--- title: "SGLang Diffusion Serving" type: concept tags: [models, api, install, hardware, user, foundational, well-established] created: 2026-08-24 updated: 2026-08-24 sources: ["raw/github_doc-docs-docs-sglang-diffusion-index-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-installation-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-api-cli-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-api-openai-api-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-api-post-processing-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-models-with-ar-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-models-with-pe-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-realtime-models-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-spectrum-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-compatibility-matrix-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-deployment-cookbook-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-support-new-models-mdx.md", "raw/github_doc-docs-docs-sglang-diffusion-environment-variables-mdx.md"] confidence: medium sglang_version: "v0.5.18" --- ## Definition SGLang Diffusion (the `sglang.multimodal_gen` module, branded "SGLang-Diffusion") is a high-performance inference framework for image and video generation, shipped alongside the LLM-serving core described in [[concepts/sglang-overview]]. It provides native SGLang pipelines, diffusers-backend support for models without a native implementation, an OpenAI-compatible HTTP server, and an optimized kernel stack built on precompiled `sgl-kernel` operators plus JIT kernels (raw/github_doc-docs-docs-sglang-diffusion-index-mdx.md). It supports broad model families — Wan, Hunyuan, Qwen-Image, FLUX, Z-Image, GLM-Image, and more — across NVIDIA, AMD, Intel XPU, Ascend, Apple Silicon, and Moore Threads platforms. ## How It Works ### Installation The standard install already bundles the optimized kernel stack (raw/github_doc-docs-docs-sglang-diffusion-installation-mdx.md): ```bash pip install --upgrade pip pip install uv uv pip install "sglang[diffusion]" --prerelease=allow ``` From source: `pip install -e "python[diffusion]"` (or the `uv` equivalent) after cloning the repo. Docker images are published at `lmsysorg/sglang` (tag `:dev` for the standard path, ROCm-specific tags such as `:v0.5.5.post2-rocm700-mi30x` for AMD Instinct GPUs). Platform-specific paths exist for ROCm (AMD), MUSA (Moore Threads — requires swapping in `python/pyproject_other.toml` and installing the `all_musa` extra), Intel XPU, Ascend NPU, and Apple MPS (`brew install ffmpeg uv`, then `uv pip install -e "python[all_mps]"`; diffusion always runs on PyTorch MPS, and the `all_mps` extra's SRT MLX backend dependencies are unrelated — `SGLANG_USE_MLX` has no effect on diffusion). ### CLI: `generate` and `serve` Two entry points cover one-off jobs and persistent serving (raw/github_doc-docs-docs-sglang-diffusion-api-cli-mdx.md): ```bash sglang generate --model-path Qwen/Qwen-Image --prompt "A beautiful sunset over the mountains" --save-output sglang serve --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers --num-gpus 4 --ulysses-degree 2 --ring-degree 2 --port 30010 ``` `sglang generate` runs one generation job and exits; HTTP-server-only arguments are ignored. `sglang serve` starts the HTTP server and keeps the model resident. Both accept `--config config.yaml` (or JSON) for structured configuration, with CLI flags overriding file values. Non-diffusers checkpoints can resolve through a self-hosted "overlay repo" registry (`SGLANG_DIFFUSION_MODEL_OVERLAY_REGISTRY`, a dev/debug override) that materializes a local componentized copy under `~/.cache/sgl_diffusion/materialized_models/` on first load. Component overrides use `---path` / `--component-paths.` (replace a component's config+weights) or `---weights-path` / `--component-weights-paths.