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1. Model Introduction

Qwen-Image-Edit-2511 is an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. Built upon the 20B Qwen-Image model, Qwen-Image-Edit-2511 successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing. Key Enhancements in Qwen-Image-Edit-2511:
  • Mitigate Image Drift: Reduces unwanted changes in non-edited regions of the image.
  • Improved Character Consistency: The model can perform imaginative edits based on an input portrait while preserving the identity and visual characteristics of the subject.
  • Multi-Person Consistency: Enhanced consistency in multi-person group photos, enabling high-fidelity fusion of two separate person images into a coherent group shot.
  • Integrated LoRA Capabilities: Selected popular community-created LoRAs are integrated directly into the base model, unlocking their effects without extra tuning (e.g., lighting enhancement, viewpoint generation).
  • Enhanced Industrial Design Generation: Special attention to practical engineering scenarios, including batch industrial product design and material replacement for industrial components.
  • Strengthened Geometric Reasoning: Stronger geometric reasoning capability for generating auxiliary construction lines for design or annotation purposes.
For more details, please refer to the official Qwen-Image-Edit-2511 HuggingFace page, the Blog, and the Tech Report.

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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.

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3. Model Deployment

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

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3.1 Basic Configuration

Qwen-Image-Edit-2511 is a 20B parameter model optimized for image editing tasks. The recommended launch configurations vary by hardware. Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform.

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3.2 Configuration Tips

Current supported optimization all listed here.
  • --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

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4. API Usage

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

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4.1 Edit an Image

Example
import base64
from openai import OpenAI

client = OpenAI(api_key="EMPTY", base_url="http://localhost:3000/v1")

response = client.images.edit(
    model="Qwen/Qwen-Image-Edit-2511",
    image=open("input.png", "rb"),
    prompt="Change the color of the taxi to black.",
    n=1,
    response_format="b64_json",
)

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

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4.2 Advanced Usage

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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-Edit-2511
Advanced Usage
  • DBCache Parameters: DBCache controls block-level caching behavior:
ParameterEnv VariableDefaultDescription
FnSGLANG_CACHE_DIT_FN1Number of first blocks to always compute
BnSGLANG_CACHE_DIT_BN0Number of last blocks to always compute
WSGLANG_CACHE_DIT_WARMUP4Warmup steps before caching starts
RSGLANG_CACHE_DIT_RDT0.24Residual difference threshold
MCSGLANG_CACHE_DIT_MC3Maximum continuous cached steps
  • TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:
ParameterEnv VariableDefaultDescription
EnableSGLANG_CACHE_DIT_TAYLORSEERfalseEnable TaylorSeer calibrator
OrderSGLANG_CACHE_DIT_TS_ORDER1Taylor 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-Edit-2511

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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.
  • --image-encoder-cpu-offload: Use CPU offload for image 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”.

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5. Benchmark

Test Environment:
  • Hardware: NVIDIA B200 GPU (1x)
  • Model: Qwen/Qwen-Image-Edit-2511
  • sglang diffusion version: 0.5.6.post2

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5.1 Speedup Benchmark

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5.1.1 Edit a image

Server Command:
Output
sglang serve --model-path Qwen/Qwen-Image-Edit-2511 --port 30000
Benchmark Command:
Output
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --backend sglang-image --dataset vbench --task ti2i --num-prompts 1 --max-concurrency 1
Result:
Output
================= Serving Benchmark Result =================
Backend:                                 sglang-image
Model:                                   Qwen/Qwen-Image-Edit-2511
Dataset:                                 vbench
Task:                                    ti2i
--------------------------------------------------
Benchmark duration (s):                  35.31
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.03
Latency Mean (s):                        35.3053
Latency Median (s):                      35.3053
Latency P99 (s):                         35.3053
--------------------------------------------------
Peak Memory Max (MB):                    47959.35
Peak Memory Mean (MB):                   47959.35
Peak Memory Median (MB):                 47959.35
============================================================

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5.1.2 Edit a image with high concurrency

Benchmark Command:
Output
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --backend sglang-image --dataset vbench --task ti2i --num-prompts 20 --max-concurrency 20
Result:
Output
================= Serving Benchmark Result =================
Backend:                                 sglang-image
Model:                                   Qwen/Qwen-Image-Edit-2511
Dataset:                                 vbench
Task:                                    ti2i
--------------------------------------------------
Benchmark duration (s):                  286.11
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     20/20
--------------------------------------------------
Request throughput (req/s):              0.07
Latency Mean (s):                        150.0428
Latency Median (s):                      150.0600
Latency P99 (s):                         283.3843
--------------------------------------------------
Peak Memory Max (MB):                    47971.82
Peak Memory Mean (MB):                   47971.49
Peak Memory Median (MB):                 47971.29
============================================================