up up

.github/workflows/pr_modular_tests.yml

@@ -75,9 +75,27 @@ jobs:
         if: ${{ failure() }}
         run: |
           echo "Repo consistency check failed. Please ensure the right dependency versions are installed with 'pip install -e .[quality]' and run 'make fix-copies'" >> $GITHUB_STEP_SUMMARY
+  check_auto_docs:
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v6
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.10"
+      - name: Install dependencies
+        run: |
+          pip install --upgrade pip
+          pip install .[quality]
+      - name: Check auto docs
+        run: make modular-autodoctrings
+      - name: Check if failure
+        if: ${{ failure() }}
+        run: |
+          echo "Auto docstring checks failed. Please run `python utils/modular_auto_docstring.py --fix_and_overwrite`." >> $GITHUB_STEP_SUMMARY
 
   run_fast_tests:
-    needs: [check_code_quality, check_repository_consistency]
+    needs: [check_code_quality, check_repository_consistency, check_auto_docs]
     name: Fast PyTorch Modular Pipeline CPU tests
 
     runs-on:

Makefile

@@ -70,6 +70,10 @@ fix-copies:
 	python utils/check_copies.py --fix_and_overwrite
 	python utils/check_dummies.py --fix_and_overwrite
 
+# Auto docstrings in modular blocks
+modular-autodoctrings:
+	python utils/modular_auto_docstring.py
+
 # Run tests for the library
 
 test:

src/diffusers/modular_pipelines/modular_pipeline.py

@@ -39,8 +39,11 @@ from .modular_pipeline_utils import (
     InputParam,
     InsertableDict,
     OutputParam,
+    combine_inputs,
+    combine_outputs,
     format_components,
     format_configs,
+    format_workflow,
     make_doc_string,
 )
 
@@ -242,6 +245,7 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 
     config_name = "modular_config.json"
     model_name = None
+    _workflow_map = None
 
     @classmethod
     def _get_signature_keys(cls, obj):
@@ -297,6 +301,35 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
     def outputs(self) -> List[OutputParam]:
         return self._get_outputs()
 
+    # currentlyonly ConditionalPipelineBlocks and SequentialPipelineBlocks support `get_execution_blocks`
+    def get_execution_blocks(self, **kwargs):
+        """
+        Get the block(s) that would execute given the inputs. Must be implemented by subclasses that support
+        conditional block selection.
+
+        Args:
+            **kwargs: Input names and values. Only trigger inputs affect block selection.
+        """
+        raise NotImplementedError(f"`get_execution_blocks` is not implemented for {self.__class__.__name__}")
+
+    # currently only SequentialPipelineBlocks support workflows
+    @property
+    def workflow_names(self):
+        """
+        Returns a list of available workflow names. Must be implemented by subclasses that define `_workflow_map`.
+        """
+        raise NotImplementedError(f"`workflow_names` is not implemented for {self.__class__.__name__}")
+
+    def get_workflow(self, workflow_name: str):
+        """
+        Get the execution blocks for a specific workflow. Must be implemented by subclasses that define
+        `_workflow_map`.
+
+        Args:
+            workflow_name: Name of the workflow to retrieve.
+        """
+        raise NotImplementedError(f"`get_workflow` is not implemented for {self.__class__.__name__}")
+
     @classmethod
     def from_pretrained(
         cls,
@@ -434,72 +467,6 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
                     if current_value is not param:  # Using identity comparison to check if object was modified
                         state.set(param_name, param, input_param.kwargs_type)
 
-    @staticmethod
-    def combine_inputs(*named_input_lists: List[Tuple[str, List[InputParam]]]) -> List[InputParam]:
-        """
-        Combines multiple lists of InputParam objects from different blocks. For duplicate inputs, updates only if
-        current default value is None and new default value is not None. Warns if multiple non-None default values
-        exist for the same input.
-
-        Args:
-            named_input_lists: List of tuples containing (block_name, input_param_list) pairs
-
-        Returns:
-            List[InputParam]: Combined list of unique InputParam objects
-        """
-        combined_dict = {}  # name -> InputParam
-        value_sources = {}  # name -> block_name
-
-        for block_name, inputs in named_input_lists:
-            for input_param in inputs:
-                if input_param.name is None and input_param.kwargs_type is not None:
-                    input_name = "*_" + input_param.kwargs_type
-                else:
-                    input_name = input_param.name
-                if input_name in combined_dict:
-                    current_param = combined_dict[input_name]
-                    if (
-                        current_param.default is not None
-                        and input_param.default is not None
-                        and current_param.default != input_param.default
-                    ):
-                        warnings.warn(
-                            f"Multiple different default values found for input '{input_name}': "
-                            f"{current_param.default} (from block '{value_sources[input_name]}') and "
-                            f"{input_param.default} (from block '{block_name}'). Using {current_param.default}."
-                        )
-                    if current_param.default is None and input_param.default is not None:
-                        combined_dict[input_name] = input_param
-                        value_sources[input_name] = block_name
-                else:
-                    combined_dict[input_name] = input_param
-                    value_sources[input_name] = block_name
-
-        return list(combined_dict.values())
-
-    @staticmethod
-    def combine_outputs(*named_output_lists: List[Tuple[str, List[OutputParam]]]) -> List[OutputParam]:
-        """
-        Combines multiple lists of OutputParam objects from different blocks. For duplicate outputs, keeps the first
-        occurrence of each output name.
-
-        Args:
-            named_output_lists: List of tuples containing (block_name, output_param_list) pairs
-
-        Returns:
-            List[OutputParam]: Combined list of unique OutputParam objects
-        """
-        combined_dict = {}  # name -> OutputParam
-
-        for block_name, outputs in named_output_lists:
-            for output_param in outputs:
-                if (output_param.name not in combined_dict) or (
-                    combined_dict[output_param.name].kwargs_type is None and output_param.kwargs_type is not None
-                ):
-                    combined_dict[output_param.name] = output_param
-
-        return list(combined_dict.values())
-
     @property
     def input_names(self) -> List[str]:
         return [input_param.name for input_param in self.inputs if input_param.name is not None]
@@ -531,7 +498,8 @@ class ModularPipelineBlocks(ConfigMixin, PushToHubMixin):
 class ConditionalPipelineBlocks(ModularPipelineBlocks):
     """
     A Pipeline Blocks that conditionally selects a block to run based on the inputs. Subclasses must implement the
-    `select_block` method to define the logic for selecting the block.
+    `select_block` method to define the logic for selecting the block. Currently, we only support selection logic based
+    on the presence or absence of inputs (i.e., whether they are `None` or not)
 
     This class inherits from [`ModularPipelineBlocks`]. Check the superclass documentation for the generic methods the
     library implements for all the pipeline blocks (such as loading or saving etc.)
@@ -539,15 +507,20 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
     > [!WARNING] > This is an experimental feature and is likely to change in the future.
 
     Attributes:
-        block_classes: List of block classes to be used
-        block_names: List of prefixes for each block
-        block_trigger_inputs: List of input names that select_block() uses to determine which block to run
+        block_classes: List of block classes to be used. Must have the same length as `block_names`.
+        block_names: List of names for each block. Must have the same length as `block_classes`.
+        block_trigger_inputs: List of input names that `select_block()` uses to determine which block to run.
+            For `ConditionalPipelineBlocks`, this does not need to correspond to `block_names` and `block_classes`. For
+            `AutoPipelineBlocks`, this must have the same length as `block_names` and `block_classes`, where each
+            element specifies the trigger input for the corresponding block.
+        default_block_name: Name of the default block to run when no trigger inputs match.
+            If None, this block can be skipped entirely when no trigger inputs are provided.
     """
 
     block_classes = []
     block_names = []
     block_trigger_inputs = []
-    default_block_name = None  # name of the default block if no trigger inputs are provided, if None, this block can be skipped if no trigger inputs are provided
+    default_block_name = None
 
     def __init__(self):
         sub_blocks = InsertableDict()
@@ -611,7 +584,7 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[Tuple[str, Any]]:
         named_inputs = [(name, block.inputs) for name, block in self.sub_blocks.items()]
-        combined_inputs = self.combine_inputs(*named_inputs)
+        combined_inputs = 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_inputs:
@@ -623,15 +596,16 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
     @property
     def intermediate_outputs(self) -> List[str]:
         named_outputs = [(name, block.intermediate_outputs) for name, block in self.sub_blocks.items()]
-        combined_outputs = self.combine_outputs(*named_outputs)
+        combined_outputs = combine_outputs(*named_outputs)
         return combined_outputs
 
     @property
     def outputs(self) -> List[str]:
         named_outputs = [(name, block.outputs) for name, block in self.sub_blocks.items()]
-        combined_outputs = self.combine_outputs(*named_outputs)
+        combined_outputs = combine_outputs(*named_outputs)
         return combined_outputs
 
+    # used for `__repr__`
     def _get_trigger_inputs(self) -> set:
         """
         Returns a set of all unique trigger input values found in this block and nested blocks.
@@ -660,11 +634,6 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
 
         return all_triggers
 
-    @property
-    def trigger_inputs(self):
-        """All trigger inputs including from nested blocks."""
-        return self._get_trigger_inputs()
-
     def select_block(self, **kwargs) -> Optional[str]:
         """
         Select the block to run based on the trigger inputs. Subclasses must implement this method to define the logic
@@ -704,6 +673,39 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
             logger.error(error_msg)
             raise
 
+    def get_execution_blocks(self, **kwargs) -> Optional["ModularPipelineBlocks"]:
+        """
+        Get the block(s) that would execute given the inputs.
+
+        Recursively resolves nested ConditionalPipelineBlocks until reaching either:
+        - A leaf block (no sub_blocks) → returns single `ModularPipelineBlocks`
+        - A `SequentialPipelineBlocks` → delegates to its `get_execution_blocks()` which returns
+        a `SequentialPipelineBlocks` containing the resolved execution blocks
+
+        Args:
+            **kwargs: Input names and values. Only trigger inputs affect block selection.
+
+        Returns:
+            - `ModularPipelineBlocks`: A leaf block or resolved `SequentialPipelineBlocks`
+            - `None`: If this block would be skipped (no trigger matched and no default)
+        """
+        trigger_kwargs = {name: kwargs.get(name) for name in self.block_trigger_inputs if name is not None}
+        block_name = self.select_block(**trigger_kwargs)
+
+        if block_name is None:
+            block_name = self.default_block_name
+
+        if block_name is None:
+            return None
+
+        block = self.sub_blocks[block_name]
+
+        # Recursively resolve until we hit a leaf block or a SequentialPipelineBlocks
+        if block.sub_blocks:
+            return block.get_execution_blocks(**kwargs)
+
+        return block
+
     def __repr__(self):
         class_name = self.__class__.__name__
         base_class = self.__class__.__bases__[0].__name__
@@ -711,11 +713,11 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
             f"{class_name}(\n  Class: {base_class}\n" if base_class and base_class != "object" else f"{class_name}(\n"
         )
 
-        if self.trigger_inputs:
+        if self._get_trigger_inputs():
             header += "\n"
             header += "  " + "=" * 100 + "\n"
             header += "  This pipeline contains blocks that are selected at runtime based on inputs.\n"
-            header += f"  Trigger Inputs: {sorted(self.trigger_inputs)}\n"
+            header += f"  Trigger Inputs: {sorted(self._get_trigger_inputs())}\n"
             header += "  " + "=" * 100 + "\n\n"
 
         # Format description with proper indentation
@@ -782,24 +784,56 @@ class ConditionalPipelineBlocks(ModularPipelineBlocks):
 
 class AutoPipelineBlocks(ConditionalPipelineBlocks):
     """
-    A Pipeline Blocks that automatically selects a block to run based on the presence of trigger inputs.
+        A Pipeline Blocks that automatically selects a block to run based on the presence of trigger inputs.
+
+        This is a specialized version of `ConditionalPipelineBlocks` where:
+        - Each block has one corresponding trigger input (1:1 mapping)
+        - Block selection is automatic: the first block whose trigger input is present gets selected
+        - `block_trigger_inputs` must have the same length as `block_names` and `block_classes`
+        - Use `None` in `block_trigger_inputs` to specify the default block, i.e the block that will run if no trigger
+          inputs are present
+
+        Attributes:
+            block_classes:
+                List of block classes to be used. Must have the same length as `block_names` and
+                `block_trigger_inputs`.
+            block_names:
+                List of names for each block. Must have the same length as `block_classes` and `block_trigger_inputs`.
+            block_trigger_inputs:
+                List of input names where each element specifies the trigger input for the corresponding block. Use
+                `None` to mark the default block.
+
+        Example:
+    ```python
+        class MyAutoBlock(AutoPipelineBlocks):
+            block_classes = [InpaintEncoderBlock, ImageEncoderBlock, TextEncoderBlock]
+            block_names = ["inpaint", "img2img", "text2img"]
+            block_trigger_inputs = ["mask_image", "image", None]  # text2img is the default
+    ```
+
+        With this definition:
+        - As long as `mask_image` is provided, "inpaint" block runs (regardless of `image` being provided or not)
+        - If `mask_image` is not provided but `image` is provided, "img2img" block runs
+        - Otherwise, "text2img" block runs (default, trigger is `None`)
     """
 
     def __init__(self):
         super().__init__()
 
+        if self.default_block_name is not None:
+            raise ValueError(
+                f"In {self.__class__.__name__}, do not set `default_block_name` for AutoPipelineBlocks. "
+                f"Use `None` in `block_trigger_inputs` to specify the default block."
+            )
+
         if not (len(self.block_classes) == len(self.block_names) == len(self.block_trigger_inputs)):
             raise ValueError(
                 f"In {self.__class__.__name__}, the number of block_classes, block_names, and block_trigger_inputs must be the same."
             )
 
-    @property
-    def default_block_name(self) -> Optional[str]:
-        """Derive default_block_name from block_trigger_inputs (None entry)."""
         if None in self.block_trigger_inputs:
             idx = self.block_trigger_inputs.index(None)
-            return self.block_names[idx]
-        return None
+            self.default_block_name = self.block_names[idx]
 
     def select_block(self, **kwargs) -> Optional[str]:
         """Select block based on which trigger input is present (not None)."""
@@ -853,6 +887,29 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
                     expected_configs.append(config)
         return expected_configs
 
+    @property
+    def workflow_names(self):
+        if self._workflow_map is None:
+            raise NotImplementedError(
+                f"workflows is not supported because _workflow_map is not set for {self.__class__.__name__}"
+            )
+
+        return list(self._workflow_map.keys())
+
+    def get_workflow(self, workflow_name: str):
+        if self._workflow_map is None:
+            raise NotImplementedError(
+                f"workflows is not supported because _workflow_map is not set for {self.__class__.__name__}"
+            )
+
+        if workflow_name not in self._workflow_map:
+            raise ValueError(f"Workflow {workflow_name} not found in {self.__class__.__name__}")
+
+        trigger_inputs = self._workflow_map[workflow_name]
+        workflow_blocks = self.get_execution_blocks(**trigger_inputs)
+
+        return workflow_blocks
+
     @classmethod
     def from_blocks_dict(
         cls, blocks_dict: Dict[str, Any], description: Optional[str] = None
@@ -948,7 +1005,7 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             # filter out them here so they do not end up as intermediate_outputs
             if name not in inp_names:
                 named_outputs.append((name, block.intermediate_outputs))
-        combined_outputs = self.combine_outputs(*named_outputs)
+        combined_outputs = combine_outputs(*named_outputs)
         return combined_outputs
 
     # YiYi TODO: I think we can remove the outputs property
@@ -972,6 +1029,7 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
                 raise
         return pipeline, state
 
+    # used for `trigger_inputs` property
     def _get_trigger_inputs(self):
         """
         Returns a set of all unique trigger input values found in the blocks.
@@ -995,89 +1053,50 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
         return fn_recursive_get_trigger(self.sub_blocks)
 
-    @property
-    def trigger_inputs(self):
-        return self._get_trigger_inputs()
-
-    def _traverse_trigger_blocks(self, active_inputs):
+    def get_execution_blocks(self, **kwargs) -> "SequentialPipelineBlocks":
         """
-        Traverse blocks and select which ones would run given the active inputs.
+        Get the blocks that would execute given the specified inputs.
 
         Args:
-            active_inputs: Dict of input names to values that are "present"
+            **kwargs: Input names and values. Only trigger inputs affect block selection.
 
         Returns:
-            OrderedDict of block_name -> block that would execute
+            SequentialPipelineBlocks containing only the blocks that would execute
         """
+        # Copy kwargs so we can add outputs as we traverse
+        active_inputs = dict(kwargs)
 
         def fn_recursive_traverse(block, block_name, active_inputs):
             result_blocks = OrderedDict()
 
             # ConditionalPipelineBlocks (includes AutoPipelineBlocks)
             if isinstance(block, ConditionalPipelineBlocks):
-                trigger_kwargs = {name: active_inputs.get(name) for name in block.block_trigger_inputs}
-                selected_block_name = block.select_block(**trigger_kwargs)
-
-                if selected_block_name is None:
-                    selected_block_name = block.default_block_name
-
-                if selected_block_name is None:
+                block = block.get_execution_blocks(**active_inputs)
+                if block is None:
                     return result_blocks
 
-                selected_block = block.sub_blocks[selected_block_name]
-
-                if selected_block.sub_blocks:
-                    result_blocks.update(fn_recursive_traverse(selected_block, block_name, active_inputs))
-                else:
-                    result_blocks[block_name] = selected_block
-                    if hasattr(selected_block, "outputs"):
-                        for out in selected_block.outputs:
-                            active_inputs[out.name] = True
-
-                return result_blocks
-
-            # SequentialPipelineBlocks or LoopSequentialPipelineBlocks
-            if block.sub_blocks:
+            # Has sub_blocks (SequentialPipelineBlocks/ConditionalPipelineBlocks)
+            if block.sub_blocks and not isinstance(block, LoopSequentialPipelineBlocks):
                 for sub_block_name, sub_block in block.sub_blocks.items():
-                    blocks_to_update = fn_recursive_traverse(sub_block, sub_block_name, active_inputs)
-                    blocks_to_update = {f"{block_name}.{k}": v for k, v in blocks_to_update.items()}
-                    result_blocks.update(blocks_to_update)
+                    nested_blocks = fn_recursive_traverse(sub_block, sub_block_name, active_inputs)
+                    nested_blocks = {f"{block_name}.{k}": v for k, v in nested_blocks.items()}
+                    result_blocks.update(nested_blocks)
             else:
+                # Leaf block: single ModularPipelineBlocks or LoopSequentialPipelineBlocks
                 result_blocks[block_name] = block
-                if hasattr(block, "outputs"):
-                    for out in block.outputs:
+                # Add outputs to active_inputs so subsequent blocks can use them as triggers
+                if hasattr(block, "intermediate_outputs"):
+                    for out in block.intermediate_outputs:
                         active_inputs[out.name] = True
 
             return result_blocks
 
         all_blocks = OrderedDict()
         for block_name, block in self.sub_blocks.items():
-            blocks_to_update = fn_recursive_traverse(block, block_name, active_inputs)
-            all_blocks.update(blocks_to_update)
-        return all_blocks
+            nested_blocks = fn_recursive_traverse(block, block_name, active_inputs)
+            all_blocks.update(nested_blocks)
 
-    def get_execution_blocks(self, **kwargs):
-        """
-        Get the blocks that would execute given the specified inputs.
-
-        Args:
-            **kwargs: Input names and values. Only trigger inputs affect block selection.
-                    Pass any inputs that would be non-None at runtime.
-
-        Returns:
-            SequentialPipelineBlocks containing only the blocks that would execute
-
-        Example:
-            # Get blocks for inpainting workflow blocks = pipeline.get_execution_blocks(prompt="a cat", mask=mask,
-            image=image)
-
-            # Get blocks for text2image workflow blocks = pipeline.get_execution_blocks(prompt="a cat")
-        """
-        # Filter out None values
-        active_inputs = {k: v for k, v in kwargs.items() if v is not None}
-
-        blocks_triggered = self._traverse_trigger_blocks(active_inputs)
-        return SequentialPipelineBlocks.from_blocks_dict(blocks_triggered)
+        return SequentialPipelineBlocks.from_blocks_dict(all_blocks)
 
     def __repr__(self):
         class_name = self.__class__.__name__
@@ -1086,18 +1105,23 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
             f"{class_name}(\n  Class: {base_class}\n" if base_class and base_class != "object" else f"{class_name}(\n"
         )
 
-        if self.trigger_inputs:
+        if self._workflow_map is None and self._get_trigger_inputs():
             header += "\n"
             header += "  " + "=" * 100 + "\n"
             header += "  This pipeline contains blocks that are selected at runtime based on inputs.\n"
-            header += f"  Trigger Inputs: {[inp for inp in self.trigger_inputs if inp is not None]}\n"
+            header += f"  Trigger Inputs: {[inp for inp in self._get_trigger_inputs() if inp is not None]}\n"
             # Get first trigger input as example
-            example_input = next(t for t in self.trigger_inputs if t is not None)
+            example_input = next(t for t in self._get_trigger_inputs() if t is not None)
             header += f"  Use `get_execution_blocks()` to see selected blocks (e.g. `get_execution_blocks({example_input}=...)`).\n"
             header += "  " + "=" * 100 + "\n\n"
 
+        description = self.description
+        if self._workflow_map is not None:
+            workflow_str = format_workflow(self._workflow_map)
+            description = f"{self.description}\n\n{workflow_str}"
+
         # Format description with proper indentation
-        desc_lines = self.description.split("\n")
+        desc_lines = description.split("\n")
         desc = []
         # First line with "Description:" label
         desc.append(f"  Description: {desc_lines[0]}")
@@ -1145,10 +1169,15 @@ class SequentialPipelineBlocks(ModularPipelineBlocks):
 
     @property
     def doc(self):
+        description = self.description
+        if self._workflow_map is not None:
+            workflow_str = format_workflow(self._workflow_map)
+            description = f"{self.description}\n\n{workflow_str}"
+
         return make_doc_string(
             self.inputs,
             self.outputs,
-            self.description,
+            description=description,
             class_name=self.__class__.__name__,
             expected_components=self.expected_components,
             expected_configs=self.expected_configs,
@@ -1281,7 +1310,7 @@ class LoopSequentialPipelineBlocks(ModularPipelineBlocks):
     @property
     def intermediate_outputs(self) -> List[str]:
         named_outputs = [(name, block.intermediate_outputs) for name, block in self.sub_blocks.items()]
-        combined_outputs = self.combine_outputs(*named_outputs)
+        combined_outputs = combine_outputs(*named_outputs)
         for output in self.loop_intermediate_outputs:
             if output.name not in {output.name for output in combined_outputs}:
                 combined_outputs.append(output)

src/diffusers/modular_pipelines/modular_pipeline_utils.py

@@ -14,10 +14,12 @@
 
 import inspect
 import re
+import warnings
 from collections import OrderedDict
 from dataclasses import dataclass, field, fields
-from typing import Any, Dict, List, Literal, Optional, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Type, Union
 
+import PIL.Image
 import torch
 
 from ..configuration_utils import ConfigMixin, FrozenDict
@@ -323,11 +325,192 @@ class ConfigSpec:
     description: Optional[str] = None
 
 
-# YiYi Notes: both inputs and intermediate_inputs are InputParam objects
-# however some fields are not relevant for intermediate_inputs
-# e.g. unlike inputs, required only used in docstring for intermediate_inputs, we do not check if a required intermediate inputs is passed
-# default is not used for intermediate_inputs, we only use default from inputs, so it is ignored if it is set for intermediate_inputs
-# -> should we use different class for inputs and intermediate_inputs?
+# ======================================================
+# InputParam and OutputParam templates
+# ======================================================
+
+INPUT_PARAM_TEMPLATES = {
+    "prompt": {
+        "type_hint": str,
+        "required": True,
+        "description": "The prompt or prompts to guide image generation.",
+    },
+    "negative_prompt": {
+        "type_hint": str,
+        "description": "The prompt or prompts not to guide the image generation.",
+    },
+    "max_sequence_length": {
+        "type_hint": int,
+        "default": 512,
+        "description": "Maximum sequence length for prompt encoding.",
+    },
+    "height": {
+        "type_hint": int,
+        "description": "The height in pixels of the generated image.",
+    },
+    "width": {
+        "type_hint": int,
+        "description": "The width in pixels of the generated image.",
+    },
+    "num_inference_steps": {
+        "type_hint": int,
+        "default": 50,
+        "description": "The number of denoising steps.",
+    },
+    "num_images_per_prompt": {
+        "type_hint": int,
+        "default": 1,
+        "description": "The number of images to generate per prompt.",
+    },
+    "generator": {
+        "type_hint": torch.Generator,
+        "description": "Torch generator for deterministic generation.",
+    },
+    "sigmas": {
+        "type_hint": List[float],
+        "description": "Custom sigmas for the denoising process.",
+    },
+    "strength": {
+        "type_hint": float,
+        "default": 0.9,
+        "description": "Strength for img2img/inpainting.",
+    },
+    "image": {
+        "type_hint": Union[PIL.Image.Image, List[PIL.Image.Image]],
+        "required": True,
+        "description": "Reference image(s) for denoising. Can be a single image or list of images.",
+    },
+    "latents": {
+        "type_hint": torch.Tensor,
+        "description": "Pre-generated noisy latents for image generation.",
+    },
+    "timesteps": {
+        "type_hint": torch.Tensor,
+        "description": "Timesteps for the denoising process.",
+    },
+    "output_type": {
+        "type_hint": str,
+        "default": "pil",
+        "description": "Output format: 'pil', 'np', 'pt'.",
+    },
+    "attention_kwargs": {
+        "type_hint": Dict[str, Any],
+        "description": "Additional kwargs for attention processors.",
+    },
+    "denoiser_input_fields": {
+        "name": None,
+        "kwargs_type": "denoiser_input_fields",
+        "description": "conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
+    },
+    # inpainting
+    "mask_image": {
+        "type_hint": PIL.Image.Image,
+        "required": True,
+        "description": "Mask image for inpainting.",
+    },
+    "padding_mask_crop": {
+        "type_hint": int,
+        "description": "Padding for mask cropping in inpainting.",
+    },
+    # controlnet
+    "control_image": {
+        "type_hint": PIL.Image.Image,
+        "required": True,
+        "description": "Control image for ControlNet conditioning.",
+    },
+    "control_guidance_start": {
+        "type_hint": float,
+        "default": 0.0,
+        "description": "When to start applying ControlNet.",
+    },
+    "control_guidance_end": {
+        "type_hint": float,
+        "default": 1.0,
+        "description": "When to stop applying ControlNet.",
+    },
+    "controlnet_conditioning_scale": {
+        "type_hint": float,
+        "default": 1.0,
+        "description": "Scale for ControlNet conditioning.",
+    },
+    "layers": {
+        "type_hint": int,
+        "default": 4,
+        "description": "Number of layers to extract from the image",
+    },
+    # common intermediate inputs
+    "prompt_embeds": {
+        "type_hint": torch.Tensor,
+        "required": True,
+        "description": "text embeddings used to guide the image generation. Can be generated from text_encoder step.",
+    },
+    "prompt_embeds_mask": {
+        "type_hint": torch.Tensor,
+        "required": True,
+        "description": "mask for the text embeddings. Can be generated from text_encoder step.",
+    },
+    "negative_prompt_embeds": {
+        "type_hint": torch.Tensor,
+        "description": "negative text embeddings used to guide the image generation. Can be generated from text_encoder step.",
+    },
+    "negative_prompt_embeds_mask": {
+        "type_hint": torch.Tensor,
+        "description": "mask for the negative text embeddings. Can be generated from text_encoder step.",
+    },
+    "image_latents": {
+        "type_hint": torch.Tensor,
+        "required": True,
+        "description": "image latents used to guide the image generation. Can be generated from vae_encoder step.",
+    },
+    "batch_size": {
+        "type_hint": int,
+        "default": 1,
+        "description": "Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.",
+    },
+    "dtype": {
+        "type_hint": torch.dtype,
+        "default": torch.float32,
+        "description": "The dtype of the model inputs, can be generated in input step.",
+    },
+}
+
+OUTPUT_PARAM_TEMPLATES = {
+    "images": {
+        "type_hint": List[PIL.Image.Image],
+        "description": "Generated images.",
+    },
+    "latents": {
+        "type_hint": torch.Tensor,
+        "description": "Denoised latents.",
+    },
+    # intermediate outputs
+    "prompt_embeds": {
+        "type_hint": torch.Tensor,
+        "kwargs_type": "denoiser_input_fields",
+        "description": "The prompt embeddings.",
+    },
+    "prompt_embeds_mask": {
+        "type_hint": torch.Tensor,
+        "kwargs_type": "denoiser_input_fields",
+        "description": "The encoder attention mask.",
+    },
+    "negative_prompt_embeds": {
+        "type_hint": torch.Tensor,
+        "kwargs_type": "denoiser_input_fields",
+        "description": "The negative prompt embeddings.",
+    },
+    "negative_prompt_embeds_mask": {
+        "type_hint": torch.Tensor,
+        "kwargs_type": "denoiser_input_fields",
+        "description": "The negative prompt embeddings mask.",
+    },
+    "image_latents": {
+        "type_hint": torch.Tensor,
+        "description": "The latent representation of the input image.",
+    },
+}
+
+
 @dataclass
 class InputParam:
     """Specification for an input parameter."""
@@ -337,11 +520,31 @@ class InputParam:
     default: Any = None
     required: bool = False
     description: str = ""
-    kwargs_type: str = None  # YiYi Notes: remove this feature (maybe)
+    kwargs_type: str = None
 
     def __repr__(self):
         return f"<{self.name}: {'required' if self.required else 'optional'}, default={self.default}>"
 
+    @classmethod
+    def template(cls, template_name: str, note: str = None, **overrides) -> "InputParam":
+        """Get template for name if exists, otherwise raise ValueError."""
+        if template_name not in INPUT_PARAM_TEMPLATES:
+            raise ValueError(f"InputParam template for {template_name} not found")
+
+        template_kwargs = INPUT_PARAM_TEMPLATES[template_name].copy()
+
+        # Determine the actual param name:
+        # 1. From overrides if provided
+        # 2. From template if present
+        # 3. Fall back to template_name
+        name = overrides.pop("name", template_kwargs.pop("name", template_name))
+
+        if note and "description" in template_kwargs:
+            template_kwargs["description"] = f"{template_kwargs['description']} ({note})"
+
+        template_kwargs.update(overrides)
+        return cls(name=name, **template_kwargs)
+
 
 @dataclass
 class OutputParam:
@@ -350,13 +553,33 @@ class OutputParam:
     name: str
     type_hint: Any = None
     description: str = ""
-    kwargs_type: str = None  # YiYi notes: remove this feature (maybe)
+    kwargs_type: str = None
 
     def __repr__(self):
         return (
             f"<{self.name}: {self.type_hint.__name__ if hasattr(self.type_hint, '__name__') else str(self.type_hint)}>"
         )
 
+    @classmethod
+    def template(cls, template_name: str, note: str = None, **overrides) -> "OutputParam":
+        """Get template for name if exists, otherwise raise ValueError."""
+        if template_name not in OUTPUT_PARAM_TEMPLATES:
+            raise ValueError(f"OutputParam template for {template_name} not found")
+
+        template_kwargs = OUTPUT_PARAM_TEMPLATES[template_name].copy()
+
+        # Determine the actual param name:
+        # 1. From overrides if provided
+        # 2. From template if present
+        # 3. Fall back to template_name
+        name = overrides.pop("name", template_kwargs.pop("name", template_name))
+
+        if note and "description" in template_kwargs:
+            template_kwargs["description"] = f"{template_kwargs['description']} ({note})"
+
+        template_kwargs.update(overrides)
+        return cls(name=name, **template_kwargs)
+
 
 def format_inputs_short(inputs):
     """
@@ -509,10 +732,12 @@ def format_params(params, header="Args", indent_level=4, max_line_length=115):
             desc = re.sub(r"\[(.*?)\]\((https?://[^\s\)]+)\)", r"[\1](\2)", param.description)
             wrapped_desc = wrap_text(desc, desc_indent, max_line_length)
             param_str += f"\n{desc_indent}{wrapped_desc}"
+        else:
+            param_str += f"\n{desc_indent}TODO: Add description."
 
         formatted_params.append(param_str)
 
-    return "\n\n".join(formatted_params)
+    return "\n".join(formatted_params)
 
 
 def format_input_params(input_params, indent_level=4, max_line_length=115):
@@ -582,7 +807,7 @@ def format_components(components, indent_level=4, max_line_length=115, add_empty
         loading_field_values = []
         for field_name in component.loading_fields():
             field_value = getattr(component, field_name)
-            if field_value is not None:
+            if field_value:
                 loading_field_values.append(f"{field_name}={field_value}")
 
         # Add loading field information if available
@@ -636,6 +861,30 @@ def format_configs(configs, indent_level=4, max_line_length=115, add_empty_lines
     return "\n".join(formatted_configs)
 
 
+def format_workflow(workflow_map):
+    """Format a workflow map into a readable string representation.
+
+    Args:
+        workflow_map: Dictionary mapping workflow names to trigger inputs
+
+    Returns:
+        A formatted string representing all workflows
+    """
+    if workflow_map is None:
+        return ""
+
+    lines = ["Supported workflows:"]
+    for workflow_name, trigger_inputs in workflow_map.items():
+        required_inputs = [k for k, v in trigger_inputs.items() if v]
+        if required_inputs:
+            inputs_str = ", ".join(f"`{t}`" for t in required_inputs)
+            lines.append(f"  - `{workflow_name}`: requires {inputs_str}")
+        else:
+            lines.append(f"  - `{workflow_name}`: default (no additional inputs required)")
+
+    return "\n".join(lines)
+
+
 def make_doc_string(
     inputs,
     outputs,
@@ -669,17 +918,17 @@ def make_doc_string(
     # Add description
     if description:
         desc_lines = description.strip().split("\n")
-        aligned_desc = "\n".join("  " + line for line in desc_lines)
+        aligned_desc = "\n".join("  " + line.rstrip() for line in desc_lines)
         output += aligned_desc + "\n\n"
 
     # Add components section if provided
     if expected_components and len(expected_components) > 0:
-        components_str = format_components(expected_components, indent_level=2)
+        components_str = format_components(expected_components, indent_level=2, add_empty_lines=False)
         output += components_str + "\n\n"
 
     # Add configs section if provided
     if expected_configs and len(expected_configs) > 0:
-        configs_str = format_configs(expected_configs, indent_level=2)
+        configs_str = format_configs(expected_configs, indent_level=2, add_empty_lines=False)
         output += configs_str + "\n\n"
 
     # Add inputs section
@@ -690,3 +939,69 @@ def make_doc_string(
     output += format_output_params(outputs, indent_level=2)
 
     return output
+
+
+def combine_inputs(*named_input_lists: List[Tuple[str, List[InputParam]]]) -> List[InputParam]:
+    """
+    Combines multiple lists of InputParam objects from different blocks. For duplicate inputs, updates only if current
+    default value is None and new default value is not None. Warns if multiple non-None default values exist for the
+    same input.
+
+    Args:
+        named_input_lists: List of tuples containing (block_name, input_param_list) pairs
+
+    Returns:
+        List[InputParam]: Combined list of unique InputParam objects
+    """
+    combined_dict = {}  # name -> InputParam
+    value_sources = {}  # name -> block_name
+
+    for block_name, inputs in named_input_lists:
+        for input_param in inputs:
+            if input_param.name is None and input_param.kwargs_type is not None:
+                input_name = "*_" + input_param.kwargs_type
+            else:
+                input_name = input_param.name
+            if input_name in combined_dict:
+                current_param = combined_dict[input_name]
+                if (
+                    current_param.default is not None
+                    and input_param.default is not None
+                    and current_param.default != input_param.default
+                ):
+                    warnings.warn(
+                        f"Multiple different default values found for input '{input_name}': "
+                        f"{current_param.default} (from block '{value_sources[input_name]}') and "
+                        f"{input_param.default} (from block '{block_name}'). Using {current_param.default}."
+                    )
+                if current_param.default is None and input_param.default is not None:
+                    combined_dict[input_name] = input_param
+                    value_sources[input_name] = block_name
+            else:
+                combined_dict[input_name] = input_param
+                value_sources[input_name] = block_name
+
+    return list(combined_dict.values())
+
+
+def combine_outputs(*named_output_lists: List[Tuple[str, List[OutputParam]]]) -> List[OutputParam]:
+    """
+    Combines multiple lists of OutputParam objects from different blocks. For duplicate outputs, keeps the first
+    occurrence of each output name.
+
+    Args:
+        named_output_lists: List of tuples containing (block_name, output_param_list) pairs
+
+    Returns:
+        List[OutputParam]: Combined list of unique OutputParam objects
+    """
+    combined_dict = {}  # name -> OutputParam
+
+    for block_name, outputs in named_output_lists:
+        for output_param in outputs:
+            if (output_param.name not in combined_dict) or (
+                combined_dict[output_param.name].kwargs_type is None and output_param.kwargs_type is not None
+            ):
+                combined_dict[output_param.name] = output_param
+
+    return list(combined_dict.values())

src/diffusers/modular_pipelines/qwenimage/before_denoise.py

@@ -118,7 +118,40 @@ def get_timesteps(scheduler, num_inference_steps, strength):
 # ====================
 
 
+# auto_docstring
 class QwenImagePrepareLatentsStep(ModularPipelineBlocks):
+    """
+    Prepare initial random noise for the generation process
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          dtype (`dtype`, *optional*, defaults to torch.float32):
+              The dtype of the model inputs, can be generated in input step.
+
+      Outputs:
+          height (`int`):
+              if not set, updated to default value
+          width (`int`):
+              if not set, updated to default value
+          latents (`Tensor`):
+              The initial latents to use for the denoising process
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -134,28 +167,20 @@ class QwenImagePrepareLatentsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("latents"),
-            InputParam(name="height"),
-            InputParam(name="width"),
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="generator"),
-            InputParam(
-                name="batch_size",
-                required=True,
-                type_hint=int,
-                description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.",
-            ),
-            InputParam(
-                name="dtype",
-                required=True,
-                type_hint=torch.dtype,
-                description="The dtype of the model inputs, can be generated in input step.",
-            ),
+            InputParam.template("latents"),
+            InputParam.template("height"),
+            InputParam.template("width"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("generator"),
+            InputParam.template("batch_size"),
+            InputParam.template("dtype"),
         ]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
+            OutputParam(name="height", type_hint=int, description="if not set, updated to default value"),
+            OutputParam(name="width", type_hint=int, description="if not set, updated to default value"),
             OutputParam(
                 name="latents",
                 type_hint=torch.Tensor,
@@ -209,7 +234,42 @@ class QwenImagePrepareLatentsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageLayeredPrepareLatentsStep(ModularPipelineBlocks):
+    """
+    Prepare initial random noise (B, layers+1, C, H, W) for the generation process
+
+      Components:
+          pachifier (`QwenImageLayeredPachifier`)
+
+      Inputs:
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          layers (`int`, *optional*, defaults to 4):
+              Number of layers to extract from the image
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          dtype (`dtype`, *optional*, defaults to torch.float32):
+              The dtype of the model inputs, can be generated in input step.
+
+      Outputs:
+          height (`int`):
+              if not set, updated to default value
+          width (`int`):
+              if not set, updated to default value
+          latents (`Tensor`):
+              The initial latents to use for the denoising process
+    """
+
     model_name = "qwenimage-layered"
 
     @property
@@ -225,29 +285,21 @@ class QwenImageLayeredPrepareLatentsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("latents"),
-            InputParam(name="height"),
-            InputParam(name="width"),
-            InputParam(name="layers", default=4),
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="generator"),
-            InputParam(
-                name="batch_size",
-                required=True,
-                type_hint=int,
-                description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can be generated in input step.",
-            ),
-            InputParam(
-                name="dtype",
-                required=True,
-                type_hint=torch.dtype,
-                description="The dtype of the model inputs, can be generated in input step.",
-            ),
+            InputParam.template("latents"),
+            InputParam.template("height"),
+            InputParam.template("width"),
+            InputParam.template("layers"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("generator"),
+            InputParam.template("batch_size"),
+            InputParam.template("dtype"),
         ]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
+            OutputParam(name="height", type_hint=int, description="if not set, updated to default value"),
+            OutputParam(name="width", type_hint=int, description="if not set, updated to default value"),
             OutputParam(
                 name="latents",
                 type_hint=torch.Tensor,
@@ -301,7 +353,31 @@ class QwenImageLayeredPrepareLatentsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImagePrepareLatentsWithStrengthStep(ModularPipelineBlocks):
+    """
+    Step that adds noise to image latents for image-to-image/inpainting. Should be run after set_timesteps,
+    prepare_latents. Both noise and image latents should alreadybe patchified.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          latents (`Tensor`):
+              The initial random noised, can be generated in prepare latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (Can be
+              generated from vae encoder and updated in input step.)
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+
+      Outputs:
+          initial_noise (`Tensor`):
+              The initial random noised used for inpainting denoising.
+          latents (`Tensor`):
+              The scaled noisy latents to use for inpainting/image-to-image denoising.
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -323,12 +399,7 @@ class QwenImagePrepareLatentsWithStrengthStep(ModularPipelineBlocks):
                 type_hint=torch.Tensor,
                 description="The initial random noised, can be generated in prepare latent step.",
             ),
-            InputParam(
-                name="image_latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The image latents to use for the denoising process. Can be generated in vae encoder and packed in input step.",
-            ),
+            InputParam.template("image_latents", note="Can be generated from vae encoder and updated in input step."),
             InputParam(
                 name="timesteps",
                 required=True,
@@ -345,6 +416,11 @@ class QwenImagePrepareLatentsWithStrengthStep(ModularPipelineBlocks):
                 type_hint=torch.Tensor,
                 description="The initial random noised used for inpainting denoising.",
             ),
+            OutputParam(
+                name="latents",
+                type_hint=torch.Tensor,
+                description="The scaled noisy latents to use for inpainting/image-to-image denoising.",
+            ),
         ]
 
     @staticmethod
@@ -382,7 +458,29 @@ class QwenImagePrepareLatentsWithStrengthStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageCreateMaskLatentsStep(ModularPipelineBlocks):
+    """
+    Step that creates mask latents from preprocessed mask_image by interpolating to latent space.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          processed_mask_image (`Tensor`):
+              The processed mask to use for the inpainting process.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          dtype (`dtype`, *optional*, defaults to torch.float32):
+              The dtype of the model inputs, can be generated in input step.
+
+      Outputs:
+          mask (`Tensor`):
+              The mask to use for the inpainting process.
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -404,9 +502,9 @@ class QwenImageCreateMaskLatentsStep(ModularPipelineBlocks):
                 type_hint=torch.Tensor,
                 description="The processed mask to use for the inpainting process.",
             ),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="dtype", required=True),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("dtype"),
         ]
 
     @property
@@ -450,7 +548,27 @@ class QwenImageCreateMaskLatentsStep(ModularPipelineBlocks):
 # ====================
 
 
+# auto_docstring
 class QwenImageSetTimestepsStep(ModularPipelineBlocks):
+    """
+    Step that sets the scheduler's timesteps for text-to-image generation. Should be run after prepare latents step.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          latents (`Tensor`):
+              The initial random noised latents for the denoising process. Can be generated in prepare latents step.
+
+      Outputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -466,13 +584,13 @@ class QwenImageSetTimestepsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="num_inference_steps", default=50),
-            InputParam(name="sigmas"),
+            InputParam.template("num_inference_steps"),
+            InputParam.template("sigmas"),
             InputParam(
                 name="latents",
                 required=True,
                 type_hint=torch.Tensor,
-                description="The latents to use for the denoising process, used to calculate the image sequence length.",
+                description="The initial random noised latents for the denoising process. Can be generated in prepare latents step.",
             ),
         ]
 
@@ -516,7 +634,27 @@ class QwenImageSetTimestepsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageLayeredSetTimestepsStep(ModularPipelineBlocks):
+    """
+    Set timesteps step for QwenImage Layered with custom mu calculation based on image_latents.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process.
+    """
+
     model_name = "qwenimage-layered"
 
     @property
@@ -532,15 +670,17 @@ class QwenImageLayeredSetTimestepsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("num_inference_steps", default=50, type_hint=int),
-            InputParam("sigmas", type_hint=List[float]),
-            InputParam("image_latents", required=True, type_hint=torch.Tensor),
+            InputParam.template("num_inference_steps"),
+            InputParam.template("sigmas"),
+            InputParam.template("image_latents"),
         ]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
-            OutputParam(name="timesteps", type_hint=torch.Tensor),
+            OutputParam(
+                name="timesteps", type_hint=torch.Tensor, description="The timesteps to use for the denoising process."
+            ),
         ]
 
     @torch.no_grad()
@@ -574,7 +714,32 @@ class QwenImageLayeredSetTimestepsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageSetTimestepsWithStrengthStep(ModularPipelineBlocks):
+    """
+    Step that sets the scheduler's timesteps for image-to-image generation, and inpainting. Should be run after prepare
+    latents step.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          latents (`Tensor`):
+              The latents to use for the denoising process. Can be generated in prepare latents step.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+
+      Outputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process.
+          num_inference_steps (`int`):
+              The number of denoising steps to perform at inference time. Updated based on strength.
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -590,15 +755,15 @@ class QwenImageSetTimestepsWithStrengthStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="num_inference_steps", default=50),
-            InputParam(name="sigmas"),
+            InputParam.template("num_inference_steps"),
+            InputParam.template("sigmas"),
             InputParam(
-                name="latents",
+                "latents",
                 required=True,
                 type_hint=torch.Tensor,
-                description="The latents to use for the denoising process, used to calculate the image sequence length.",
+                description="The latents to use for the denoising process. Can be generated in prepare latents step.",
             ),
-            InputParam(name="strength", default=0.9),
+            InputParam.template("strength", default=0.9),
         ]
 
     @property
@@ -607,7 +772,12 @@ class QwenImageSetTimestepsWithStrengthStep(ModularPipelineBlocks):
             OutputParam(
                 name="timesteps",
                 type_hint=torch.Tensor,
-                description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",
+                description="The timesteps to use for the denoising process.",
+            ),
+            OutputParam(
+                name="num_inference_steps",
+                type_hint=int,
+                description="The number of denoising steps to perform at inference time. Updated based on strength.",
             ),
         ]
 
@@ -654,7 +824,29 @@ class QwenImageSetTimestepsWithStrengthStep(ModularPipelineBlocks):
 ## RoPE inputs for denoiser
 
 
+# auto_docstring
 class QwenImageRoPEInputsStep(ModularPipelineBlocks):
+    """
+    Step that prepares the RoPE inputs for the denoising process. Should be place after prepare_latents step
+
+      Inputs:
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+
+      Outputs:
+          img_shapes (`List`):
+              The shapes of the images latents, used for RoPE calculation
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -666,11 +858,11 @@ class QwenImageRoPEInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="batch_size", required=True),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="prompt_embeds_mask"),
-            InputParam(name="negative_prompt_embeds_mask"),
+            InputParam.template("batch_size"),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("prompt_embeds_mask"),
+            InputParam.template("negative_prompt_embeds_mask"),
         ]
 
     @property
@@ -702,7 +894,34 @@ class QwenImageRoPEInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageEditRoPEInputsStep(ModularPipelineBlocks):
+    """
+    Step that prepares the RoPE inputs for denoising process. This is used in QwenImage Edit. Should be placed after
+    prepare_latents step
+
+      Inputs:
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          image_height (`int`):
+              The height of the reference image. Can be generated in input step.
+          image_width (`int`):
+              The width of the reference image. Can be generated in input step.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+
+      Outputs:
+          img_shapes (`List`):
+              The shapes of the images latents, used for RoPE calculation
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -712,13 +931,23 @@ class QwenImageEditRoPEInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="batch_size", required=True),
-            InputParam(name="image_height", required=True),
-            InputParam(name="image_width", required=True),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="prompt_embeds_mask"),
-            InputParam(name="negative_prompt_embeds_mask"),
+            InputParam.template("batch_size"),
+            InputParam(
+                name="image_height",
+                required=True,
+                type_hint=int,
+                description="The height of the reference image. Can be generated in input step.",
+            ),
+            InputParam(
+                name="image_width",
+                required=True,
+                type_hint=int,
+                description="The width of the reference image. Can be generated in input step.",
+            ),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("prompt_embeds_mask"),
+            InputParam.template("negative_prompt_embeds_mask"),
         ]
 
     @property
@@ -756,7 +985,39 @@ class QwenImageEditRoPEInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageEditPlusRoPEInputsStep(ModularPipelineBlocks):
+    """
+    Step that prepares the RoPE inputs for denoising process. This is used in QwenImage Edit Plus.
+      Unlike Edit, Edit Plus handles lists of image_height/image_width for multiple reference images. Should be placed
+      after prepare_latents step.
+
+      Inputs:
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          image_height (`List`):
+              The heights of the reference images. Can be generated in input step.
+          image_width (`List`):
+              The widths of the reference images. Can be generated in input step.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+
+      Outputs:
+          img_shapes (`List`):
+              The shapes of the image latents, used for RoPE calculation
+          txt_seq_lens (`List`):
+              The sequence lengths of the prompt embeds, used for RoPE calculation
+          negative_txt_seq_lens (`List`):
+              The sequence lengths of the negative prompt embeds, used for RoPE calculation
+    """
+
     model_name = "qwenimage-edit-plus"
 
     @property
@@ -770,13 +1031,23 @@ class QwenImageEditPlusRoPEInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="batch_size", required=True),
-            InputParam(name="image_height", required=True, type_hint=List[int]),
-            InputParam(name="image_width", required=True, type_hint=List[int]),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="prompt_embeds_mask"),
-            InputParam(name="negative_prompt_embeds_mask"),
+            InputParam.template("batch_size"),
+            InputParam(
+                name="image_height",
+                required=True,
+                type_hint=List[int],
+                description="The heights of the reference images. Can be generated in input step.",
+            ),
+            InputParam(
+                name="image_width",
+                required=True,
+                type_hint=List[int],
+                description="The widths of the reference images. Can be generated in input step.",
+            ),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("prompt_embeds_mask"),
+            InputParam.template("negative_prompt_embeds_mask"),
         ]
 
     @property
@@ -832,7 +1103,37 @@ class QwenImageEditPlusRoPEInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageLayeredRoPEInputsStep(ModularPipelineBlocks):
+    """
+    Step that prepares the RoPE inputs for the denoising process. Should be place after prepare_latents step
+
+      Inputs:
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          layers (`int`, *optional*, defaults to 4):
+              Number of layers to extract from the image
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+
+      Outputs:
+          img_shapes (`List`):
+              The shapes of the image latents, used for RoPE calculation
+          txt_seq_lens (`List`):
+              The sequence lengths of the prompt embeds, used for RoPE calculation
+          negative_txt_seq_lens (`List`):
+              The sequence lengths of the negative prompt embeds, used for RoPE calculation
+          additional_t_cond (`Tensor`):
+              The additional t cond, used for RoPE calculation
+    """
+
     model_name = "qwenimage-layered"
 
     @property
@@ -844,12 +1145,12 @@ class QwenImageLayeredRoPEInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="batch_size", required=True),
-            InputParam(name="layers", required=True),
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
-            InputParam(name="prompt_embeds_mask"),
-            InputParam(name="negative_prompt_embeds_mask"),
+            InputParam.template("batch_size"),
+            InputParam.template("layers"),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("prompt_embeds_mask"),
+            InputParam.template("negative_prompt_embeds_mask"),
         ]
 
     @property
@@ -914,7 +1215,34 @@ class QwenImageLayeredRoPEInputsStep(ModularPipelineBlocks):
 
 
 ## ControlNet inputs for denoiser
+
+
+# auto_docstring
 class QwenImageControlNetBeforeDenoiserStep(ModularPipelineBlocks):
+    """
+    step that prepare inputs for controlnet. Insert before the Denoise Step, after set_timesteps step.
+
+      Components:
+          controlnet (`QwenImageControlNetModel`)
+
+      Inputs:
+          control_guidance_start (`float`, *optional*, defaults to 0.0):
+              When to start applying ControlNet.
+          control_guidance_end (`float`, *optional*, defaults to 1.0):
+              When to stop applying ControlNet.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning.
+          control_image_latents (`Tensor`):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+
+      Outputs:
+          controlnet_keep (`List`):
+              The controlnet keep values
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -930,12 +1258,17 @@ class QwenImageControlNetBeforeDenoiserStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("control_guidance_start", default=0.0),
-            InputParam("control_guidance_end", default=1.0),
-            InputParam("controlnet_conditioning_scale", default=1.0),
-            InputParam("control_image_latents", required=True),
+            InputParam.template("control_guidance_start"),
+            InputParam.template("control_guidance_end"),
+            InputParam.template("controlnet_conditioning_scale"),
             InputParam(
-                "timesteps",
+                name="control_image_latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step.",
+            ),
+            InputParam(
+                name="timesteps",
                 required=True,
                 type_hint=torch.Tensor,
                 description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",

src/diffusers/modular_pipelines/qwenimage/decoders.py

@@ -12,10 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List, Union
+from typing import Any, Dict, List
 
-import numpy as np
-import PIL
 import torch
 
 from ...configuration_utils import FrozenDict
@@ -31,7 +29,30 @@ logger = logging.get_logger(__name__)
 
 
 # after denoising loop (unpack latents)
+
+
+# auto_docstring
 class QwenImageAfterDenoiseStep(ModularPipelineBlocks):
+    """
+    Step that unpack the latents from 3D tensor (batch_size, sequence_length, channels) into 5D tensor (batch_size,
+    channels, 1, height, width)
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          latents (`Tensor`):
+              The latents to decode, can be generated in the denoise step.
+
+      Outputs:
+          latents (`Tensor`):
+              The denoisedlatents unpacked to B, C, 1, H, W
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -49,13 +70,21 @@ class QwenImageAfterDenoiseStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="height", required=True),
-            InputParam(name="width", required=True),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
             InputParam(
                 name="latents",
                 required=True,
                 type_hint=torch.Tensor,
-                description="The latents to decode, can be generated in the denoise step",
+                description="The latents to decode, can be generated in the denoise step.",
+            ),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                name="latents", type_hint=torch.Tensor, description="The denoisedlatents unpacked to B, C, 1, H, W"
             ),
         ]
 
@@ -72,7 +101,29 @@ class QwenImageAfterDenoiseStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageLayeredAfterDenoiseStep(ModularPipelineBlocks):
+    """
+    Unpack latents from (B, seq, C*4) to (B, C, layers+1, H, W) after denoising.
+
+      Components:
+          pachifier (`QwenImageLayeredPachifier`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          layers (`int`, *optional*, defaults to 4):
+              Number of layers to extract from the image
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents. (unpacked to B, C, layers+1, H, W)
+    """
+
     model_name = "qwenimage-layered"
 
     @property
@@ -88,10 +139,21 @@ class QwenImageLayeredAfterDenoiseStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("latents", required=True, type_hint=torch.Tensor),
-            InputParam("height", required=True, type_hint=int),
-            InputParam("width", required=True, type_hint=int),
-            InputParam("layers", required=True, type_hint=int),
+            InputParam(
+                name="latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The denoised latents to decode, can be generated in the denoise step.",
+            ),
+            InputParam.template("height", required=True),
+            InputParam.template("width", required=True),
+            InputParam.template("layers"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam.template("latents", note="unpacked to B, C, layers+1, H, W"),
         ]
 
     @torch.no_grad()
@@ -112,7 +174,26 @@ class QwenImageLayeredAfterDenoiseStep(ModularPipelineBlocks):
 
 
 # decode step
+
+
+# auto_docstring
 class QwenImageDecoderStep(ModularPipelineBlocks):
+    """
+    Step that decodes the latents to images
+
+      Components:
+          vae (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -134,19 +215,13 @@ class QwenImageDecoderStep(ModularPipelineBlocks):
                 name="latents",
                 required=True,
                 type_hint=torch.Tensor,
-                description="The latents to decode, can be generated in the denoise step",
+                description="The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step.",
             ),
         ]
 
     @property
-    def intermediate_outputs(self) -> List[str]:
-        return [
-            OutputParam(
-                "images",
-                type_hint=Union[List[PIL.Image.Image], List[torch.Tensor], List[np.array]],
-                description="The generated images, can be a PIL.Image.Image, torch.Tensor or a numpy array",
-            )
-        ]
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [OutputParam.template("images", note="tensor output of the vae decoder.")]
 
     @torch.no_grad()
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
@@ -176,7 +251,26 @@ class QwenImageDecoderStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageLayeredDecoderStep(ModularPipelineBlocks):
+    """
+    Decode unpacked latents (B, C, layers+1, H, W) into layer images.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage-layered"
 
     @property
@@ -198,14 +292,19 @@ class QwenImageLayeredDecoderStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("latents", required=True, type_hint=torch.Tensor),
-            InputParam("output_type", default="pil", type_hint=str),
+            InputParam(
+                name="latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise step.",
+            ),
+            InputParam.template("output_type"),
         ]
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
-            OutputParam(name="images", type_hint=List[List[PIL.Image.Image]]),
+            OutputParam.template("images"),
         ]
 
     @torch.no_grad()
@@ -251,7 +350,27 @@ class QwenImageLayeredDecoderStep(ModularPipelineBlocks):
 
 
 # postprocess the decoded images
+
+
+# auto_docstring
 class QwenImageProcessImagesOutputStep(ModularPipelineBlocks):
+    """
+    postprocess the generated image
+
+      Components:
+          image_processor (`VaeImageProcessor`)
+
+      Inputs:
+          images (`Tensor`):
+              the generated image tensor from decoders step
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -272,15 +391,19 @@ class QwenImageProcessImagesOutputStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("images", required=True, description="the generated image from decoders step"),
             InputParam(
-                name="output_type",
-                default="pil",
-                type_hint=str,
-                description="The type of the output images, can be 'pil', 'np', 'pt'",
+                name="images",
+                required=True,
+                type_hint=torch.Tensor,
+                description="the generated image tensor from decoders step",
             ),
+            InputParam.template("output_type"),
         ]
 
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [OutputParam.template("images")]
+
     @staticmethod
     def check_inputs(output_type):
         if output_type not in ["pil", "np", "pt"]:
@@ -301,7 +424,28 @@ class QwenImageProcessImagesOutputStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageInpaintProcessImagesOutputStep(ModularPipelineBlocks):
+    """
+    postprocess the generated image, optional apply the mask overally to the original image..
+
+      Components:
+          image_mask_processor (`InpaintProcessor`)
+
+      Inputs:
+          images (`Tensor`):
+              the generated image tensor from decoders step
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+          mask_overlay_kwargs (`Dict`, *optional*):
+              The kwargs for the postprocess step to apply the mask overlay. generated in
+              InpaintProcessImagesInputStep.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -322,16 +466,24 @@ class QwenImageInpaintProcessImagesOutputStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("images", required=True, description="the generated image from decoders step"),
             InputParam(
-                name="output_type",
-                default="pil",
-                type_hint=str,
-                description="The type of the output images, can be 'pil', 'np', 'pt'",
+                name="images",
+                required=True,
+                type_hint=torch.Tensor,
+                description="the generated image tensor from decoders step",
+            ),
+            InputParam.template("output_type"),
+            InputParam(
+                name="mask_overlay_kwargs",
+                type_hint=Dict[str, Any],
+                description="The kwargs for the postprocess step to apply the mask overlay. generated in InpaintProcessImagesInputStep.",
             ),
-            InputParam("mask_overlay_kwargs"),
         ]
 
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [OutputParam.template("images")]
+
     @staticmethod
     def check_inputs(output_type, mask_overlay_kwargs):
         if output_type not in ["pil", "np", "pt"]:

src/diffusers/modular_pipelines/qwenimage/denoise.py

@@ -50,7 +50,7 @@ class QwenImageLoopBeforeDenoiser(ModularPipelineBlocks):
     def inputs(self) -> List[InputParam]:
         return [
             InputParam(
-                "latents",
+                name="latents",
                 required=True,
                 type_hint=torch.Tensor,
                 description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
@@ -80,17 +80,12 @@ class QwenImageEditLoopBeforeDenoiser(ModularPipelineBlocks):
     def inputs(self) -> List[InputParam]:
         return [
             InputParam(
-                "latents",
+                name="latents",
                 required=True,
                 type_hint=torch.Tensor,
                 description="The initial latents to use for the denoising process. Can be generated in prepare_latent step.",
             ),
-            InputParam(
-                "image_latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The initial image latents to use for the denoising process. Can be encoded in vae_encoder step and packed in prepare_image_latents step.",
-            ),
+            InputParam.template("image_latents"),
         ]
 
     @torch.no_grad()
@@ -134,29 +129,12 @@ class QwenImageLoopBeforeDenoiserControlNet(ModularPipelineBlocks):
                 type_hint=torch.Tensor,
                 description="The control image to use for the denoising process. Can be generated in prepare_controlnet_inputs step.",
             ),
+            InputParam.template("controlnet_conditioning_scale", note="updated in prepare_controlnet_inputs step."),
             InputParam(
-                "controlnet_conditioning_scale",
-                type_hint=float,
-                description="The controlnet conditioning scale value to use for the denoising process. Can be generated in prepare_controlnet_inputs step.",
-            ),
-            InputParam(
-                "controlnet_keep",
+                name="controlnet_keep",
                 required=True,
                 type_hint=List[float],
-                description="The controlnet keep values to use for the denoising process. Can be generated in prepare_controlnet_inputs step.",
-            ),
-            InputParam(
-                "num_inference_steps",
-                required=True,
-                type_hint=int,
-                description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
-            ),
-            InputParam(
-                kwargs_type="denoiser_input_fields",
-                description=(
-                    "All conditional model inputs for the denoiser. "
-                    "It should contain prompt_embeds/negative_prompt_embeds."
-                ),
+                description="The controlnet keep values. Can be generated in prepare_controlnet_inputs step.",
             ),
         ]
 
@@ -217,28 +195,13 @@ class QwenImageLoopDenoiser(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("attention_kwargs"),
-            InputParam(
-                "latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The latents to use for the denoising process. Can be generated in prepare_latents step.",
-            ),
-            InputParam(
-                "num_inference_steps",
-                required=True,
-                type_hint=int,
-                description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
-            ),
-            InputParam(
-                kwargs_type="denoiser_input_fields",
-                description="conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
-            ),
+            InputParam.template("attention_kwargs"),
+            InputParam.template("denoiser_input_fields"),
             InputParam(
                 "img_shapes",
                 required=True,
                 type_hint=List[Tuple[int, int]],
-                description="The shape of the image latents for RoPE calculation. Can be generated in prepare_additional_inputs step.",
+                description="The shape of the image latents for RoPE calculation. can be generated in prepare_additional_inputs step.",
             ),
         ]
 
@@ -317,23 +280,8 @@ class QwenImageEditLoopDenoiser(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam("attention_kwargs"),
-            InputParam(
-                "latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The latents to use for the denoising process. Can be generated in prepare_latents step.",
-            ),
-            InputParam(
-                "num_inference_steps",
-                required=True,
-                type_hint=int,
-                description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
-            ),
-            InputParam(
-                kwargs_type="denoiser_input_fields",
-                description="conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
-            ),
+            InputParam.template("attention_kwargs"),
+            InputParam.template("denoiser_input_fields"),
             InputParam(
                 "img_shapes",
                 required=True,
@@ -415,7 +363,7 @@ class QwenImageLoopAfterDenoiser(ModularPipelineBlocks):
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
         return [
-            OutputParam("latents", type_hint=torch.Tensor, description="The denoised latents."),
+            OutputParam.template("latents"),
         ]
 
     @torch.no_grad()
@@ -456,24 +404,19 @@ class QwenImageLoopAfterDenoiserInpaint(ModularPipelineBlocks):
                 type_hint=torch.Tensor,
                 description="The mask to use for the inpainting process. Can be generated in inpaint prepare latents step.",
             ),
-            InputParam(
-                "image_latents",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The image latents to use for the inpainting process. Can be generated in inpaint prepare latents step.",
-            ),
+            InputParam.template("image_latents"),
             InputParam(
                 "initial_noise",
                 required=True,
                 type_hint=torch.Tensor,
                 description="The initial noise to use for the inpainting process. Can be generated in inpaint prepare latents step.",
             ),
-            InputParam(
-                "timesteps",
-                required=True,
-                type_hint=torch.Tensor,
-                description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",
-            ),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam.template("latents"),
         ]
 
     @torch.no_grad()
@@ -515,17 +458,12 @@ class QwenImageDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
     def loop_inputs(self) -> List[InputParam]:
         return [
             InputParam(
-                "timesteps",
+                name="timesteps",
                 required=True,
                 type_hint=torch.Tensor,
                 description="The timesteps to use for the denoising process. Can be generated in set_timesteps step.",
             ),
-            InputParam(
-                "num_inference_steps",
-                required=True,
-                type_hint=int,
-                description="The number of inference steps to use for the denoising process. Can be generated in set_timesteps step.",
-            ),
+            InputParam.template("num_inference_steps", required=True),
         ]
 
     @torch.no_grad()
@@ -557,7 +495,42 @@ class QwenImageDenoiseLoopWrapper(LoopSequentialPipelineBlocks):
 
 
 # Qwen Image (text2image, image2image)
+
+
+# auto_docstring
 class QwenImageDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageLoopBeforeDenoiser`
+       - `QwenImageLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+      This block supports text2image and image2image tasks for QwenImage.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. can be generated in prepare_additional_inputs step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
 
     block_classes = [
@@ -570,8 +543,8 @@ class QwenImageDenoiseStep(QwenImageDenoiseLoopWrapper):
     @property
     def description(self) -> str:
         return (
-            "Denoise step that iteratively denoise the latents. \n"
-            "Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method \n"
+            "Denoise step that iteratively denoise the latents.\n"
+            "Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method\n"
             "At each iteration, it runs blocks defined in `sub_blocks` sequencially:\n"
             " - `QwenImageLoopBeforeDenoiser`\n"
             " - `QwenImageLoopDenoiser`\n"
@@ -581,7 +554,47 @@ class QwenImageDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image (inpainting)
+# auto_docstring
 class QwenImageInpaintDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageLoopBeforeDenoiser`
+       - `QwenImageLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+       - `QwenImageLoopAfterDenoiserInpaint`
+      This block supports inpainting tasks for QwenImage.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. can be generated in prepare_additional_inputs step.
+          mask (`Tensor`):
+              The mask to use for the inpainting process. Can be generated in inpaint prepare latents step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          initial_noise (`Tensor`):
+              The initial noise to use for the inpainting process. Can be generated in inpaint prepare latents step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageLoopBeforeDenoiser,
@@ -606,7 +619,47 @@ class QwenImageInpaintDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image (text2image, image2image) with controlnet
+# auto_docstring
 class QwenImageControlNetDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageLoopBeforeDenoiser`
+       - `QwenImageLoopBeforeDenoiserControlNet`
+       - `QwenImageLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+      This block supports text2img/img2img tasks with controlnet for QwenImage.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) controlnet (`QwenImageControlNetModel`) transformer
+          (`QwenImageTransformer2DModel`) scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          control_image_latents (`Tensor`):
+              The control image to use for the denoising process. Can be generated in prepare_controlnet_inputs step.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning. (updated in prepare_controlnet_inputs step.)
+          controlnet_keep (`List`):
+              The controlnet keep values. Can be generated in prepare_controlnet_inputs step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. can be generated in prepare_additional_inputs step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageLoopBeforeDenoiser,
@@ -631,7 +684,54 @@ class QwenImageControlNetDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image (inpainting) with controlnet
+# auto_docstring
 class QwenImageInpaintControlNetDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageLoopBeforeDenoiser`
+       - `QwenImageLoopBeforeDenoiserControlNet`
+       - `QwenImageLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+       - `QwenImageLoopAfterDenoiserInpaint`
+      This block supports inpainting tasks with controlnet for QwenImage.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) controlnet (`QwenImageControlNetModel`) transformer
+          (`QwenImageTransformer2DModel`) scheduler (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          control_image_latents (`Tensor`):
+              The control image to use for the denoising process. Can be generated in prepare_controlnet_inputs step.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning. (updated in prepare_controlnet_inputs step.)
+          controlnet_keep (`List`):
+              The controlnet keep values. Can be generated in prepare_controlnet_inputs step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. can be generated in prepare_additional_inputs step.
+          mask (`Tensor`):
+              The mask to use for the inpainting process. Can be generated in inpaint prepare latents step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          initial_noise (`Tensor`):
+              The initial noise to use for the inpainting process. Can be generated in inpaint prepare latents step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageLoopBeforeDenoiser,
@@ -664,7 +764,42 @@ class QwenImageInpaintControlNetDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image Edit (image2image)
+# auto_docstring
 class QwenImageEditDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageEditLoopBeforeDenoiser`
+       - `QwenImageEditLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+      This block supports QwenImage Edit.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. Can be generated in prepare_additional_inputs step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditLoopBeforeDenoiser,
@@ -687,7 +822,47 @@ class QwenImageEditDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image Edit (inpainting)
+# auto_docstring
 class QwenImageEditInpaintDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageEditLoopBeforeDenoiser`
+       - `QwenImageEditLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+       - `QwenImageLoopAfterDenoiserInpaint`
+      This block supports inpainting tasks for QwenImage Edit.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. Can be generated in prepare_additional_inputs step.
+          mask (`Tensor`):
+              The mask to use for the inpainting process. Can be generated in inpaint prepare latents step.
+          initial_noise (`Tensor`):
+              The initial noise to use for the inpainting process. Can be generated in inpaint prepare latents step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditLoopBeforeDenoiser,
@@ -712,7 +887,42 @@ class QwenImageEditInpaintDenoiseStep(QwenImageDenoiseLoopWrapper):
 
 
 # Qwen Image Layered (image2image)
+# auto_docstring
 class QwenImageLayeredDenoiseStep(QwenImageDenoiseLoopWrapper):
+    """
+    Denoise step that iteratively denoise the latents.
+      Its loop logic is defined in `QwenImageDenoiseLoopWrapper.__call__` method At each iteration, it runs blocks
+      defined in `sub_blocks` sequencially:
+       - `QwenImageEditLoopBeforeDenoiser`
+       - `QwenImageEditLoopDenoiser`
+       - `QwenImageLoopAfterDenoiser`
+      This block supports QwenImage Layered.
+
+      Components:
+          guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`)
+
+      Inputs:
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          latents (`Tensor`):
+              The initial latents to use for the denoising process. Can be generated in prepare_latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          img_shapes (`List`):
+              The shape of the image latents for RoPE calculation. Can be generated in prepare_additional_inputs step.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-layered"
     block_classes = [
         QwenImageEditLoopBeforeDenoiser,

src/diffusers/modular_pipelines/qwenimage/inputs.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 
@@ -109,7 +109,44 @@ def calculate_dimension_from_latents(latents: torch.Tensor, vae_scale_factor: in
     return height, width
 
 
+# auto_docstring
 class QwenImageTextInputsStep(ModularPipelineBlocks):
+    """
+    Text input processing step that standardizes text embeddings for the pipeline.
+      This step:
+        1. Determines `batch_size` and `dtype` based on `prompt_embeds`
+        2. Ensures all text embeddings have consistent batch sizes (batch_size * num_images_per_prompt)
+
+      This block should be placed after all encoder steps to process the text embeddings before they are used in
+      subsequent pipeline steps.
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -129,26 +166,22 @@ class QwenImageTextInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="prompt_embeds", required=True, kwargs_type="denoiser_input_fields"),
-            InputParam(name="prompt_embeds_mask", required=True, kwargs_type="denoiser_input_fields"),
-            InputParam(name="negative_prompt_embeds", kwargs_type="denoiser_input_fields"),
-            InputParam(name="negative_prompt_embeds_mask", kwargs_type="denoiser_input_fields"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("prompt_embeds"),
+            InputParam.template("prompt_embeds_mask"),
+            InputParam.template("negative_prompt_embeds"),
+            InputParam.template("negative_prompt_embeds_mask"),
         ]
 
     @property
-    def intermediate_outputs(self) -> List[str]:
+    def intermediate_outputs(self) -> List[OutputParam]:
         return [
-            OutputParam(
-                "batch_size",
-                type_hint=int,
-                description="Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt",
-            ),
-            OutputParam(
-                "dtype",
-                type_hint=torch.dtype,
-                description="Data type of model tensor inputs (determined by `prompt_embeds`)",
-            ),
+            OutputParam(name="batch_size", type_hint=int, description="The batch size of the prompt embeddings"),
+            OutputParam(name="dtype", type_hint=torch.dtype, description="The data type of the prompt embeddings"),
+            OutputParam.template("prompt_embeds", note="batch-expanded"),
+            OutputParam.template("prompt_embeds_mask", note="batch-expanded"),
+            OutputParam.template("negative_prompt_embeds", note="batch-expanded"),
+            OutputParam.template("negative_prompt_embeds_mask", note="batch-expanded"),
         ]
 
     @staticmethod
@@ -221,20 +254,76 @@ class QwenImageTextInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
-    """Input step for QwenImage: update height/width, expand batch, patchify."""
+    """
+    Input processing step that:
+        1. For image latent inputs: Updates height/width if None, patchifies, and expands batch size
+        2. For additional batch inputs: Expands batch dimensions to match final batch size
+
+      Configured inputs:
+        - Image latent inputs: ['image_latents']
+
+      This block should be placed after the encoder steps and the text input step.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and
+              batch-expanded)
+    """
 
     model_name = "qwenimage"
 
     def __init__(
         self,
-        image_latent_inputs: List[str] = ["image_latents"],
-        additional_batch_inputs: List[str] = [],
+        image_latent_inputs: Optional[List[InputParam]] = None,
+        additional_batch_inputs: Optional[List[InputParam]] = None,
     ):
+        # by default, process `image_latents`
+        if image_latent_inputs is None:
+            image_latent_inputs = [InputParam.template("image_latents")]
+        if additional_batch_inputs is None:
+            additional_batch_inputs = []
+
         if not isinstance(image_latent_inputs, list):
-            image_latent_inputs = [image_latent_inputs]
+            raise ValueError(f"image_latent_inputs must be a list, but got {type(image_latent_inputs)}")
+        else:
+            for input_param in image_latent_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(f"image_latent_inputs must be a list of InputParam, but got {type(input_param)}")
+
         if not isinstance(additional_batch_inputs, list):
-            additional_batch_inputs = [additional_batch_inputs]
+            raise ValueError(f"additional_batch_inputs must be a list, but got {type(additional_batch_inputs)}")
+        else:
+            for input_param in additional_batch_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(
+                        f"additional_batch_inputs must be a list of InputParam, but got {type(input_param)}"
+                    )
 
         self._image_latent_inputs = image_latent_inputs
         self._additional_batch_inputs = additional_batch_inputs
@@ -252,9 +341,9 @@ class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
         if self._image_latent_inputs or self._additional_batch_inputs:
             inputs_info = "\n\nConfigured inputs:"
             if self._image_latent_inputs:
-                inputs_info += f"\n  - Image latent inputs: {self._image_latent_inputs}"
+                inputs_info += f"\n  - Image latent inputs: {[p.name for p in self._image_latent_inputs]}"
             if self._additional_batch_inputs:
-                inputs_info += f"\n  - Additional batch inputs: {self._additional_batch_inputs}"
+                inputs_info += f"\n  - Additional batch inputs: {[p.name for p in self._additional_batch_inputs]}"
 
         placement_section = "\n\nThis block should be placed after the encoder steps and the text input step."
 
@@ -269,23 +358,19 @@ class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         inputs = [
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="batch_size", required=True),
-            InputParam(name="height"),
-            InputParam(name="width"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("batch_size"),
+            InputParam.template("height"),
+            InputParam.template("width"),
         ]
-
-        for image_latent_input_name in self._image_latent_inputs:
-            inputs.append(InputParam(name=image_latent_input_name))
-
-        for input_name in self._additional_batch_inputs:
-            inputs.append(InputParam(name=input_name))
+        # default is `image_latents`
+        inputs += self._image_latent_inputs + self._additional_batch_inputs
 
         return inputs
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
-        return [
+        outputs = [
             OutputParam(
                 name="image_height",
                 type_hint=int,
@@ -298,11 +383,43 @@ class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
             ),
         ]
 
+        # `height`/`width` are not new outputs, but they will be updated if any image latent inputs are provided
+        if len(self._image_latent_inputs) > 0:
+            outputs.append(
+                OutputParam(name="height", type_hint=int, description="if not provided, updated to image height")
+            )
+            outputs.append(
+                OutputParam(name="width", type_hint=int, description="if not provided, updated to image width")
+            )
+
+        # image latent inputs are modified in place (patchified and batch-expanded)
+        for input_param in self._image_latent_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (patchified and batch-expanded)",
+                )
+            )
+
+        # additional batch inputs (batch-expanded only)
+        for input_param in self._additional_batch_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (batch-expanded)",
+                )
+            )
+
+        return outputs
+
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
         # Process image latent inputs
-        for image_latent_input_name in self._image_latent_inputs:
+        for input_param in self._image_latent_inputs:
+            image_latent_input_name = input_param.name
             image_latent_tensor = getattr(block_state, image_latent_input_name)
             if image_latent_tensor is None:
                 continue
@@ -331,7 +448,8 @@ class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
             setattr(block_state, image_latent_input_name, image_latent_tensor)
 
         # Process additional batch inputs (only batch expansion)
-        for input_name in self._additional_batch_inputs:
+        for input_param in self._additional_batch_inputs:
+            input_name = input_param.name
             input_tensor = getattr(block_state, input_name)
             if input_tensor is None:
                 continue
@@ -349,20 +467,76 @@ class QwenImageAdditionalInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
-    """Input step for QwenImage Edit Plus: handles list of latents with different sizes."""
+    """
+    Input processing step for Edit Plus that:
+        1. For image latent inputs (list): Collects heights/widths, patchifies each, concatenates, expands batch
+        2. For additional batch inputs: Expands batch dimensions to match final batch size
+        Height/width defaults to last image in the list.
+
+      Configured inputs:
+        - Image latent inputs: ['image_latents']
+
+      This block should be placed after the encoder steps and the text input step.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          image_height (`List`):
+              The image heights calculated from the image latents dimension
+          image_width (`List`):
+              The image widths calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified,
+              concatenated, and batch-expanded)
+    """
 
     model_name = "qwenimage-edit-plus"
 
     def __init__(
         self,
-        image_latent_inputs: List[str] = ["image_latents"],
-        additional_batch_inputs: List[str] = [],
+        image_latent_inputs: Optional[List[InputParam]] = None,
+        additional_batch_inputs: Optional[List[InputParam]] = None,
     ):
+        if image_latent_inputs is None:
+            image_latent_inputs = [InputParam.template("image_latents")]
+        if additional_batch_inputs is None:
+            additional_batch_inputs = []
+
         if not isinstance(image_latent_inputs, list):
-            image_latent_inputs = [image_latent_inputs]
+            raise ValueError(f"image_latent_inputs must be a list, but got {type(image_latent_inputs)}")
+        else:
+            for input_param in image_latent_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(f"image_latent_inputs must be a list of InputParam, but got {type(input_param)}")
+
         if not isinstance(additional_batch_inputs, list):
-            additional_batch_inputs = [additional_batch_inputs]
+            raise ValueError(f"additional_batch_inputs must be a list, but got {type(additional_batch_inputs)}")
+        else:
+            for input_param in additional_batch_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(
+                        f"additional_batch_inputs must be a list of InputParam, but got {type(input_param)}"
+                    )
 
         self._image_latent_inputs = image_latent_inputs
         self._additional_batch_inputs = additional_batch_inputs
@@ -381,9 +555,9 @@ class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
         if self._image_latent_inputs or self._additional_batch_inputs:
             inputs_info = "\n\nConfigured inputs:"
             if self._image_latent_inputs:
-                inputs_info += f"\n  - Image latent inputs: {self._image_latent_inputs}"
+                inputs_info += f"\n  - Image latent inputs: {[p.name for p in self._image_latent_inputs]}"
             if self._additional_batch_inputs:
-                inputs_info += f"\n  - Additional batch inputs: {self._additional_batch_inputs}"
+                inputs_info += f"\n  - Additional batch inputs: {[p.name for p in self._additional_batch_inputs]}"
 
         placement_section = "\n\nThis block should be placed after the encoder steps and the text input step."
 
@@ -398,23 +572,20 @@ class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         inputs = [
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="batch_size", required=True),
-            InputParam(name="height"),
-            InputParam(name="width"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("batch_size"),
+            InputParam.template("height"),
+            InputParam.template("width"),
         ]
 
-        for image_latent_input_name in self._image_latent_inputs:
-            inputs.append(InputParam(name=image_latent_input_name))
-
-        for input_name in self._additional_batch_inputs:
-            inputs.append(InputParam(name=input_name))
+        # default is `image_latents`
+        inputs += self._image_latent_inputs + self._additional_batch_inputs
 
         return inputs
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
-        return [
+        outputs = [
             OutputParam(
                 name="image_height",
                 type_hint=List[int],
@@ -427,11 +598,43 @@ class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
             ),
         ]
 
+        # `height`/`width` are updated if any image latent inputs are provided
+        if len(self._image_latent_inputs) > 0:
+            outputs.append(
+                OutputParam(name="height", type_hint=int, description="if not provided, updated to image height")
+            )
+            outputs.append(
+                OutputParam(name="width", type_hint=int, description="if not provided, updated to image width")
+            )
+
+        # image latent inputs are modified in place (patchified, concatenated, and batch-expanded)
+        for input_param in self._image_latent_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (patchified, concatenated, and batch-expanded)",
+                )
+            )
+
+        # additional batch inputs (batch-expanded only)
+        for input_param in self._additional_batch_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (batch-expanded)",
+                )
+            )
+
+        return outputs
+
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
         # Process image latent inputs
-        for image_latent_input_name in self._image_latent_inputs:
+        for input_param in self._image_latent_inputs:
+            image_latent_input_name = input_param.name
             image_latent_tensor = getattr(block_state, image_latent_input_name)
             if image_latent_tensor is None:
                 continue
@@ -476,7 +679,8 @@ class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
             setattr(block_state, image_latent_input_name, packed_image_latent_tensors)
 
         # Process additional batch inputs (only batch expansion)
-        for input_name in self._additional_batch_inputs:
+        for input_param in self._additional_batch_inputs:
+            input_name = input_param.name
             input_tensor = getattr(block_state, input_name)
             if input_tensor is None:
                 continue
@@ -494,22 +698,75 @@ class QwenImageEditPlusAdditionalInputsStep(ModularPipelineBlocks):
         return components, state
 
 
-# YiYi TODO: support define config default component from the ModularPipeline level.
-# it is same as QwenImageAdditionalInputsStep, but with layered pachifier.
+# same as QwenImageAdditionalInputsStep, but with layered pachifier.
+
+
+# auto_docstring
 class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
-    """Input step for QwenImage Layered: update height/width, expand batch, patchify with layered pachifier."""
+    """
+    Input processing step for Layered that:
+        1. For image latent inputs: Updates height/width if None, patchifies with layered pachifier, and expands batch
+           size
+        2. For additional batch inputs: Expands batch dimensions to match final batch size
+
+      Configured inputs:
+        - Image latent inputs: ['image_latents']
+
+      This block should be placed after the encoder steps and the text input step.
+
+      Components:
+          pachifier (`QwenImageLayeredPachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified
+              with layered pachifier and batch-expanded)
+    """
 
     model_name = "qwenimage-layered"
 
     def __init__(
         self,
-        image_latent_inputs: List[str] = ["image_latents"],
-        additional_batch_inputs: List[str] = [],
+        image_latent_inputs: Optional[List[InputParam]] = None,
+        additional_batch_inputs: Optional[List[InputParam]] = None,
     ):
+        if image_latent_inputs is None:
+            image_latent_inputs = [InputParam.template("image_latents")]
+        if additional_batch_inputs is None:
+            additional_batch_inputs = []
+
         if not isinstance(image_latent_inputs, list):
-            image_latent_inputs = [image_latent_inputs]
+            raise ValueError(f"image_latent_inputs must be a list, but got {type(image_latent_inputs)}")
+        else:
+            for input_param in image_latent_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(f"image_latent_inputs must be a list of InputParam, but got {type(input_param)}")
+
         if not isinstance(additional_batch_inputs, list):
-            additional_batch_inputs = [additional_batch_inputs]
+            raise ValueError(f"additional_batch_inputs must be a list, but got {type(additional_batch_inputs)}")
+        else:
+            for input_param in additional_batch_inputs:
+                if not isinstance(input_param, InputParam):
+                    raise ValueError(
+                        f"additional_batch_inputs must be a list of InputParam, but got {type(input_param)}"
+                    )
 
         self._image_latent_inputs = image_latent_inputs
         self._additional_batch_inputs = additional_batch_inputs
@@ -527,9 +784,9 @@ class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
         if self._image_latent_inputs or self._additional_batch_inputs:
             inputs_info = "\n\nConfigured inputs:"
             if self._image_latent_inputs:
-                inputs_info += f"\n  - Image latent inputs: {self._image_latent_inputs}"
+                inputs_info += f"\n  - Image latent inputs: {[p.name for p in self._image_latent_inputs]}"
             if self._additional_batch_inputs:
-                inputs_info += f"\n  - Additional batch inputs: {self._additional_batch_inputs}"
+                inputs_info += f"\n  - Additional batch inputs: {[p.name for p in self._additional_batch_inputs]}"
 
         placement_section = "\n\nThis block should be placed after the encoder steps and the text input step."
 
@@ -544,21 +801,18 @@ class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         inputs = [
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="batch_size", required=True),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("batch_size"),
         ]
+        # default is `image_latents`
 
-        for image_latent_input_name in self._image_latent_inputs:
-            inputs.append(InputParam(name=image_latent_input_name))
-
-        for input_name in self._additional_batch_inputs:
-            inputs.append(InputParam(name=input_name))
+        inputs += self._image_latent_inputs + self._additional_batch_inputs
 
         return inputs
 
     @property
     def intermediate_outputs(self) -> List[OutputParam]:
-        return [
+        outputs = [
             OutputParam(
                 name="image_height",
                 type_hint=int,
@@ -569,15 +823,44 @@ class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
                 type_hint=int,
                 description="The image width calculated from the image latents dimension",
             ),
-            OutputParam(name="height", type_hint=int, description="The height of the image output"),
-            OutputParam(name="width", type_hint=int, description="The width of the image output"),
         ]
 
+        if len(self._image_latent_inputs) > 0:
+            outputs.append(
+                OutputParam(name="height", type_hint=int, description="if not provided, updated to image height")
+            )
+            outputs.append(
+                OutputParam(name="width", type_hint=int, description="if not provided, updated to image width")
+            )
+
+        # Add outputs for image latent inputs (patchified with layered pachifier and batch-expanded)
+        for input_param in self._image_latent_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (patchified with layered pachifier and batch-expanded)",
+                )
+            )
+
+        # Add outputs for additional batch inputs (batch-expanded only)
+        for input_param in self._additional_batch_inputs:
+            outputs.append(
+                OutputParam(
+                    name=input_param.name,
+                    type_hint=input_param.type_hint,
+                    description=input_param.description + " (batch-expanded)",
+                )
+            )
+
+        return outputs
+
     def __call__(self, components: QwenImageModularPipeline, state: PipelineState) -> PipelineState:
         block_state = self.get_block_state(state)
 
         # Process image latent inputs
-        for image_latent_input_name in self._image_latent_inputs:
+        for input_param in self._image_latent_inputs:
+            image_latent_input_name = input_param.name
             image_latent_tensor = getattr(block_state, image_latent_input_name)
             if image_latent_tensor is None:
                 continue
@@ -608,7 +891,8 @@ class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
             setattr(block_state, image_latent_input_name, image_latent_tensor)
 
         # Process additional batch inputs (only batch expansion)
-        for input_name in self._additional_batch_inputs:
+        for input_param in self._additional_batch_inputs:
+            input_name = input_param.name
             input_tensor = getattr(block_state, input_name)
             if input_tensor is None:
                 continue
@@ -626,7 +910,34 @@ class QwenImageLayeredAdditionalInputsStep(ModularPipelineBlocks):
         return components, state
 
 
+# auto_docstring
 class QwenImageControlNetInputsStep(ModularPipelineBlocks):
+    """
+    prepare the `control_image_latents` for controlnet. Insert after all the other inputs steps.
+
+      Inputs:
+          control_image_latents (`Tensor`):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          batch_size (`int`, *optional*, defaults to 1):
+              Number of prompts, the final batch size of model inputs should be batch_size * num_images_per_prompt. Can
+              be generated in input step.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+
+      Outputs:
+          control_image_latents (`Tensor`):
+              The control image latents (patchified and batch-expanded).
+          height (`int`):
+              if not provided, updated to control image height
+          width (`int`):
+              if not provided, updated to control image width
+    """
+
     model_name = "qwenimage"
 
     @property
@@ -636,11 +947,28 @@ class QwenImageControlNetInputsStep(ModularPipelineBlocks):
     @property
     def inputs(self) -> List[InputParam]:
         return [
-            InputParam(name="control_image_latents", required=True),
-            InputParam(name="batch_size", required=True),
-            InputParam(name="num_images_per_prompt", default=1),
-            InputParam(name="height"),
-            InputParam(name="width"),
+            InputParam(
+                name="control_image_latents",
+                required=True,
+                type_hint=torch.Tensor,
+                description="The control image latents to use for the denoising process. Can be generated in controlnet vae encoder step.",
+            ),
+            InputParam.template("batch_size"),
+            InputParam.template("num_images_per_prompt"),
+            InputParam.template("height"),
+            InputParam.template("width"),
+        ]
+
+    @property
+    def intermediate_outputs(self) -> List[OutputParam]:
+        return [
+            OutputParam(
+                name="control_image_latents",
+                type_hint=torch.Tensor,
+                description="The control image latents (patchified and batch-expanded).",
+            ),
+            OutputParam(name="height", type_hint=int, description="if not provided, updated to control image height"),
+            OutputParam(name="width", type_hint=int, description="if not provided, updated to control image width"),
         ]
 
     @torch.no_grad()

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage.py

@@ -12,14 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
-
-import PIL.Image
 import torch
 
 from ...utils import logging
 from ..modular_pipeline import AutoPipelineBlocks, ConditionalPipelineBlocks, SequentialPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict, OutputParam
+from ..modular_pipeline_utils import InputParam, InsertableDict, OutputParam
 from .before_denoise import (
     QwenImageControlNetBeforeDenoiserStep,
     QwenImageCreateMaskLatentsStep,
@@ -59,11 +56,91 @@ logger = logging.get_logger(__name__)
 
 
 # ====================
-# 1. VAE ENCODER
+# 1. TEXT ENCODER
 # ====================
 
 
+# auto_docstring
+class QwenImageAutoTextEncoderStep(AutoPipelineBlocks):
+    """
+    Text encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block.
+
+      Components:
+          text_encoder (`Qwen2_5_VLForConditionalGeneration`): The text encoder to use tokenizer (`Qwen2Tokenizer`):
+          The tokenizer to use guider (`ClassifierFreeGuidance`)
+
+      Inputs:
+          prompt (`str`, *optional*):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          max_sequence_length (`int`, *optional*, defaults to 1024):
+              Maximum sequence length for prompt encoding.
+
+      Outputs:
+          prompt_embeds (`Tensor`):
+              The prompt embeddings.
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask.
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings.
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask.
+    """
+
+    model_name = "qwenimage"
+    block_classes = [QwenImageTextEncoderStep()]
+    block_names = ["text_encoder"]
+    block_trigger_inputs = ["prompt"]
+
+    @property
+    def description(self) -> str:
+        return "Text encoder step that encodes the text prompt into a text embedding. This is an auto pipeline block."
+        " - `QwenImageTextEncoderStep` (text_encoder) is used when `prompt` is provided."
+        " - if `prompt` is not provided, step will be skipped."
+
+
+# ====================
+# 2. VAE ENCODER
+# ====================
+
+
+# auto_docstring
 class QwenImageInpaintVaeEncoderStep(SequentialPipelineBlocks):
+    """
+    This step is used for processing image and mask inputs for inpainting tasks. It:
+       - Resizes the image to the target size, based on `height` and `width`.
+       - Processes and updates `image` and `mask_image`.
+       - Creates `image_latents`.
+
+      Components:
+          image_mask_processor (`InpaintProcessor`) vae (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          mask_image (`Image`):
+              Mask image for inpainting.
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          padding_mask_crop (`int`, *optional*):
+              Padding for mask cropping in inpainting.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          processed_image (`Tensor`):
+              The processed image
+          processed_mask_image (`Tensor`):
+              The processed mask image
+          mask_overlay_kwargs (`Dict`):
+              The kwargs for the postprocess step to apply the mask overlay
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
+
     model_name = "qwenimage"
     block_classes = [QwenImageInpaintProcessImagesInputStep(), QwenImageVaeEncoderStep()]
     block_names = ["preprocess", "encode"]
@@ -78,7 +155,31 @@ class QwenImageInpaintVaeEncoderStep(SequentialPipelineBlocks):
         )
 
 
+# auto_docstring
 class QwenImageImg2ImgVaeEncoderStep(SequentialPipelineBlocks):
+    """
+    Vae encoder step that preprocess andencode the image inputs into their latent representations.
+
+      Components:
+          image_processor (`VaeImageProcessor`) vae (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          processed_image (`Tensor`):
+              The processed image
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
+
     model_name = "qwenimage"
 
     block_classes = [QwenImageProcessImagesInputStep(), QwenImageVaeEncoderStep()]
@@ -89,7 +190,6 @@ class QwenImageImg2ImgVaeEncoderStep(SequentialPipelineBlocks):
         return "Vae encoder step that preprocess andencode the image inputs into their latent representations."
 
 
-# Auto VAE encoder
 class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks):
     block_classes = [QwenImageInpaintVaeEncoderStep, QwenImageImg2ImgVaeEncoderStep]
     block_names = ["inpaint", "img2img"]
@@ -107,7 +207,33 @@ class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks):
 
 
 # optional controlnet vae encoder
+# auto_docstring
 class QwenImageOptionalControlNetVaeEncoderStep(AutoPipelineBlocks):
+    """
+    Vae encoder step that encode the image inputs into their latent representations.
+      This is an auto pipeline block.
+       - `QwenImageControlNetVaeEncoderStep` (controlnet) is used when `control_image` is provided.
+       - if `control_image` is not provided, step will be skipped.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) controlnet (`QwenImageControlNetModel`) control_image_processor
+          (`VaeImageProcessor`)
+
+      Inputs:
+          control_image (`Image`, *optional*):
+              Control image for ControlNet conditioning.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          control_image_latents (`Tensor`):
+              The latents representing the control image
+    """
+
     block_classes = [QwenImageControlNetVaeEncoderStep]
     block_names = ["controlnet"]
     block_trigger_inputs = ["control_image"]
@@ -123,14 +249,65 @@ class QwenImageOptionalControlNetVaeEncoderStep(AutoPipelineBlocks):
 
 
 # ====================
-# 2. DENOISE (input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise)
+# 3. DENOISE (input -> prepare_latents -> set_timesteps -> prepare_rope_inputs -> denoise -> after_denoise)
 # ====================
 
 
 # assemble input steps
+# auto_docstring
 class QwenImageImg2ImgInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the img2img denoising step. It:
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and
+              batch-expanded)
+    """
+
     model_name = "qwenimage"
-    block_classes = [QwenImageTextInputsStep(), QwenImageAdditionalInputsStep(image_latent_inputs=["image_latents"])]
+    block_classes = [QwenImageTextInputsStep(), QwenImageAdditionalInputsStep()]
     block_names = ["text_inputs", "additional_inputs"]
 
     @property
@@ -140,12 +317,69 @@ class QwenImageImg2ImgInputStep(SequentialPipelineBlocks):
         " - update height/width based `image_latents`, patchify `image_latents`."
 
 
+# auto_docstring
 class QwenImageInpaintInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the inpainting denoising step. It:
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`, *optional*):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and
+              batch-expanded)
+          processed_mask_image (`Tensor`):
+              The processed mask image (batch-expanded)
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageTextInputsStep(),
         QwenImageAdditionalInputsStep(
-            image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"]
+            additional_batch_inputs=[
+                InputParam(name="processed_mask_image", type_hint=torch.Tensor, description="The processed mask image")
+            ]
         ),
     ]
     block_names = ["text_inputs", "additional_inputs"]
@@ -158,7 +392,42 @@ class QwenImageInpaintInputStep(SequentialPipelineBlocks):
 
 
 # assemble prepare latents steps
+# auto_docstring
 class QwenImageInpaintPrepareLatentsStep(SequentialPipelineBlocks):
+    """
+    This step prepares the latents/image_latents and mask inputs for the inpainting denoising step. It:
+       - Add noise to the image latents to create the latents input for the denoiser.
+       - Create the pachified latents `mask` based on the processedmask image.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          latents (`Tensor`):
+              The initial random noised, can be generated in prepare latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (Can be
+              generated from vae encoder and updated in input step.)
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          processed_mask_image (`Tensor`):
+              The processed mask to use for the inpainting process.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          dtype (`dtype`, *optional*, defaults to torch.float32):
+              The dtype of the model inputs, can be generated in input step.
+
+      Outputs:
+          initial_noise (`Tensor`):
+              The initial random noised used for inpainting denoising.
+          latents (`Tensor`):
+              The scaled noisy latents to use for inpainting/image-to-image denoising.
+          mask (`Tensor`):
+              The mask to use for the inpainting process.
+    """
+
     model_name = "qwenimage"
     block_classes = [QwenImagePrepareLatentsWithStrengthStep(), QwenImageCreateMaskLatentsStep()]
     block_names = ["add_noise_to_latents", "create_mask_latents"]
@@ -176,7 +445,49 @@ class QwenImageInpaintPrepareLatentsStep(SequentialPipelineBlocks):
 
 
 # Qwen Image (text2image)
+# auto_docstring
 class QwenImageCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs
+    (timesteps, latents, rope inputs etc.).
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageTextInputsStep(),
@@ -199,9 +510,63 @@ class QwenImageCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.)."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image (inpainting)
+# auto_docstring
 class QwenImageInpaintCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint
+    task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`, *optional*):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageInpaintInputStep(),
@@ -226,9 +591,61 @@ class QwenImageInpaintCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image (image2image)
+# auto_docstring
 class QwenImageImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img
+    task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageImg2ImgInputStep(),
@@ -253,9 +670,66 @@ class QwenImageImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image (text2image) with controlnet
+# auto_docstring
 class QwenImageControlNetCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs
+    (timesteps, latents, rope inputs etc.).
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet
+          (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          control_image_latents (`Tensor`):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          control_guidance_start (`float`, *optional*, defaults to 0.0):
+              When to start applying ControlNet.
+          control_guidance_end (`float`, *optional*, defaults to 1.0):
+              When to stop applying ControlNet.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageTextInputsStep(),
@@ -282,9 +756,72 @@ class QwenImageControlNetCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "step that denoise noise into image for text2image task. It includes the denoise loop, as well as prepare the inputs (timesteps, latents, rope inputs etc.)."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image (inpainting) with controlnet
+# auto_docstring
 class QwenImageControlNetInpaintCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint
+    task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet
+          (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`, *optional*):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+          control_image_latents (`Tensor`):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          control_guidance_start (`float`, *optional*, defaults to 0.0):
+              When to start applying ControlNet.
+          control_guidance_end (`float`, *optional*, defaults to 1.0):
+              When to stop applying ControlNet.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageInpaintInputStep(),
@@ -313,9 +850,70 @@ class QwenImageControlNetInpaintCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for inpaint task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image (image2image) with controlnet
+# auto_docstring
 class QwenImageControlNetImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img
+    task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) controlnet
+          (`QwenImageControlNetModel`) guider (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          control_image_latents (`Tensor`):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          control_guidance_start (`float`, *optional*, defaults to 0.0):
+              When to start applying ControlNet.
+          control_guidance_end (`float`, *optional*, defaults to 1.0):
+              When to stop applying ControlNet.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage"
     block_classes = [
         QwenImageImg2ImgInputStep(),
@@ -344,6 +942,12 @@ class QwenImageControlNetImg2ImgCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Before denoise step that prepare the inputs (timesteps, latents, rope inputs etc.) for the denoise step for img2img task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Auto denoise step for QwenImage
 class QwenImageAutoCoreDenoiseStep(ConditionalPipelineBlocks):
@@ -402,19 +1006,36 @@ class QwenImageAutoCoreDenoiseStep(ConditionalPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(
-                name="latents", type_hint=torch.Tensor, description="The latents generated by the denoising step"
-            ),
+            OutputParam.template("latents"),
         ]
 
 
 # ====================
-# 3. DECODE
+# 4. DECODE
 # ====================
 
 
 # standard decode step works for most tasks except for inpaint
+# auto_docstring
 class QwenImageDecodeStep(SequentialPipelineBlocks):
+    """
+    Decode step that decodes the latents to images and postprocess the generated image.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage"
     block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
     block_names = ["decode", "postprocess"]
@@ -425,7 +1046,30 @@ class QwenImageDecodeStep(SequentialPipelineBlocks):
 
 
 # Inpaint decode step
+# auto_docstring
 class QwenImageInpaintDecodeStep(SequentialPipelineBlocks):
+    """
+    Decode step that decodes the latents to images and postprocess the generated image, optional apply the mask
+    overally to the original image.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_mask_processor (`InpaintProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+          mask_overlay_kwargs (`Dict`, *optional*):
+              The kwargs for the postprocess step to apply the mask overlay. generated in
+              InpaintProcessImagesInputStep.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage"
     block_classes = [QwenImageDecoderStep(), QwenImageInpaintProcessImagesOutputStep()]
     block_names = ["decode", "postprocess"]
@@ -452,11 +1096,11 @@ class QwenImageAutoDecodeStep(AutoPipelineBlocks):
 
 
 # ====================
-# 4. AUTO BLOCKS & PRESETS
+# 5. AUTO BLOCKS & PRESETS
 # ====================
 AUTO_BLOCKS = InsertableDict(
     [
-        ("text_encoder", QwenImageTextEncoderStep()),
+        ("text_encoder", QwenImageAutoTextEncoderStep()),
         ("vae_encoder", QwenImageAutoVaeEncoderStep()),
         ("controlnet_vae_encoder", QwenImageOptionalControlNetVaeEncoderStep()),
         ("denoise", QwenImageAutoCoreDenoiseStep()),
@@ -465,24 +1109,119 @@ AUTO_BLOCKS = InsertableDict(
 )
 
 
+# auto_docstring
 class QwenImageAutoBlocks(SequentialPipelineBlocks):
+    """
+    Auto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage.
+
+      Supported workflows:
+        - `text2image`: requires `prompt`
+        - `image2image`: requires `prompt`, `image`
+        - `inpainting`: requires `prompt`, `mask_image`, `image`
+        - `controlnet_text2image`: requires `prompt`, `control_image`
+        - `controlnet_image2image`: requires `prompt`, `image`, `control_image`
+        - `controlnet_inpainting`: requires `prompt`, `mask_image`, `image`, `control_image`
+
+      Components:
+          text_encoder (`Qwen2_5_VLForConditionalGeneration`): The text encoder to use tokenizer (`Qwen2Tokenizer`):
+          The tokenizer to use guider (`ClassifierFreeGuidance`) image_mask_processor (`InpaintProcessor`) vae
+          (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`) controlnet (`QwenImageControlNetModel`)
+          control_image_processor (`VaeImageProcessor`) pachifier (`QwenImagePachifier`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          prompt (`str`, *optional*):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          max_sequence_length (`int`, *optional*, defaults to 1024):
+              Maximum sequence length for prompt encoding.
+          mask_image (`Image`, *optional*):
+              Mask image for inpainting.
+          image (`Union[Image, List]`, *optional*):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          padding_mask_crop (`int`, *optional*):
+              Padding for mask cropping in inpainting.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          control_image (`Image`, *optional*):
+              Control image for ControlNet conditioning.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          latents (`Tensor`):
+              Pre-generated noisy latents for image generation.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          image_latents (`Tensor`, *optional*):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          control_image_latents (`Tensor`, *optional*):
+              The control image latents to use for the denoising process. Can be generated in controlnet vae encoder
+              step.
+          control_guidance_start (`float`, *optional*, defaults to 0.0):
+              When to start applying ControlNet.
+          control_guidance_end (`float`, *optional*, defaults to 1.0):
+              When to stop applying ControlNet.
+          controlnet_conditioning_scale (`float`, *optional*, defaults to 1.0):
+              Scale for ControlNet conditioning.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+          mask_overlay_kwargs (`Dict`, *optional*):
+              The kwargs for the postprocess step to apply the mask overlay. generated in
+              InpaintProcessImagesInputStep.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage"
 
     block_classes = AUTO_BLOCKS.values()
     block_names = AUTO_BLOCKS.keys()
 
+    # Workflow map defines the trigger conditions for each workflow.
+    # How to define:
+    #   - Only include required inputs and trigger inputs (inputs that determine which blocks run)
+    #   - `True` means the workflow triggers when the input is not None (most common case)
+    #   - Use specific values (e.g., `{"strength": 0.5}`) if your `select_block` logic depends on the value
+
+    _workflow_map = {
+        "text2image": {"prompt": True},
+        "image2image": {"prompt": True, "image": True},
+        "inpainting": {"prompt": True, "mask_image": True, "image": True},
+        "controlnet_text2image": {"prompt": True, "control_image": True},
+        "controlnet_image2image": {"prompt": True, "image": True, "control_image": True},
+        "controlnet_inpainting": {"prompt": True, "mask_image": True, "image": True, "control_image": True},
+    }
+
     @property
     def description(self):
-        return (
-            "Auto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage.\n"
-            + "- for image-to-image generation, you need to provide `image`\n"
-            + "- for inpainting, you need to provide `mask_image` and `image`, optionally you can provide `padding_mask_crop` \n"
-            + "- to run the controlnet workflow, you need to provide `control_image`\n"
-            + "- for text-to-image generation, all you need to provide is `prompt`"
-        )
+        return "Auto Modular pipeline for text-to-image, image-to-image, inpainting, and controlnet tasks using QwenImage."
 
