[skip ci][Doc] Refine the Diffusion Features User Guide (#1928)

Signed-off-by: Didan Deng <33117903+wtomin@users.noreply.github.com>

docs/user_guide/diffusion/parallelism/overview.md

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+# Parallelism Acceleration Guide
+
+This guide covers the parallelism methods in vLLM-Omni for speeding up diffusion model inference and reducing per-device memory requirements.
+
+## Supported Methods
+
+| Method | Description |
+|--------|-------------|
+| **[Tensor Parallelism](tensor_parallel.md)** | Shards DiT weights across GPUs to reduce per-GPU memory |
+| **[Sequence Parallelism](sequence_parallel.md)** | Splits sequence dimension across GPUs (Ulysses-SP, Ring-Attention, or hybrid) for high-resolution images and videos |
+| **[CFG-Parallel](cfg_parallel.md)** | Runs CFG positive/negative branches on separate GPUs for ~1.8x speedup on guided generation |
+| **[VAE Patch Parallelism](vae_patch_parallel.md)** | Distributes VAE decode spatially across GPUs to reduce peak VAE memory |
+| **[HSDP](hsdp.md)** | Shards full model weights via PyTorch FSDP2 to enable large-model inference on memory-constrained GPUs |
+| **[Expert Parallelism](expert_parallel.md)** | Shards MoE expert blocks across GPUs for MoE models (e.g. HunyuanImage3.0) |
+
+See [Supported Models](../../diffusion_features.md#supported-models) for per-model compatibility.