diff --git a/src/diffusers/modular_pipelines/modular_pipeline.py b/src/diffusers/modular_pipelines/modular_pipeline.py
index eede0b76d1..9252d6a739 100644
--- a/src/diffusers/modular_pipelines/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/modular_pipeline.py
@@ -76,139 +76,59 @@ class PipelineState:
     [`PipelineState`] stores the state of a pipeline. It is used to pass data between pipeline blocks.
     """
 
-    inputs: Dict[str, Any] = field(default_factory=dict)
-    intermediates: Dict[str, Any] = field(default_factory=dict)
-    input_kwargs: Dict[str, List[str]] = field(default_factory=dict)
-    intermediate_kwargs: Dict[str, List[str]] = field(default_factory=dict)
+    values: Dict[str, Any] = field(default_factory=dict)
+    kwargs_mapping: Dict[str, List[str]] = field(default_factory=dict)
 
-    def set_input(self, key: str, value: Any, kwargs_type: str = None):
+    def set(self, key: str, value: Any, kwargs_type: str = None):
         """
-        Add an input to the immutable pipeline state, i.e, pipeline_state.inputs.
-
-        The kwargs_type parameter allows you to associate inputs with specific input types. For example, if you call
-        set_input(prompt_embeds=..., kwargs_type="guider_kwargs"), this input will be automatically fetched when a
-        pipeline block has "guider_kwargs" in its expected_inputs list.
+        Add a value to the pipeline state.
 
         Args:
-            key (str): The key for the input
-            value (Any): The input value
-            kwargs_type (str): The kwargs_type with which the input is associated
+            key (str): The key for the value
+            value (Any): The value to store
+            kwargs_type (str): The kwargs_type with which the value is associated
         """
-        self.inputs[key] = value
+        self.values[key] = value
+
         if kwargs_type is not None:
-            if kwargs_type not in self.input_kwargs:
-                self.input_kwargs[kwargs_type] = [key]
+            if kwargs_type not in self.kwargs_mapping:
+                self.kwargs_mapping[kwargs_type] = [key]
             else:
-                self.input_kwargs[kwargs_type].append(key)
+                self.kwargs_mapping[kwargs_type].append(key)
 
-    def set_intermediate(self, key: str, value: Any, kwargs_type: str = None):
+    def get(self, keys: Union[str, List[str]], default: Any = None) -> Union[Any, Dict[str, Any]]:
         """
-        Add an intermediate value to the mutable pipeline state, i.e, pipeline_state.intermediates.
-
-        The kwargs_type parameter allows you to associate intermediate values with specific input types. For example,
-        if you call set_intermediate(latents=..., kwargs_type="latents_kwargs"), this intermediate value will be
-        automatically fetched when a pipeline block has "latents_kwargs" in its expected_intermediate_inputs list.
+        Get one or multiple values from the pipeline state.
 
         Args:
-            key (str): The key for the intermediate value
-            value (Any): The intermediate value
-            kwargs_type (str): The kwargs_type with which the intermediate value is associated
-        """
-        self.intermediates[key] = value
-        if kwargs_type is not None:
-            if kwargs_type not in self.intermediate_kwargs:
-                self.intermediate_kwargs[kwargs_type] = [key]
-            else:
-                self.intermediate_kwargs[kwargs_type].append(key)
-
-    def get_input(self, key: str, default: Any = None) -> Any:
-        """
-        Get an input from the pipeline state.
-
-        Args:
-            key (str): The key for the input
-            default (Any): The default value to return if the input is not found
+            keys (Union[str, List[str]]): Key or list of keys for the values
+            default (Any): The default value to return if not found
 
         Returns:
-            Any: The input value
+            Union[Any, Dict[str, Any]]: Single value if keys is str, dictionary of values if keys is list
         """
-        value = self.inputs.get(key, default)
-        if value is not None:
-            return deepcopy(value)
+        if isinstance(keys, str):
+            return self.values.get(keys, default)
+        return {key: self.values.get(key, default) for key in keys}
 
-    def get_inputs(self, keys: List[str], default: Any = None) -> Dict[str, Any]:
+    def get_by_kwargs(self, kwargs_type: str) -> Dict[str, Any]:
         """
-        Get multiple inputs from the pipeline state.
-
-        Args:
-            keys (List[str]): The keys for the inputs
-            default (Any): The default value to return if the input is not found
-
-        Returns:
-            Dict[str, Any]: Dictionary of inputs with matching keys
-        """
-        return {key: self.inputs.get(key, default) for key in keys}
-
-    def get_inputs_kwargs(self, kwargs_type: str) -> Dict[str, Any]:
-        """
-        Get all inputs with matching kwargs_type.
+        Get all values with matching kwargs_type.
 
         Args:
             kwargs_type (str): The kwargs_type to filter by
 
         Returns:
-            Dict[str, Any]: Dictionary of inputs with matching kwargs_type
+            Dict[str, Any]: Dictionary of values with matching kwargs_type
         """
-        input_names = self.input_kwargs.get(kwargs_type, [])
-        return self.get_inputs(input_names)
-
-    def get_intermediate_kwargs(self, kwargs_type: str) -> Dict[str, Any]:
-        """
-        Get all intermediates with matching kwargs_type.
-
-        Args:
-            kwargs_type (str): The kwargs_type to filter by
-
-        Returns:
-            Dict[str, Any]: Dictionary of intermediates with matching kwargs_type
-        """
-        intermediate_names = self.intermediate_kwargs.get(kwargs_type, [])
-        return self.get_intermediates(intermediate_names)
-
-    def get_intermediate(self, key: str, default: Any = None) -> Any:
-        """
-        Get an intermediate value from the pipeline state.
-
-        Args:
-            key (str): The key for the intermediate value
-            default (Any): The default value to return if the intermediate value is not found
-
-        Returns:
-            Any: The intermediate value
-        """
-        return self.intermediates.get(key, default)
-
-    def get_intermediates(self, keys: List[str], default: Any = None) -> Dict[str, Any]:
-        """
-        Get multiple intermediate values from the pipeline state.
-
-        Args:
-            keys (List[str]): The keys for the intermediate values
-            default (Any): The default value to return if the intermediate value is not found
-
-        Returns:
-            Dict[str, Any]: Dictionary of intermediate values with matching keys
-        """
-        return {key: self.intermediates.get(key, default) for key in keys}
+        value_names = self.kwargs_mapping.get(kwargs_type, [])
+        return self.get(value_names)
 
     def to_dict(self) -> Dict[str, Any]:
         """
         Convert PipelineState to a dictionary.
-
-        Returns:
-            Dict[str, Any]: Dictionary containing all attributes of the PipelineState
         """
-        return {**self.__dict__, "inputs": self.inputs, "intermediates": self.intermediates}
+        return {**self.__dict__}
 
     def __repr__(self):
         def format_value(v):
@@ -219,23 +139,16 @@ class PipelineState:
             else:
                 return repr(v)
 
