diff --git a/python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py b/python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py
index 9144c81a6..6fa8b8f11 100644
--- a/python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py
+++ b/python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py
@@ -7,7 +7,6 @@ Base class for composed pipelines.
 This module defines the base class for pipelines that are composed of multiple stages.
 """
 
-import argparse
 import os
 from abc import ABC, abstractmethod
 from typing import Any, cast
@@ -15,7 +14,6 @@ from typing import Any, cast
 import torch
 from tqdm import tqdm
 
-from sglang.multimodal_gen.configs.pipeline_configs import PipelineConfig
 from sglang.multimodal_gen.runtime.loader.component_loader import (
     PipelineComponentLoader,
 )
@@ -49,7 +47,6 @@ class ComposedPipelineBase(ABC):
     _extra_config_module_map: dict[str, str] = {}
     server_args: ServerArgs | None = None
     modules: dict[str, Any] = {}
-    post_init_called: bool = False
     executor: PipelineExecutor | None = None
 
     # the name of the pipeline it associated with, in diffusers
@@ -92,6 +89,8 @@ class ComposedPipelineBase(ABC):
         logger.info("Loading pipeline modules...")
         self.modules = self.load_modules(server_args, loaded_modules)
 
+        self.__post_init__()
+
     def build_executor(self, server_args: ServerArgs):
         # TODO
         from sglang.multimodal_gen.runtime.pipelines_core.executors.parallel_executor import (
@@ -101,48 +100,13 @@ class ComposedPipelineBase(ABC):
         # return SyncExecutor(server_args=server_args)
         return ParallelExecutor(server_args=server_args)
 
-    def post_init(self) -> None:
+    def __post_init__(self) -> None:
         assert self.server_args is not None, "server_args must be set"
-        if self.post_init_called:
-            return
-        self.post_init_called = True
-
         self.initialize_pipeline(self.server_args)
 
         logger.info("Creating pipeline stages...")
         self.create_pipeline_stages(self.server_args)
 
-    @classmethod
-    def from_pretrained(
-        cls,
-        model_path: str,
-        device: str | None = None,
-        torch_dtype: torch.dtype | None = None,
-        pipeline_config: str | PipelineConfig | None = None,
-        args: argparse.Namespace | None = None,
-        required_config_modules: list[str] | None = None,
-        loaded_modules: dict[str, torch.nn.Module] | None = None,
-        **kwargs,
-    ) -> "ComposedPipelineBase":
-        """
-        Load a pipeline from a pretrained model.
-        loaded_modules: Optional[Dict[str, torch.nn.Module]] = None,
-        If provided, loaded_modules will be used instead of loading from config/pretrained weights.
-        """
-        kwargs["model_path"] = model_path
-        server_args = ServerArgs.from_kwargs(**kwargs)
-
-        logger.info("server_args in from_pretrained: %s", server_args)
-
-        pipe = cls(
-            model_path,
-            server_args,
-            required_config_modules=required_config_modules,
-            loaded_modules=loaded_modules,
-        )
-        pipe.post_init()
-        return pipe
-
     def get_module(self, module_name: str, default_value: Any = None) -> Any:
         if module_name not in self.modules:
             return default_value
@@ -357,8 +321,6 @@ class ComposedPipelineBase(ABC):
         Returns:
             Req: The batch with the generated video or image.
         """
-        if not self.post_init_called:
-            self.post_init()
 
         if self.is_lora_set() and not self.is_lora_effective():
             logger.warning(