cuda-kernels

To write and benchmark custom CUDA kernels for HuggingFace Diffusers, use the structured workflow to develop high-performance kernels like RMSNorm or RoPE, validate them against PyTorch baselines, and compare optimized performance using benchmarking templates.

Yes, you can integrate pre-compiled CUDA kernels into Transformers pipelines using the HuggingFace Kernels Hub get_kernel guidance, which supports loading and injecting optimized kernels directly into your diffusers and transformers workflows.

This benchmarking workflow supports building and validating high-performance CUDA kernels for operations including RMSNorm, RoPE, GEGLU, and AdaLN, comparing their optimized performance against standard PyTorch baselines.

Yes, you need PyTorch, Diffusers, Transformers, and the kernels dependency installed to run the scripts, benchmark performance, and inject optimized CUDA kernels into your video generation and transformer workloads.

The best way to compare optimized CUDA kernels against PyTorch baselines is using the provided benchmarking templates and example workflows, which enable micro-benchmarks and video generation performance comparisons.

Limitations involve end-to-end kernel development constraints and pipeline integration compatibility; validate your custom Diffusers CUDA kernels against PyTorch baselines first to ensure video generation outputs remain accurate before full deployment.