sglang-diffusion-benchmark-profile

To profile denoise latency bottlenecks in diffusion models, you can run reproducible benchmarks and capture torch.profiler traces to rank CUDA hotspots and inspect end-to-end latency. This skill applies to image and video generation workloads running on SGLang.

You can compare baseline and optimized performance for SGLang diffusion workloads by running fixed-shape benchmark presets, saving perf dumps, and analyzing profiler traces to identify configuration issues or genuine kernel opportunities.

Yes, CUDA-capable GPUs are required to benchmark diffusion performance and capture profiler traces. You also need SGLang installed, and optional Hugging Face authentication is supported for accessing gated models.

You should use torch profiler to diagnose diffusion hotspots when a new change appears slower, requiring you to reproduce the workload, inspect the denoising trace, and determine if the issue is fast-path enablement or a kernel opportunity.

Yes, you can classify diffusion bottlenecks against existing fused kernels, packed QKV paths, attention backends, normalization optimizations, and distributed overlap families before handing off kernel work.

The best way to validate GPU memory behavior for video generation models is to run checked-in benchmark presets that record peak GPU memory alongside denoise latency, allowing you to compare baseline and modified runs.