Qwen-Image - SGLang Documentation

1. Model Introduction

Qwen-Image is a 20B text-to-image model built for strong prompt following and precise rendering of English and Chinese text. It is especially useful for posters, signs, diagrams, and dense layouts where typography and spatial relationships matter as much as general image quality. This page covers generation rather than editing: use Qwen-Image-Edit when an existing image, subject identity, or untouched region must be preserved. The full checkpoint is memory-heavy, while the validated ModelOpt NVFP4 release provides a supported low-precision deployment option with an expected quality tradeoff.

2. SGLang-diffusion Installation

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements. Please refer to the official SGLang-diffusion installation guide for installation instructions.

3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

3.1 Basic Configuration

Qwen-Image is a text-to-image model. The recommended launch configurations vary by hardware. SGLang supports serving Qwen-Image on NVIDIA B200, B300, H200, H100, AMD MI300X, MI325X, MI355X GPUs and Ascend A2, A3 NPUs. Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform. For the validated ModelOpt NVFP4 checkpoint on Blackwell, load the published Qwen-Image-2512 NVFP4 repo directly:

Command

sglang serve \
  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
  --ulysses-degree=1 \
  --ring-degree=1

For high-resolution B200 generations, the FlashInfer CUTLASS FP4 GEMM backend can be faster than the default TensorRT-LLM FP4 GEMM backend:

Command

SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND=cutlass \
sglang generate \
  --model-path lmsys/qwen-image-2512-modelopt-nvfp4-sglang \
  --width 2048 --height 2048 \
  --prompt "A tiny astronaut reading a book under a glass greenhouse" \
  --save-output

3.2 Fixed-resolution latency on two H200 GPUs

For Qwen/Qwen-Image-2512 at 1024x1024, use breakable CUDA graph (BCG) to reduce launch overhead across graph-safe DiT segments while retaining explicit breakpoints around unsupported operations. This recipe was validated on two NVIDIA H200 GPUs with 50 denoising steps and no classifier-free guidance:

Command

sglang serve \
  --model-path Qwen/Qwen-Image-2512 \
  --model-type diffusion \
  --num-gpus 2 \
  --tp-size 2 \
  --performance-mode speed \
  --dit-layerwise-offload false \
  --enable-torch-compile false \
  --enable-breakable-cuda-graph \
  --warmup-mode server \
  --warmup-resolutions 1024x1024

Declare every production resolution in --warmup-resolutions. A request at an uncaptured resolution runs eagerly, so omitting 1024x1024 removes the gain from this recipe. Graph capture used about 5 GB more peak memory per GPU in the validation run. On CUDA, the TP path dispatches supported collectives through SRT CustomAllReduceV2. At 1024x1024, Qwen-Image reduces 24 MiB row-parallel outputs; the diffusion runtime reserves a 32 MiB V2 workspace so these collectives do not fall back to NCCL. If profiling shows large NCCL all-reduce kernels again, first confirm that V2 is enabled and the requested shape fits the workspace. BCG changed floating-point execution order but not the sampling algorithm. The fixed-seed output measured 0.984 SSIM and 39.7 dB PSNR against eager output; use eager execution when you require bit-exact output. Regional torch.compile was also tested on this profile and did not improve steady-state latency.

3.3 Configuration Tips

See Performance Optimization for acceleration features and their runtime requirements.

--vae-path: Path to a custom VAE model or HuggingFace model ID (e.g., fal/FLUX.2-Tiny-AutoEncoder). If not specified, the VAE will be loaded from the main model path.
--num-gpus: Number of GPUs to use
--tp-size: Tensor parallelism size (only for the encoder; should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
--sp-degree: Sequence parallelism size (typically should match the number of GPUs)
--ulysses-degree: The degree of DeepSpeed-Ulysses-style SP in USP
--ring-degree: The degree of ring attention-style SP in USP

AMD ROCm Notes: Requires SGLang >= v0.5.8.

4. API Usage

For complete API documentation, please refer to the official API usage guide.

4.1 Generate an Image

Example

import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:30000/v1")

response = client.images.generate(
    model="Qwen/Qwen-Image",
    prompt="A logo With Bold Large text: SGL Diffusion",
    n=1,
    response_format="b64_json",
)

# Save the generated image
image_bytes = base64.b64decode(response.data[0].b64_json)
with open("output.png", "wb") as f:
    f.write(image_bytes)

4.2 Advanced Usage

4.2.1 Cache-DiT Acceleration

SGLang integrates Cache-DiT, a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to 7.4x inference speedup with minimal quality loss. You can set SGLANG_CACHE_DIT_ENABLED=True to enable it. For more details, please refer to the SGLang Cache-DiT documentation. Basic Usage

Command

SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path Qwen/Qwen-Image

Advanced Usage

DBCache Parameters: DBCache controls block-level caching behavior:

Parameter	Env Variable	Default	Description
Fn	`SGLANG_CACHE_DIT_FN`	1	Number of first blocks to always compute
Bn	`SGLANG_CACHE_DIT_BN`	0	Number of last blocks to always compute
W	`SGLANG_CACHE_DIT_WARMUP`	4	Warmup steps before caching starts
R	`SGLANG_CACHE_DIT_RDT`	0.24	Residual difference threshold
MC	`SGLANG_CACHE_DIT_MC`	3	Maximum continuous cached steps

TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:

