[diffusion] chore: remove useless params (#15925)

python/sglang/multimodal_gen/runtime/loader/component_loader.py

@@ -668,10 +668,6 @@ class TransformerLoader(ComponentLoader):
                 "Only diffusers format is supported."
             )
 
-        if server_args.override_transformer_cls_name is not None:
-            cls_name = server_args.override_transformer_cls_name
-            logger.info("Overriding transformer cls_name to %s", cls_name)
-
         server_args.model_paths["transformer"] = component_model_path
 
         # Config from Diffusers supersedes sgl_diffusion's model config

python/sglang/multimodal_gen/runtime/pipelines_core/stages/base.py

@@ -191,31 +191,26 @@ class PipelineStage(ABC):
         """
         stage_name = self.__class__.__name__
         # Check if verification is enabled (simple approach for prototype)
-        enable_verification = getattr(server_args, "enable_stage_verification", False)
 
-        if enable_verification:
+        # Pre-execution input verification
-            # Pre-execution input verification
+        try:
-            try:
+            input_result = self.verify_input(batch, server_args)
-                input_result = self.verify_input(batch, server_args)
+            self._run_verification(input_result, stage_name, "input")
-                self._run_verification(input_result, stage_name, "input")
+        except Exception as e:
-            except Exception as e:
+            logger.error("Input verification failed for %s: %s", stage_name, str(e))
-                logger.error("Input verification failed for %s: %s", stage_name, str(e))
+            raise
-                raise
 
         # Execute the actual stage logic with unified profiling
         with StageProfiler(stage_name, logger=logger, timings=batch.timings):
             result = self.forward(batch, server_args)
 
-        if enable_verification:
+        # Post-execution output verification
-            # Post-execution output verification
+        try:
-            try:
+            output_result = self.verify_output(result, server_args)
-                output_result = self.verify_output(result, server_args)
+            self._run_verification(output_result, stage_name, "output")
-                self._run_verification(output_result, stage_name, "output")
+        except Exception as e:
-            except Exception as e:
+            logger.error("Output verification failed for %s: %s", stage_name, str(e))
-                logger.error(
+            raise
-                    "Output verification failed for %s: %s", stage_name, str(e)
-                )
-                raise
 
         return result
 

python/sglang/multimodal_gen/runtime/pipelines_core/stages/decoding.py

@@ -200,7 +200,6 @@ class DecodingStage(PipelineStage):
                 - trajectory_latents (optional): Latents at different timesteps
                 - trajectory_timesteps (optional): Corresponding timesteps
             server_args: Configuration containing:
-                - output_type: "latent" to skip decoding, otherwise decode to pixels
                 - vae_cpu_offload: Whether to offload VAE to CPU after decoding
                 - model_loaded: Track VAE loading state
                 - model_paths: Path to VAE model if loading needed
@@ -213,10 +212,7 @@ class DecodingStage(PipelineStage):
         # load vae if not already loaded (used for memory constrained devices)
         self.load_model()
 
-        if server_args.output_type == "latent":
+        frames = self.decode(batch.latents, server_args)
-            frames = batch.latents
-        else:
-            frames = self.decode(batch.latents, server_args)
 
         # decode trajectory latents if needed
         if batch.return_trajectory_decoded:

python/sglang/multimodal_gen/runtime/server_args.py

@@ -145,31 +145,6 @@ def _sanitize_for_logging(obj: Any, key_hint: str | None = None) -> Any:
         return "<unserializable>"
 
 
-class ExecutionMode(str, Enum):
-    """
-    Enumeration for different pipeline modes.
-
-    Inherits from str to allow string comparison for backward compatibility.
-    """
-
-    INFERENCE = "inference"
-
-    @classmethod
-    def from_string(cls, value: str) -> "ExecutionMode":
-        """Convert string to ExecutionMode enum."""
-        try:
-            return cls(value.lower())
-        except ValueError:
-            raise ValueError(
-                f"Invalid mode: {value}. Must be one of: {', '.join([m.value for m in cls])}"
-            ) from None
-
-    @classmethod
-    def choices(cls) -> list[str]:
-        """Get all available choices as strings for argparse."""
-        return [mode.value for mode in cls]
-
-
 @dataclasses.dataclass
 class ServerArgs:
     # Model and path configuration (for convenience)
@@ -178,14 +153,7 @@ class ServerArgs:
     # Attention
     attention_backend: str = None
 