-        inputs = "\n".join(f"    {k}: {format_value(v)}" for k, v in self.inputs.items())
-        intermediates = "\n".join(f"    {k}: {format_value(v)}" for k, v in self.intermediates.items())
-
-        # Format input_kwargs and intermediate_kwargs
-        input_kwargs_str = "\n".join(f"    {k}: {v}" for k, v in self.input_kwargs.items())
-        intermediate_kwargs_str = "\n".join(f"    {k}: {v}" for k, v in self.intermediate_kwargs.items())
+        values_str = "\n".join(f"    {k}: {format_value(v)}" for k, v in self.values.items())
+        kwargs_mapping_str = "\n".join(f"    {k}: {v}" for k, v in self.kwargs_mapping.items())
 
         return (
             f"PipelineState(\n"
-            f"  inputs={{\n{inputs}\n  }},\n"
-            f"  intermediates={{\n{intermediates}\n  }},\n"
-            f"  input_kwargs={{\n{input_kwargs_str}\n  }},\n"
-            f"  intermediate_kwargs={{\n{intermediate_kwargs_str}\n  }}\n"
+            f"  values={{\n{values_str}\n  }},\n"
+            f"  kwargs_mapping={{\n{kwargs_mapping_str}\n  }}\n"
             f")"
         )
 
-
 @dataclass
 class BlockState:
     """
@@ -322,7 +235,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
     </Tip>
     """
 
-    config_name = "config.json"
+    config_name = "modular_config.json"
     model_name = None
 
     @classmethod
@@ -334,6 +247,14 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
         return expected_modules, optional_parameters
 
+    def __init__(self):
+        self.sub_blocks = InsertableDict()
+
+    @property
+    def description(self) -> str:
+        """Description of the block. Must be implemented by subclasses."""
+        return ""
+
     @property
     def expected_components(self) -> List[ComponentSpec]:
         return []
@@ -343,8 +264,8 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         return []
 
     @property
-    def intermediate_inputs(self) -> List[OutputParam]:
-        """List of intermediate output parameters. Must be implemented by subclasses."""
+    def inputs(self) -> List[InputParam]:
+        """List of input parameters. Must be implemented by subclasses."""
         return []
 
     @property
@@ -352,6 +273,13 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
         """List of intermediate output parameters. Must be implemented by subclasses."""
         return []
 
+    def _get_outputs(self):
+        return self.intermediate_outputs
+
+    @property
+    def outputs(self) -> List[OutputParam]:
+        return self._get_outputs()
+
     @classmethod
     def from_pretrained(
         cls,
@@ -436,12 +364,12 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
     def get_block_state(self, state: PipelineState) -> dict:
         """Get all inputs and intermediates in one dictionary"""
         data = {}
-        state_inputs = self.inputs + self.intermediate_inputs
+        state_inputs = self.inputs
 
         # Check inputs
         for input_param in state_inputs:
             if input_param.name:
-                value = state.get_input(input_param.name) or state.get_intermediate(input_param.name)
+                value = state.get(input_param.name)
                 if input_param.required and value is None:
                     raise ValueError(f"Required input '{input_param.name}' is missing")
                 elif value is not None or (value is None and input_param.name not in data):
@@ -451,7 +379,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
                 # if kwargs_type is provided, get all inputs with matching kwargs_type
                 if input_param.kwargs_type not in data:
                     data[input_param.kwargs_type] = {}
-                inputs_kwargs = state.get_inputs_kwargs(input_param.kwargs_type) or state.get_intermediate_kwargs(
+                inputs_kwargs = state.get_by_kwargs(input_param.kwargs_type) or state.get_kwargs(
                     input_param.kwargs_type
                 )
                 if inputs_kwargs:
@@ -467,25 +395,28 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
             if not hasattr(block_state, output_param.name):
                 raise ValueError(f"Intermediate output '{output_param.name}' is missing in block state")
             param = getattr(block_state, output_param.name)
-            state.set_intermediate(output_param.name, param, output_param.kwargs_type)
+            state.set(output_param.name, param, output_param.kwargs_type)
 
-        for input_param in self.intermediate_inputs:
+        for input_param in self.inputs:
             if input_param.name and hasattr(block_state, input_param.name):
                 param = getattr(block_state, input_param.name)
                 # Only add if the value is different from what's in the state
-                current_value = state.get_intermediate(input_param.name)
+                current_value = state.get(input_param.name)
                 if current_value is not param:  # Using identity comparison to check if object was modified
-                    state.set_intermediate(input_param.name, param, input_param.kwargs_type)
+                    state.set(input_param.name, param, input_param.kwargs_type)
             elif input_param.kwargs_type:
                 # if it is a kwargs type, e.g. "guider_input_fields", it is likely to be a list of parameters
                 # we need to first find out which inputs are and loop through them.
-                intermediate_kwargs = state.get_intermediate_kwargs(input_param.kwargs_type)
+                intermediate_kwargs = state.get_by_kwargs(input_param.kwargs_type)
                 for param_name, current_value in intermediate_kwargs.items():
-                    if not hasattr(block_state, param_name):
-                        continue
-                    param = getattr(block_state, param_name)
-                    if current_value is not param:  # Using identity comparison to check if object was modified
-                        state.set_intermediate(param_name, param, input_param.kwargs_type)
+                    try:
+                        if not hasattr(block_state, param_name):
+                            continue
+                        param = getattr(block_state, param_name)
+                        if current_value is not param:  # Using identity comparison to check if object was modified
+                            state.set(param_name, param, input_param.kwargs_type)
+                    except:
+                        import ipdb; ipdb.set_trace()
 