Parameter	Env Variable	Default	Description
Enable	`SGLANG_CACHE_DIT_TAYLORSEER`	false	Enable TaylorSeer calibrator
Order	`SGLANG_CACHE_DIT_TS_ORDER`	1	Taylor expansion order (1 or 2)

Combined Configuration Example:

Command

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=2 \
SGLANG_CACHE_DIT_BN=1 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.4 \
SGLANG_CACHE_DIT_MC=4 \
SGLANG_CACHE_DIT_TAYLORSEER=true \
SGLANG_CACHE_DIT_TS_ORDER=2 \
sglang serve --model-path Qwen/Qwen-Image

4.2.2 CPU Offload

--dit-cpu-offload: Use CPU offload for DiT inference. Enable if run out of memory.
--text-encoder-cpu-offload: Use CPU offload for text encoder inference.
--vae-cpu-offload: Use CPU offload for VAE.
--pin-cpu-memory: Pin memory for CPU offload. Only added as a temp workaround if it throws “CUDA error: invalid argument”.

4.2.3 Known LoRA examples

Use --lora-path at startup or the LoRA management API to load an adapter. Known Qwen-Image examples include:

5. Benchmark

Test Environment:

Hardware: AMD Instinct MI300X GPU (1x)
Model: Qwen/Qwen-Image
Docker Image: lmsysorg/sglang:v0.5.8-rocm700-mi30x
sglang diffusion version: 0.5.8

5.1 Speedup Benchmark

5.1.1 Generate an image

AMD MI300X
Ascend A3

Server Command:

Command

sglang serve --model-path Qwen/Qwen-Image \
    --ulysses-degree=1 --ring-degree=1 --port 30000

Benchmark Command:

Command

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --dataset vbench --task text-to-image --num-prompts 1 --max-concurrency 1

Result:

Output

================= Serving Benchmark Result =================
Task:                                    text-to-image
Model:                                   Qwen/Qwen-Image
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  29.04
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.03
Latency Mean (s):                        29.0378
Latency Median (s):                      29.0378
Latency P99 (s):                         29.0378
--------------------------------------------------
Peak Memory Max (MB):                    48018.83
Peak Memory Mean (MB):                   48018.83
Peak Memory Median (MB):                 48018.83
============================================================

Server Command:

Command

#One A3 card has 2 npu chips
sglang serve --tp-size 2 --sp-degree 1 --model-path Qwen/Qwen-Image --num-gpus 2

Benchmark Command:

Command

python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 1 --max-concurrency 1

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-image
Model:                                        Qwen/Qwen-Image
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       36.26
Request rate:                                 inf
Max request concurrency:                      1
Successful requests:                          1/1
Completed outputs:                            1
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.03
Output throughput (outputs/s):                0.03
Latency Mean (s):                             36.26
Latency Median (s):                           36.26
Latency P90 (s):                              36.26
Latency P95 (s):                              36.26
Latency P99 (s):                              36.26
--------------------------------------------------
Peak Memory Max (MB):                         36984.00
Peak Memory Mean (MB):                        36984.00
Peak Memory Median (MB):                      36984.00
------------------------------------------------------------

5.1.2 Generate images with high concurrency

AMD MI300X
Ascend A3

Benchmark Command:

Command

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --dataset vbench --task text-to-image --num-prompts 20 --max-concurrency 20 --port 30000

Result:

Output

================= Serving Benchmark Result =================
Task:                                    text-to-image
Model:                                   Qwen/Qwen-Image
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  300.79
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     14/20
--------------------------------------------------
Request throughput (req/s):              0.05
Latency Mean (s):                        154.5368
Latency Median (s):                      154.8363
Latency P99 (s):                         285.4603
--------------------------------------------------
Peak Memory Max (MB):                    48030.31
Peak Memory Mean (MB):                   48030.30
Peak Memory Median (MB):                 48030.29
============================================================

Benchmark Command:

Command

python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 20 --max-concurrency 20

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-image
Model:                                        Qwen/Qwen-Image
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       300.81
Request rate:                                 inf
Max request concurrency:                      20
Successful requests:                          8/20
Completed outputs:                            8
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.03
Output throughput (outputs/s):                0.03
Latency Mean (s):                             166.61
Latency Median (s):                           167.02
Latency P90 (s):                              270.80
Latency P95 (s):                              283.48
Latency P99 (s):                              293.64
--------------------------------------------------
Peak Memory Max (MB):                         36984.00
Peak Memory Mean (MB):                        36984.00
Peak Memory Median (MB):                      36984.00
------------------------------------------------------------

​1. Model Introduction

​2. SGLang-diffusion Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 Fixed-resolution latency on two H200 GPUs

​3.3 Configuration Tips

​4. API Usage

​4.1 Generate an Image

​4.2 Advanced Usage

​4.2.1 Cache-DiT Acceleration

​4.2.2 CPU Offload

​4.2.3 Known LoRA examples

​5. Benchmark

​5.1 Speedup Benchmark

​5.1.1 Generate an image

​5.1.2 Generate images with high concurrency

1. Model Introduction

2. SGLang-diffusion Installation

3. Model Deployment

3.1 Basic Configuration

3.2 Fixed-resolution latency on two H200 GPUs

3.3 Configuration Tips

4. API Usage

4.1 Generate an Image

4.2 Advanced Usage

4.2.1 Cache-DiT Acceleration

4.2.2 CPU Offload

4.2.3 Known LoRA examples

5. Benchmark

5.1 Speedup Benchmark

5.1.1 Generate an image

5.1.2 Generate images with high concurrency