Custom CUDA kernels for Diffusers and Transformers via Codex and Claude

AI Impact Summary

Codex and Claude were used to generate production-grade CUDA kernels that integrate with PyTorch for diffusers and transformers pipelines. The skill packages domain knowledge (GPU architectures, kernel templates, and PyTorch bindings) and demonstrates end-to-end workflows, including benchmarking alongside real targets like LTX-Video and Qwen3-8B, with integration via the HuggingFace Kernel Hub. Benchmark results show notable isolated kernel speedups and meaningful end-to-end improvements on H100, highlighting a scalable path to accelerate diffusion/transformer workloads while reducing developer effort. This approach relies on standardized loading of pre-built kernels through the Kernel Hub, enabling rapid adoption across agent-powered tooling.