Auto docstring and parameter templates

Every [~modular_pipelines.ModularPipelineBlocks] has a doc property that is automatically generated from its description, inputs, intermediate_outputs, expected_components, and expected_configs. The auto docstring system keeps docstrings in sync with the block's actual interface. Parameter templates provide standardized descriptions for parameters that appear across many pipelines.

Auto docstring

Modular pipeline blocks are composable — you can nest them, chain them in sequences, and rearrange them freely. Their docstrings follow the same pattern. When a [~modular_pipelines.SequentialPipelineBlocks] aggregates inputs and outputs from its sub-blocks, the documentation should update automatically without manual rewrites.

The # auto_docstring marker generates docstrings from the block's properties. Add it above a class definition to mark the class for automatic docstring generation.

# auto_docstring
class FluxTextEncoderStep(SequentialPipelineBlocks):
    ...

Run the following command to generate and insert the docstrings.

bash

python utils/modular_auto_docstring.py --fix_and_overwrite

The utility reads the block's doc property and inserts it as the class docstring.

# auto_docstring
class FluxTextEncoderStep(SequentialPipelineBlocks):
    """
    Text input processing step that standardizes text embeddings for the pipeline.

    Inputs:
        prompt_embeds (`torch.Tensor`) *required*:
            text embeddings used to guide the image generation.
        ...

    Outputs:
        prompt_embeds (`torch.Tensor`):
            text embeddings used to guide the image generation.
        ...
    """

You can also check without overwriting, or target a specific file or directory.

bash

# Check that all marked classes have up-to-date docstrings
python utils/modular_auto_docstring.py

# Check a specific file or directory
python utils/modular_auto_docstring.py src/diffusers/modular_pipelines/flux/

If any marked class is missing a docstring, the check fails and lists the classes that need updating.

Found the following # auto_docstring markers that need docstrings:
- src/diffusers/modular_pipelines/flux/encoders.py: FluxTextEncoderStep at line 42

Run `python utils/modular_auto_docstring.py --fix_and_overwrite` to fix them.

Parameter templates

InputParam and OutputParam define a block's inputs and outputs. Create them directly or use .template() for standardized definitions of common parameters like prompt, num_inference_steps, or latents.

InputParam

[~modular_pipelines.InputParam] describes a single input to a block.

Field	Type	Description
`name`	`str`	Name of the parameter
`type_hint`	`Any`	Type annotation (e.g., `str`, `torch.Tensor`)
`default`	`Any`	Default value (if not set, parameter has no default)
`required`	`bool`	Whether the parameter is required
`description`	`str`	Human-readable description
`kwargs_type`	`str`	Group name for related parameters (e.g., `"denoiser_input_fields"`)
`metadata`	`dict`	Arbitrary additional information

Creating InputParam directly

from diffusers.modular_pipelines import InputParam

InputParam(
    name="guidance_scale",
    type_hint=float,
    default=7.5,
    description="Scale for classifier-free guidance.",
)

Using a template

InputParam.template("prompt")
# Equivalent to:
# InputParam(name="prompt", type_hint=str, required=True,
#            description="The prompt or prompts to guide image generation.")

Templates set name, type_hint, default, required, and description automatically. Override any field or add context with the note parameter.

# Override the default value
InputParam.template("num_inference_steps", default=28)

# Add a note to the description
InputParam.template("prompt_embeds", note="batch-expanded")
# description becomes: "text embeddings used to guide the image generation. ... (batch-expanded)"

OutputParam

[~modular_pipelines.OutputParam] describes a single output from a block.

Field	Type	Description
`name`	`str`	Name of the parameter
`type_hint`	`Any`	Type annotation
`description`	`str`	Human-readable description
`kwargs_type`	`str`	Group name for related parameters
`metadata`	`dict`	Arbitrary additional information

Creating OutputParam directly

from diffusers.modular_pipelines import OutputParam

OutputParam(name="image_latents", type_hint=torch.Tensor, description="Encoded image latents.")

Using a template

OutputParam.template("latents")

# Add a note to the description
OutputParam.template("prompt_embeds", note="batch-expanded")

Available templates

INPUT_PARAM_TEMPLATES and OUTPUT_PARAM_TEMPLATES are defined in modular_pipeline_utils.py. They include common parameters like prompt, image, num_inference_steps, latents, prompt_embeds, and more. Refer to the source for the full list of available template names.