` (replace only weights, keep the base config) — the component key must match `model_index.json` or a native pipeline's registered module name. `--component-residency COMPONENT=MODE` assigns `resident`, `component-offload`, or `layerwise-offload` per component (group selectors `dit`, `text_encoder`, `image_encoder`, `vae`, and `all` are also available; an exact key overrides a matching group, and a group overrides `all`). Legacy per-component flags (`--dit-cpu-offload`, `--layerwise-offload-components`, etc.) remain supported and compose with the new selectors. Use `--backend diffusers` to force vanilla diffusers pipelines when no native implementation exists or a model needs a custom pipeline class (`--trust-remote-code` for custom classes). Health endpoints separate liveness from readiness: `GET /liveness` returns `200` as soon as the HTTP server accepts requests (even during warmup); `GET /health` returns `503` during server-based warmup and `200` once ready; `GET /health_generate` is a compatibility alias that does not itself issue a generation request. Cloud storage for generated outputs (S3-compatible, including MinIO) is configured via `SGLANG_CLOUD_STORAGE_TYPE=s3` plus bucket/endpoint/credential env vars — see Environment Variables below. ### OpenAI-compatible API The HTTP server implements OpenAI-compatible image and video endpoints under `/v1` (raw/github_doc-docs-docs-sglang-diffusion-api-openai-api-mdx.md): - **Images**: `POST /v1/images/generations` (text-to-image; `quality` selects a model-owned sampling level — `lossless` or `high` — see [[concepts/diffusion-optimization]] — but this only takes effect when the target model advertises such a level; omitting `quality`, or sending OpenAI's `auto`, keeps the runtime default instead), `POST /v1/images/edits` (multipart, image + prompt), `GET /v1/images/{image_id}/content` (when `response_format=url` and no cloud storage is configured, a relative URL is returned instead of an absolute one). - **Videos**: `POST /v1/videos` (text-to-video, or image-to-video via multipart `input_reference` or a `reference_url` JSON field), `GET /v1/videos` (list/poll status), `GET /v1/videos/{video_id}/content`. - **Discovery**: `GET /v1/models` and `GET /v1/models/{name}` return the public model name plus diffusion runtime info (num_gpus, task_type, precision, pipeline class); `GET /server_info` also reports `served_model_name`. The resolved public name follows `--served-model-name`, then `--model-id`, then `--model-path` — `--model-id` selects a registered model configuration for checkpoints whose local path can't be identified, it is not a free-form deployment alias. - **LoRA management**: `POST /v1/set_lora` (load/activate one or more adapters by nickname, path, target transformer, strength, and `merge_mode`), `POST /v1/merge_lora_weights`, `POST /v1/unmerge_lora_weights` (must precede switching to a different LoRA), `GET /v1/list_loras`. Regular weights statically merge by default; FSDP-sharded weights use dynamic LoRA to avoid full-gather memory peaks. - **Output quality**: `output-quality` (`maximum`/`high`/`medium`/`low`/`default`) actually defaults to the string `"default"`, which auto-resolves to a compression value of `50` for video and `75` for image; `output-compression` (0–100, takes precedence when set) directly overrides the compression level. PNG ignores both. Note: the cited source itself is inconsistent about this field's own spelling — it's called `output_quality` (underscore) in the request-`quality` discussion elsewhere on the same page but `output-quality`/`output-compression` (hyphenated) in the parameter reference — verify the actual spelling against the API schema/code before relying on it. ### Post-processing Optional steps run after generation and can be combined — frame interpolation runs first (raising frame count), then upscaling runs on every frame (raising resolution) (raw/github_doc-docs-docs-sglang-diffusion-api-post-processing-mdx.md). Frame interpolation (`--enable-frame-interpolation`) uses **only** RIFE 4.22.lite (`elfgum/RIFE-4.22.lite`, auto-downloaded); output frame count follows `(N-1) × 2^exp + 1` where `exp` is `--frame-interpolation-exp` (default `1`). Upscaling (`--enable-upscaling`) uses Real-ESRGAN with architecture auto-detected from checkpoint keys (RRDBNet for quality, SRVGGNetCompact — the default `RealESRGAN_x4.pth` — for speed); `--upscaling-scale` (default `4`) beyond the native 4× applies a bicubic resize after the network output. ### Models with autoregressive or prompt-enhancement stages GLM-Image, Qwen Image Layered, and LongCat-Image ship a bundled autoregressive (AR) stage (raw/github_doc-docs-docs-sglang-diffusion-models-with-ar-mdx.md). Qwen Image Layered and LongCat-Image run the native Qwen2.5-VL component in-process; GLM-Image can instead delegate AR inference to a separately launched SGLang server via `--srt-encoder-url` (with `--srt-encoder-timeout` / `--srt-encoder-connection-timeout` for long-running or flaky links) — the diffusion server sends one HTTP request per AR step, so co-locating both servers on a fast local network is recommended, and startup fails fast if the AR host is offline. ERNIE-Image similarly supports built-in prompt enhancement (native Ministral3 implementation) or an external `--pe-server-url` SGLang PE server; `--layerwise-offload-components pe` streams the in-process PE decoder's layers for memory-constrained deployments (does not apply when `--pe-server-url` is set). Ascend NPU deployments running both servers on the same NPU group need distinct `HCCL_IF_BASE_PORT` / `HCCL_HOST_SOCKET_PORT_RANGE` / `HCCL_NPU_SOCKET_PORT_RANGE` ranges per process. ### Realtime and causal video models Two execution modes generate video incrementally and reuse state across chunks, unlike offline pipelines that denoise one bounded sequence and release state at completion (raw/github_doc-docs-docs-sglang-diffusion-realtime-models-mdx.md): **realtime sessions** (state persists until disconnect, served over the `/v1/realtime_video/generate` WebSocket — e.g. LingBot World, SANA-WM realtime) and **request-based causal generation** (state reused across chunks within one request, then released — e.g. LongLive 2.0, batch-streaming SANA-WM). A causal DiT is not automatically a realtime session model; the pipeline must also register a realtime adapter and implement the WebSocket lifecycle. Requests can override `realtime_causal_sink_size` and `realtime_causal_kv_cache_num_frames`. For LingBot World, `--kv-cache-quant {off,int4,int2}` compresses completed causal KV-cache chunks (lossy, disabled by default) — see [[concepts/diffusion-optimization]] for the underlying Quant-VideoGen mechanism. ### Spectrum acceleration Spectrum forecasts DiT features to skip selected denoising steps (raw/github_doc-docs-docs-sglang-diffusion-spectrum-mdx.md). It is scoped narrowly: available only on native FLUX.1, Wan, HunyuanVideo, and SD3 implementation paths (not `--backend diffusers`, not FLUX.2 yet), reachable only through `sglang generate` and Python sampling parameters (not `sglang serve` or the OpenAI server yet), and mutually exclusive with `--enable-teacache`. ```bash sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \ --prompt "A paper boat floating through a misty mountain lake" \ --enable-spectrum --save-output ``` Advanced controls (`--spectrum-window-size` default `2.0`, `--spectrum-flex-window` `0.75`, `--spectrum-warmup-steps` `5`, `--spectrum-m` `4`, `--spectrum-lam` `0.1`, `--spectrum-tau-num-steps` `50`, `--history-size` `100`, `--taylor-order` `1`, `--w` `1.0`) trade speed against fidelity; providing any override implicitly enables Spectrum, but explicit `--enable-spectrum` is preferred for new commands. `--debug` adds shadow-prediction validation work and is not representative of normal latency. ### Supported models and the compatibility matrix Pass a Hugging Face model ID (or local directory) to `--model-path` for `sglang generate`/`sglang serve`, or the equivalent Python loading helper (raw/github_doc-docs-docs-sglang-diffusion-compatibility-matrix-mdx.md — verified against the `main`-branch docs fetched 2026-08-24, not a release-tag-pinned snapshot). Broad families include, on the image side, FLUX (1/2, including klein variants and NVFP4), Z-Image, Qwen-Image (incl. Edit/Layered), LongCat-Image, SD3/3.5, SANA, FireRed-Image, JoyAI-Image, and GLM-Image/Hunyuan3D-2/ERNIE-Image/ideogram-4 as long-tail entries; on the video side, FastWan, SANA-Video, LingBot Video MoE, Wan2.1/2.2 (and TurboWan variants), LongLive 2.0, HunyuanVideo/FastHunyuan, MOVA, MiniMax-H3, Helios, LTX-2/2.5, and Cosmos3; and for realtime/world models, LingBotWorld and