Awesome
In-Domain GAN Inversion for Real Image Editing
Based on Seonghyeon Kim's Pytorch Implementation of StyleGAN2
[Paper] [Official Code] [StyleGAN2 Pytorch]
Train Encoder
python train_encoder.py
0k iter
<img src="./imgs/0k.png" width="960">
1M iter
<img src="./imgs/1M.png" width="960">
[encoder checkpoint] [generator checkpoint]
Note: The encoder architecture and loss weights are different from the original implemetation.
Interpolation
interpolate.ipynb
Domain-Guided Encoder (Initial projection)
<img src="./imgs/interpolation_domain_guided_encoder.png" width="360">
In-Domain Inversion (500 steps)
<img src="./imgs/interpolation_idinversion_500steps.png" width="360">
Inperpolation Result
<img src="./imgs/interpolation_results.png" width="960">