan_celebahq -b boundaries/stylegan_celebahq_eyeglasses_boundary.npy -i MY_LATENT_CODES_FILE.npy -o results/my_image -s wp

picture is strange, like this: