[modular] add auto_docstring & more doc related refactors

[yiyixuxu]

This PR adds a utility script utils/modular_auto_docstring.py that automatically generates docstrings for modular pipeline block classes from their doc property.

Usage

Mark classes with # auto_docstring comment:

# auto_docstring
class QwenImageAutoVaeEncoderStep(AutoPipelineBlocks):
    block_classes = [QwenImageInpaintVaeEncoderStep, QwenImageImg2ImgVaeEncoderStep]
    block_names = ["inpaint", "img2img"]
    block_trigger_inputs = ["mask_image", "image"]

    @property
    def doc(self):
        return (
            "Vae encoder step that encodes image inputs into latent representations.\n"
            "This is an auto pipeline block.\n"
            " - `QwenImageInpaintVaeEncoderStep` (inpaint) is used when `mask_image` is provided.\n"
            " - `QwenImageImg2ImgVaeEncoderStep` (img2img) is used when `image` is provided."
        )

Run the script to insert docstrings:

python utils/modular_auto_docstring.py --fix_and_overwrite

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[yiyixuxu]

our pipeline parameter are pretty consistent across different pipelines, e.g. you always have prompt, height, width, num_inference_steps, etc. I made template for these common ones, so that it is easier to define

before you need

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

now you do

InputParam.prompt()
InputParam.height(default=1024)
InputParam.num_inference_steps(default=28)
InputParam.generator()

[yiyixuxu]

these are all auto generated docstring

[yiyixuxu]

add this mark and then run

python utils/modular_auto_docstring.py --fix_and_overwrite