[Diffusion][Attention] Support per-role attention backend via CLI (#2681)

Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

vllm_omni/diffusion/model_loader/diffusers_loader.py

@@ -319,8 +319,11 @@ class DiffusersPipelineLoader:
                     elif load_format == "diffusers":
                         model = DiffusersAdapterPipeline(od_config=od_config, device=target_device)
                     elif load_format == "custom_pipeline":
+                        from vllm_omni.diffusion.config import set_current_diffusion_config
+
                         model_cls = resolve_obj_by_qualname(custom_pipeline_name)
-                        model = model_cls(od_config=od_config)
+                        with set_current_diffusion_config(od_config):
+                            model = model_cls(od_config=od_config)
                     else:
                         raise ValueError(f"Unknown load_format: {load_format}")
                 logger.debug("Loading weights on %s ...", load_device)
@@ -588,8 +591,11 @@ class DiffusersPipelineLoader:
         if load_format == "default":
             model = initialize_model(od_config)
         elif load_format == "custom_pipeline":
+            from vllm_omni.diffusion.config import set_current_diffusion_config
+
             model_cls = resolve_obj_by_qualname(custom_pipeline_name)
-            model = model_cls(od_config=od_config)
+            with set_current_diffusion_config(od_config):
+                model = model_cls(od_config=od_config)
         self.load_weights(model)
 
         # Collect all transformers to shard (some models have transformer_2 for MoE)