     @property
     def outputs(self):
         return [
-            OutputParam(name="images", type_hint=List[List[PIL.Image.Image]]),
+            OutputParam.template("images"),
         ]

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_edit.py

@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List, Optional
+from typing import Optional
 
-import PIL.Image
 import torch
 
 from ...utils import logging
 from ..modular_pipeline import AutoPipelineBlocks, ConditionalPipelineBlocks, SequentialPipelineBlocks
-from ..modular_pipeline_utils import InsertableDict, OutputParam
+from ..modular_pipeline_utils import InputParam, InsertableDict, OutputParam
 from .before_denoise import (
     QwenImageCreateMaskLatentsStep,
     QwenImageEditRoPEInputsStep,
@@ -59,8 +58,35 @@ logger = logging.get_logger(__name__)
 # ====================
 
 
+# auto_docstring
 class QwenImageEditVLEncoderStep(SequentialPipelineBlocks):
-    """VL encoder that takes both image and text prompts."""
+    """
+    QwenImage-Edit VL encoder step that encode the image and text prompts together.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) guider (`ClassifierFreeGuidance`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          prompt (`str`):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+
+      Outputs:
+          resized_image (`List`):
+              The resized images
+          prompt_embeds (`Tensor`):
+              The prompt embeddings.
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask.
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings.
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask.
+    """
 
     model_name = "qwenimage-edit"
     block_classes = [
@@ -80,7 +106,30 @@ class QwenImageEditVLEncoderStep(SequentialPipelineBlocks):
 
 
 # Edit VAE encoder
+# auto_docstring
 class QwenImageEditVaeEncoderStep(SequentialPipelineBlocks):
+    """
+    Vae encoder step that encode the image inputs into their latent representations.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) image_processor (`VaeImageProcessor`) vae
+          (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          resized_image (`List`):
+              The resized images
+          processed_image (`Tensor`):
+              The processed image
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditResizeStep(),
@@ -95,12 +144,46 @@ class QwenImageEditVaeEncoderStep(SequentialPipelineBlocks):
 
 
 # Edit Inpaint VAE encoder
+# auto_docstring
 class QwenImageEditInpaintVaeEncoderStep(SequentialPipelineBlocks):
+    """
+    This step is used for processing image and mask inputs for QwenImage-Edit inpaint tasks. It:
+       - resize the image for target area (1024 * 1024) while maintaining the aspect ratio.
+       - process the resized image and mask image.
+       - create image latents.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) image_mask_processor (`InpaintProcessor`) vae
+          (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          mask_image (`Image`):
+              Mask image for inpainting.
+          padding_mask_crop (`int`, *optional*):
+              Padding for mask cropping in inpainting.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          resized_image (`List`):
+              The resized images
+          processed_image (`Tensor`):
+              The processed image
+          processed_mask_image (`Tensor`):
+              The processed mask image
+          mask_overlay_kwargs (`Dict`):
+              The kwargs for the postprocess step to apply the mask overlay
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditResizeStep(),
         QwenImageEditInpaintProcessImagesInputStep(),
-        QwenImageVaeEncoderStep(input_name="processed_image", output_name="image_latents"),
+        QwenImageVaeEncoderStep(),
     ]
     block_names = ["resize", "preprocess", "encode"]
 
@@ -137,11 +220,64 @@ class QwenImageEditAutoVaeEncoderStep(AutoPipelineBlocks):
 
 
 # assemble input steps
+# auto_docstring
 class QwenImageEditInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the edit denoising step. It:
+       - make sure the text embeddings have consistent batch size as well as the additional inputs.
+       - update height/width based `image_latents`, patchify `image_latents`.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and
+              batch-expanded)
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageTextInputsStep(),
-        QwenImageAdditionalInputsStep(image_latent_inputs=["image_latents"]),
+        QwenImageAdditionalInputsStep(),
     ]
     block_names = ["text_inputs", "additional_inputs"]
 
@@ -154,12 +290,71 @@ class QwenImageEditInputStep(SequentialPipelineBlocks):
         )
 
 
+# auto_docstring
 class QwenImageEditInpaintInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the edit inpaint denoising step. It:
+       - make sure the text embeddings have consistent batch size as well as the additional inputs.
+       - update height/width based `image_latents`, patchify `image_latents`.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified and
+              batch-expanded)
+          processed_mask_image (`Tensor`):
+              The processed mask image (batch-expanded)
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageTextInputsStep(),
         QwenImageAdditionalInputsStep(
-            image_latent_inputs=["image_latents"], additional_batch_inputs=["processed_mask_image"]
+            additional_batch_inputs=[
+                InputParam(name="processed_mask_image", type_hint=torch.Tensor, description="The processed mask image")
+            ]
         ),
     ]
     block_names = ["text_inputs", "additional_inputs"]
@@ -174,7 +369,42 @@ class QwenImageEditInpaintInputStep(SequentialPipelineBlocks):
 
 
 # assemble prepare latents steps
+# auto_docstring
 class QwenImageEditInpaintPrepareLatentsStep(SequentialPipelineBlocks):
+    """
+    This step prepares the latents/image_latents and mask inputs for the edit inpainting denoising step. It:
+       - Add noise to the image latents to create the latents input for the denoiser.
+       - Create the patchified latents `mask` based on the processed mask image.
+
+      Components:
+          scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          latents (`Tensor`):
+              The initial random noised, can be generated in prepare latent step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (Can be
+              generated from vae encoder and updated in input step.)
+          timesteps (`Tensor`):
+              The timesteps to use for the denoising process. Can be generated in set_timesteps step.
+          processed_mask_image (`Tensor`):
+              The processed mask to use for the inpainting process.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          dtype (`dtype`, *optional*, defaults to torch.float32):
+              The dtype of the model inputs, can be generated in input step.
+
+      Outputs:
+          initial_noise (`Tensor`):
+              The initial random noised used for inpainting denoising.
+          latents (`Tensor`):
+              The scaled noisy latents to use for inpainting/image-to-image denoising.
+          mask (`Tensor`):
+              The mask to use for the inpainting process.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [QwenImagePrepareLatentsWithStrengthStep(), QwenImageCreateMaskLatentsStep()]
     block_names = ["add_noise_to_latents", "create_mask_latents"]
@@ -189,7 +419,50 @@ class QwenImageEditInpaintPrepareLatentsStep(SequentialPipelineBlocks):
 
 
 # Qwen Image Edit (image2image) core denoise step
+# auto_docstring
 class QwenImageEditCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Core denoising workflow for QwenImage-Edit edit (img2img) task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditInputStep(),
@@ -212,9 +485,62 @@ class QwenImageEditCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Core denoising workflow for QwenImage-Edit edit (img2img) task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Qwen Image Edit (inpainting) core denoise step
+# auto_docstring
 class QwenImageEditInpaintCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Core denoising workflow for QwenImage-Edit edit inpaint task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [
         QwenImageEditInpaintInputStep(),
@@ -239,6 +565,12 @@ class QwenImageEditInpaintCoreDenoiseStep(SequentialPipelineBlocks):
     def description(self):
         return "Core denoising workflow for QwenImage-Edit edit inpaint task."
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # Auto core denoise step for QwenImage Edit
 class QwenImageEditAutoCoreDenoiseStep(ConditionalPipelineBlocks):
@@ -267,6 +599,12 @@ class QwenImageEditAutoCoreDenoiseStep(ConditionalPipelineBlocks):
             "Supports edit (img2img) and edit inpainting tasks for QwenImage-Edit."
         )
 
+    @property
+    def outputs(self):
+        return [
+            OutputParam.template("latents"),
+        ]
+
 
 # ====================
 # 4. DECODE
@@ -274,7 +612,26 @@ class QwenImageEditAutoCoreDenoiseStep(ConditionalPipelineBlocks):
 
 
 # Decode step (standard)
+# auto_docstring
 class QwenImageEditDecodeStep(SequentialPipelineBlocks):
+    """
+    Decode step that decodes the latents to images and postprocess the generated image.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
     block_names = ["decode", "postprocess"]
@@ -285,7 +642,30 @@ class QwenImageEditDecodeStep(SequentialPipelineBlocks):
 
 
 # Inpaint decode step
+# auto_docstring
 class QwenImageEditInpaintDecodeStep(SequentialPipelineBlocks):
+    """
+    Decode step that decodes the latents to images and postprocess the generated image, optionally apply the mask
+    overlay to the original image.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_mask_processor (`InpaintProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+          mask_overlay_kwargs (`Dict`, *optional*):
+              The kwargs for the postprocess step to apply the mask overlay. generated in
+              InpaintProcessImagesInputStep.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage-edit"
     block_classes = [QwenImageDecoderStep(), QwenImageInpaintProcessImagesOutputStep()]
     block_names = ["decode", "postprocess"]
@@ -313,9 +693,7 @@ class QwenImageEditAutoDecodeStep(AutoPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(
-                name="latents", type_hint=torch.Tensor, description="The latents generated by the denoising step"
-            ),
+            OutputParam.template("latents"),
         ]
 
 
@@ -333,7 +711,66 @@ EDIT_AUTO_BLOCKS = InsertableDict(
 )
 
 
+# auto_docstring
 class QwenImageEditAutoBlocks(SequentialPipelineBlocks):
+    """
+    Auto Modular pipeline for edit (img2img) and edit inpaint tasks using QwenImage-Edit.
+      - for edit (img2img) generation, you need to provide `image`
+      - for edit inpainting, you need to provide `mask_image` and `image`, optionally you can provide
+        `padding_mask_crop`
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) guider (`ClassifierFreeGuidance`) image_mask_processor (`InpaintProcessor`) vae
+          (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`) pachifier (`QwenImagePachifier`) scheduler
+          (`FlowMatchEulerDiscreteScheduler`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          prompt (`str`):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          mask_image (`Image`, *optional*):
+              Mask image for inpainting.
+          padding_mask_crop (`int`, *optional*):
+              Padding for mask cropping in inpainting.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          height (`int`):
+              The height in pixels of the generated image.
+          width (`int`):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          processed_mask_image (`Tensor`, *optional*):
+              The processed mask image
+          latents (`Tensor`):
+              Pre-generated noisy latents for image generation.
+          num_inference_steps (`int`):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          strength (`float`, *optional*, defaults to 0.9):
+              Strength for img2img/inpainting.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+          mask_overlay_kwargs (`Dict`, *optional*):
+              The kwargs for the postprocess step to apply the mask overlay. generated in
+              InpaintProcessImagesInputStep.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage-edit"
     block_classes = EDIT_AUTO_BLOCKS.values()
     block_names = EDIT_AUTO_BLOCKS.keys()
@@ -349,5 +786,5 @@ class QwenImageEditAutoBlocks(SequentialPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(name="images", type_hint=List[List[PIL.Image.Image]], description="The generated images"),
+            OutputParam.template("images"),
         ]

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_edit_plus.py

@@ -12,11 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import List
-
-import PIL.Image
-import torch
-
 from ...utils import logging
 from ..modular_pipeline import SequentialPipelineBlocks
 from ..modular_pipeline_utils import InsertableDict, OutputParam
@@ -53,12 +48,41 @@ logger = logging.get_logger(__name__)
 # ====================
 
 
+# auto_docstring
 class QwenImageEditPlusVLEncoderStep(SequentialPipelineBlocks):
-    """VL encoder that takes both image and text prompts. Uses 384x384 target area."""
+    """
+    QwenImage-Edit Plus VL encoder step that encodes the image and text prompts together.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) guider (`ClassifierFreeGuidance`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          prompt (`str`):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+
+      Outputs:
+          resized_image (`List`):
+              Images resized to 1024x1024 target area for VAE encoding
+          resized_cond_image (`List`):
+              Images resized to 384x384 target area for VL text encoding
+          prompt_embeds (`Tensor`):
+              The prompt embeddings.
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask.
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings.
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask.
+    """
 
     model_name = "qwenimage-edit-plus"
     block_classes = [
-        QwenImageEditPlusResizeStep(target_area=384 * 384, output_name="resized_cond_image"),
+        QwenImageEditPlusResizeStep(),
         QwenImageEditPlusTextEncoderStep(),
     ]
     block_names = ["resize", "encode"]
@@ -73,12 +97,36 @@ class QwenImageEditPlusVLEncoderStep(SequentialPipelineBlocks):
 # ====================
 
 
+# auto_docstring
 class QwenImageEditPlusVaeEncoderStep(SequentialPipelineBlocks):
-    """VAE encoder that handles multiple images with different sizes. Uses 1024x1024 target area."""
+    """
+    VAE encoder step that encodes image inputs into latent representations.
+      Each image is resized independently based on its own aspect ratio to 1024x1024 target area.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) image_processor (`VaeImageProcessor`) vae
+          (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          resized_image (`List`):
+              Images resized to 1024x1024 target area for VAE encoding
+          resized_cond_image (`List`):
+              Images resized to 384x384 target area for VL text encoding
+          processed_image (`Tensor`):
+              The processed image
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
 
     model_name = "qwenimage-edit-plus"
     block_classes = [
-        QwenImageEditPlusResizeStep(target_area=1024 * 1024, output_name="resized_image"),
+        QwenImageEditPlusResizeStep(),
         QwenImageEditPlusProcessImagesInputStep(),
         QwenImageVaeEncoderStep(),
     ]
@@ -98,11 +146,66 @@ class QwenImageEditPlusVaeEncoderStep(SequentialPipelineBlocks):
 
 
 # assemble input steps
+# auto_docstring
 class QwenImageEditPlusInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the Edit Plus denoising step. It:
+       - Standardizes text embeddings batch size.
+       - Processes list of image latents: patchifies, concatenates along dim=1, expands batch.
+       - Outputs lists of image_height/image_width for RoPE calculation.
+       - Defaults height/width from last image in the list.
+
+      Components:
+          pachifier (`QwenImagePachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`List`):
+              The image heights calculated from the image latents dimension
+          image_width (`List`):
+              The image widths calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified,
+              concatenated, and batch-expanded)
+    """
+
     model_name = "qwenimage-edit-plus"
     block_classes = [
         QwenImageTextInputsStep(),
-        QwenImageEditPlusAdditionalInputsStep(image_latent_inputs=["image_latents"]),
+        QwenImageEditPlusAdditionalInputsStep(),
     ]
     block_names = ["text_inputs", "additional_inputs"]
 
@@ -118,7 +221,50 @@ class QwenImageEditPlusInputStep(SequentialPipelineBlocks):
 
 
 # Qwen Image Edit Plus (image2image) core denoise step
+# auto_docstring
 class QwenImageEditPlusCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Core denoising workflow for QwenImage-Edit Plus edit (img2img) task.
+
+      Components:
+          pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-edit-plus"
     block_classes = [
         QwenImageEditPlusInputStep(),
@@ -144,9 +290,7 @@ class QwenImageEditPlusCoreDenoiseStep(SequentialPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(
-                name="latents", type_hint=torch.Tensor, description="The latents generated by the denoising step"
-            ),
+            OutputParam.template("latents"),
         ]
 
 
@@ -155,7 +299,26 @@ class QwenImageEditPlusCoreDenoiseStep(SequentialPipelineBlocks):
 # ====================
 
 
+# auto_docstring
 class QwenImageEditPlusDecodeStep(SequentialPipelineBlocks):
+    """
+    Decode step that decodes the latents to images and postprocesses the generated image.
+
+      Components:
+          vae (`AutoencoderKLQwenImage`) image_processor (`VaeImageProcessor`)
+
+      Inputs:
+          latents (`Tensor`):
+              The denoised latents to decode, can be generated in the denoise step and unpacked in the after denoise
+              step.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images. (tensor output of the vae decoder.)
+    """
+
     model_name = "qwenimage-edit-plus"
     block_classes = [QwenImageDecoderStep(), QwenImageProcessImagesOutputStep()]
     block_names = ["decode", "postprocess"]
@@ -179,7 +342,53 @@ EDIT_PLUS_AUTO_BLOCKS = InsertableDict(
 )
 
 
+# auto_docstring
 class QwenImageEditPlusAutoBlocks(SequentialPipelineBlocks):
+    """
+    Auto Modular pipeline for edit (img2img) tasks using QwenImage-Edit Plus.
+      - `image` is required input (can be single image or list of images).
+      - Each image is resized independently based on its own aspect ratio.
+      - VL encoder uses 384x384 target area, VAE encoder uses 1024x1024 target area.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) guider (`ClassifierFreeGuidance`) image_processor (`VaeImageProcessor`) vae
+          (`AutoencoderKLQwenImage`) pachifier (`QwenImagePachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`)
+          transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          prompt (`str`):
+              The prompt or prompts to guide image generation.
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          height (`int`, *optional*):
+              The height in pixels of the generated image.
+          width (`int`, *optional*):
+              The width in pixels of the generated image.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage-edit-plus"
     block_classes = EDIT_PLUS_AUTO_BLOCKS.values()
     block_names = EDIT_PLUS_AUTO_BLOCKS.keys()
@@ -196,5 +405,5 @@ class QwenImageEditPlusAutoBlocks(SequentialPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(name="images", type_hint=List[List[PIL.Image.Image]], description="The generated images"),
+            OutputParam.template("images"),
         ]

src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_layered.py

@@ -12,12 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
-from typing import List
-
-import PIL.Image
-import torch
-
 from ...utils import logging
 from ..modular_pipeline import SequentialPipelineBlocks
 from ..modular_pipeline_utils import InsertableDict, OutputParam
@@ -55,8 +49,44 @@ logger = logging.get_logger(__name__)
 # ====================
 
 
+# auto_docstring
 class QwenImageLayeredTextEncoderStep(SequentialPipelineBlocks):
-    """Text encoder that takes text prompt, will generate a prompt based on image if not provided."""
+    """
+    QwenImage-Layered Text encoder step that encode the text prompt, will generate a prompt based on image if not
+    provided.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) tokenizer (`Qwen2Tokenizer`): The tokenizer to use guider (`ClassifierFreeGuidance`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          resolution (`int`, *optional*, defaults to 640):
+              The target area to resize the image to, can be 1024 or 640
+          prompt (`str`, *optional*):
+              The prompt or prompts to guide image generation.
+          use_en_prompt (`bool`, *optional*, defaults to False):
+              Whether to use English prompt template
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          max_sequence_length (`int`, *optional*, defaults to 1024):
+              Maximum sequence length for prompt encoding.
+
+      Outputs:
+          resized_image (`List`):
+              The resized images
+          prompt (`str`):
+              The prompt or prompts to guide image generation. If not provided, updated using image caption
+          prompt_embeds (`Tensor`):
+              The prompt embeddings.
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask.
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings.
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask.
+    """
 
     model_name = "qwenimage-layered"
     block_classes = [
@@ -77,7 +107,32 @@ class QwenImageLayeredTextEncoderStep(SequentialPipelineBlocks):
 