SANA-WM. See [[concepts/supported-models]] for SGLang's non-diffusion model coverage. The detailed video optimization matrix cross-references model against TeaCache/Sliding-Tile/Sage/VSA/SLA/SageSLA/SVG2/Laser/BSA/Rain-Fusion support (✅ full, ❌ none, ⭕ not applicable) — see [[concepts/diffusion-optimization]] for what each abbreviation does. A missing checkpoint alias in the matrix does not imply the family is unsupported: the runtime registry also accepts detector-based aliases and local directories matching the same family. ### Deployment and performance-mode presets `--performance-mode` (`manual`/`auto`/`speed`/`memory`, alias `--mode`; default `auto`) applies safe residency/parallelism presets without overriding explicit flags (raw/github_doc-docs-docs-sglang-diffusion-deployment-cookbook-mdx.md). `auto` checks the least-available GPU memory across selected devices: for image workloads with ≥45 GiB per GPU it keeps the DiT resident and layerwise-offloads large auxiliary encoders, below that it keeps the DiT offloaded; it may also enable FSDP+CFG parallelism on validated multi-GPU deployments, and CFG parallelism when the model defaults to CFG and no explicit parallelism policy is set. `speed` favors GPU-resident execution and disables CPU offload by default (may OOM); `manual` keeps every performance flag under explicit control. The quick decision rule: resident + no FSDP for the fastest single-GPU run that fits; component-then-layerwise offload for lower single-GPU memory; FSDP + CFG parallelism + resident sharded components for faster multi-GPU Qwen/Wan CFG generation; SP/Ulysses/Ring for sequence-length scaling; explicit TP for compatibility rather than as a default latency lever. `--served-model-name` decouples the public API identity from the checkpoint mount path (useful across replicas or hosts) — see the OpenAI API section above for resolution order. Startup and readiness probes should point at `/health` with a large failure budget (model loading and compilation can legitimately take minutes); liveness probes should use `/liveness` instead. Benchmark takeaways from the docs: Z-Image and Qwen-Image were faster single-GPU/no-FSDP than FSDP/SP in tested settings; Wan benefited from FSDP replacing DiT offload on validated multi-GPU workloads; component offload mainly helped memory, not latency. Always re-benchmark on the target resolution, frame count, step count, and GPU. See [[concepts/diffusion-optimization]] for the parallelism, caching, and kernel mechanics these modes are built on. ### Supporting new models Adding a model is a triage flow, not a fixed template (raw/github_doc-docs-docs-sglang-diffusion-support-new-models-mdx.md). Read the request path in dependency order: `registry.py` → `configs/pipeline_configs/{model}.py` → `runtime/pipelines/{model}.py` → `runtime/pipelines_core/stages/` → `runtime/models/` (only when the architecture can't be reused). Decide the smallest applicable change: a new checkpoint of an existing family needs only a registry entry and maybe a `SamplingParams`/`PipelineConfig` variant; a new native architecture needs a native pipeline and missing components; a long-tail model can start on the diffusers backend for compatibility first. Prefer native stages directly, then subclassing the narrowest native stage, then a custom single-purpose stage, and only as a last resort an aggregated `BeforeDenoisingStage` (hides multiple responsibilities and bypasses shared offload/profiling/disaggregation/batching hooks). Out-of-tree models can register without touching SGLang's source: call `ModelRegistry.register_model` and `register_pipeline` in an installed package's `__init__.py`, then set `SGLANG_EXTERNAL_MODEL_PACKAGE=` before launching (each process imports it once). Complete native support (not just single-GPU parity) also requires encoder/DiT TP+SP, `ParallelTiledVAE`-based parallel decode, and `LayerwiseOffloadableModuleMixin` layer declarations — see [[concepts/diffusion-optimization]] for what these integrate with. ### Environment variables Runtime configuration is largely environment-driven (raw/github_doc-docs-docs-sglang-diffusion-environment-variables-mdx.md): `SGLANG_DIFFUSION_TARGET_DEVICE` (default `cuda`; also `rocm`/`xpu`/`npu`/`musa`/`mps`/`cpu`), `SGLANG_DIFFUSION_ATTENTION_BACKEND` and `SGLANG_DIFFUSION_ATTENTION_CONFIG` (two separate env-level attention overrides — the former overrides the backend selection, the latter points at a JSON/YAML backend-config file — see [[concepts/diffusion-optimization]]), `SGLANG_DIFFUSION_STAGE_LOGGING`, `SGLANG_DIFFUSION_TORCH_PROFILER_DIR`, `SGLANG_DIFFUSION_CACHE_ROOT` (default `~/.cache/sgl_diffusion`), `SGLANG_DIFFUSION_CONFIG_ROOT`, `SGLANG_DIFFUSION_WORKER_MULTIPROC_METHOD` (`fork`/`spawn`), `SGLANG_DIFFUSION_IPC_A2A` (CUDA-IPC all-to-all for eligible 2-GPU Ulysses groups, default on; `0` forces NCCL) with `_TIMEOUT_MS` (default `10000`) and `_MAX_BUFFERS` (default `16`), and `SGLANG_USE_RUNAI_MODEL_STREAMER`. Platform-specific variables cover Apple MPS (`SGLANG_USE_MLX`, SRT-only, no diffusion effect), ROCm (`SGLANG_USE_ROCM_VAE`, `SGLANG_USE_ROCM_CUDNN_BENCHMARK`), and quantization (`SGLANG_DIFFUSION_FLASHINFER_FP4_GEMM_BACKEND`, `SGLANG_DIFFUSION_ENABLE_W8A8_FP8_GEMM`). Cloud storage uses `SGLANG_CLOUD_STORAGE_TYPE=s3` plus `SGLANG_S3_BUCKET_NAME`/`_ENDPOINT_URL`/`_REGION_NAME`/`_ACCESS_KEY_ID`/`_SECRET_ACCESS_KEY`. CUDA crash debugging exposes `SGLANG_KERNEL_API_LOGLEVEL` (0/1/3/5/10), `_LOGDEST`, `_DUMP_DIR`, `_DUMP_INCLUDE`/`_EXCLUDE`. The `SGLANG_CACHE_DIT_*` family (enable flag, `Fn`/`Bn`/warmup/RDT/max-continuous, TaylorSeer, SCM preset/policy/bins, plus `_SECONDARY_*` variants for dual-transformer models) configures Cache-DiT server-wide defaults — see [[concepts/diffusion-optimization]] for the caching mechanics they control. ## Key Parameters - `--model-path` / `--served-model-name` / `--model-id` / `--model-variant` — checkpoint location, public API name, registry hint, and semantic weight-partition selector, respectively. - `--component-paths.` / `---path`, `--component-weights-paths.` / `---weights-path` — replace a component's config+weights or weights-only. - `--component-residency COMPONENT=MODE` (`resident`/`component-offload`/`layerwise-offload`), plus legacy `--dit-cpu-offload`, `--layerwise-offload-components`, `--dit-offload-prefetch-size`, `--dit-layerwise-resident-layers`, `--dit-layerwise-residency-policy`. - `--performance-mode` / `--mode` (`manual`/`auto`/`speed`/`memory`). - `--num-gpus`, `--tp-size`, `--sp-degree`, `--ulysses-degree`, `--ring-degree`, `--dp-size` (see [[concepts/diffusion-optimization]] for the parallelism math). - `--quality {lossless,high}` — request-level exactness vs. validated-accelerated tradeoff (fused kernels, described in [[concepts/diffusion-optimization]]). - `--prompt`, `--negative-prompt`, `--image-path`, `--num-inference-steps`, `--seed`, `--num-outputs-per-prompt`, `--height`/`--width`/`--num-frames`/`--fps`, `--output-path`/`--output-file-name`/`--save-output`. - `--enable-frame-interpolation` / `--frame-interpolation-exp` / `--frame-interpolation-scale` / `--frame-interpolation-model-path`; `--enable-upscaling` / `--upscaling-scale` / `--upscaling-model-path`. - `--srt-encoder-url` / `--srt-encoder-timeout` / `--srt-encoder-connection-timeout` (AR delegation); `--pe-server-url` (prompt enhancement delegation). - `--enable-spectrum` and the `--spectrum-*` tuning flags. - `--log-requests`, `--log-requests-level {0-3}`, `--log-requests-format {text,json}`, `--log-requests-target`. - `SGLANG_DIFFUSION_MODEL_OVERLAY_REGISTRY`, `SGLANG_EXTERNAL_MODEL_PACKAGE`, `SGLANG_CLOUD_STORAGE_TYPE` + `SGLANG_S3_*`. ## When To Use Use SGLang Diffusion when you need to self-host image/video generation behind an OpenAI-compatible Images API and a documented subset of the OpenAI Videos API (not a blanket drop-in guarantee for video clients), with the same operational shape as SGLang's LLM serving (see [[concepts/sglang-overview]]). Reach for `sglang generate` for one-off/batch jobs and scripted benchmarking, and `sglang serve` for a persistent multi-request server. Use the AR/PE delegation paths (`--srt-encoder-url`, `--pe-server-url`) when GLM-Image's or ERNIE-Image's auxiliary stage needs independent scaling or resources. Use realtime sessions for interactive world models needing live control (camera