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Starred repositories
an unofficial implementation of the paper "Neural Preset for Color Style Transfer" (CVPR 2023), including ~300 LUT files
Powerful yet simple to use screenshot software 🖥️ 📸
Download images from Google, Bing, Baidu. 谷歌、百度、必应图片下载.
Python implementation of the Iterative Distribution Transfer algorithm, by Pitie, F. et al.
Palette-based Photo Recoloring, implementation of the approach of Huiwen Chang et al.
This is an open-source implementation of Xiao's image colour transfer algorithm published in 2006. MATLAB/Octave code is provided.
Color transfer based on the work of Xuezhong Xiao and Lizhuang Ma
A collection of algorithms for color transfer, style transfer, and colorization, complemented by objective evaluation metrics for quantitative assessment.
Control Color: Multimodal Diffusion-based Interactive Image Colorization
[NeurIPS 2024] Goal Conditioned Reinforcement Learning for Photo Finishing Tuning
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
Python implementation of colour transfer algorithm based on linear Monge-Kantorovitch solution
Official PyTorch implementation of DiffuseMix : Label-Preserving Data Augmentation with Diffusion Models (CVPR'2024)
[AAAI 2021] HR-Depth : High Resolution Self-Supervised Depth Estimation
Official Implementation of 'Fast AutoAugment' in PyTorch.
Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow
Automatic architecture search and hyperparameter optimization for PyTorch
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
Bolt is a deep learning library with high performance and heterogeneous flexibility.
PaddleSlim is an open-source library for deep model compression and architecture search.
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.
Count the MACs / FLOPs of your PyTorch model.