     @staticmethod
     def combine_inputs(*named_input_lists: List[Tuple[str, List[InputParam]]]) -> List[InputParam]:
@@ -557,10 +488,6 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
     def input_names(self) -> List[str]:
         return [input_param.name for input_param in self.inputs]
 
-    @property
-    def intermediate_input_names(self) -> List[str]:
-        return [input_param.name for input_param in self.intermediate_inputs]
-
     @property
     def intermediate_output_names(self) -> List[str]:
         return [output_param.name for output_param in self.intermediate_outputs]
@@ -737,31 +664,31 @@ class PipelineBlock(ModularPipelineBlocks):
             if not hasattr(block_state, output_param.name):
                 raise ValueError(f"Intermediate output '{output_param.name}' is missing in block state")
             param = getattr(block_state, output_param.name)
-            state.set_intermediate(output_param.name, param, output_param.kwargs_type)
+            state.set(output_param.name, param, output_param.kwargs_type)
 
         for input_param in self.intermediate_inputs:
             if hasattr(block_state, input_param.name):
                 param = getattr(block_state, input_param.name)
                 # Only add if the value is different from what's in the state
-                current_value = state.get_intermediate(input_param.name)
+                current_value = state.get(input_param.name)
                 if current_value is not param:  # Using identity comparison to check if object was modified
-                    state.set_intermediate(input_param.name, param, input_param.kwargs_type)
+                    state.set(input_param.name, param, input_param.kwargs_type)
 
         for input_param in self.intermediate_inputs:
             if input_param.name and hasattr(block_state, input_param.name):
                 param = getattr(block_state, input_param.name)
                 # Only add if the value is different from what's in the state
-                current_value = state.get_intermediate(input_param.name)
+                current_value = state.get(input_param.name)
                 if current_value is not param:  # Using identity comparison to check if object was modified
-                    state.set_intermediate(input_param.name, param, input_param.kwargs_type)
+                    state.set(input_param.name, param, input_param.kwargs_type)
             elif input_param.kwargs_type:
                 # if it is a kwargs type, e.g. "guider_input_fields", it is likely to be a list of parameters
                 # we need to first find out which inputs are and loop through them.
-                intermediate_kwargs = state.get_intermediate_kwargs(input_param.kwargs_type)
+                intermediate_kwargs = state.get_kwargs(input_param.kwargs_type)
                 for param_name, current_value in intermediate_kwargs.items():
                     param = getattr(block_state, param_name)
                     if current_value is not param:  # Using identity comparison to check if object was modified
-                        state.set_intermediate(param_name, param, input_param.kwargs_type)
+                        state.set(param_name, param, input_param.kwargs_type)
 
 
 class AutoPipelineBlocks(ModularPipelineBlocks):
@@ -852,22 +779,6 @@ class AutoPipelineBlocks(ModularPipelineBlocks):
 
         return list(required_by_all)
 
-    # YiYi TODO: maybe we do not need this, it is only used in docstring,
-    # intermediate_inputs is by default required, unless you manually handle it inside the block
-    @property
-    def required_intermediate_inputs(self) -> List[str]:
-        if None not in self.block_trigger_inputs:
-            return []
-        first_block = next(iter(self.sub_blocks.values()))
-        required_by_all = set(getattr(first_block, "required_intermediate_inputs", set()))
-
-        # Intersect with required inputs from all other blocks
-        for block in list(self.sub_blocks.values())[1:]:
-            block_required = set(getattr(block, "required_intermediate_inputs", set()))
-            required_by_all.intersection_update(block_required)
-
-        return list(required_by_all)
-
     # YiYi TODO: add test for this
     @property
     def inputs(self) -> List[Tuple[str, Any]]:
@@ -881,18 +792,6 @@ class AutoPipelineBlocks(ModularPipelineBlocks):
                 input_param.required = False
         return combined_inputs
 
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        named_inputs = [(name, block.intermediate_inputs) for name, block in self.sub_blocks.items()]
-        combined_inputs = self.combine_inputs(*named_inputs)
-        # mark Required inputs only if that input is required by all the blocks
-        for input_param in combined_inputs:
-            if input_param.name in self.required_intermediate_inputs:
-                input_param.required = True
-            else:
-                input_param.required = False
-        return combined_inputs
-
     @property
     def intermediate_outputs(self) -> List[str]:
         named_outputs = [(name, block.intermediate_outputs) for name, block in self.sub_blocks.items()]
@@ -911,10 +810,10 @@ class AutoPipelineBlocks(ModularPipelineBlocks):
 
         block = self.trigger_to_block_map.get(None)
         for input_name in self.block_trigger_inputs:
-            if input_name is not None and state.get_input(input_name) is not None:
+            if input_name is not None and state.get(input_name) is not None:
                 block = self.trigger_to_block_map[input_name]
                 break
-            elif input_name is not None and state.get_intermediate(input_name) is not None:
+            elif input_name is not None and state.get(input_name) is not None:
                 block = self.trigger_to_block_map[input_name]
                 break
 
@@ -1133,6 +1032,34 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             sub_blocks[block_name] = block_cls()
         self.sub_blocks = sub_blocks
 
+    def _get_inputs(self):
+        inputs = []
+        outputs = set()
+
+        # Go through all blocks in order
+        for block in self.sub_blocks.values():
+            # Add inputs that aren't in outputs yet
+            for inp in block.inputs:
+                if inp.name not in outputs and inp.name not in {input.name for input in inputs}:
+                    inputs.append(inp)
+
+             # Only add outputs if the block cannot be skipped
+            should_add_outputs = True
+            if hasattr(block, "block_trigger_inputs") and None not in block.block_trigger_inputs:
+                should_add_outputs = False
+
+            if should_add_outputs:
+                # Add this block's outputs
+                block_intermediate_outputs = [out.name for out in block.intermediate_outputs]
+                outputs.update(block_intermediate_outputs)
+
+        return inputs
+
+    # YiYi TODO: add test for this
+    @property
+    def inputs(self) -> List[Tuple[str, Any]]:
+        return self._get_inputs()
+
     @property
     def required_inputs(self) -> List[str]:
         # Get the first block from the dictionary
@@ -1146,65 +1073,11 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
         return list(required_by_any)
 
