FastVideo Kernel
CUDA kernels for FastVideo video generation.
Installation
Standard Installation (Local Development)
This will automatically detect your GPU architecture. If an NVIDIA Hopper (H100/sm_90a) GPU is detected, ThunderKittens kernels will be enabled. Otherwise, they will be skipped, and the package will use Triton fallbacks at runtime.
git submodule update --init --recursive
cd fastvideo-kernel
./build.sh
Rocm Build
If you are in a rocm environment without the compilation toolchaine of CUDA.
cd fastvideo-kernel
./build.sh --rocm
Usage
Sliding Tile Attention (STA) & Video Sparse Attention (VSA)
For detailed usage, please check the Attention Documentation.
from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_attn_varlen
# Example: Sliding Tile Attention
out = sliding_tile_attention(q, k, v, window_sizes, text_len)
# Example: Video Sparse Attention (with Triton fallback)
out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
# Example: VMoBA
out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
Benchmark
VSA (block-sparse) TFLOPs
After building/installing fastvideo-kernel, run:
cd fastvideo-kernel
python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
TurboDiffusion Kernels
This package also includes kernels from TurboDiffusion, including INT8 GEMM, Quantization, RMSNorm and LayerNorm.
Requirements
- Runtime:
- NVIDIA H100 (sm_90a) for C++ optimized kernels.
- Any CUDA GPU for Triton-based fallbacks.
- Build:
- CUDA Toolkit 12.3+
- C++20 compatible compiler (GCC 10+, Clang 11+)
Acknowledgement
This package structure and build system are based on sgl-kernel from the SGLang project.
The implementation of turbodiffusion kernels is adapted from TurboDiffusion. If you use these kernels, please cite:
@article{zhang2025turbodiffusion,
title={TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times},
author={Zhang, Jintao and Zheng, Kaiwen and Jiang, Kai and Wang, Haoxu and Stoica, Ion and Gonzalez, Joseph E and Chen, Jianfei and Zhu, Jun},
journal={arXiv preprint arXiv:2512.16093},
year={2025}
}