[diffusion] add tp support for qwen-image and refactor some tests (#830)

Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

docs/user_guide/diffusion/parallelism_acceleration.md

@@ -23,13 +23,23 @@ The following table shows which models are currently supported by parallelism me
 | **LongCat-Image** | `meituan-longcat/LongCat-Image` | ✅ | ✅ | ❌ | ❌ |
 | **LongCat-Image-Edit** | `meituan-longcat/LongCat-Image-Edit` | ✅ | ✅ | ❌ | ❌ |
 | **Ovis-Image** | `OvisAI/Ovis-Image` | ❌ | ❌ | ❌ | ❌ |
-| **Qwen-Image** | `Qwen/Qwen-Image` | ✅ | ✅ | ✅ | ❌ |
+| **Qwen-Image** | `Qwen/Qwen-Image` | ✅ | ✅ | ✅ | ✅ |
 | **Qwen-Image-Edit** | `Qwen/Qwen-Image-Edit` | ✅ | ✅ | ✅ | ❌ |
 | **Qwen-Image-Edit-2509** | `Qwen/Qwen-Image-Edit-2509` | ✅ | ✅ | ✅ | ❌ |
 | **Qwen-Image-Layered** | `Qwen/Qwen-Image-Layered` | ✅ | ✅ | ✅ | ❌ |
 | **Z-Image** | `Tongyi-MAI/Z-Image-Turbo` | ❌ | ❌ | ❌ | ✅ (TP=2 only) |
 | **Stable-Diffusion3.5** | `stabilityai/stable-diffusion-3.5` | ❌ | ❌ | ❌ | ❌ |
 
+
+!!! note "TP Limitations for Diffusion Models"
+    We currently implement Tensor Parallelism (TP) only for the DiT (Diffusion Transformer) blocks. This is because the `text_encoder` component in vLLM-Omni uses the original Transformers implementation, which does not yet support TP.
+
+    - Good news: The text_encoder typically has minimal impact on overall inference performance.
+    - Bad news: When TP is enabled, every TP process retains a full copy of the text_encoder weights, leading to significant GPU memory waste.
+
+    We are actively refactoring this design to address this. For details and progress, please refer to [Issue #771](https://github.com/vllm-project/vllm-omni/issues/771).
+
+
 !!! note "Why Z-Image is TP=2 only"
     Z-Image Turbo is currently limited to `tensor_parallel_size` of **1 or 2** due to model shape divisibility constraints.
     For example, the model has `n_heads=30` and a final projection out dimension of `64`, so valid TP sizes must divide both 30 and 64; the only common divisors are **1 and 2**.