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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.

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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

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.

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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 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)

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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 black-forest-labs/FLUX.1-dev
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 black-forest-labs/FLUX.1-dev

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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.
  • --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

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5.1 Speedup Benchmark

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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:
Output
sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000
Benchmark Command:
Output
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
============================================================

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5.1.2 Generate images with high concurrency

Server Command :
Output
sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000
Benchmark Command :
Output
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
============================================================