diff --git a/docs/.nav.yml b/docs/.nav.yml
index 455a0525056..b340794f802 100644
--- a/docs/.nav.yml
+++ b/docs/.nav.yml
@@ -64,6 +64,7 @@ nav:
         - FP8: user_guide/diffusion/quantization/fp8.md
         - Int8: user_guide/diffusion/quantization/int8.md
         - GGUF: user_guide/diffusion/quantization/gguf.md
+      - Attention Backends: user_guide/diffusion/attention_backends.md
       - Frame Interpolation: user_guide/diffusion/frame_interpolation.md
       - Parallelism:
         - Overview: user_guide/diffusion/parallelism/overview.md
diff --git a/docs/user_guide/diffusion/attention_backends.md b/docs/user_guide/diffusion/attention_backends.md
new file mode 100644
index 00000000000..692bcc0f9d0
--- /dev/null
+++ b/docs/user_guide/diffusion/attention_backends.md
@@ -0,0 +1,120 @@
+# Diffusion Attention Backends
+
+This document describes the diffusion attention backends available in vLLM-Omni, how to select them, and how to use SageAttention.
+
+## Overview
+
+Diffusion attention backend selection is controlled by the `DIFFUSION_ATTENTION_BACKEND` environment variable and resolved in `vllm_omni.diffusion.attention.selector`.
+
+This backend is used by diffusion attention layers such as the DiT attention in video and image generation models.
+
+On CUDA, the practical choices today are:
+
+- `FLASH_ATTN`: FlashAttention backend. This is the default on supported CUDA systems when FlashAttention is installed.
+- `TORCH_SDPA`: PyTorch `scaled_dot_product_attention`.
+- `SAGE_ATTN`: SageAttention backend, if `sageattention` is installed.
+
+If `DIFFUSION_ATTENTION_BACKEND` is unset, vLLM-Omni asks the current platform to choose the default backend. On CUDA, that normally means `FLASH_ATTN` when available, otherwise `TORCH_SDPA`.
+
+## Backend Options
+
+| Value | Notes |
+|---|---|
+| `FLASH_ATTN` | Default on CUDA when FlashAttention is available. Good default for most diffusion workloads. |
+| `TORCH_SDPA` | Most conservative fallback. Useful for debugging or compatibility. |
+| `SAGE_ATTN` | Requires `sageattention`. Can improve performance on some workloads, but output quality must be validated model-by-model. |
+
+## Selection Priority
+
+Diffusion attention backend selection follows this order:
+
+1. `DIFFUSION_ATTENTION_BACKEND`
+2. Platform default
+
+Example:
+
+```bash
+export DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN
+```
+
+## SageAttention Installation
+
+vLLM-Omni expects SageAttention to be installed into the same Python environment as vLLM-Omni.
+
+Build from source:
+
+```bash
+git clone https://github.com/thu-ml/SageAttention.git
+cd SageAttention
+
+export EXT_PARALLEL=4 NVCC_APPEND_FLAGS="--threads 8" MAX_JOBS=32
+pip install . --no-build-isolation
+```
+
+Quick check:
+
+```bash
+python -c "import sageattention; print(sageattention.__file__)"
+```
+
+## Usage
+
+### Enable SageAttention
+
+Example: HunyuanVideo-1.5 text-to-video
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN python examples/offline_inference/text_to_video/text_to_video.py \
+    --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 480 --width 832 --num-frames 33 \
+    --num-inference-steps 30 --seed 42 --guidance-scale 6.0 \
+    --tensor-parallel-size 2 \
+    --output ../tmp/hv15_modelopt_sage.mp4
+```
+
+Example: Wan2.2 TI2V 5B
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=SAGE_ATTN python examples/offline_inference/text_to_video/text_to_video.py \
+    --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 704 --width 1280 --num-frames 49 \
+    --num-inference-steps 30 --seed 42 --guidance-scale 5.0 \
+    --tensor-parallel-size 2 \
+    --output outputs/wan22_sage.mp4
+```
+
+### Compare Against FlashAttention
+
+Unset the backend override, or explicitly use `FLASH_ATTN`:
+
+```bash
+python examples/offline_inference/text_to_video/text_to_video.py \
+    --model Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+    --prompt "A dog running across a field of golden wheat." \
+    --height 704 --width 1280 --num-frames 49 \
+    --num-inference-steps 30 --seed 42 --guidance-scale 5.0 \
+    --tensor-parallel-size 2 \
+    --output outputs/wan22_fa3.mp4
+```
+
+## Validation Guidance
+
+Do not assume that a faster attention backend is numerically interchangeable with `FLASH_ATTN`.
+
+Always compare:
+
+- End-to-end runtime
+- DiT / diffusion stage runtime
+- Output quality against a known-good baseline
+
+At minimum, keep the same:
+
+- model
+- prompt
+- seed
+- resolution
+- frame count
+- inference steps
+- parallel config
diff --git a/docs/user_guide/examples/offline_inference/text_to_video.md b/docs/user_guide/examples/offline_inference/text_to_video.md
index 4288c089c60..a09dbfc979f 100644
--- a/docs/user_guide/examples/offline_inference/text_to_video.md
+++ b/docs/user_guide/examples/offline_inference/text_to_video.md
@@ -5,6 +5,8 @@ Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/offline_inf
 
 A unified script for text-to-video generation. Supports multiple models with model-aware defaults.
 
+For backend selection and SageAttention usage, see the [Diffusion Attention Backends](../../diffusion/attention_backends.md) guide.
+
 ## Supported Models
 
 | Model | Default Resolution | Default Frames | Default Steps | Guidance | VRAM (BF16) |