Z-Image-Turbo

1. Model Introduction

Z-Image is a powerful and highly efficient image generation model family with 6B parameters, developed by Tongyi-MAI. It adopts a Scalable Single-Stream DiT (S3-DiT) architecture, where text, visual semantic tokens, and image VAE tokens are concatenated at the sequence level to serve as a unified input stream, maximizing parameter efficiency compared to dual-stream approaches.

Z-Image-Turbo is a distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It is powered by two core techniques: Decoupled-DMD (few-step distillation) and DMDR (fusing DMD with Reinforcement Learning).

Key Features:

• Sub-second Inference Latency: Achieves sub-second inference on enterprise-grade H800 GPUs and fits comfortably within 16GB VRAM consumer devices
• Photorealistic Image Generation: Excels in high-quality photorealistic image generation with rich aesthetics
• Bilingual Text Rendering: Supports accurate bilingual text rendering in both English and Chinese
• Robust Instruction Adherence: Strong prompt following and instruction adherence capabilities
• #1 Open-Source Model: Ranked 8th overall and #1 among open-source models on the Artificial Analysis Text-to-Image Leaderboard

For more details, please refer to the Z-Image-Turbo HuggingFace page, the GitHub repository, and the technical report (arXiv).

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.

Hardware Platform

MI300XMI325XMI355XB200H200H100

Generated Command

sglang serve \
  --model-path Tongyi-MAI/Z-Image-Turbo \
  --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

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

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

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:

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".

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

Server Command:

sglang serve --model-path Tongyi-MAI/Z-Image-Turbo \
    --ulysses-degree=1 --ring-degree=1 --port 30000

Benchmark Command:

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

Result:

================= 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
============================================================

5.1.2 Generate images with high concurrency

Benchmark Command:

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

Result:

================= 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
============================================================