cache-dit
Speed up diffusion model inference with caching, parallelism, and quantization
All Skills in This Repository (7)
Pure Emerald Level Indicatorsptq-workflow-integration
Integrate a new PTQ workflow into cache-dit with a public API and validation.
cute-dsl-kernel
Implement and optimize CuTe DSL GPU kernels across NVIDIA architectures.
operator-migration
Migrate native CUDA, Triton, or custom operators into cache-dit with import safety.
cutlass-cpp-kernel
Implement and optimize CUTLASS and CuTe C++ kernels with validation.
triton-kernel
Write optimized Triton GPU kernels for deep learning operations.
cuda-cpp-kernel
Implements, debugs, and optimizes CUDA C++ and PTX kernels across NVIDIA GPU architectures.
cache-dit-model-integration
Integrates new DiT models into cache-dit with cache, parallelism, and CLI support.
Frequently Asked Questions
FAQPage SchemaHow to install cache-dit?▼
Run `npx skills add vipshop/cache-dit --all -g -y` in your terminal to install all skills in this suite globally.
How to speed up diffusion model inference?▼
Cache-DiT applies hybrid caching, context parallelism, and quantization on top of Diffusers pipelines, delivering up to 9x speedup without changing your model code.
Which models does cache-dit support?▼
It supports over 40 DiT pipeline families and 120+ variants from HuggingFace Diffusers, including FLUX, Qwen-Image, CogVideoX, Wan, and HunyuanVideo.
Can AI agents write GPU kernels with these skills?▼
Yes. The bundled skills guide agents through writing and optimizing CUDA, Triton, CuTe DSL, and CUTLASS kernels with architecture-specific profiling and validation steps.
Does cache-dit work with vLLM and ComfyUI?▼
Yes. It integrates natively with SGLang Diffusion, vLLM-Omni, TensorRT-LLM, and ComfyUI, and runs on NVIDIA, AMD, and Ascend hardware.
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