Sglang Diffusion Performance

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

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.

Section 2: Lossy Optimizations

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

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, 2 GPUs: LTX-2 two-stage

sglang generate --model-path Lightricks/LTX-2 \
  --pipeline-class-name LTX2TwoStagePipeline \
  --prompt "A cat and a dog baking a cake together in a kitchen." \
  --width 768 --height 512 \
  --num-frames 121 \
  --seed 42 --num-gpus 2 --enable-cfg-parallel \
  --enable-torch-compile --warmup --save-output

Note: this generate recipe is aligned with the nightly comparison case ltx2_twostage_t2v. The nightly config omits explicit steps and guidance, so this command omits them too and uses runtime defaults. LTX2TwoStagePipeline is a native path and auto-resolves the spatial upsampler plus distilled LoRA from the same model snapshot unless you override them.

Nightly-aligned model, 2 GPUs: LTX-2.3 TI2V two-stage

sglang generate --model-path Lightricks/LTX-2.3 \
  --pipeline-class-name LTX2TwoStagePipeline \
  --prompt "The cat starts walking slowly towards the camera." \
  --image-path "${ASSET_DIR}/cat.png" \
  --width 768 --height 512 \
  --num-frames 121 \
  --seed 42 --num-gpus 2 --cfg-parallel-size 2 \
  --enable-torch-compile --warmup --save-output

Note: this matches the nightly comparison case ltx2.3_twostage_ti2v_2gpus. The nightly config omits explicit steps and guidance, so this command omits them too and uses runtime defaults. Download ${ASSET_DIR}/cat.png with the benchmark/profile skill before running it.

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.

Skill-only stress target, 2 GPUs: LTX-2.3 two-stage high resolution

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 a high-resolution stress target for the native LTX-2.3 two-stage path. It matches the skill-only ltx23-two-stage benchmark preset, not a nightly comparison case.

Maximum speed, image model, single GPU, lossless

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

Note: for image models, per-layer compute is smaller, so layerwise offload may not fully hide H2D transfer. Disable DiT layerwise and CPU offload if VRAM allows; otherwise a large image DiT can stay resident on CPU and make the denoise loop H2D-bound.

Image-edit baselines: JoyAI and FireRed

sglang generate --backend=sglang \
  --model-path jdopensource/JoyAI-Image-Edit-Diffusers \
  --prompt "Make the cat wear a red hat" \
  --image-path "${ASSET_DIR}/cat.png" \
  --width 1024 --height 1024 \
  --num-inference-steps 40 --guidance-scale 4.0 \
  --num-gpus 2 --enable-cfg-parallel --ulysses-degree 1 \
  --dit-layerwise-offload false --dit-cpu-offload false \
  --enable-torch-compile --warmup --save-output

sglang generate --backend=sglang \
  --model-path FireRedTeam/FireRed-Image-Edit-1.1 \
  --prompt "Make the cat wear a red hat" \
  --image-path "${ASSET_DIR}/cat.png" \
  --width 1024 --height 1024 \
  --num-inference-steps 40 --guidance-scale 4.0 \
  --num-gpus 2 --enable-cfg-parallel --ulysses-degree 1 \
  --dit-layerwise-offload false --dit-cpu-offload false \
  --enable-torch-compile --warmup --save-output

Use FireRedTeam/FireRed-Image-Edit-1.0 in the same command when comparing FireRed 1.0. These are native image-edit paths; keep the reference image, prompt, seed, and output size fixed when comparing denoise numbers. On H100, 2-GPU CFG parallel was faster than the otherwise matching 2-GPU Ulysses command: FireRed 1.0 improved from 13419.15 ms to 10955.90 ms, and FireRed 1.1 improved from 13414.72 ms to 10934.21 ms.

Hunyuan3D shape baseline

OUTPUT_DIR=$(python3 "$ENV_PY" print-output-dir --kind benchmarks --mkdir)
CONFIG_DIR="${OUTPUT_DIR}/generated_configs"
mkdir -p "${CONFIG_DIR}"
printf '{"paint_enable": false}\n' > "${CONFIG_DIR}/hunyuan3d-shape.json"

sglang generate --backend=sglang \
  --model-path tencent/Hunyuan3D-2 \
  --prompt "generate 3d mesh" \
  --image-path "${ASSET_DIR}/cat.png" \
  --config "${CONFIG_DIR}/hunyuan3d-shape.json" \
  --num-inference-steps 50 --guidance-scale 5.0 \
  --dit-layerwise-offload false --dit-cpu-offload false \
  --enable-torch-compile --warmup --save-output

For Hunyuan3D, treat Hunyuan3DShapeDenoisingStage as the primary latency metric. Mesh export and paint stages are useful end-to-end checks but should not drive DiT optimization decisions.

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 native path (SageAttention + Cache-DiT)

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

Add native Cache-DiT knobs such as SGLANG_CACHE_DIT_SCM_PRESET=medium, SGLANG_CACHE_DIT_RDT=0.24, or SGLANG_CACHE_DIT_TAYLORSEER=true only after you have a BF16 baseline output to compare against.

For a diffusers-backend Cache-DiT YAML/JSON config baseline, make the fallback explicit:

sglang generate --backend diffusers --model-path <MODEL> \
  --cache-dit-config <config.yaml> \
  --dit-layerwise-offload false \
  --prompt "..." --save-output

Model-Specific Starting Points

Use these as first commands to benchmark, not as universal winners.

Open PR Watchlist

As of 2026-05-02, these performance PRs were open. Treat them as direction and prior art until merged:

• Fusion/kernel: #24025 LTX2 QK norm, #24059 Helios norm modulation, #24117 Z-Image packed QKV, #19488 Wan elementwise cross-block fusion, #19249 Z-Image gate/norm fusion, #20429 Qwen-Image layernorm/modulation, #20530 MOVA RMSNorm+RoPE.
• VAE/decode: #22531 LTX2 parallel VAE, #20927 batched tiled VAE decode.
• Runtime/parallel/cache: #22805 FLUX.2 packed QKV for A2A, #21742 hybrid attention schedule, #24053 USP replicated-prefix fix, #21613 TeaCache refactor, #24227 WanVideo TeaCache fix, #18764 dynamic batching, #24200 disaggregated diffusion.

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 native optimizations (fused kernels, SP, native Cache-DiT env knobs, etc.). --backend diffusers falls back to Diffusers pipelines and is the path that accepts --cache-dit-config plus diffusers attention backend names.
• 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 GroupNorm+SiLU, Z-Image residual-form modulation, fused diffusion QK norm + RoPE, packed QKV/NVFP4 expectations, 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.