LlamaIndex is the leading framework for building LLM-powered agents over your data.

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

🦜🔗 The platform for reliable agents.

ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS@2023 Spotlight, TPAMI@2024)

🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/

fixed official code for paper "A Closer Look at Parameter-Efficient Tuning in Diffusion Models".

[ICCV 2025 Highlight] 🌟🌟🌟 Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts

非常详细的大模型LLM的学习教程与笔记

一个基于langchain实现RAG的简单示例

A C++ header-only HTTP/HTTPS server and client library

Cross-platform, efficient, customizable, and robust asynchronous HTTP(S)/WebSocket server C++ library with the right balance between performance and ease of use

ICCV 2025-CTMSR:Consistency Trajectory Matching for One-Step Generative Super-Resolution

The official implementation of [CVPR 2025] "5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks".

CVPR2025 - CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution

(CVPR 2025) Adversarial Diffusion Compression for Real-World Image Super-Resolution [PyTorch]

[CVPR 2024] SinSR: Diffusion-Based Image Super-Resolution in a Single Step

A collection of resources on modern C++

Modern C++ Programming Course (C++03/11/14/17/20/23/26)

A cheatsheet of modern C++ language and library features.

Arbitrary-steps Image Super-resolution via Diffusion Inversion (CVPR 2025)

[CVPR 2025] Official implementation for "Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution".

[CVPR 2025] Official code repository for "Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach"

[AAAI 26] Official PyTorch implementation of Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency Adaptation

Code for PID: Physics-Informed Diffusion Model for Infrared Image Generation

A Unified Library for Parameter-Efficient and Modular Transfer Learning

DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging （CVPR 25）

[CVPR 2022] This is the official code for the paper "Toward Fast, Flexible, and Robust Low-Light Image Enhancement".

Official pytorch implementation for "LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models"