hyperstyle
restyle-encoder
| hyperstyle | restyle-encoder | |
|---|---|---|
| 3 | 2 | |
| 991 | 1,021 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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hyperstyle
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Recent announcement from Emad
Hypernetworks and training the VAE for SD are already publicly available.
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[D] SOTA StyleGAN inversion explained - HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing 5-minute digest (by Casual GAN Papers)
arxiv / code / demo
restyle-encoder
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amazing april 7th
Found relevant code at https://yuval-alaluf.github.io/restyle-encoder/ + all code implementations here
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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?
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
DirectVoxGO - Direct voxel grid optimization for fast radiance field reconstruction.
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
perceiver-pytorch - Implementation of Perceiver, General Perception with Iterative Attention, in Pytorch
StyleGAN_PyTorch - The implementation of StyleGAN on PyTorch 1.0.1
stylegan3-editing - Official Implementation of "Third Time's the Charm? Image and Video Editing with StyleGAN3" (AIM ECCVW 2022) https://arxiv.org/abs/2201.13433
SAM - Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754
SteganoGAN - SteganoGAN is a tool for creating steganographic images using adversarial training.
StyleFlow - StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)
neat - [ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving