PyTorch-StudioGAN Alternatives

PyTorch-StudioGAN reviews and mentions

• [R] GigaGAN: A Large-scale Modified GAN Architecture for Text-to-Image Synthesis. Better FID Score than Stable Diffusion v1.5, DALL·E 2, and Parti-750M. Generates 512px outputs at 0.13s. Native Prompt mixing, Prompt Interpolation and Style Mixing. A GigaGAN Upscaler is also introduced (Up to 4K)
  1 project | /r/MachineLearning | 9 Mar 2023

  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.

• [P] Implementations of 30 representative GANs and Comprehensive Benchmark for GAN, AR, and Diffusion Models (link in comments).
  1 project | /r/MachineLearning | 10 Jul 2022

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

  1 project | /r/MachineLearning | 10 Jul 2022

  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.

• [P], [R] Implementations of 30 Representative GANs and Comprehensive Benchmark for GAN, AR, and Diffusion Models (link in comments).
  1 project | /r/MachineLearning | 10 Jul 2022
• [P] Implementations of 37 GAN-related papers using PyTorch including BigGAN and StyleGAN2-ADA (link in comment)
  1 project | /r/MachineLearning | 13 Nov 2021
• [P] 40 Implementations of GAN-related papers including BigGAN and StyleGAN2 in a unified training pipeline
  1 project | /r/MachineLearning | 12 Nov 2021
• [R] Rebooting ACGAN: A new GAN that achieves SOTA results and harmonizes with various architectures, adversarial losses, and even differentiable augmentations (Neurips 2021).
  1 project | /r/MachineLearning | 10 Nov 2021

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

• [N] LAMA AI's weekly news, updates, and events.
  1 project | /r/MachineLearning | 1 Mar 2021

  StudioGAN is introduced: A PyTorch library for SoTA GAN models

• PyTorch GAN Library that provides implementations of 18+ SOTA GANs with pretrained_model, configs, logs, and checkpoints (link in comments)
  1 project | /r/MachineLearning | 23 Feb 2021

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

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