[Chore]: refactor out unused/redundant params in diffusion pipelines (#1235)

Signed-off-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

docs/features/custom_pipeline.md

@@ -54,20 +54,24 @@ Create a custom pipeline class that extends an existing pipeline. In this exampl
 
 ```python
 # custom_pipeline.py
-from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
+from vllm_omni.diffusion.data import OmniDiffusionConfig
 from vllm_omni.diffusion.models.qwen_image.pipeline_qwen_image_edit import QwenImageEditPipeline
+from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
 import torch
 
 class CustomPipeline(QwenImageEditPipeline):
     def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
         super().__init__(od_config=od_config, prefix=prefix)
 
-    def forward(self, req, prompt=None, negative_prompt=None, **kwargs):
+    def forward(self, req: DiffusionRequestBatch):
+        # Optionally customize sampling parameters
+        actual_num_steps = req.sampling_params.num_inference_steps or 50
+        req.sampling_params.num_inference_steps = actual_num_steps
+
         # Call parent's forward to get normal output
-        output = super().forward(req=req, prompt=prompt, negative_prompt=negative_prompt, **kwargs)
+        output = super().forward(req=req)
 
         # Add custom trajectory data
-        actual_num_steps = req.sampling_params.num_inference_steps or kwargs.get('num_inference_steps', 50)
         output.trajectory_timesteps = torch.linspace(1000, 0, actual_num_steps, dtype=torch.float32)
         output.trajectory_latents = torch.randn(actual_num_steps, 1, 16, 64, 64, dtype=torch.float32)