GAN library
PaddlePaddle GAN library providing implementations and applications of many generative adversarial networks for image generation, style transfer, super-resolution, face restoration, and video editing.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose when you prefer PyTorch and need a GAN library dedicated to a broad set of generative models; the framework difference means a port is required.
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose for high-resolution (2048x1024) conditional image synthesis with a dedicated PyTorch repo, rather than PaddleGAN's broader library.
Synthesizing and manipulating 2048x1024 images with conditional GANs
Choose for multimodal unsupervised image translation with a lightweight single-purpose PyTorch implementation.
Multimodal Unsupervised Image-to-Image Translation
Choose for faster unpaired translation (CUT) with a smaller memory/training footprint than CycleGAN, in a dedicated PyTorch codebase.
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
Choose for classic unsupervised image translation if you prefer the original UNIT implementation in PyTorch.
Unsupervised Image-to-Image Translation
Choose for the canonical pix2pix model if you need to match a legacy Torch codebase and don't need other GAN applications.
Image-to-image translation with conditional adversarial nets
Choose for a focused photo-to-cartoon model with minimal dependencies instead of PaddleGAN's full framework.
人像卡通化探索项目 (photo-to-cartoon translation project)
Choose for exemplar-based high-resolution portrait style transfer with a dedicated PyTorch repo.
[CVPR 2022] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer
Choose for controllable high-resolution portrait video style transfer with this dedicated PyTorch implementation.
[SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
Choose for a minimal DCGAN implementation in TensorFlow as an educational baseline.
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
Choose for video restoration with the original EDVR PyTorch implementation, if you don't need PaddleGAN's other features.
Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.
Choose for a tiny composable GAN framework with a simple API if you want minimal code, but it's outdated and less featureful.
Composable GAN framework with api and user interface
Choose for edge/color-guided progressive image reconstruction with a dedicated implementation.
:fire: PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain. :fire: 图像翻译，条件GAN，AI绘画
Choose for practical real-world face restoration with a widely-used PyTorch model instead of PaddleGAN's suite.
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Choose for diffusion-based real-world image super-resolution with a dedicated PyTorch implementation.
[IJCV2024] Exploiting Diffusion Prior for Real-World Image Super-Resolution
These projects were analysed and named PaddleGAN among their alternatives. The relationship is not symmetric — how PaddleGAN rates them is a separate judgement, made when PaddleGAN is analysed in its own right.
calls PaddleGAN “Choose if you use PaddlePaddle and need a broad GAN library with image-to-image translation support.”
Image-to-Image Translation in PyTorch
calls PaddleGAN “Choose when you are working in PaddlePaddle and want a broader GAN toolbox with ready-to-use image translation and style-transfer applications instead of the original Torch CycleGAN code.”
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
calls PaddleGAN “when you want a broad GAN library on PaddlePaddle with many image-to-image applications.”
Image-to-image translation with conditional adversarial nets