sglang-diffusion-performance

To optimize SGLang diffusion performance, you can use guided benchmarking to identify optimal backends, degrees of parallelism, and offload configurations tailored to your specific GPU and VRAM constraints.

Tuning diffusion inference requires setting exact SGLang flags to enable or disable components like torch.compile, Ulysses, offloads, and attention backends to achieve the fastest inference speed with acceptable memory usage.

Yes, you can benchmark native SGLang diffusion performance across multiple GPUs by applying profile-driven recommendations and parameter presets to compare baseline performance results.

The best way to prepare for SGLang diffusion benchmarking involves following operational guidance on environment setup and flag usage to accurately reproduce performance results across different hardware configurations.

Yes, torch.compile is a supported component in SGLang diffusion performance tuning, where you can enable or disable it alongside Ulysses and attention backends to identify the optimal configuration for your model.

You should evaluate disabling offloads or Ulysses components during SGLang diffusion tuning if your profile-driven benchmarks indicate they negatively impact inference speed or exceed your target VRAM budget.