From f6d53d6d16269764190b9b58206ec51a04598d76 Mon Sep 17 00:00:00 2001 From: Mick Date: Tue, 9 Jun 2026 11:44:31 +0800 Subject: [PATCH] docs: update SANA-WM cookbook serve examples (#27626) --- docs_new/cookbook/diffusion/SANA-WM/SANA-WM.mdx | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs_new/cookbook/diffusion/SANA-WM/SANA-WM.mdx b/docs_new/cookbook/diffusion/SANA-WM/SANA-WM.mdx index e59f838b5..a2cb20647 100644 --- a/docs_new/cookbook/diffusion/SANA-WM/SANA-WM.mdx +++ b/docs_new/cookbook/diffusion/SANA-WM/SANA-WM.mdx @@ -44,9 +44,9 @@ For more details, see the [SANA-WM paper (arXiv)](https://arxiv.org/abs/2605.151 ## 2. Installation -SGLang-diffusion offers multiple installation methods depending on your hardware platform. Please refer to the [official SGLang-diffusion installation guide](https://github.com/sgl-project/sglang/blob/main/python/sglang/multimodal_gen/docs/install.md). +SGLang-diffusion offers multiple installation methods depending on your hardware platform. Please refer to the [SGLang Diffusion installation guide](../../../docs/sglang-diffusion/installation). -SANA-WM adds the `SanaWMTransformer3DModel` + GDN kernels, the `SanaWMTwoStagePipeline` (dense bidirectional + chunk-causal streaming), and the `SanaWMRealtimePipeline` with the `/v1/realtime_video` WebSocket router. The diffusion server CLI is invoked as `python -m sglang.multimodal_gen.runtime.entrypoints.cli.main`. +SANA-WM adds the `SanaWMTransformer3DModel` + GDN kernels, the `SanaWMTwoStagePipeline` (dense bidirectional + chunk-causal streaming), and the `SanaWMRealtimePipeline` with the `/v1/realtime_video` WebSocket router. Use `sglang serve` to launch the diffusion server. ## 3. Model Setup @@ -87,7 +87,7 @@ The **bidirectional** checkpoint generates the whole clip in **one shot** (full Launch with the two-stage pipeline and **no** `--streaming` flag (dense is the default — `streaming` defaults to `False`): ```bash Command -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_bidirectional \ --pipeline-class-name SanaWMTwoStagePipeline \ --host 127.0.0.1 --port 30000 @@ -122,7 +122,7 @@ The **streaming** checkpoint generates a **full camera-controlled clip in one re Launch with the two-stage pipeline + the streaming flags: ```bash Command -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_streaming \ --pipeline-class-name SanaWMTwoStagePipeline \ --streaming --refiner-chunked \ @@ -167,7 +167,7 @@ The response is a `VideoResponse`; fetch the rendered MP4 via the returned refer Launch with the realtime pipeline **pinned** — the checkpoint defaults to `SanaWMTwoStagePipeline`, so realtime must be selected explicitly (see §3). The `/v1/realtime_video` router is always mounted and becomes functional once the realtime config is active, because `SanaWMRealtimeConfig` has a registered realtime adapter (`SanaWMRealtimeAdapter`). ```bash Command -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_streaming \ --pipeline-class-name SanaWMRealtimePipeline \ --host 127.0.0.1 --port 30000 @@ -177,20 +177,20 @@ Common launch variants: ```bash Command # recommended multi-GPU realtime profile -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_streaming \ --pipeline-class-name SanaWMRealtimePipeline \ --num-gpus 8 --sp-degree 8 \ --host 127.0.0.1 --port 30000 # single GPU -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_streaming \ --pipeline-class-name SanaWMRealtimePipeline \ --num-gpus 1 --host 127.0.0.1 --port 30000 # offload DiT + text encoder to CPU (tight VRAM) -python -m sglang.multimodal_gen.runtime.entrypoints.cli.main serve \ +sglang serve \ --model-path Efficient-Large-Model/SANA-WM_streaming \ --pipeline-class-name SanaWMRealtimePipeline \ --host 127.0.0.1 --port 30000 \