docs: split diffusion feature guides

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

docs/user_guide/diffusion_features.md

@@ -106,7 +106,10 @@ The following tables show which models support each feature:
 
 > Notes:
 
-> 1. CPU Offload has two methods: Module-wise (default for models with DiT + text encoder) and Layerwise. The tables below show **Layerwise support** only. Split models like Cosmos3 (no separate text encoder) swap their reasoner/generator components for module-wise offload; see the [CPU Offload Guide](diffusion/cpu_offload.md).
+> 1. CPU Offload has three strategies: model-level, layerwise, and distributed
+>    layerwise. The tables below show **layerwise support** only. Split models
+>    like Cosmos3 swap their reasoner/generator components for model-level
+>    offload; see the [CPU Offload Guide](diffusion/cpu_offload.md).
 > 2. The **💾Quantization** column is collapsed for readability. See [Quantization](quantization/overview.md) for per-method and per-model support details.
 
 ### ImageGen
@@ -214,7 +217,7 @@ The following tables show which models support each feature:
     3. CPU Offloading (Layerwise) and CPU Offloading (Module-wise) are not compatible.
     4. The CPU Offloading (Layerwise) row describes local layerwise offload.
        Multi-device Distributed Layerwise Offload has a separate topology and
-       compatibility matrix in the [CPU Offloading guide](diffusion/cpu_offload.md#distributed-layerwise-offloading).
+       compatibility matrix in the [Distributed Layerwise Offloading guide](diffusion/cpu_offload/distributed_layerwise.md).
     5. The compatibility matrix uses FP8 as the representative quantization method.
     6. Step Execution is not compatible with any diffusion cache backend. LoRA is supported, but each scheduled batch must use a single adapter (requests with different `lora_request` or `lora_scale` are kept in separate batches).