sglang-diffusion-performance

name	sglang-diffusion-performance
description	Use when choosing the fastest SGLang Diffusion flags for a model, GPU, and VRAM budget.

SGLang Diffusion Performance Tuning

Use this skill when the user wants the fastest command line, lower VRAM, or the right performance flags for a specific model and GPU setup.

Before running any sglang generate command below inside the diffusion container:

• use python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-benchmark-profile/scripts/diffusion_skill_env.py to derive the repo root, verify write access, and choose idle GPU(s)
• export HF_TOKEN first when the selected model lives in a gated Hugging Face repo such as black-forest-labs/FLUX.*
• export FLASHINFER_DISABLE_VERSION_CHECK=1
• cd to the repo root resolved from sglang.__file__

Native Backend Gate

Performance numbers are useful only when the intended backend actually ran.

• Treat any log containing Falling back to diffusers backend, Using diffusers backend, or Loaded diffusers pipeline as invalid for native SGLang performance tuning.
• Use --backend diffusers only for an explicit diffusers baseline. For native recipes, leave the default backend or pin --backend sglang.
• If a fallback happened, fix pipeline registration/model-path/config issues first, then rerun. Do not compare perf dumps collected from a fallback run.
• When the runtime auto-selects parallel settings because the user omitted them, keep the result as an auto-tuned baseline. For reproducible tuning, pin --num-gpus, --ulysses-degree, --ring-degree, and --enable-cfg-parallel explicitly.

Reference: SGLang-Diffusion Advanced Optimizations Blog

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Section 1: Lossless Optimizations

These options are intended to preserve output quality. In practice, some paths (most notably torch.compile) can still introduce small floating-point drift, so validate on your target model when numerical parity matters.

Option	CLI Flag / Env Var	What It Does	Speedup	Limitations / Notes
torch.compile	--enable-torch-compile	Applies torch.compile to the DiT forward pass, fusing ops and reducing kernel launch overhead.	~1.2–1.5x on denoising	First request is slow (compilation). May cause minor precision drifts due to PyTorch issue #145213. Pair with --warmup for best results.
Warmup	--warmup	Runs dummy forward passes to warm up CUDA caches, JIT, and torch.compile. Eliminates cold-start penalty.	Removes first-request latency spike	Adds startup time. Without --warmup-resolutions, warmup happens on first request.
Warmup Resolutions	--warmup-resolutions 256x256 720x720	Pre-compiles and warms up specific resolutions at server startup (instead of lazily on first request).	Faster first request per resolution	Each resolution adds to startup time. Serving mode only; useful when you know your target resolutions in advance.
Multi-GPU (SP)	--num-gpus N --ulysses-degree N	Sequence parallelism across GPUs. Shards sequence tokens (not frames) to minimize padding.	Near-linear scaling with N GPUs	Requires NCCL; inter-GPU bandwidth matters. ulysses_degree * ring_degree = sp_degree. For Wan2.2 video, start by benchmarking pure Ulysses before assuming a mixed Ulysses/Ring layout is fastest.
CFG Parallel	--enable-cfg-parallel	Runs conditional and unconditional CFG branches in parallel across GPUs. For CFG models on multi-GPU, benchmark this against pure Ulysses on your topology instead of assuming one always wins.	Often faster than pure SP for CFG models	Requires num_gpus >= 2. Halves the Ulysses group size (e.g. 8 GPU → two 4-GPU groups). Only for models that use CFG. Nightly coverage configs may intentionally use smaller Ulysses groups to keep ring behavior exercised; that does not automatically make them the lowest-latency choice.
Layerwise Offload	--dit-layerwise-offload	Async layer-by-layer H2D prefetch with compute overlap. Only ~2 DiT layers reside on GPU at a time, dramatically reducing VRAM. For some video models the copy stream can be almost fully hidden behind compute.	Saves VRAM (40 GB → ~11 GB for Wan A14B); can be near-zero speed cost on the right workload	Enabled by default for Wan/MOVA video models. Incompatible with Cache-DiT. For image models or highly parallelized setups (many GPUs, small per-GPU compute), the copy stream may not be fully hidden and can cause slowdown.
Offload Prefetch Size	--dit-offload-prefetch-size F	Fine-grained control over layerwise offload: how many layers to prefetch ahead. 0.0 = 1 layer (min VRAM), 0.1 = 10% of layers, ≥1 = absolute layer count.	Tune for cases where default offload has copy stream interference (e.g. image models). 0.05–0.1 is a good starting point.	Values ≥ 0.5 approach no-offload VRAM with worse performance. Use lower values when copy overlap is weak; disable offload when memory allows and latency dominates.
FSDP Inference	--use-fsdp-inference	Uses PyTorch FSDP to shard model weights across GPUs with prefetch. Low latency, low VRAM.	Reduces per-GPU VRAM	Mutually exclusive with --dit-layerwise-offload. More overhead than SP on high-bandwidth interconnects.
CPU Offload (components)	--text-encoder-cpu-offload, --image-encoder-cpu-offload, --vae-cpu-offload, --dit-cpu-offload	Offloads specific pipeline components to CPU when not in use.	Reduces peak VRAM	Adds H2D transfer latency when the component is needed. Auto-enabled for low-VRAM GPUs (<30 GB). Tip: after the first request completes, the console prints a peak VRAM analysis with suggestions on which offload flags can be safely disabled — look for the "Components that could stay resident" log line.
Pin CPU Memory	--pin-cpu-memory	Uses pinned (page-locked) memory for CPU offload transfers.	Faster H2D transfers	Slightly higher host memory usage. Enabled by default; disable only as workaround for CUDA errors.
Attention Backend (lossless)	--attention-backend fa	Selects a lossless attention kernel for SGLang-native pipelines: fa (FlashAttention 2/3/4 alias) or torch_sdpa.	FA is usually faster than SDPA on long sequences	FA requires compatible GPU (Ampere+). For --backend diffusers, valid backend names differ; use the names documented in docs/diffusion/performance/attention_backends.md.
Parallel Folding	(automatic when SP > 1)	Reuses the SP process group as TP for the T5 text encoder, so text encoding is parallelized "for free".	Faster text encoding on multi-GPU	Automatic; no user action needed. Only applies to T5-based pipelines.

