Qwen-Image-Edit-2511
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.
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-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.
sglang serve \ --model-path Qwen/Qwen-Image-Edit-2511 \ --ulysses-degree=1 \ --ring-degree=1
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
4. API Usage
For complete API documentation, please refer to the official API usage guide.
4.1 Edit an Image
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)
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
SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path Qwen/Qwen-Image-Edit-2511
Advanced Usage
-
DBCache Parameters: DBCache controls block-level caching behavior:
Parameter Env Variable Default Description Fn SGLANG_CACHE_DIT_FN1 Number of first blocks to always compute Bn SGLANG_CACHE_DIT_BN0 Number of last blocks to always compute W SGLANG_CACHE_DIT_WARMUP4 Warmup steps before caching starts R SGLANG_CACHE_DIT_RDT0.24 Residual difference threshold MC SGLANG_CACHE_DIT_MC3 Maximum continuous cached steps -
TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:
Parameter Env Variable Default Description Enable SGLANG_CACHE_DIT_TAYLORSEERfalse Enable TaylorSeer calibrator Order SGLANG_CACHE_DIT_TS_ORDER1 Taylor expansion order (1 or 2) Combined Configuration Example:
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
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".
5. Benchmark
Test Environment:
- Hardware: NVIDIA B200 GPU (1x)
- Model: Qwen/Qwen-Image-Edit-2511
- sglang diffusion version: 0.5.6.post2
5.1 Speedup Benchmark
5.1.1 Edit a image
Server Command:
sglang serve --model-path Qwen/Qwen-Image-Edit-2511 --port 30000
Benchmark Command:
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
--backend sglang-image --dataset vbench --task ti2i --num-prompts 1 --max-concurrency 1
Result:
================= 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
============================================================
5.1.2 Edit a image with high concurrency
Benchmark Command:
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
--backend sglang-image --dataset vbench --task ti2i --num-prompts 20 --max-concurrency 20
Result:
================= 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
============================================================