docs: split diffusion feature guides

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

docs/user_guide/diffusion/attention_backends/dense_backends.md

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+# Dense Attention Backends
+
+Use these backends when you want dense attention without backend-specific
+sparsity. Start with `TORCH_SDPA` for correctness comparisons, then benchmark
+the fastest compatible kernel for your model, shape, and hardware.
+
+For selection precedence and per-role configuration, see the
+[attention backend overview](../attention_backends.md).
+
+## `TORCH_SDPA`
+
+`TORCH_SDPA` calls PyTorch `scaled_dot_product_attention` and lets PyTorch's
+dispatcher choose the implementation. It is always available and is the most
+conservative reference when validating another backend.
+
+```bash
+vllm-omni serve <model> --diffusion-attention-backend TORCH_SDPA
+```
+
+## `FLASH_ATTN`
+
+`FLASH_ATTN` uses the installed FlashAttention implementation. On Blackwell it
+prefers the CuTe-based FlashAttention 4 path and falls back to
+FlashAttention 3 or 2 when that path is unavailable. On Hopper, Ada, and
+Ampere it is the preferred automatic route when a compatible package is
+installed.
+
+```bash
+vllm-omni serve <model> --diffusion-attention-backend FLASH_ATTN
+```
+
+### FlashAttention 4 on Blackwell
+
+Install the optional CUDA 13 extra:
+
+```bash
+pip install 'vllm-omni[fa4]'
+```
+
+Version `4.0.0b18` is required; earlier beta wheels had known JIT failures on
+Blackwell. If the CuTe path is unavailable, the backend falls back to the
+compatible FlashAttention 3 or 2 path.
+
+## `CUDNN_ATTN`
+
+`CUDNN_ATTN` pins PyTorch SDPA to `CUDNN_ATTENTION`. It is particularly useful
+for mask-heavy DiTs and is automatically preferred on Blackwell when cuDNN
+9.5 or newer is available and the higher-priority TRTLLM route is not
+compatible.
+
+```bash
+vllm-omni serve <model> --diffusion-attention-backend CUDNN_ATTN
+```
+
+### LTX-2.0 limitation
+
+LTX-2 audio attention has a symbolic head dimension during `torch.compile`
+tracing. The cuDNN SDPA selector rejects that symbolic dimension and Dynamo
+aborts compilation. This is tracked in
+[issue #3121](https://github.com/vllm-project/vllm-omni/issues/3121).
+
+Use `FLASHINFER_ATTN` or `TORCH_SDPA` as a workaround:
+
+```bash
+DIFFUSION_ATTENTION_BACKEND=FLASHINFER_ATTN \
+  python examples/offline_inference/text_to_video/text_to_video.py \
+  --model Lightricks/LTX-2 ...
+```
+
+## `FLASHINFER_ATTN`
+
+`FLASHINFER_ATTN` uses FlashInfer's batch-prefill wrapper. It is an explicit
+option on CUDA platforms and an automatic Blackwell fallback when FlashInfer
+is installed but cuDNN is too old for `CUDNN_ATTN`.
+
+```bash
+vllm-omni serve <model> --diffusion-attention-backend FLASHINFER_ATTN
+```
+
+### FlashInfer quantized attention
+
+The backend accepts an `AttentionSpec.quant` block. For QK16/V8, keep Q and K
+in FP16 or BF16 and use FP8 E4M3 for V:
+
+```python
+from vllm_omni.diffusion.data import (
+    AttentionConfig,
+    AttentionSpec,
+    AttnQuantSpec,
+    OmniDiffusionConfig,
+)
+
+config = OmniDiffusionConfig(
+    diffusion_attention_config=AttentionConfig(
+        default=AttentionSpec(
+            backend="FLASHINFER_ATTN",
+            quant=AttnQuantSpec(
+                dtype_qk="bfloat16",
+                dtype_vo="fp8_e4m3",
+            ),
+        ),
+    ),
+    ...,
+)
+```
+
+`dtype_qk` controls Q and K; `dtype_vo` controls V. Mixed-dtype
+configurations require FlashInfer 0.6.16rc1 or newer. The shared quantization
+schema is also consumed by TRTLLM, but each backend validates its own allowed
+fields and values; see [TRTLLM SAGE quantization](trtllm.md#sage-quantization).