-    # YiYi TODO: maybe we do not need this, it is only used in docstring,
-    # intermediate_inputs is by default required, unless you manually handle it inside the block
-    @property
-    def required_intermediate_inputs(self) -> List[str]:
-        required_intermediate_inputs = []
-        for input_param in self.intermediate_inputs:
-            if input_param.required:
-                required_intermediate_inputs.append(input_param.name)
-        return required_intermediate_inputs
-
-    # YiYi TODO: add test for this
-    @property
-    def inputs(self) -> List[Tuple[str, Any]]:
-        return self.get_inputs()
-
-    def get_inputs(self):
-        named_inputs = [(name, block.inputs) for name, block in self.sub_blocks.items()]
-        combined_inputs = self.combine_inputs(*named_inputs)
-        # mark Required inputs only if that input is required any of the blocks
-        for input_param in combined_inputs:
-            if input_param.name in self.required_inputs:
-                input_param.required = True
-            else:
-                input_param.required = False
-        return combined_inputs
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return self.get_intermediate_inputs()
-
-    def get_intermediate_inputs(self):
-        inputs = []
-        outputs = set()
-        added_inputs = set()
-
-        # Go through all blocks in order
-        for block in self.sub_blocks.values():
-            # Add inputs that aren't in outputs yet
-            for inp in block.intermediate_inputs:
-                if inp.name not in outputs and inp.name not in added_inputs:
-                    inputs.append(inp)
-                    added_inputs.add(inp.name)
-
-            # Only add outputs if the block cannot be skipped
-            should_add_outputs = True
-            if hasattr(block, "block_trigger_inputs") and None not in block.block_trigger_inputs:
-                should_add_outputs = False
-
-            if should_add_outputs:
-                # Add this block's outputs
-                block_intermediate_outputs = [out.name for out in block.intermediate_outputs]
-                outputs.update(block_intermediate_outputs)
-        return inputs
-
     @property
     def intermediate_outputs(self) -> List[str]:
         named_outputs = []
         for name, block in self.sub_blocks.items():
-            inp_names = {inp.name for inp in block.intermediate_inputs}
+            inp_names = {inp.name for inp in block.inputs}
             # so we only need to list new variables as intermediate_outputs, but if user wants to list these they modified it's still fine (a.k.a we don't enforce)
             # filter out them here so they do not end up as intermediate_outputs
             if name not in inp_names:
@@ -1422,7 +1295,6 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
     def doc(self):
         return make_doc_string(
             self.inputs,
-            self.intermediate_inputs,
             self.outputs,
             self.description,
             class_name=self.__class__.__name__,
@@ -1472,16 +1344,6 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
         """List of input parameters. Must be implemented by subclasses."""
         return []
 
-    @property
-    def loop_intermediate_inputs(self) -> List[InputParam]:
-        """List of intermediate input parameters. Must be implemented by subclasses."""
-        return []
-
-    @property
-    def loop_intermediate_outputs(self) -> List[OutputParam]:
-        """List of intermediate output parameters. Must be implemented by subclasses."""
-        return []
-
     @property
     def loop_required_inputs(self) -> List[str]:
         input_names = []
@@ -1491,12 +1353,9 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
         return input_names
 
     @property
-    def loop_required_intermediate_inputs(self) -> List[str]:
-        input_names = []
-        for input_param in self.loop_intermediate_inputs:
-            if input_param.required:
-                input_names.append(input_param.name)
-        return input_names
+    def loop_intermediate_outputs(self) -> List[OutputParam]:
+        """List of intermediate output parameters. Must be implemented by subclasses."""
+        return []
 
     # modified from SequentialPipelineBlocks to include loop_expected_components
     @property
@@ -1524,43 +1383,16 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
                 expected_configs.append(config)
         return expected_configs
 
-    # modified from SequentialPipelineBlocks to include loop_inputs
-    def get_inputs(self):
-        named_inputs = [(name, block.inputs) for name, block in self.sub_blocks.items()]
-        named_inputs.append(("loop", self.loop_inputs))
-        combined_inputs = self.combine_inputs(*named_inputs)
-        # mark Required inputs only if that input is required any of the blocks
-        for input_param in combined_inputs:
-            if input_param.name in self.required_inputs:
-                input_param.required = True
-            else:
-                input_param.required = False
-        return combined_inputs
-
-    @property
-    # Copied from diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks.inputs
-    def inputs(self):
-        return self.get_inputs()
-
-    # modified from SequentialPipelineBlocks to include loop_intermediate_inputs
-    @property
-    def intermediate_inputs(self):
-        intermediates = self.get_intermediate_inputs()
-        intermediate_names = [input.name for input in intermediates]
-        for loop_intermediate_input in self.loop_intermediate_inputs:
-            if loop_intermediate_input.name not in intermediate_names:
-                intermediates.append(loop_intermediate_input)
-        return intermediates
-
-    # modified from SequentialPipelineBlocks
-    def get_intermediate_inputs(self):
+    def _get_inputs(self):
         inputs = []
+        inputs.extend(self.loop_inputs)
         outputs = set()
 
-        # Go through all blocks in order
-        for block in self.sub_blocks.values():
+        for name, block in self.sub_blocks.items():
             # Add inputs that aren't in outputs yet
-            inputs.extend(input_name for input_name in block.intermediate_inputs if input_name.name not in outputs)
+            for inp in block.inputs:
+                if inp.name not in outputs and inp not in inputs:
+                    inputs.append(inp)
 
