FLUX - SGLang Documentation

1. Model Introduction

FLUX is a family of rectified flow transformer models developed by Black Forest Labs for high-quality image generation from text descriptions. FLUX.1-dev is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. Key Features:

Cutting-edge Output Quality: Second only to the state-of-the-art FLUX.1 [pro] model
Competitive Prompt Following: Matches the performance of closed-source alternatives
Guidance Distillation: Trained using guidance distillation for improved efficiency
Open Weights: Available for personal, scientific, and commercial purposes under the FLUX [dev] Non-Commercial License

FLUX.2-dev is a 32 billion parameter rectified flow transformer capable of generating, editing, and combining images based on text instructions. Key Features:

State-of-the-art Performance: Leading open model in text-to-image generation, single-reference editing, and multi-reference editing
No Finetuning Required: Character, object, and style reference without additional training in one model
Guidance Distillation: Trained using guidance distillation for improved efficiency
Open Weights: Available for personal, scientific, and commercial purposes under the FLUX [dev] Non-Commercial License

For more details, please refer to the FLUX.1-dev HuggingFace page, FLUX.2-dev HuggingFace page, and the official blog post.

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

FLUX models are optimized for high-quality image generation. The recommended launch configurations vary by hardware and model version. Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and model version. SGLang supports serving FLUX on NVIDIA B200, H200, H100, and AMD MI355X, MI325X, MI300X GPUs.

3.2 Configuration Tips

Currently supported optimizations are 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 Generate an Image

Example

import base64
from openai import OpenAI

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

response = client.images.generate(
    model="black-forest-labs/FLUX.1-dev",
    prompt="A cat holding a sign that says hello world",
    size="1024x1024",
    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 black-forest-labs/FLUX.1-dev

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 black-forest-labs/FLUX.1-dev

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

5.1 Speedup Benchmark

5.1.1 Generate a image

Test Environment:

Hardware: NVIDIA B200 GPU (1x)
Model: black-forest-labs/FLUX.1-dev
sglang diffusion version: 0.5.6.post2

Server Command:

Command

sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000

Benchmark Command:

Command

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --backend sglang-video --dataset vbench --task t2v --num-prompts 1 --max-concurrency 1

Result:

Output

================= Serving Benchmark Result =================
Backend:                                 sglang-image
Model:                                   black-forest-labs/FLUX.1-dev
Dataset:                                 vbench
Task:                                    t2v
--------------------------------------------------
Benchmark duration (s):                  50.97
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.02
Latency Mean (s):                        50.9681
Latency Median (s):                      50.9681
Latency P99 (s):                         50.9681
--------------------------------------------------
Peak Memory Max (MB):                    27905.19
Peak Memory Mean (MB):                   27905.19
Peak Memory Median (MB):                 27905.19
============================================================

5.1.2 Generate images with high concurrency

Server Command :

Command

sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000

Benchmark Command :

Command

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --backend sglang-image --dataset vbench --task t2v --num-prompts 20 --max-concurrency 20

Result :

Output

================= Serving Benchmark Result =================
Backend:                                 sglang-image
Model:                                   black-forest-labs/FLUX.1-dev
Dataset:                                 vbench
Task:                                    t2v
--------------------------------------------------
Benchmark duration (s):                  111.79
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     20/20
--------------------------------------------------
Request throughput (req/s):              0.18
Latency Mean (s):                        67.0646
Latency Median (s):                      66.9691
Latency P99 (s):                         110.8949
--------------------------------------------------
Peak Memory Max (MB):                    27917.19
Peak Memory Mean (MB):                   27916.59
Peak Memory Median (MB):                 27917.19
============================================================

​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

​5. Benchmark

​5.1 Speedup Benchmark

​5.1.1 Generate a 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

5. Benchmark

5.1 Speedup Benchmark

5.1.1 Generate a image

5.1.2 Generate images with high concurrency