vipshop / cache-dit

🤗🎉A PyTorch-native Inference Engine with Hybrid Cache Acceleration and Massive Parallelism for DiTs **🤗Why Cache-DiT❓❓**Cache-DiT is built on top of the 🤗Diffusers library and now supports nearly ALL DiTs from Diffusers. It provides hybrid cache acceleration (DBCache, TaylorSeer, SCM, etc.) and comprehensive parallelism optimizations, including Context Parallelism, Tensor Parallelism, hybrid 2D or 3D parallelism, and dedicated extra parallelism support for Text Encoder, VAE, and ControlNet. Cache-DiT is compatible with compilation, CPU Offloading, and quantization, fully integrates with SGLang Diffusion, vLLM-Omni, ComfyUI, and runs natively on NVIDIA GPUs, Ascend NPUs and AMD GPUs. Cache-DiT is **fast**, **easy to use**, and **flexible** for various DiTs (online docs at 📘readthedocs.io). (Let's go beyond Cache-DiT) 👉 SGLang Diffusion | vLLM-Omni | Diffusers 🚀Quick Start You can install the cache-dit from PyPI or from source: Then accelerate your DiTs with just **♥️one line♥️** of code ~ For more advanced features, please refer to our online documentation at 📘readthedocs.io. 🌐Community Integration • 🎉ComfyUI x Cache-DiT • 🎉(Intel) llm-scaler x Cache-DiT • 🎉Diffusers x Cache-DiT • 🎉SGLang Diffusion x Cache-DiT • 🎉vLLM-Omni x Cache-DiT • 🎉Nunchaku x Cache-DiT • 🎉SD.Next x Cache-DiT • 🎉stable-diffusion.cpp x Cache-DiT • 🎉jetson-containers x Cache-DiT ©️Acknowledgements Special thanks to vipshop's Computer Vision AI Team for supporting document, testing and deployment of this project. We learned the design and reused code from the following projects: Diffusers, SGLang, vLLM, vLLM-Omni, ParaAttention, xDiT and TaylorSeer. ©️Citations