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docs(diffusion): add per-model tuning decision table to performance guide (#38148)

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Xiaoyu Zhang
2026-09-06 15:26:40 +08:00
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@@ -147,6 +147,27 @@ These techniques can change the denoising path, numerical representation, or gen
6. Profile if the bottleneck is unclear.
7. Add `quality=high`, caching, progressive resolution, or quantization only after comparing output quality against your acceptance target.
## Per-model tuning starting points
Warmup and breakable CUDA graph (BCG) solve different problems. `--warmup-mode request` runs a warmup copy derived from the first request to prime one-time compilation and caches; it does not remove recurring Python launches from each denoising step. BCG captures supported DiT segments and can reduce that recurring launch overhead for captured shapes. Use the table below as a first experiment, then keep a lever only when profiling confirms that it addresses the active bottleneck.
| Observed bottleneck or constraint | Example models | First experiment | What to verify |
| --- | --- | --- | --- |
| The first execution of a shape pays substantial compilation or cache setup | ERNIE-Image-Turbo, GLM-Image, FastWan / TurboWan family, FLUX.2-klein-4B, LingBot-World, LongLive, Cosmos3, SANA, LongCat-Image-Edit-Turbo | Compare `--warmup-mode request` with `off` | Separate warmup time, first-real-request latency, and warmed steady-state latency. Request-based warmup is primarily a benchmark aid and still consumes time before the first real request completes. |
| A supported fixed-shape pipeline shows recurring host-launch gaps between GPU kernels | GLM-Image, Z-Image-Turbo, Qwen-Image-2512, Ideogram-4, LongCat-Image, LTX-2 / LTX-2.3, SANA-1.5 1.6B, JoyEcho | Compare eager execution with `--enable-breakable-cuda-graph` | Confirm graph capture and replay in the server log, then compare steady-state latency and GPU memory. Pass additional production shapes through `--warmup-resolutions`; without it, BCG captures only the model's default warmup resolution. |
| The model does not fit while both DiT stages are resident | Wan2.2-T2V-A14B, Wan2.2-I2V-A14B, LingBot-Video-MoE | Start with `--dit-layerwise-offload` | Verify peak GPU memory first, then measure the transfer overhead. Add warmup separately if first-run compilation is also material. |
| Dynamic masks, metadata, or image conditions make graph replay ineffective | Qwen-Image-Edit family, FireRed-Image-Edit | Keep eager execution as the baseline | Profile before attempting BCG. Dynamic per-step host work can outweigh graph replay savings, and unsupported pipelines fall back to eager execution. |
| Kernels or collectives already dominate a many-step workload | Qwen-Image, FLUX.1-dev, FLUX.2-dev, LTX-2, Wan2.1 14B, HunyuanVideo, MOVA | Warm the baseline, then profile before enabling another lever | A small host gap limits the benefit available from BCG. Prioritize kernels, attention, parallelism, or residency according to the trace. |
| A one-shot or very few-step workload cannot amortize warmup | FastVideo-FastH3 (4-step) | Start with `--warmup-mode off` | Compare end-to-end latency including warmup. Use request warmup only when intentionally separating cold-start setup from the measured request. |
The example assignments come from single-GPU NVIDIA B300/GB300 profiles and are directional rather than an exhaustive compatibility list. A model can match more than one row as resolution, frame count, step count, parallelism, or GPU type changes; the measured bottleneck takes precedence over the model name.
BCG is enabled only for model and pipeline configurations accepted by the runtime support check. It captures the default warmup shape automatically. Use `--warmup-resolutions` for additional served resolutions; for video and variable prompt lengths, set `--warmup-num-frames` and `--bcg-text-buckets` to cover the intended workload. Requests that do not match a captured signature fall back to eager execution.
Warmup and BCG do not intentionally trade output quality for speed, but that is not a guarantee of byte-identical output. Different execution paths can introduce numerical differences, and some pipelines customize the scheduler or schedule used by a synthetic warmup request. Before production rollout, compare output hashes when bitwise stability is required, otherwise run the project's quality acceptance check.
Performance results are configuration-dependent. Record the exact checkpoint revision, GPU, precision, resolution, frame count, step count, parallelism, command line, and whether the measurement includes warmup. Re-profile with [Profiling](/docs/sglang-diffusion/profiling) on the target workload rather than transferring a percentage from another model or shape.
## Diagnostics
[Profiling](/docs/sglang-diffusion/profiling) is not an optimization technique by itself. It belongs in the performance workflow because it tells you which stage, kernel, or denoising step is worth optimizing before you change multiple levers.