Z-Image-Turbo - SGLang Documentation

1. Model Introduction

Z-Image-Turbo is a distilled 6B single-stream DiT for fast text-to-image generation. It reaches its intended operating point in 8 function evaluations and is particularly strong at photorealistic scenes, prompt adherence, and English/Chinese text rendering. Choose it when latency and a relatively small deployment footprint matter more than the editability or maximum capacity of larger image models. It is a generation-only checkpoint; use Qwen-Image-Edit or FLUX.2 when the request includes source images or identity-preserving edits.

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

Z-Image-Turbo is optimized for high-quality image generation with only 8 inference steps. 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.

3.2 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="Tongyi-MAI/Z-Image-Turbo",
    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 Tongyi-MAI/Z-Image-Turbo

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 Tongyi-MAI/Z-Image-Turbo

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 Z-Image-Turbo examples include:

5. Benchmark

Test Environment:

Hardware: AMD Instinct MI300X GPU (1x)
Model: Tongyi-MAI/Z-Image-Turbo
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 Tongyi-MAI/Z-Image-Turbo \
    --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:                                   Tongyi-MAI/Z-Image-Turbo
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  1.84
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.54
Latency Mean (s):                        1.8435
Latency Median (s):                      1.8435
Latency P99 (s):                         1.8435
--------------------------------------------------
Peak Memory Max (MB):                    30689.20
Peak Memory Mean (MB):                   30689.20
Peak Memory Median (MB):                 30689.20
============================================================

Server Command:

Command

#One A3 card has 2 npu chips
sglang serve --model-path Tongyi-MAI/Z-Image-Turbo --tp-size 2 --sp-degree 1 --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:                                        Tongyi-MAI/Z-Image-Turbo
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       2.43
Request rate:                                 inf
Max request concurrency:                      1
Successful requests:                          1/1
Completed outputs:                            1
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.41
Output throughput (outputs/s):                0.41
Latency Mean (s):                             2.43
Latency Median (s):                           2.43
Latency P90 (s):                              2.43
Latency P95 (s):                              2.43
Latency P99 (s):                              2.43
--------------------------------------------------
Peak Memory Max (MB):                         11052.00
Peak Memory Mean (MB):                        11052.00
Peak Memory Median (MB):                      11052.00
------------------------------------------------------------

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

Result:

Output

================= Serving Benchmark Result =================
Task:                                    text-to-image
Model:                                   Tongyi-MAI/Z-Image-Turbo
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  35.32
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     20/20
--------------------------------------------------
Request throughput (req/s):              0.57
Latency Mean (s):                        18.5672
Latency Median (s):                      18.5573
Latency P99 (s):                         34.9880
--------------------------------------------------
Peak Memory Max (MB):                    30689.26
Peak Memory Mean (MB):                   30689.21
Peak Memory Median (MB):                 30689.21
============================================================

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:                                        /models/Tongyi-MAI/Z-Image-Turbo/Z-Image-Turbo
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       49.08
Request rate:                                 inf
Max request concurrency:                      20
Successful requests:                          20/20
Completed outputs:                            20
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.41
Output throughput (outputs/s):                0.41
Latency Mean (s):                             25.78
Latency Median (s):                           25.77
Latency P90 (s):                              44.42
Latency P95 (s):                              46.75
Latency P99 (s):                              48.61
--------------------------------------------------
Peak Memory Max (MB):                         11054.00
Peak Memory Mean (MB):                        11054.00
Peak Memory Median (MB):                      11054.00
------------------------------------------------------------

​1. Model Introduction

​2. SGLang-diffusion Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 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 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