Thanks for the interesting work. I would like to ask how did you get s space? In the paper, it is said that it is obtained by the In-domain gan method, but I read the In-domain gan paper to get W space. Is s space obtained on W space or is it a separate space?

By the way, zongze, can you share your pretrained face segmentation model? @betterze And, how about the speed of gradient computation (I see that you use 1K images to compute gradient)? I find in my experiments, the speed of only an image using your code is very slow... (maybe 1 image need 40 minutes), thus do you have any ways to speed it up?

Originally posted by @sunpeng1996 in https://github.com/betterze/StyleSpace/issues/4#issuecomment-826317931

Your work is really good! But I have some detail question. When detect locally-active style channels, how to calculate the gradient map, what are the specific details?

Hi, zongze, good to see you again.

I notice that both in StyleSpace and StyleCLIP, it is difficult to manipulate more fine-grained attributes such as nose (big/small), single and double eyelids, ear size, and so on. Do you have any ideas about these fine-grained attribute controls?

If possible, could you share the pytorch version of GetCode.py? I want to get the latent codes "s" of the pretrained stylegan2-ada model. metfaces.pkl

Hello :)

I have a simple question. I know I can turn a dlatent vector of shape 18x512 into a style vector of 26 entries, and I saw you defined an operation in tensorflow called 'G_synthesis_1/dlatents_in:0' to transform one into the other, which is embedded into the function W2S. I would really like to reverse this operation, having something like S2W, but I cannot find anywhere in the code how to do that. I saw your table in the supplementary material of the paper, I got there is a mapping between W+ and S, but I cannot get it.

Could you help me? Thanks in advance and best regards,

Francesco

1. Is the pretrained classifiers for car and LSUN bedroom available?
2. How do I create or find a pretrained classifier for a model I trained from scratch?
3. This notebook seems to be very specialized for FFHQ. Any advise or notebook you used for other classes?

Wonderful work!

Hello, I'm looking forward to your paper. If you look at your paper, there is a case that uses real face manipulation instead of a generated image, please tell me how to do input other image it with code.

I'm really impressed for your great works.

Actually, we would like to add more control attributes for modifying facial image. Instead of applying a pre-trained weights for a binary classifier, we want to train the classifier for a new attribute. Where can we get a training code for the binary classifier based on stylegan discriminator? Thank you in advance.

It takes quite a long time and to generate the gradient maps, is it possible to share the top32 channels that are locally active at different facial parts as shown in Figure 3 in the paper? Thanks a lot!

I downloaded the 40 classifiers here, but when i try to run it on an image it gives strange exceptions.

If i run the classifier with:

classifier.run( image , None )

where image is:

image = np.asarray(Image.open('drive/MyDrive/sample_img.jpg').resize((256,256) ) #(256, 256, 3)
image = np.expand_dims(image, axis=0) #(1, 256, 256, 3) = (NWHC)
image = convert_images_from_uint8(images=image,nhwc_to_nchw=True) #(1, 3, 256, 256) = (NCHW)

( NB: convert_images_from_uint8 defined here )

this error comes: UnimplementedError (see above for traceback): Generic conv implementation only supports NHWC tensor format for now. [[node celebahq-classifier-20-goatee/_Run/celebahq-classifier-20-goatee/FromRGB_lod0/Conv2D (defined at :159) ]]

But if i comment the function convert_images_from_uint8, thus giving an NWHC image, this arises:

InvalidArgumentError: input depth must be evenly divisible by filter depth: 256 vs 3 [[{{node celebahq-classifier-20-goatee/_Run/celebahq-classifier-20-goatee/FromRGB_lod0/Conv2D}}]]

It seems to me that there are some problems/confusion among the dimensions, since the 256 appearing in the error is not a random number. How can i fix it?

Hi!

I am a big fan of your work. For another paper I am working on, it would be great to have your pretrained LSUN styleGAN generator. Is it possible to make that publicly available? Thanks!

I am having all sorts of troubles with SLURM for following the section on Localized Channels.

Is it possible to just run it directly without using SLURM?

I have some few questions:

1. What is the difference of Localized Channels vs Attribute-Specific Channels? Is Localized Channel required to get the attribute-specific channel?
2. My understanding from the paper is that you can approximate the channels (e.g. 4_56) using N samples that has the desired attribute. Say, car with grass. Can we approximate that channel without using pretrained classifiers and just some sample images?

Hi Betterze,

I want to say it is a great work, but I want to know how to test with my own W. Could you share with me the code on how to input my own W?

Best

Hi,

first of all thanks for this very interesting analysis you have done! I was wondering whether you could share any observations regarding training time. Does the number of epochs the model has been trained influence the number of locally active or single attribute channels?