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Section 2: Lossy Optimizations

These options trade output quality for speed or VRAM savings. Results will differ from the baseline.

Option	CLI Flag / Env Var	What It Does	Speedup	Quality Impact / Limitations
Approximate Attention	--attention-backend sage_attn / sage_attn_3 / sliding_tile_attn / video_sparse_attn / sparse_video_gen_2_attn / vmoba_attn / sla_attn / sage_sla_attn	Replaces exact attention with approximate or sparse variants. sage_attn: INT8/FP8 quantized Q·K; sliding_tile_attn: spatial-temporal tile skipping; others: model-specific sparse patterns.	~1.5–2x on attention (varies by backend)	Quality degradation varies by backend and model. sage_attn is the most general; sparse backends (sliding_tile_attn, video_sparse_attn, etc.) are video-model-specific and may require config files (e.g. --mask-strategy-file-path for STA). Requires corresponding packages installed.
Cache-DiT	SGLANG_CACHE_DIT_ENABLED=true + --cache-dit-config <path>	Caches intermediate residuals across denoising steps and skips redundant computations via a Selective Computation Mask (SCM).	~1.5–2x on supported models	Quality depends on SCM config. Incompatible with --dit-layerwise-offload. Requires correct per-model config YAML.
Quantized Models (Nunchaku / SVDQuant)	--enable-svdquant --transformer-weights-path <path> + optional --quantization-precision int4|nvfp4, --quantization-rank 32	W4A4-style quantization via Nunchaku. Reduces DiT weight memory by ~4x. Precision/rank can be auto-inferred from weight filename or set explicitly.	~1.5–2x compute speedup	Lossy quantization; quality depends on rank and precision. Requires pre-quantized weights. Ampere (SM8x) or SM12x only (no Hopper SM90). Higher rank = better quality but more memory.
Pre-quantized Transformer Override	--transformer-path <dir-or-repo> / --transformer-weights-path <path>	Load a quantized transformer component or raw transformer weights. For converted ModelOpt FP8/NVFP4 directories, prefer --transformer-path; use --transformer-weights-path for weight-only artifacts the model loader expects.	~1.3–1.5x compute (dtype dependent)	Requires a validated quantized transformer override, such as one produced by the ModelOpt helper tools. Quality is usually slightly worse than BF16 and depends on the format, fallback layers, and calibration scope.
Component Precision Override	--dit-precision fp16, --vae-precision fp16|bf16	On-the-fly dtype conversion for individual components. E.g. convert a BF16 model to FP16 at load time, or run VAE in BF16 instead of FP32.	Reduces memory; FP16 can be faster on some GPUs	May affect numerical stability. VAE is FP32 by default for accuracy; lowering it is lossy. DiT defaults to BF16.
Fewer Inference Steps	--num-inference-steps N (sampling param)	Reduces the number of denoising steps. Fewer steps = faster.	Linear speedup	Quality degrades with too few steps. Model-dependent optimal range.

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Quick Recipes

Maximum speed, video model, multi-GPU, lossless (Wan A14B, 8 GPUs)

sglang generate --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
  --num-gpus 8 --enable-cfg-parallel --ulysses-degree 4 \
  --enable-torch-compile --warmup \
  --text-encoder-cpu-offload true \
  --prompt "..." --save-output

Note: --dit-layerwise-offload is enabled by default for Wan/MOVA video models and is often a good default, but still benchmark it on your exact workload if latency matters.