             # Only add outputs if the block cannot be skipped
             should_add_outputs = True
@@ -1571,8 +1403,20 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
                 # Add this block's outputs
                 block_intermediate_outputs = [out.name for out in block.intermediate_outputs]
                 outputs.update(block_intermediate_outputs)
+
+        for input_param in inputs:
+            if input_param.name in self.required_inputs:
+                input_param.required = True
+            else:
+                input_param.required = False
+
         return inputs
 
+    @property
+    # Copied from diffusers.modular_pipelines.modular_pipeline.SequentialPipelineBlocks.inputs
+    def inputs(self):
+        return self._get_inputs()
+
     # modified from SequentialPipelineBlocks, if any additionan input required by the loop is required by the block
     @property
     def required_inputs(self) -> List[str]:
@@ -1590,19 +1434,6 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
 
         return list(required_by_any)
 
-    # YiYi TODO: maybe we do not need this, it is only used in docstring,
-    # intermediate_inputs is by default required, unless you manually handle it inside the block
-    @property
-    def required_intermediate_inputs(self) -> List[str]:
-        required_intermediate_inputs = []
-        for input_param in self.intermediate_inputs:
-            if input_param.required:
-                required_intermediate_inputs.append(input_param.name)
-        for input_param in self.loop_intermediate_inputs:
-            if input_param.required:
-                required_intermediate_inputs.append(input_param.name)
-        return required_intermediate_inputs
-
     # YiYi TODO: this need to be thought about more
     # modified from SequentialPipelineBlocks to include loop_intermediate_outputs
     @property
@@ -1892,96 +1723,6 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
             params[input_param.name] = input_param.default
         return params
 
-    def __call__(self, state: PipelineState = None, output: Union[str, List[str]] = None, **kwargs):
-        """
-        Execute the pipeline by running the pipeline blocks with the given inputs.
-
-        Args:
-            state (`PipelineState`, optional):
-                PipelineState instance contains inputs and intermediate values. If None, a new `PipelineState` will be
-                created based on the user inputs and the pipeline blocks's requirement.
-            output (`str` or `List[str]`, optional):
-                Optional specification of what to return:
-                   - None: Returns the complete `PipelineState` with all inputs and intermediates (default)
-                   - str: Returns a specific intermediate value from the state (e.g. `output="image"`)
-                   - List[str]: Returns a dictionary of specific intermediate values (e.g. `output=["image",
-                     "latents"]`)
-
-
-        Examples:
-            ```python
-            # Get complete pipeline state
-            state = pipeline(prompt="A beautiful sunset", num_inference_steps=20)
-            print(state.intermediates)  # All intermediate outputs
-
-            # Get specific output
-            image = pipeline(prompt="A beautiful sunset", output="image")
-
-            # Get multiple specific outputs
-            results = pipeline(prompt="A beautiful sunset", output=["image", "latents"])
-            image, latents = results["image"], results["latents"]
-
-            # Continue from previous state
-            state = pipeline(prompt="A beautiful sunset")
-            new_state = pipeline(state=state, output="image")  # Continue processing
-            ```
-
-        Returns:
-            - If `output` is None: Complete `PipelineState` containing all inputs and intermediates
-            - If `output` is str: The specific intermediate value from the state (e.g. `output="image"`)
-            - If `output` is List[str]: Dictionary mapping output names to their values from the state (e.g.
-              `output=["image", "latents"]`)
-        """
-        if state is None:
-            state = PipelineState()
-
-        # Make a copy of the input kwargs
-        passed_kwargs = kwargs.copy()
-
-        # Add inputs to state, using defaults if not provided in the kwargs or the state
-        # if same input already in the state, will override it if provided in the kwargs
-        intermediate_inputs = [inp.name for inp in self.blocks.intermediate_inputs]
-        for expected_input_param in self.blocks.inputs:
-            name = expected_input_param.name
-            default = expected_input_param.default
-            kwargs_type = expected_input_param.kwargs_type
-            if name in passed_kwargs:
-                if name not in intermediate_inputs:
-                    state.set_input(name, passed_kwargs.pop(name), kwargs_type)
-                else:
-                    state.set_input(name, passed_kwargs[name], kwargs_type)
-            elif name not in state.inputs:
-                state.set_input(name, default, kwargs_type)
-
-        for expected_intermediate_param in self.blocks.intermediate_inputs:
-            name = expected_intermediate_param.name
-            kwargs_type = expected_intermediate_param.kwargs_type
-            if name in passed_kwargs:
-                state.set_intermediate(name, passed_kwargs.pop(name), kwargs_type)
-
-        # Warn about unexpected inputs
-        if len(passed_kwargs) > 0:
-            warnings.warn(f"Unexpected input '{passed_kwargs.keys()}' provided. This input will be ignored.")
-        # Run the pipeline
-        with torch.no_grad():
-            try:
-                _, state = self.blocks(self, state)
-            except Exception:
-                error_msg = f"Error in block: ({self.blocks.__class__.__name__}):\n"
-                logger.error(error_msg)
-                raise
-
-        if output is None:
-            return state
-
-        elif isinstance(output, str):
-            return state.get_intermediate(output)
-
-        elif isinstance(output, (list, tuple)):
-            return state.get_intermediates(output)
-        else:
-            raise ValueError(f"Output '{output}' is not a valid output type")
-
     def load_default_components(self, **kwargs):
         """
         Load from_pretrained components using the loading specs in the config dict.
@@ -2805,3 +2546,87 @@ class ModularPipeline(ConfigMixin, PushToHubMixin):
         for sub_block_name, sub_block in self.blocks.sub_blocks.items():
             if hasattr(sub_block, "set_progress_bar_config"):
                 sub_block.set_progress_bar_config(**kwargs)
+
+    def __call__(self, state: PipelineState = None, output: Union[str, List[str]] = None, **kwargs):
+        """
+        Execute the pipeline by running the pipeline blocks with the given inputs.
+
+        Args:
+            state (`PipelineState`, optional):
+                PipelineState instance contains inputs and intermediate values. If None, a new `PipelineState` will be
+                created based on the user inputs and the pipeline blocks's requirement.
+            output (`str` or `List[str]`, optional):
+                Optional specification of what to return:
+                   - None: Returns the complete `PipelineState` with all inputs and intermediates (default)
+                   - str: Returns a specific intermediate value from the state (e.g. `output="image"`)
+                   - List[str]: Returns a dictionary of specific intermediate values (e.g. `output=["image",
+                     "latents"]`)
+
+
+        Examples:
+            ```python
+            # Get complete pipeline state
+            state = pipeline(prompt="A beautiful sunset", num_inference_steps=20)
+            print(state.intermediates)  # All intermediate outputs
+
+            # Get specific output
+            image = pipeline(prompt="A beautiful sunset", output="image")
+
+            # Get multiple specific outputs
+            results = pipeline(prompt="A beautiful sunset", output=["image", "latents"])
+            image, latents = results["image"], results["latents"]
+
+            # Continue from previous state
+            state = pipeline(prompt="A beautiful sunset")
+            new_state = pipeline(state=state, output="image")  # Continue processing
+            ```
+
+        Returns:
+            - If `output` is None: Complete `PipelineState` containing all inputs and intermediates
+            - If `output` is str: The specific intermediate value from the state (e.g. `output="image"`)
+            - If `output` is List[str]: Dictionary mapping output names to their values from the state (e.g.
+              `output=["image", "latents"]`)
+        """
+        if state is None:
+            state = PipelineState()
+
+        # Make a copy of the input kwargs
+        passed_kwargs = kwargs.copy()
+
+        # Add inputs to state, using defaults if not provided in the kwargs or the state
+        # if same input already in the state, will override it if provided in the kwargs
+        intermediate_inputs = [inp.name for inp in self.blocks.inputs]
+        for expected_input_param in self.blocks.inputs:
+            name = expected_input_param.name
+            default = expected_input_param.default
+            kwargs_type = expected_input_param.kwargs_type
+            if name in passed_kwargs:
+                if name not in intermediate_inputs:
+                    state.set(name, passed_kwargs.pop(name), kwargs_type)
+                else:
+                    state.set(name, passed_kwargs[name], kwargs_type)
+            elif name not in state.values:
+                state.set(name, default, kwargs_type)
+
+        # Warn about unexpected inputs
+        if len(passed_kwargs) > 0:
+            warnings.warn(f"Unexpected input '{passed_kwargs.keys()}' provided. This input will be ignored.")
+        # Run the pipeline
+        with torch.no_grad():
+            try:
+                _, state = self.blocks(self, state)
+            except Exception:
+                error_msg = f"Error in block: ({self.blocks.__class__.__name__}):\n"
+                logger.error(error_msg)
+                raise
+
+        if output is None:
+            return state
+
+        if isinstance(output, str):
+            return state.get(output)
+
+        elif isinstance(output, (list, tuple)):
+            return state.get(output)
+        else:
+            raise ValueError(f"Output '{output}' is not a valid output type")
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py
index 1800a613ec..61487cde15 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/before_denoise.py
@@ -27,7 +27,7 @@ from ...schedulers import EulerDiscreteScheduler
 from ...utils import logging
 from ...utils.torch_utils import randn_tensor, unwrap_module
 from ..modular_pipeline import (
-    PipelineBlock,
+    ModularPipelineBlocks,
     PipelineState,
 )
 from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
@@ -195,7 +195,7 @@ def prepare_latents_img2img(
     return latents
 
 
-class StableDiffusionXLInputStep(PipelineBlock):
+class StableDiffusionXLInputStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -213,11 +213,6 @@ class StableDiffusionXLInputStep(PipelineBlock):
     def inputs(self) -> List[InputParam]:
         return [
             InputParam("num_images_per_prompt", default=1),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "prompt_embeds",
                 required=True,
@@ -394,7 +389,7 @@ class StableDiffusionXLInputStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLImg2ImgSetTimestepsStep(PipelineBlock):
+class StableDiffusionXLImg2ImgSetTimestepsStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -421,11 +416,6 @@ class StableDiffusionXLImg2ImgSetTimestepsStep(PipelineBlock):
             InputParam("denoising_start"),
             # YiYi TODO: do we need num_images_per_prompt here?
             InputParam("num_images_per_prompt", default=1),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "batch_size",
                 required=True,
@@ -543,7 +533,7 @@ class StableDiffusionXLImg2ImgSetTimestepsStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLSetTimestepsStep(PipelineBlock):
+class StableDiffusionXLSetTimestepsStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -611,7 +601,7 @@ class StableDiffusionXLSetTimestepsStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
+class StableDiffusionXLInpaintPrepareLatentsStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -640,11 +630,6 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
                 "`num_inference_steps`. A value of 1, therefore, essentially ignores `image`. Note that in the case of "
                 "`denoising_start` being declared as an integer, the value of `strength` will be ignored.",
             ),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam("generator"),
             InputParam(
                 "batch_size",
@@ -890,7 +875,7 @@ class StableDiffusionXLInpaintPrepareLatentsStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLImg2ImgPrepareLatentsStep(PipelineBlock):
+class StableDiffusionXLImg2ImgPrepareLatentsStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -910,11 +895,6 @@ class StableDiffusionXLImg2ImgPrepareLatentsStep(PipelineBlock):
             InputParam("latents"),
             InputParam("num_images_per_prompt", default=1),
             InputParam("denoising_start"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam("generator"),
             InputParam(
                 "latent_timestep",
@@ -971,7 +951,7 @@ class StableDiffusionXLImg2ImgPrepareLatentsStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLPrepareLatentsStep(PipelineBlock):
+class StableDiffusionXLPrepareLatentsStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -992,11 +972,6 @@ class StableDiffusionXLPrepareLatentsStep(PipelineBlock):
             InputParam("width"),
             InputParam("latents"),
             InputParam("num_images_per_prompt", default=1),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam("generator"),
             InputParam(
                 "batch_size",
@@ -1082,7 +1057,7 @@ class StableDiffusionXLPrepareLatentsStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(PipelineBlock):
+class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -1119,11 +1094,6 @@ class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(PipelineBlock):
             InputParam("num_images_per_prompt", default=1),
             InputParam("aesthetic_score", default=6.0),
             InputParam("negative_aesthetic_score", default=2.0),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam(
                 "latents",
                 required=True,
@@ -1306,7 +1276,7 @@ class StableDiffusionXLImg2ImgPrepareAdditionalConditioningStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLPrepareAdditionalConditioningStep(PipelineBlock):
+class StableDiffusionXLPrepareAdditionalConditioningStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -1335,11 +1305,6 @@ class StableDiffusionXLPrepareAdditionalConditioningStep(PipelineBlock):
             InputParam("crops_coords_top_left", default=(0, 0)),
             InputParam("negative_crops_coords_top_left", default=(0, 0)),
             InputParam("num_images_per_prompt", default=1),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam(
                 "latents",
                 required=True,
@@ -1489,7 +1454,7 @@ class StableDiffusionXLPrepareAdditionalConditioningStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLControlNetInputStep(PipelineBlock):
+class StableDiffusionXLControlNetInputStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -1517,11 +1482,6 @@ class StableDiffusionXLControlNetInputStep(PipelineBlock):
             InputParam("controlnet_conditioning_scale", default=1.0),
             InputParam("guess_mode", default=False),
             InputParam("num_images_per_prompt", default=1),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "latents",
                 required=True,
@@ -1708,7 +1668,7 @@ class StableDiffusionXLControlNetInputStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLControlNetUnionInputStep(PipelineBlock):
+class StableDiffusionXLControlNetUnionInputStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
index e9f627636e..44010792cd 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/decoders.py
@@ -24,7 +24,7 @@ from ...models import AutoencoderKL
 from ...models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 from ...utils import logging
 from ..modular_pipeline import (
-    PipelineBlock,
+    ModularPipelineBlocks,
     PipelineState,
 )
 from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
@@ -33,7 +33,7 @@ from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-class StableDiffusionXLDecodeStep(PipelineBlock):
+class StableDiffusionXLDecodeStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -56,11 +56,6 @@ class StableDiffusionXLDecodeStep(PipelineBlock):
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
             InputParam("output_type", default="pil"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "latents",
                 required=True,
@@ -157,7 +152,7 @@ class StableDiffusionXLDecodeStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLInpaintOverlayMaskStep(PipelineBlock):
+class StableDiffusionXLInpaintOverlayMaskStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
index 7fe4a472ee..96df9711cc 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/denoise.py
@@ -25,7 +25,7 @@ from ...utils import logging
 from ..modular_pipeline import (
     BlockState,
     LoopSequentialPipelineBlocks,
-    PipelineBlock,
+    ModularPipelineBlocks,
     PipelineState,
 )
 from ..modular_pipeline_utils import ComponentSpec, InputParam, OutputParam
@@ -37,7 +37,7 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 # YiYi experimenting composible denoise loop
 # loop step (1): prepare latent input for denoiser
-class StableDiffusionXLLoopBeforeDenoiser(PipelineBlock):
+class StableDiffusionXLLoopBeforeDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -55,7 +55,7 @@ class StableDiffusionXLLoopBeforeDenoiser(PipelineBlock):
         )
 