actions, prompt updates) and request-based causal generation for long videos that stream chunk-by-chunk without needing a persistent WebSocket. Use Spectrum only after validating quality/latency on the exact model, shape, hardware, and sampling settings you plan to deploy — it is explicitly approximate. ## Risks & Pitfalls - The `quality` field in a **video response body** is unrelated to the `quality` sampling parameter — it is fixed Sora-compatible metadata always reported as `"standard"`, not a reflection of what actually ran. - `/health` is not a liveness probe: a long server-based warmup can legitimately hold it at `503` for minutes; use `/liveness` for that purpose instead. - `--model-id` is not a free-form deployment alias — it selects a registry configuration; use `--served-model-name` to control the name clients see. - The AR delegation path (GLM-Image `--srt-encoder-url`) issues one HTTP request per AR step; cross-region encoder/diffusion placement measurably degrades latency, and the diffusion server refuses to start if the AR host is unreachable at startup. - Ascend NPU deployments running two servers (AR + diffusion) on one NPU group must assign non-overlapping `HCCL_*` port ranges or the servers will conflict. - Spectrum is approximate, scoped to only FLUX.1/Wan/HunyuanVideo/SD3 native paths, unavailable via `sglang serve`/the OpenAI server, and mutually exclusive with TeaCache — do not assume it composes with an arbitrary deployment. - `SGLANG_DIFFUSION_MODEL_OVERLAY_REGISTRY` is documented as a dev/debug override, not a production configuration mechanism. - `SGLANG_EXTERNAL_MODEL_PACKAGE` must be set before process startup; each process imports the package exactly once, and `overwrite=True` is required to intentionally replace a built-in pipeline. - A missing model ID in the compatibility matrix does not mean unsupported — check for detector-based aliases or a local directory match before concluding a family is unavailable. - HunyuanVideo/FastHunyuan default to tiled VAE decode for a reason: overriding `--vae-config.parallel-decode-mode` to `spatial`/`spatial_shard` at their documented shapes can request hundreds of GiB per rank for the causal mask — only use those modes at smaller validated shapes. ## Related Concepts - [[concepts/diffusion-optimization]] — the performance/acceleration stack (caching, attention backends, kernels, parallelism, disaggregation, batching, quantization, profiling) that the deployment and performance-mode levers described here build on. - [[concepts/sglang-overview]] — the LLM-serving core SGLang Diffusion ships alongside. - [[concepts/supported-models]] — SGLang's non-diffusion (LLM/multimodal/embedding/reward) model coverage. - [[concepts/quantization]] — SGLang's LLM-side quantization support, contrasted with diffusion checkpoint quantization in [[concepts/diffusion-optimization]]. - [[concepts/parallelism-and-disaggregation]] — SGLang's LLM-side TP/PP/EP/DP and PD disaggregation, contrasted with the diffusion-specific CFG/TP/SP parallelism and encoder/denoiser/decoder disaggregation in [[concepts/diffusion-optimization]]. - [[concepts/server-apis]] — the LLM-serving OpenAI/native/Anthropic/Ollama API surface, alongside which the diffusion `/v1/images` and `/v1/videos` endpoints are served. ## Sources - raw/github_doc-docs-docs-sglang-diffusion-index-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-installation-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-api-cli-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-api-openai-api-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-api-post-processing-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-models-with-ar-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-models-with-pe-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-realtime-models-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-spectrum-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-compatibility-matrix-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-deployment-cookbook-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-support-new-models-mdx.md - raw/github_doc-docs-docs-sglang-diffusion-environment-variables-mdx.md