--- title: FLUX metatags: description: "Deploy FLUX diffusion models with SGLang - 12B/32B rectified flow transformers for high-quality text-to-image generation." --- import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx'; import { FluxDeployment } from '/src/snippets/diffusion/flux-deployment.jsx'; ## 1. Model Introduction [FLUX](https://blackforestlabs.ai/) is Black Forest Labs' rectified-flow image model family. [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) is the smaller 12B text-to-image checkpoint; [FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) is a 32B model that adds instruction-based editing plus single- and multi-reference composition. FLUX is a strong default when prompt adherence, polished image quality, or reference consistency matters. The tradeoff is deployment weight: FLUX.2 needs substantially more memory than FLUX.1, and the dev checkpoints use the FLUX non-commercial license, so review the model license before production use. | Checkpoint | Best fit | Main limitation | | --- | --- | --- | | `black-forest-labs/FLUX.1-dev` | High-quality text-to-image with the lighter FLUX deployment | No native multi-reference editing path | | `black-forest-labs/FLUX.2-dev` | Text-to-image, editing, and reference-guided composition in one model | 32B model with a larger memory footprint | ## 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](https://docs.sglang.io/docs/sglang-diffusion/installation) 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, Ascend A2, A3 NPUs Series NPUs and Intel Arc B-series graphics(codename: BMG (Battlemage)). ### 3.2 Configuration Tips See [Performance Optimization](/docs/sglang-diffusion/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 ### 3.3 Breakable CUDA graph for FLUX.1-dev For repeated `quality=lossless` requests, FLUX.1-dev supports breakable CUDA graph (BCG) execution with the native backend. BCG captures DiT segments during startup and replays them for the warmed resolutions. It can reduce recurring host launch overhead without enabling `torch.compile`. The following configuration was validated on two NVIDIA H200 GPUs with BF16 weights, PyTorch 2.13, and CUDA 13.0. Set `HF_TOKEN` to a Hugging Face token with access to the checkpoint before downloading it. ```bash CUDA_VISIBLE_DEVICES=0,1 sglang generate \ --model-path black-forest-labs/FLUX.1-dev \ --backend sglang \ --num-gpus 2 --tp-size 2 \ --component-residency dit=resident \ --enable-torch-compile false \ --enable-breakable-cuda-graph \ --warmup-resolutions 1024x1024 \ --quality lossless \ --width 1024 --height 1024 \ --num-inference-steps 50 --guidance-scale 3.5 --seed 42 \ --prompt "A futuristic cyberpunk city at night, neon lights reflecting on wet streets" ``` Confirm `[Diffusion BCG] captured` in the log, then compare warmed request latency with eager execution on the same GPUs. Capture time and graph memory are additional startup costs. Add other served resolutions to `--warmup-resolutions`; a request with an uncaptured signature falls back to eager. FLUX.1-dev uses a fixed 512-token T5 conditioning sequence, so changing `--bcg-text-buckets` does not create additional prompt-length graphs. Its request-gated DiT fusions at `quality=high` and `extra-high` cannot be combined with BCG: the runtime rejects those requests because the captured graph uses the lossless branches. FLUX.2 and quantized transformer overrides require separate validation. ## 4. API Usage For complete API documentation, please refer to the [official API usage guide](/docs/sglang-diffusion/api/openai_api). ### 4.1 Generate an Image ```python 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](https://github.com/vipshop/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](/docs/sglang-diffusion/cache_dit). **Basic Usage** ```bash 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: ```bash 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". #### 4.2.3 Known LoRA examples Use `--lora-path` at startup or the [LoRA management API](/docs/sglang-diffusion/api/openai_api#lora-management) to load an adapter. Known FLUX examples include: - [`dvyio/flux-lora-simple-illustration`](https://huggingface.co/dvyio/flux-lora-simple-illustration) - [`XLabs-AI/flux-furry-lora`](https://huggingface.co/XLabs-AI/flux-furry-lora) - [`XLabs-AI/flux-RealismLora`](https://huggingface.co/XLabs-AI/flux-RealismLora) ## 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**: ```shell Command sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000 ``` **Benchmark Command**: ```shell Command python3 -m sglang.multimodal_gen.benchmarks.bench_serving \ --dataset vbench --task text-to-image --num-prompts 1 --max-concurrency 1 ``` **Result**: ```text Output ================= Serving Benchmark Result ================= Model: black-forest-labs/FLUX.1-dev Dataset: vbench Task: text-to-image -------------------------------------------------- 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 ============================================================ ``` **Server Command**: ```shell Command #One A3 Series card has 2 npu chips sglang serve --tp-size 2 --sp-degree 1 --model-path black-forest-labs/FLUX.1-dev --num-gpus 2 ``` **Benchmark Command**: ```shell Command python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 1 --max-concurrency 1 ``` **Result**: ```text Output ================= Serving Benchmark Result ================= Task: text-to-image Model: black-forest-labs/FLUX.1-dev Dataset: vbench -------------------------------------------------- Benchmark duration (s): 16.30 Request rate: inf Max request concurrency: 1 Successful requests: 1/1 Completed outputs: 1 Outputs per prompt: 1 -------------------------------------------------- Request throughput (req/s): 0.06 Output throughput (outputs/s): 0.06 Latency Mean (s): 16.30 Latency Median (s): 16.30 Latency P90 (s): 16.30 Latency P95 (s): 16.30 Latency P99 (s): 16.30 -------------------------------------------------- Peak Memory Max (MB): 19972.00 Peak Memory Mean (MB): 19972.00 Peak Memory Median (MB): 19972.00 ------------------------------------------------------------ ``` #### 5.1.2 Generate images with high concurrency **Server Command** : ```shell Command sglang serve --model-path black-forest-labs/FLUX.1-dev --port 30000 ``` **Benchmark Command** : ```shell Command python3 -m sglang.multimodal_gen.benchmarks.bench_serving \ --dataset vbench --task text-to-image --num-prompts 20 --max-concurrency 20 ``` **Result** : ```text Output ================= Serving Benchmark Result ================= Model: black-forest-labs/FLUX.1-dev Dataset: vbench Task: text-to-image -------------------------------------------------- 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 ============================================================ ``` **Server Command** : ```shell Command #One A3 Series card has 2 npu chips sglang serve --tp-size 2 --sp-degree 1 --model-path black-forest-labs/FLUX.1-dev --num-gpus 2 ``` **Benchmark Command** : ```shell Command python -m sglang.multimodal_gen.benchmarks.bench_serving --dataset vbench --task text-to-image --num-prompts 20 --max-concurrency 20 ``` **Result** : ```text Output ================= Serving Benchmark Result ================= Task: text-to-image Model: black-forest-labs/FLUX.1-dev Dataset: vbench -------------------------------------------------- Benchmark duration (s): 300.85 Request rate: inf Max request concurrency: 20 Successful requests: 18/20 Completed outputs: 18 Outputs per prompt: 1 -------------------------------------------------- Request throughput (req/s): 0.06 Output throughput (outputs/s): 0.06 Latency Mean (s): 155.16 Latency Median (s): 155.11 Latency P90 (s): 266.30 Latency P95 (s): 280.15 Latency P99 (s): 291.23 -------------------------------------------------- Peak Memory Max (MB): 19972.00 Peak Memory Mean (MB): 19972.00 Peak Memory Median (MB): 19972.00 ------------------------------------------------------------ ``` ## 6. Run in ComfyUI import { ComfyUISupport } from '/src/snippets/diffusion/comfyui-support.jsx';