Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

Contrastive Learning for Compact Single Image Dehazing, CVPR2021

Code for "Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models" [TPAMI 2023]

Here is the code for the TPAMI paper: Advancing Real-World Image Dehazing:Perspective, Modules, and Training.

Semantic-Aware Discriminator for Image Super-Resolution

[ECCV2024] Pixel-Aware Stable Diffusion for Realistic Image Super-Resolution and Personalized Stylization

Multi-Organ Foundation Model for Universal Ultrasound Image Segmentation with Task Prompt and Anatomical Prior

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Official repo for consistency models.

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs https://arxiv.org/abs/2112.07804

A very simple implementation of cyclegan, which is based on pytorch.

Official PyTorch implementation of AdaDiff described in the paper (https://arxiv.org/abs/2207.05876).

An SSG tool for quickly building modern documentation sites. 🚀️🚀️🚀️

High-Resolution Image Synthesis with Latent Diffusion Models

Datasets, Transforms and Models specific to Computer Vision

操作系统课程期末大作业Lab实验代码仓库

Pytorch implementation of Mip-NeRF360

上班摸鱼时间写的调用ChatGLM的马老师聊天机器人

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Copy formulas in Latex format from any website and save them in a markdown file.

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.