-    # Running mode
-    mode: ExecutionMode = ExecutionMode.INFERENCE
-
-    # Cache strategy
-    cache_strategy: str = "none"
-
     # Distributed executor backend
-    distributed_executor_backend: str = "mp"
     nccl_port: Optional[int] = None
 
     # HuggingFace specific parameters
@@ -224,8 +192,6 @@ class ServerArgs:
     # Will adapt only q, k, v, o by default.
     lora_target_modules: list[str] | None = None
 
-    output_type: str = "pil"
-
     # CPU offload parameters
     dit_cpu_offload: bool = True
     dit_layerwise_offload: bool = False
@@ -266,9 +232,6 @@ class ServerArgs:
 
     scheduler_port: int = 5555
 
-    # Stage verification
-    enable_stage_verification: bool = True
-
     # Prompt text file for batch processing
     prompt_file_path: str | None = None
 
@@ -280,7 +243,6 @@ class ServerArgs:
             "vae": True,
         }
     )
-    override_transformer_cls_name: str | None = None
 
     # # DMD parameters
     # dmd_denoising_steps: List[int] | None = field(default=None)
@@ -369,24 +331,6 @@ class ServerArgs:
             help="The attention backend to use. If not specified, the backend is automatically selected based on hardware and installed packages.",
         )
 
-        # Running mode
-        parser.add_argument(
-            "--mode",
-            type=str,
-            choices=ExecutionMode.choices(),
-            default=ServerArgs.mode.value,
-            help="The mode to run SGLang-diffusion",
-        )
-
-        # distributed_executor_backend
-        parser.add_argument(
-            "--distributed-executor-backend",
-            type=str,
-            choices=["mp"],
-            default=ServerArgs.distributed_executor_backend,
-            help="The distributed executor backend to use",
-        )
-
         # HuggingFace specific parameters
         parser.add_argument(
             "--trust-remote-code",
@@ -466,15 +410,6 @@ class ServerArgs:
             help="Set timeout for torch.distributed initialization.",
         )
 
-        # Output type
-        parser.add_argument(
-            "--output-type",
-            type=str,
-            default=ServerArgs.output_type,
-            choices=["pil"],
-            help="Output type for the generated video",
-        )
-
         # Prompt text file for batch processing
         parser.add_argument(
             "--prompt-file-path",
@@ -600,19 +535,6 @@ class ServerArgs:
             help="Whether to use webui for better display",
         )
 
-        # Stage verification
-        parser.add_argument(
-            "--enable-stage-verification",
-            action=StoreBoolean,
-            default=ServerArgs.enable_stage_verification,
-            help="Enable input/output verification for pipeline stages",
-        )
-        parser.add_argument(
-            "--override-transformer-cls-name",
-            type=str,
-            default=ServerArgs.override_transformer_cls_name,
-            help="Override transformer cls name",
-        )
         # LoRA
         parser.add_argument(
             "--lora-path",
@@ -760,10 +682,6 @@ class ServerArgs:
 
     @classmethod
     def from_kwargs(cls, **kwargs: Any) -> "ServerArgs":
-        # Convert mode string to enum if necessary
-        if "mode" in kwargs and isinstance(kwargs["mode"], str):
-            kwargs["mode"] = ExecutionMode.from_string(kwargs["mode"])
-
         kwargs["pipeline_config"] = PipelineConfig.from_kwargs(kwargs)
         return cls(**kwargs)
 
@@ -885,14 +803,6 @@ class ServerArgs:
         else:
             self.disable_autocast = False
 
-        # Validate mode consistency
-        assert isinstance(
-            self.mode, ExecutionMode
-        ), f"Mode must be an ExecutionMode enum, got {type(self.mode)}"
-        assert (
-            self.mode in ExecutionMode.choices()
-        ), f"Invalid execution mode: {self.mode}"
-
         if self.tp_size == -1:
             self.tp_size = 1