Comments (6)
These (also used here) seem to work well.
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Hey @molo32 ,
The reason why this does not work is because those boundaries were found based on StyleGAN1.
In this project we use StyleGAN2, which has a completely different latent space than StyleGAN1. Hence also different boundaries
The boundaries I have uploaded were trained on StyleGAN2, that's why they work
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Hey @JK737353
There are several ways to edit images using StyleGAN after the initial inversion done by PTI.
You can use directions from InterfaceGAN, GANSpace, Sefa, StyleCLIP, and many more amazing works.
Creating an editing direction on your own is a feasible task, nevertheless not an easy one. Therefore I would suggest searching for a framework from the ones I have mentioned above which manipulates the latent code to your desired need,
And if you don't find one, create one yourself using the InterfaceGAN framework for example
See https://github.com/orpatashnik/StyleCLIP or https://github.com/genforce/interfacegan for more information about editing frameworks
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Hey @molo32 ,
I personally do not possess any more editing directions for InterfaceGAN. You can try to create ones on your own.
See https://github.com/genforce/interfacegan for more details.
A different option would be to use other editing techniques - such as GanSPACE, SeFa, StyleFlow and StyleCLIP
Although I did not upload support for SeFa and StyleFlow, I have received messages from different users which combined the methods successfully
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hi danielroich, can you tell me where to find the interfacegan latents for gender and glasses, the repository only has age smile pose.
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try to use the boundary of ffhq that are in
https://github.com/genforce/interfacegan/tree/master/boundaries
i turned them npy into pt
but it seems that it does not work, it modifies the face but it does it very badly.
to go from npy to pt just use torch.load and np.load.
am i missing something?
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Related Issues (20)
- The more smile, the more black HOT 5
- How did you find the directions? HOT 1
- Unexpected size of W features in generated_images = self.G.synthesis(w, noise_mode='const', force_fp32=True) HOT 2
- opt multi image one time? HOT 1
- The input parameters of the Generator HOT 1
- checkpoint HOT 3
- SG and SG2 issue HOT 2
- GPU error HOT 1
- Effect of lpips type HOT 1
- run_pit: assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution) HOT 3
- [question] What kind of pre-processing would a model that doesn't generate faces require? HOT 1
- Custom Images - Run on full images HOT 3
- How can i get "car images " w_pivot?
- Applying PTI to other non style-GAN generators
- Failed to run 512x512 images
- How to improve the invert image to be more similar to the input image? HOT 1
- May I ask how the pt file for editing attributes was obtained?
- insetGan+PTI?
- Can I run it on Windows?
- how to get the inversion of my custom images with the latent codes and tuned generators?
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