encoder4editing
FixNoise
| encoder4editing | FixNoise | |
|---|---|---|
| 2 | 2 | |
| 912 | 179 | |
| - | - | |
| 0.0 | 3.9 | |
| 10 months ago | 12 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
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encoder4editing
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[R] a Metric for finding the best StyleGAN Latent Encoders
Right now we have encoders like pSp and restyle or encoder4editing, but how can we tell which one performs better than the other?
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Edit a human face image with text-to-image using Google Colab notebook StyleCLIP by orpatashnik. 3 transformations shown. Details in a comment.
If you want to edit an existing image, the GitHub page says to use encoder4editing, but it currently has no code. If that is remedied, then set experiment_type=edit and latent_path to the output file generated by encoder4editing. If you use experiment_type=edit and latent_path=None, a random StyleGAN image is used.
FixNoise
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[R] Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN (CVPRW 2022)
Paper: https://arxiv.org/abs/2204.14079
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[R]Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Github: https://github.com/LeeDongYeun/FixNoise
What are some alternatives?
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
PixelAlchemist - Semantic image editing in realtime with a multi-parameter interface for StyleCLIP global directions
DualStyleGAN - [CVPR 2022] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
PTI - Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744
stylegan2-projecting-images - Projecting images to latent space with StyleGAN2.
GAN-Anime-Characters - Applied several Generative Adversarial Networks (GAN) techniques such as: DCGAN, WGAN and StyleGAN to generate Anime Faces and Handwritten Digits.