stylegan2-pytorch
maua-stylegan2
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| stylegan2-pytorch | maua-stylegan2 | |
|---|---|---|
| 3 | 2 | |
| 2,659 | 179 | |
| - | - | |
| 0.0 | 0.0 | |
| 6 months ago | almost 3 years ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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stylegan2-pytorch
- Converting a .pkl file to a .pt for StyleGan2 Model
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I'm stumped with installing PyTorch.
Originally I wanted to run https://github.com/JCBrouwer/maua-stylegan2. I was trying to run the convert_weight.py but it resulted in shape mismatch errors in torch torch.Size([1, 512, 4, 4]) vs torch.Size([1]), so I tried the version here https://github.com/rosinality/stylegan2-pytorch/blob/master/convert_weight.py and the result was the same.
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[D] Node Collapse with StyleGAN2 - ada
Used this implementation (https://github.com/rosinality/stylegan2-pytorch) to better understand the code. IMO it is written way more clearly than the official implementation. You should spend a lot of time reviewing the code until you understand what each line is doing. It isn't helpful that their aren't very many comments, but you should be able to recognize different architectures and calculations from the paper. Understanding the details is key. Don't let your eyes glaze over any part of it.
maua-stylegan2
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I'm stumped with installing PyTorch.
Originally I wanted to run https://github.com/JCBrouwer/maua-stylegan2. I was trying to run the convert_weight.py but it resulted in shape mismatch errors in torch torch.Size([1, 512, 4, 4]) vs torch.Size([1]), so I tried the version here https://github.com/rosinality/stylegan2-pytorch/blob/master/convert_weight.py and the result was the same.
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Pretrained 1792x1024 StyleGAN2 model
You don't even need to really do model surgery. All the convolutions will accept arbitrary dimensions. You can just use network bending padding operations to get any output size you like Vadim Epstein's repo does something slightly different which let's you even use different latents per section: https://github.com/eps696/stylegan2ada Or mine which has the simpler, single latent version https://github.com/JCBrouwer/maua-stylegan2 Or for training, then all you have to do is change the size of your constant layer Or just graft on some more upsamples Either way though, there's not too much point to training at weird rectangular resolutions. You'll get pretty much identical results by just forcefully resizing to a square and then stretching the generated versions back out to square Unless you've got a ridiculous amount of VRAM, larger models don't really make too much sense either. Especially because it'll be hard to find 10k images at such a big resolution
What are some alternatives?
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.
stylegan2ada - StyleGAN2-ada for practice
stylegan-waifu-generator - Generate your waifu with styleGAN, stylegan老婆生成器
stylegan2-surgery - StyleGAN2 fork with scripts and convenience modifications for creative media synthesis
PyTorch-StudioGAN - StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
BasicSR - Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.