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DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
The fundamental package for scientific computing with Python.
AIHawk aims to easy job hunt process by automating the job application process. Utilizing artificial intelligence, it enables users to apply for multiple jobs in a tailored way.
A generative world for general-purpose robotics & embodied AI learning.
Official inference repo for FLUX.1 models
Fast and memory-efficient exact attention
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
Lets make video diffusion practical!
High-Resolution 3D Assets Generation with Large Scale Hunyuan3D Diffusion Models.
[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer
Official repo for paper "Structured 3D Latents for Scalable and Versatile 3D Generation" (CVPR'25 Spotlight).
A collaboration friendly studio for NeRFs
[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation
This repository contains the official implementation of "FastVLM: Efficient Vision Encoding for Vision Language Models" - CVPR 2025
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.
Efficient Triton Kernels for LLM Training
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
An easy to use PyTorch to TensorRT converter
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer
A PyTorch native platform for training generative AI models
Speech To Speech: an effort for an open-sourced and modular GPT4-o
StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and te…
🚀 Efficient implementations of state-of-the-art linear attention models