[Frontend][Model] Support batch request with refined OmniDiffusionReq… (#797)

Signed-off-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com>

docs/user_guide/diffusion/cache_dit_acceleration.md

@@ -18,6 +18,7 @@ Enable cache-dit acceleration by simply setting `cache_backend="cache_dit"`. Cac
 
 ```python
 from vllm_omni.entrypoints.omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 # Simplest way: just enable cache-dit with default parameters
 omni = Omni(
@@ -27,7 +28,7 @@ omni = Omni(
 
 images = omni.generate(
     "a beautiful landscape",
-    num_inference_steps=50,
+    OmniDiffusionSamplingParams(num_inference_steps=50),
 )
 ```
 

docs/user_guide/diffusion/parallelism_acceleration.md

@@ -67,10 +67,12 @@ omni = Omni(
 )
 
 outputs = omni.generate(
-    prompt="a cat reading a book",
-    num_inference_steps=9,
-    width=512,
-    height=512,
+    "a cat reading a book",
+    OmniDiffusionSamplingParams(
+        num_inference_steps=9,
+        width=512,
+        height=512,
+    ),
 )
 ```
 
@@ -83,6 +85,7 @@ outputs = omni.generate(
 An example of offline inference script using [Ulysses-SP](https://arxiv.org/pdf/2309.14509) is shown below:
 ```python
 from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ulysses_degree = 2
 
@@ -91,7 +94,10 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=2)
 )
 
-outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
+)
 ```
 
 See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.
@@ -133,6 +139,7 @@ Ring-Attention ([arxiv paper](https://arxiv.org/abs/2310.01889)) splits the inpu
 An example of offline inference script using Ring-Attention is shown below:
 ```python
 from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 ring_degree = 2
 
@@ -141,7 +148,10 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ring_degree=2)
 )
 
-outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
+)
 ```
 
 See `examples/offline_inference/text_to_image/text_to_image.py` for a complete working example.
@@ -183,6 +193,7 @@ You can combine both Ulysses-SP and Ring-Attention for larger scale parallelism.
 
 ```python
 from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 from vllm_omni.diffusion.data import DiffusionParallelConfig
 
 # Hybrid: 2 Ulysses × 2 Ring = 4 GPUs total
@@ -191,7 +202,10 @@ omni = Omni(
     parallel_config=DiffusionParallelConfig(ulysses_degree=2, ring_degree=2)
 )
 
-outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50, width=2048, height=2048)
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(num_inference_steps=50, width=2048, height=2048),
+)
 ```
 
 ##### Online Serving
@@ -374,11 +388,15 @@ omni = Omni(
 )
 
 outputs = omni.generate(
-    prompt="turn this cat to a dog",
-    negative_prompt="low quality, blurry",
-    true_cfg_scale=4.0,
-    pil_image=input_image,
-    num_inference_steps=50,
+    {
+        "prompt": "turn this cat to a dog",
+        "negative_prompt": "low quality, blurry",
+    },
+    OmniDiffusionSamplingParams(
+        true_cfg_scale=4.0,
+        pil_image=input_image,
+        num_inference_steps=50,
+    ),
 )
 ```
 

docs/user_guide/diffusion/teacache.md

@@ -8,6 +8,7 @@ Enable TeaCache by setting `cache_backend` to `"tea_cache"`:
 
 ```python
 from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 # Simple configuration - model_type is automatically extracted from pipeline.__class__.__name__
 omni = Omni(
@@ -17,7 +18,12 @@ omni = Omni(
         "rel_l1_thresh": 0.2  # Optional, defaults to 0.2
     }
 )
-outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(
+        num_inference_steps=50,
+    ),
+)
 ```
 
 ### Using Environment Variable
@@ -68,13 +74,19 @@ Controls the balance between speed and quality. Lower values prioritize quality,
 
 ```python
 from vllm_omni import Omni
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
 
 omni = Omni(
     model="Qwen/Qwen-Image",
     cache_backend="tea_cache",
     cache_config={"rel_l1_thresh": 0.2}
 )
-outputs = omni.generate(prompt="A cat sitting on a windowsill", num_inference_steps=50)
+outputs = omni.generate(
+    "A cat sitting on a windowsill",
+    OmniDiffusionSamplingParams(
+        num_inference_steps=50,
+    ),
+)
 ```
 
 ## Performance Tuning