     @property
-    def intermediate_inputs(self) -> List[str]:
+    def inputs(self) -> List[str]:
         return [
             InputParam(
                 "latents",
@@ -73,7 +73,7 @@ class StableDiffusionXLLoopBeforeDenoiser(PipelineBlock):
 
 
 # loop step (1): prepare latent input for denoiser (with inpainting)
-class StableDiffusionXLInpaintLoopBeforeDenoiser(PipelineBlock):
+class StableDiffusionXLInpaintLoopBeforeDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -91,7 +91,7 @@ class StableDiffusionXLInpaintLoopBeforeDenoiser(PipelineBlock):
         )
 
     @property
-    def intermediate_inputs(self) -> List[str]:
+    def inputs(self) -> List[str]:
         return [
             InputParam(
                 "latents",
@@ -144,7 +144,7 @@ class StableDiffusionXLInpaintLoopBeforeDenoiser(PipelineBlock):
 
 
 # loop step (2): denoise the latents with guidance
-class StableDiffusionXLLoopDenoiser(PipelineBlock):
+class StableDiffusionXLLoopDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -171,11 +171,6 @@ class StableDiffusionXLLoopDenoiser(PipelineBlock):
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
             InputParam("cross_attention_kwargs"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "num_inference_steps",
                 required=True,
@@ -249,7 +244,7 @@ class StableDiffusionXLLoopDenoiser(PipelineBlock):
 
 
 # loop step (2): denoise the latents with guidance (with controlnet)
-class StableDiffusionXLControlNetLoopDenoiser(PipelineBlock):
+class StableDiffusionXLControlNetLoopDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -277,11 +272,6 @@ class StableDiffusionXLControlNetLoopDenoiser(PipelineBlock):
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
             InputParam("cross_attention_kwargs"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam(
                 "controlnet_cond",
                 required=True,
@@ -449,7 +439,7 @@ class StableDiffusionXLControlNetLoopDenoiser(PipelineBlock):
 
 
 # loop step (3): scheduler step to update latents
-class StableDiffusionXLLoopAfterDenoiser(PipelineBlock):
+class StableDiffusionXLLoopAfterDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -470,11 +460,6 @@ class StableDiffusionXLLoopAfterDenoiser(PipelineBlock):
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
             InputParam("eta", default=0.0),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam("generator"),
         ]
 
@@ -520,7 +505,7 @@ class StableDiffusionXLLoopAfterDenoiser(PipelineBlock):
 
 
 # loop step (3): scheduler step to update latents (with inpainting)
-class StableDiffusionXLInpaintLoopAfterDenoiser(PipelineBlock):
+class StableDiffusionXLInpaintLoopAfterDenoiser(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -542,11 +527,6 @@ class StableDiffusionXLInpaintLoopAfterDenoiser(PipelineBlock):
     def inputs(self) -> List[Tuple[str, Any]]:
         return [
             InputParam("eta", default=0.0),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[str]:
-        return [
             InputParam("generator"),
             InputParam(
                 "timesteps",
@@ -660,7 +640,7 @@ class StableDiffusionXLDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
         ]
 
