PyTorch-StudioGAN
anycost-gan
| PyTorch-StudioGAN | anycost-gan | |
|---|---|---|
| 9 | 1 | |
| 3,366 | 769 | |
| -0.1% | 0.3% | |
| 6.1 | 2.5 | |
| 9 months ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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PyTorch-StudioGAN
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[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)
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.
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[P] Implementations of 30 representative GANs and Comprehensive Benchmark for GAN, AR, and Diffusion Models (link in comments).
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.
- [P], [R] Implementations of 30 Representative GANs and Comprehensive Benchmark for GAN, AR, and Diffusion Models (link in comments).
- [P] Implementations of 37 GAN-related papers using PyTorch including BigGAN and StyleGAN2-ADA (link in comment)
- [P] 40 Implementations of GAN-related papers including BigGAN and StyleGAN2 in a unified training pipeline
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[R] Rebooting ACGAN: A new GAN that achieves SOTA results and harmonizes with various architectures, adversarial losses, and even differentiable augmentations (Neurips 2021).
Code for https://arxiv.org/abs/2111.01118 found: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
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[N] LAMA AI's weekly news, updates, and events.
StudioGAN is introduced: A PyTorch library for SoTA GAN models
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PyTorch GAN Library that provides implementations of 18+ SOTA GANs with pretrained_model, configs, logs, and checkpoints (link in comments)
Github: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
anycost-gan
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