 
 # Edit VAE encoder
+# auto_docstring
 class QwenImageLayeredVaeEncoderStep(SequentialPipelineBlocks):
+    """
+    Vae encoder step that encode the image inputs into their latent representations.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) image_processor (`VaeImageProcessor`) vae
+          (`AutoencoderKLQwenImage`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          resolution (`int`, *optional*, defaults to 640):
+              The target area to resize the image to, can be 1024 or 640
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+
+      Outputs:
+          resized_image (`List`):
+              The resized images
+          processed_image (`Tensor`):
+              The processed image
+          image_latents (`Tensor`):
+              The latent representation of the input image.
+    """
+
     model_name = "qwenimage-layered"
     block_classes = [
         QwenImageLayeredResizeStep(),
@@ -98,11 +153,60 @@ class QwenImageLayeredVaeEncoderStep(SequentialPipelineBlocks):
 
 
 # assemble input steps
+# auto_docstring
 class QwenImageLayeredInputStep(SequentialPipelineBlocks):
+    """
+    Input step that prepares the inputs for the layered denoising step. It:
+       - make sure the text embeddings have consistent batch size as well as the additional inputs.
+       - update height/width based `image_latents`, patchify `image_latents`.
+
+      Components:
+          pachifier (`QwenImageLayeredPachifier`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+
+      Outputs:
+          batch_size (`int`):
+              The batch size of the prompt embeddings
+          dtype (`dtype`):
+              The data type of the prompt embeddings
+          prompt_embeds (`Tensor`):
+              The prompt embeddings. (batch-expanded)
+          prompt_embeds_mask (`Tensor`):
+              The encoder attention mask. (batch-expanded)
+          negative_prompt_embeds (`Tensor`):
+              The negative prompt embeddings. (batch-expanded)
+          negative_prompt_embeds_mask (`Tensor`):
+              The negative prompt embeddings mask. (batch-expanded)
+          image_height (`int`):
+              The image height calculated from the image latents dimension
+          image_width (`int`):
+              The image width calculated from the image latents dimension
+          height (`int`):
+              if not provided, updated to image height
+          width (`int`):
+              if not provided, updated to image width
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step. (patchified
+              with layered pachifier and batch-expanded)
+    """
+
     model_name = "qwenimage-layered"
     block_classes = [
         QwenImageTextInputsStep(),
-        QwenImageLayeredAdditionalInputsStep(image_latent_inputs=["image_latents"]),
+        QwenImageLayeredAdditionalInputsStep(),
     ]
     block_names = ["text_inputs", "additional_inputs"]
 
@@ -116,7 +220,48 @@ class QwenImageLayeredInputStep(SequentialPipelineBlocks):
 
 
 # Qwen Image Layered (image2image) core denoise step
+# auto_docstring
 class QwenImageLayeredCoreDenoiseStep(SequentialPipelineBlocks):
+    """
+    Core denoising workflow for QwenImage-Layered img2img task.
+
+      Components:
+          pachifier (`QwenImageLayeredPachifier`) scheduler (`FlowMatchEulerDiscreteScheduler`) guider
+          (`ClassifierFreeGuidance`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          prompt_embeds (`Tensor`):
+              text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          prompt_embeds_mask (`Tensor`):
+              mask for the text embeddings. Can be generated from text_encoder step.
+          negative_prompt_embeds (`Tensor`, *optional*):
+              negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
+          negative_prompt_embeds_mask (`Tensor`, *optional*):
+              mask for the negative text embeddings. Can be generated from text_encoder step.
+          image_latents (`Tensor`):
+              image latents used to guide the image generation. Can be generated from vae_encoder step.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          layers (`int`, *optional*, defaults to 4):
+              Number of layers to extract from the image
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+
+      Outputs:
+          latents (`Tensor`):
+              Denoised latents.
+    """
+
     model_name = "qwenimage-layered"
     block_classes = [
         QwenImageLayeredInputStep(),
@@ -142,9 +287,7 @@ class QwenImageLayeredCoreDenoiseStep(SequentialPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(
-                name="latents", type_hint=torch.Tensor, description="The latents generated by the denoising step"
-            ),
+            OutputParam.template("latents"),
         ]
 
 
@@ -162,7 +305,54 @@ LAYERED_AUTO_BLOCKS = InsertableDict(
 )
 
 
+# auto_docstring
 class QwenImageLayeredAutoBlocks(SequentialPipelineBlocks):
+    """
+    Auto Modular pipeline for layered denoising tasks using QwenImage-Layered.
+
+      Components:
+          image_resize_processor (`VaeImageProcessor`) text_encoder (`Qwen2_5_VLForConditionalGeneration`) processor
+          (`Qwen2VLProcessor`) tokenizer (`Qwen2Tokenizer`): The tokenizer to use guider (`ClassifierFreeGuidance`)
+          image_processor (`VaeImageProcessor`) vae (`AutoencoderKLQwenImage`) pachifier (`QwenImageLayeredPachifier`)
+          scheduler (`FlowMatchEulerDiscreteScheduler`) transformer (`QwenImageTransformer2DModel`)
+
+      Inputs:
+          image (`Union[Image, List]`):
+              Reference image(s) for denoising. Can be a single image or list of images.
+          resolution (`int`, *optional*, defaults to 640):
+              The target area to resize the image to, can be 1024 or 640
+          prompt (`str`, *optional*):
+              The prompt or prompts to guide image generation.
+          use_en_prompt (`bool`, *optional*, defaults to False):
+              Whether to use English prompt template
+          negative_prompt (`str`, *optional*):
+              The prompt or prompts not to guide the image generation.
+          max_sequence_length (`int`, *optional*, defaults to 1024):
+              Maximum sequence length for prompt encoding.
+          generator (`Generator`, *optional*):
+              Torch generator for deterministic generation.
+          num_images_per_prompt (`int`, *optional*, defaults to 1):
+              The number of images to generate per prompt.
+          latents (`Tensor`, *optional*):
+              Pre-generated noisy latents for image generation.
+          layers (`int`, *optional*, defaults to 4):
+              Number of layers to extract from the image
+          num_inference_steps (`int`, *optional*, defaults to 50):
+              The number of denoising steps.
+          sigmas (`List`, *optional*):
+              Custom sigmas for the denoising process.
+          attention_kwargs (`Dict`, *optional*):
+              Additional kwargs for attention processors.
+          **denoiser_input_fields (`None`, *optional*):
+              conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.
+          output_type (`str`, *optional*, defaults to pil):
+              Output format: 'pil', 'np', 'pt'.
+
+      Outputs:
+          images (`List`):
+              Generated images.
+    """
+
     model_name = "qwenimage-layered"
     block_classes = LAYERED_AUTO_BLOCKS.values()
     block_names = LAYERED_AUTO_BLOCKS.keys()
@@ -174,5 +364,5 @@ class QwenImageLayeredAutoBlocks(SequentialPipelineBlocks):
     @property
     def outputs(self):
         return [
-            OutputParam(name="images", type_hint=List[List[PIL.Image.Image]], description="The generated images"),
+            OutputParam.template("images"),
         ]

src/diffusers/modular_pipelines/z_image/denoise.py

@@ -131,7 +131,7 @@ class ZImageLoopDenoiser(ModularPipelineBlocks):
             ),
             InputParam(
                 kwargs_type="denoiser_input_fields",
-                description="conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
+                description="The conditional model inputs for the denoiser: e.g. prompt_embeds, negative_prompt_embeds, etc.",
             ),
         ]
         guider_input_names = []

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -84,7 +84,6 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
         >>> from diffusers.utils import load_image
 
-
         >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
         >>> feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(

src/diffusers/pipelines/hidream_image/pipeline_hidream_image.py

@@ -53,7 +53,6 @@ EXAMPLE_DOC_STRING = """
         >>> from transformers import AutoTokenizer, LlamaForCausalLM
         >>> from diffusers import HiDreamImagePipeline
 
-
         >>> tokenizer_4 = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
         >>> text_encoder_4 = LlamaForCausalLM.from_pretrained(
         ...     "meta-llama/Meta-Llama-3.1-8B-Instruct",

src/diffusers/pipelines/pag/pipeline_pag_controlnet_sd_xl_img2img.py

@@ -85,7 +85,6 @@ EXAMPLE_DOC_STRING = """
         >>> from diffusers import ControlNetModel, StableDiffusionXLControlNetPAGImg2ImgPipeline, AutoencoderKL
         >>> from diffusers.utils import load_image
 
-
         >>> depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
         >>> feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
         >>> controlnet = ControlNetModel.from_pretrained(

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -459,7 +459,6 @@ class StableDiffusionLatentUpscalePipeline(DiffusionPipeline, StableDiffusionMix
         >>> from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline
         >>> import torch
 
-
         >>> pipeline = StableDiffusionPipeline.from_pretrained(
         ...     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16
         ... )

utils/modular_auto_docstring.py

@@ -0,0 +1,300 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Auto Docstring Generator for Modular Pipeline Blocks
+
+This script scans Python files for classes that have `# auto_docstring` comment above them
+and inserts/updates the docstring from the class's `doc` property.
+
+Run from the root of the repo:
+    python utils/modular_auto_docstring.py [path] [--fix_and_overwrite]
+
+Examples:
+    # Check for auto_docstring markers (will error if found without proper docstring)
+    python utils/modular_auto_docstring.py
+
+    # Check specific directory
+    python utils/modular_auto_docstring.py src/diffusers/modular_pipelines/
+
+    # Fix and overwrite the docstrings
+    python utils/modular_auto_docstring.py --fix_and_overwrite
+
+Usage in code:
+    # auto_docstring
+    class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks):
+        # docstring will be automatically inserted here
+
+        @property
+        def doc(self):
+            return "Your docstring content..."
+"""
+
+import argparse
+import ast
+import glob
+import importlib
+import os
+import re
+import sys
+
+
+# All paths are set with the intent you should run this script from the root of the repo
+DIFFUSERS_PATH = "src/diffusers"
+REPO_PATH = "."
+
+# Pattern to match the auto_docstring comment
+AUTO_DOCSTRING_PATTERN = re.compile(r"^\s*#\s*auto_docstring\s*$")
+
+
+def setup_diffusers_import():
+    """Setup import path to use the local diffusers module."""
+    src_path = os.path.join(REPO_PATH, "src")
+    if src_path not in sys.path:
+        sys.path.insert(0, src_path)
+
+
+def get_module_from_filepath(filepath: str) -> str:
+    """Convert a filepath to a module name."""
+    filepath = os.path.normpath(filepath)
+
+    if filepath.startswith("src" + os.sep):
+        filepath = filepath[4:]
+
+    if filepath.endswith(".py"):
+        filepath = filepath[:-3]
+
+    module_name = filepath.replace(os.sep, ".")
+    return module_name
+
+
+def load_module(filepath: str):
+    """Load a module from filepath."""
+    setup_diffusers_import()
+    module_name = get_module_from_filepath(filepath)
+
+    try:
+        module = importlib.import_module(module_name)
+        return module
+    except Exception as e:
+        print(f"Warning: Could not import module {module_name}: {e}")
+        return None
+
+
+def get_doc_from_class(module, class_name: str) -> str:
+    """Get the doc property from an instantiated class."""
+    if module is None:
+        return None
+
+    cls = getattr(module, class_name, None)
+    if cls is None:
+        return None
+
+    try:
+        instance = cls()
+        if hasattr(instance, "doc"):
+            return instance.doc
+    except Exception as e:
+        print(f"Warning: Could not instantiate {class_name}: {e}")
+
+    return None
+
+
+def find_auto_docstring_classes(filepath: str) -> list:
+    """
+    Find all classes in a file that have # auto_docstring comment above them.
+
+    Returns list of (class_name, class_line_number, has_existing_docstring, docstring_end_line)
+    """
+    with open(filepath, "r", encoding="utf-8", newline="\n") as f:
+        lines = f.readlines()
+
+    # Parse AST to find class locations and their docstrings
+    content = "".join(lines)
+    try:
+        tree = ast.parse(content)
+    except SyntaxError as e:
+        print(f"Syntax error in {filepath}: {e}")
+        return []
+
+    # Build a map of class_name -> (class_line, has_docstring, docstring_end_line)
+    class_info = {}
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ClassDef):
+            has_docstring = False
+            docstring_end_line = node.lineno  # default to class line
+
+            if node.body and isinstance(node.body[0], ast.Expr):
+                first_stmt = node.body[0]
+                if isinstance(first_stmt.value, ast.Constant) and isinstance(first_stmt.value.value, str):
+                    has_docstring = True
+                    docstring_end_line = first_stmt.end_lineno or first_stmt.lineno
+
+            class_info[node.name] = (node.lineno, has_docstring, docstring_end_line)
+
+    # Now scan for # auto_docstring comments
+    classes_to_update = []
+
+    for i, line in enumerate(lines):
+        if AUTO_DOCSTRING_PATTERN.match(line):
+            # Found the marker, look for class definition on next non-empty, non-comment line
+            j = i + 1
+            while j < len(lines):
+                next_line = lines[j].strip()
+                if next_line and not next_line.startswith("#"):
+                    break
+                j += 1
+
+            if j < len(lines) and lines[j].strip().startswith("class "):
+                # Extract class name
+                match = re.match(r"class\s+(\w+)", lines[j].strip())
+                if match:
+                    class_name = match.group(1)
+                    if class_name in class_info:
+                        class_line, has_docstring, docstring_end_line = class_info[class_name]
+                        classes_to_update.append((class_name, class_line, has_docstring, docstring_end_line))
+
+    return classes_to_update
+
+
+def strip_class_name_line(doc: str, class_name: str) -> str:
+    """Remove the 'class ClassName' line from the doc if present."""
+    lines = doc.strip().split("\n")
+    if lines and lines[0].strip() == f"class {class_name}":
+        # Remove the class line and any blank line following it
+        lines = lines[1:]
+        while lines and not lines[0].strip():
+            lines = lines[1:]
+    return "\n".join(lines)
+
+
+def format_docstring(doc: str, indent: str = "    ") -> str:
+    """Format a doc string as a properly indented docstring."""
+    lines = doc.strip().split("\n")
+
+    if len(lines) == 1:
+        return f'{indent}"""{lines[0]}"""\n'
+    else:
+        result = [f'{indent}"""\n']
+        for line in lines:
+            if line.strip():
+                result.append(f"{indent}{line}\n")
+            else:
+                result.append("\n")
+        result.append(f'{indent}"""\n')
+        return "".join(result)
+
+
+def process_file(filepath: str, overwrite: bool = False) -> list:
+    """
+    Process a file and find/insert docstrings for # auto_docstring marked classes.
+
+    Returns list of classes that need updating.
+    """
+    classes_to_update = find_auto_docstring_classes(filepath)
+
+    if not classes_to_update:
+        return []
+
+    if not overwrite:
+        # Just return the list of classes that need updating
+        return [(filepath, cls_name, line) for cls_name, line, _, _ in classes_to_update]
+
+    # Load the module to get doc properties
+    module = load_module(filepath)
+
+    with open(filepath, "r", encoding="utf-8", newline="\n") as f:
+        lines = f.readlines()
+
+    # Process in reverse order to maintain line numbers
+    updated = False
+    for class_name, class_line, has_docstring, docstring_end_line in reversed(classes_to_update):
+        doc = get_doc_from_class(module, class_name)
+
+        if doc is None:
+            print(f"Warning: Could not get doc for {class_name} in {filepath}")
+            continue
+
+        # Remove the "class ClassName" line since it's redundant in a docstring
+        doc = strip_class_name_line(doc, class_name)
+
+        # Format the new docstring with 4-space indent
+        new_docstring = format_docstring(doc, "    ")
+
+        if has_docstring:
+            # Replace existing docstring (line after class definition to docstring_end_line)
+            # class_line is 1-indexed, we want to replace from class_line+1 to docstring_end_line
+            lines = lines[:class_line] + [new_docstring] + lines[docstring_end_line:]
+        else:
+            # Insert new docstring right after class definition line
+            # class_line is 1-indexed, so lines[class_line-1] is the class line
+            # Insert at position class_line (which is right after the class line)
+            lines = lines[:class_line] + [new_docstring] + lines[class_line:]
+
+        updated = True
+        print(f"Updated docstring for {class_name} in {filepath}")
+
+    if updated:
+        with open(filepath, "w", encoding="utf-8", newline="\n") as f:
+            f.writelines(lines)
+
+    return [(filepath, cls_name, line) for cls_name, line, _, _ in classes_to_update]
+
+
+def check_auto_docstrings(path: str = None, overwrite: bool = False):
+    """
+    Check all files for # auto_docstring markers and optionally fix them.
+    """
+    if path is None:
+        path = DIFFUSERS_PATH
+
+    if os.path.isfile(path):
+        all_files = [path]
+    else:
+        all_files = glob.glob(os.path.join(path, "**/*.py"), recursive=True)
+
+    all_markers = []
+
+    for filepath in all_files:
+        markers = process_file(filepath, overwrite)
+        all_markers.extend(markers)
+
+    if not overwrite and len(all_markers) > 0:
+        message = "\n".join([f"- {f}: {cls} at line {line}" for f, cls, line in all_markers])
+        raise ValueError(
+            f"Found the following # auto_docstring markers that need docstrings:\n{message}\n\n"
+            f"Run `python utils/modular_auto_docstring.py --fix_and_overwrite` to fix them."
+        )
+
+    if overwrite and len(all_markers) > 0:
+        print(f"\nUpdated {len(all_markers)} docstring(s).")
+    elif len(all_markers) == 0:
+        print("No # auto_docstring markers found.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Check and fix # auto_docstring markers in modular pipeline blocks",
+    )
+    parser.add_argument("path", nargs="?", default=None, help="File or directory to process (default: src/diffusers)")
+    parser.add_argument(
+        "--fix_and_overwrite",
+        action="store_true",
+        help="Whether to fix the docstrings by inserting them from doc property.",
+    )
+
+    args = parser.parse_args()
+
+    check_auto_docstrings(args.path, args.fix_and_overwrite)