     @property
-    def loop_intermediate_inputs(self) -> List[InputParam]:
+    def loop_inputs(self) -> List[InputParam]:
         return [
             InputParam(
                 "timesteps",
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py
index bd0e962140..8926d6c1fb 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/encoders.py
@@ -35,7 +35,7 @@ from ...utils import (
     scale_lora_layers,
     unscale_lora_layers,
 )
-from ..modular_pipeline import PipelineBlock, PipelineState
+from ..modular_pipeline import ModularPipelineBlocks, PipelineState
 from ..modular_pipeline_utils import ComponentSpec, ConfigSpec, InputParam, OutputParam
 from .modular_pipeline import StableDiffusionXLModularPipeline
 
@@ -57,7 +57,7 @@ def retrieve_latents(
         raise AttributeError("Could not access latents of provided encoder_output")
 
 
-class StableDiffusionXLIPAdapterStep(PipelineBlock):
+class StableDiffusionXLIPAdapterStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -215,7 +215,7 @@ class StableDiffusionXLIPAdapterStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLTextEncoderStep(PipelineBlock):
+class StableDiffusionXLTextEncoderStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -576,7 +576,7 @@ class StableDiffusionXLTextEncoderStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLVaeEncoderStep(PipelineBlock):
+class StableDiffusionXLVaeEncoderStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -601,11 +601,6 @@ class StableDiffusionXLVaeEncoderStep(PipelineBlock):
             InputParam("image", required=True),
             InputParam("height"),
             InputParam("width"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam("generator"),
             InputParam("dtype", type_hint=torch.dtype, description="Data type of model tensor inputs"),
             InputParam(
@@ -691,7 +686,7 @@ class StableDiffusionXLVaeEncoderStep(PipelineBlock):
         return components, state
 
 
-class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
+class StableDiffusionXLInpaintVaeEncoderStep(ModularPipelineBlocks):
     model_name = "stable-diffusion-xl"
 
     @property
@@ -726,11 +721,6 @@ class StableDiffusionXLInpaintVaeEncoderStep(PipelineBlock):
             InputParam("image", required=True),
             InputParam("mask_image", required=True),
             InputParam("padding_mask_crop"),
-        ]
-
-    @property
-    def intermediate_inputs(self) -> List[InputParam]:
-        return [
             InputParam("dtype", type_hint=torch.dtype, description="The dtype of the model inputs"),
             InputParam("generator"),
         ]
diff --git a/src/diffusers/modular_pipelines/stable_diffusion_xl/modular_pipeline.py b/src/diffusers/modular_pipelines/stable_diffusion_xl/modular_pipeline.py
index fc030fae56..0ee37f5201 100644
--- a/src/diffusers/modular_pipelines/stable_diffusion_xl/modular_pipeline.py
+++ b/src/diffusers/modular_pipelines/stable_diffusion_xl/modular_pipeline.py
@@ -247,10 +247,6 @@ SDXL_INPUTS_SCHEMA = {
     "control_mode": InputParam(
         "control_mode", type_hint=List[int], required=True, description="Control mode for union controlnet"
     ),
-}
-
-
-SDXL_INTERMEDIATE_INPUTS_SCHEMA = {
     "prompt_embeds": InputParam(
         "prompt_embeds",
         type_hint=torch.Tensor,
@@ -271,13 +267,6 @@ SDXL_INTERMEDIATE_INPUTS_SCHEMA = {
     "preprocess_kwargs": InputParam(
         "preprocess_kwargs", type_hint=Optional[dict], description="Kwargs for ImageProcessor"
     ),
-    "latents": InputParam(
-        "latents", type_hint=torch.Tensor, required=True, description="Initial latents for denoising process"
-    ),
-    "timesteps": InputParam("timesteps", type_hint=torch.Tensor, required=True, description="Timesteps for inference"),
-    "num_inference_steps": InputParam(
-        "num_inference_steps", type_hint=int, required=True, description="Number of denoising steps"
-    ),
     "latent_timestep": InputParam(
         "latent_timestep", type_hint=torch.Tensor, required=True, description="Initial noise level timestep"
     ),
diff --git a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
index 2e7c90d8e4..b199e42f98 100644
--- a/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
+++ b/tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py
@@ -119,11 +119,10 @@ class SDXLModularIPAdapterTests:
 
         _ = blocks.sub_blocks.pop("ip_adapter")
         parameters = blocks.input_names
-        intermediate_parameters = blocks.intermediate_input_names
         assert "ip_adapter_image" not in parameters, (
             "`ip_adapter_image` argument must be removed from the `__call__` method"
         )
-        assert "ip_adapter_image_embeds" not in intermediate_parameters, (
+        assert "ip_adapter_image_embeds" not in parameters, (
             "`ip_adapter_image_embeds` argument must be supported by the `__call__` method"
         )
 
diff --git a/tests/modular_pipelines/test_modular_pipelines_common.py b/tests/modular_pipelines/test_modular_pipelines_common.py
index 24c36b9fa9..6848e1906d 100644
--- a/tests/modular_pipelines/test_modular_pipelines_common.py
+++ b/tests/modular_pipelines/test_modular_pipelines_common.py
@@ -139,7 +139,6 @@ class ModularPipelineTesterMixin:
     def test_pipeline_call_signature(self):
         pipe = self.get_pipeline()
         input_parameters = pipe.blocks.input_names
-        intermediate_parameters = pipe.blocks.intermediate_input_names
         optional_parameters = pipe.default_call_parameters
 
         def _check_for_parameters(parameters, expected_parameters, param_type):
@@ -149,7 +148,6 @@ class ModularPipelineTesterMixin:
             )
 
         _check_for_parameters(self.params, input_parameters, "input")
-        _check_for_parameters(self.intermediate_params, intermediate_parameters, "intermediate")
         _check_for_parameters(self.optional_params, optional_parameters, "optional")
 
     def test_inference_batch_consistent(self, batch_sizes=[2], batch_generator=True):