For Wan2.2 specifically:

• the nightly-aligned 4-GPU benchmark may use --enable-cfg-parallel --ulysses-degree=2 to keep CFG and ring behavior covered
• that is a coverage choice, not a guaranteed best-performance choice
• for pure latency tuning, benchmark pure Ulysses too, for example --ulysses-degree=4 --ring-degree=1 on 4 GPUs
• on 8 GPUs, compare pure --ulysses-degree=8 against --enable-cfg-parallel --ulysses-degree=4

Nightly-aligned model, single GPU: LTX-2 two-stage

sglang generate --model-path Lightricks/LTX-2 \
  --pipeline-class-name LTX2TwoStagePipeline \
  --prompt "A beautiful sunset over the ocean" \
  --negative-prompt "shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static." \
  --width 1536 --height 1024 \
  --num-frames 121 --fps 24 \
  --seed 1234 --num-gpus 1 \
  --enable-torch-compile --warmup --save-output

Note: this generate recipe is aligned with the nightly comparison case ltx2_twostage_t2v. LTX2TwoStagePipeline is a native path and auto-resolves the spatial upsampler plus distilled LoRA from the same model snapshot unless you override them.

Native baseline, 2 GPUs: LTX-2.3 one-stage

sglang generate --model-path Lightricks/LTX-2.3 \
  --prompt "A beautiful sunset over the ocean" \
  --negative-prompt "shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static." \
  --width 768 --height 512 \
  --num-frames 121 --fps 24 \
  --num-inference-steps 30 --guidance-scale 3.0 \
  --seed 1234 --num-gpus 2 \
  --enable-torch-compile --warmup --save-output

Note: use this as the native LTX2Pipeline baseline for LTX-2.3. It keeps the validated one-stage resolution and explicit LTX-2.3 sampling defaults, and matches the ltx23-one-stage benchmark preset in sglang-diffusion-benchmark-profile.

Benchmark target, 2 GPUs: LTX-2.3 two-stage

sglang generate --model-path Lightricks/LTX-2.3 \
  --pipeline-class-name LTX2TwoStagePipeline \
  --prompt "A beautiful sunset over the ocean" \
  --negative-prompt "shaky, glitchy, low quality, worst quality, deformed, distorted, disfigured, motion smear, motion artifacts, fused fingers, bad anatomy, weird hand, ugly, transition, static." \
  --width 1536 --height 1024 \
  --num-frames 121 --fps 24 \
  --num-inference-steps 30 --guidance-scale 3.0 \
  --seed 1234 --num-gpus 2 \
  --enable-torch-compile --warmup --save-output

Note: this is the recommended benchmark command for the LTX-2.3 two-stage path. It uses the native LTX2TwoStagePipeline and matches the ltx23-two-stage benchmark preset in sglang-diffusion-benchmark-profile.

Maximum speed, image model, single GPU, lossless

sglang generate --model-path <IMAGE_MODEL> \
  --enable-torch-compile --warmup \
  --dit-layerwise-offload false \
  --prompt "..." --save-output

Note: for image models, per-layer compute is smaller, so layerwise offload may not fully hide H2D transfer. Disable it if VRAM allows.

Low VRAM, decent speed (single GPU)

sglang generate --model-path <MODEL> \
  --enable-torch-compile --warmup \
  --dit-layerwise-offload --dit-offload-prefetch-size 0.1 \
  --text-encoder-cpu-offload true --vae-cpu-offload true \
  --prompt "..." --save-output

Maximum speed, lossy (SageAttention + Cache-DiT)

SGLANG_CACHE_DIT_ENABLED=true sglang generate --model-path <MODEL> \
  --attention-backend sage_attn \
  --cache-dit-config <config.yaml> \
  --enable-torch-compile --warmup \
  --dit-layerwise-offload false \
  --prompt "..." --save-output

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Tips

• Benchmarking: always use --warmup and look for the line ending with (with warmup excluded) for accurate timing.
• Perf dump: use --perf-dump-path result.json to save structured metrics, then compare with python python/sglang/multimodal_gen/benchmarks/compare_perf.py baseline.json result.json.
• Offload tuning: after the first request, the runtime logs peak GPU memory and which components could stay resident. Use this to decide which --*-cpu-offload flags to disable.
• Backend selection: --backend sglang (default, auto-detected) enables all native optimizations (fused kernels, SP, etc.). --backend diffusers falls back to vanilla Diffusers pipelines but supports --cache-dit-config and diffusers attention backends.
• Wan2.2-I2V sizing: explicit --width/--height on Wan2.2-I2V-A14B control the target area while preserving the condition-image aspect ratio.
• Mainline diffusion fast paths: before proposing a new kernel or overlap scheme, check sglang-diffusion-benchmark-profile/existing-fast-paths.md. It covers Z-Image residual-form modulation, fused diffusion QK norm + RoPE, and existing multi-GPU overlap families such as Ulysses / USP and turbo-layer async all-to-all.
• NVFP4 trace interpretation: on FLUX.2 NVFP4 and Nunchaku-style checkpoints, packed QKV is expected. SGLang intentionally uses fused projection modules such as to_qkv / to_added_qkv instead of separate to_q / to_k / to_v, so a split-QKV trace usually means the quantized path did not engage rather than a brand new fusion opportunity.
• Hotspot workflow split: use sglang-diffusion-benchmark-profile to prove and classify a slowdown with perf dumps plus torch.profiler; hand concrete kernel work to sglang-diffusion-ako4all-kernel or another specialized optimization skill instead of expanding the benchmark skill.