encoder4editing
DualStyleGAN
encoder4editing | DualStyleGAN | |
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2 | 3 | |
912 | 1,557 | |
- | - | |
0.0 | 0.7 | |
10 months ago | about 1 year 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.
DualStyleGAN
- DualStyleGAN: A High-Resolution Portrait Style Transfer
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[R][P] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer + Hugging Face Gradio Web Demo
Github: https://github.com/williamyang1991/DualStyleGAN
- "Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer", Yang et al 2022
What are some alternatives?
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
Cartoon-StyleGAN - Fine-tuning StyleGAN2 for Cartoon Face Generation
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
StyleCLIPDraw - Styled text-to-drawing synthesis method. Featured at IJCAI 2022 and the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design
restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699
cartoonify - Deploy and scale serverless machine learning app - in 4 steps.
FixNoise - Official Pytorch Implementation for "Fix the Noise: Disentangling Source Feature for Controllable Domain Translation" (CVPR 2023, CVPRW 2022 Best paper)
TargetCLIP - [ECCV 2022] Official PyTorch implementation of the paper Image-Based CLIP-Guided Essence Transfer.
PTI - Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744