docs: split diffusion feature guides

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

docs/user_guide/diffusion/cpu_offload/model_level.md

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+# Model-Level Offloading
+
+Model-level, or sequential, offloading keeps only the pipeline component group
+currently executing on the accelerator. It is the simplest offload strategy
+and is selected with `--enable-cpu-offload`.
+
+## How it works
+
+Pre-forward hooks enforce mutual exclusion between DiT and encoder modules:
+
+- before an encoder runs, the DiT moves to CPU;
+- before a DiT runs, encoders and other DiTs move to CPU; and
+- VAE modules remain on the accelerator.
+
+Pinned host memory reduces transfer overhead. Transfers occur at phase
+boundaries, so cold-start and encoder-to-denoiser transitions become slower.
+
+## Usage
+
+```python
+from vllm_omni import Omni
+
+omni = Omni(
+    model="Wan-AI/Wan2.2-T2V-A14B-Diffusers",
+    enable_cpu_offload=True,
+)
+```
+
+```bash
+vllm serve Wan-AI/Wan2.2-T2V-A14B-Diffusers \
+  --omni --enable-cpu-offload
+```
+
+## Model integration
+
+Pipelines should implement `SupportsComponentDiscovery`:
+
+```python
+from typing import ClassVar
+
+from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery
+
+
+class MyPipeline(nn.Module, SupportsComponentDiscovery):
+    _dit_modules: ClassVar[list[str]] = ["transformer"]
+    _encoder_modules: ClassVar[list[str]] = ["text_encoder", "vision_model"]
+    _vae_modules: ClassVar[list[str]] = ["vae"]
+    _resident_modules: ClassVar[list[str]] = []
+```
+
+All entries may be dotted paths. DiT and encoder lists are both required for
+mutual exclusion. VAE modules are pinned but not swapped; resident modules are
+small modules that must stay on the accelerator for layerwise paths.
+
+## Split-model components
+
+Some models, such as Cosmos3, split one transformer into mutually exclusive
+components that run in different phases. The pipeline exposes
+`enable_omni_model_cpu_offload`, and the backend delegates to the model-local
+contexts:
+
+```python
+class Cosmos3VFMTransformer(nn.Module):
+    def forward(self, ...):
+        with self._offload_context("reasoner"):
+            ...
+        with self._offload_context("generator"):
+            ...
+```
+
+This preserves the same invariant—exactly one component is device resident—
+while reusing sequential `.to()` movers.
+
+## Limitations
+
+- Single device only.
+- Higher cold-start latency.
+- Transfers between encoder and denoising phases add latency.
+
+See the [model-level design](../../../design/feature/model_level_offload.md)
+for lifecycle and extension invariants.