PyTorch-StudioGAN
pix2pixHD
PyTorch-StudioGAN | pix2pixHD | |
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9 | 6 | |
3,366 | 6,530 | |
-0.1% | 0.5% | |
6.1 | 0.0 | |
9 months ago | 11 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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, DALLĀ·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
pix2pixHD
- How do I run more than 200 epochs in training a Pix2PixHD model?
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NVIDIA DLSS Now Available in Over 150 Games, Including Dying Light 2 Stay Human, Sifu and Phantasy Star Online 2 New Genesis
Well, maybe not, considering things like pix2pix can generate detail from just solid shapes and colors.
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Image to hand drawn
Sources: U2Net, ArtLine, Pix2PixHD, APDrawingGAN
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[P] I made FaceShop! Instance segmentation + CGAN for editing faces (badly)
Pix2PixHD (from DeepSIM)
Uses a mix of instance segmentation (BiSeNet) and conditional GAN, and is heavily inspired by the Pix2PixHD and DeepSIM papers. Will have more details when I wake up!
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How to access a class object when I use torch.nn.DataParallel()?
I used Pix2PixHD implementation in GitHub if you want to see the full code.
What are some alternatives?
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
BigGAN-PyTorch - The author's officially unofficial PyTorch BigGAN implementation.
stylegan2-pytorch - Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch
sofgan - [TOG 2022] SofGAN: A Portrait Image Generator with Dynamic Styling
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
face-parsing.PyTorch - Using modified BiSeNet for face parsing in PyTorch
anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
generative-inpainting-pytorch - A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)
contrastive-unpaired-translation - Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.