stylegan2-pytorch VS PyTorch-StudioGAN

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.

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.

Given the first author I'd expect it to land in StudioGAN sometime in the future. Training it from scratch will definitely be costly though.

Github Link: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN

Github Link: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN Paper Link: https://arxiv.org/abs/2206.09479 I would like to introduce PyTorch-StudioGAN library, which I have been maintaining for the past two years. StudioGAN is a PyTorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT, RQ-Transformer), and Diffusion models (LSGM++, CLD-SGM, ADM-G-U). [Features] * Coverage: StudioGAN is a self-contained library that provides 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 13 regularization modules, 6 augmentation modules, 8 evaluation metrics, and 5 evaluation backbones. Among these configurations, we formulate 30 GANs as representatives. * Flexibility: Each modularized option is managed through a configuration system that works through a YAML file, so users can train a large combination of GANs by mix-matching distinct options. * Reproducibility: With StudioGAN, users can compare and debug various GANs with the unified computing environment without concerning about hidden details and tricks. * Plentifulness: StudioGAN provides a large collection of pre-trained GAN models, training logs, and evaluation results. * Versatility: StudioGAN supports 5 types of acceleration methods with synchronized batch normalization for training: a single GPU training, data-parallel training (DP), distributed data-parallel training (DDP), multi-node distributed data-parallel training (MDDP), and mixed-precision training.

Code for https://arxiv.org/abs/2111.01118 found: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN

StudioGAN is introduced: A PyTorch library for SoTA GAN models

Github: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN