custom-blocks

name	custom-blocks
description	Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that consumers can load via `ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)`.

What this skill is for

A ModularPipelineBlocks subclass is a unit of pipeline logic — input/output spec plus a __call__ — that slots into diffusers' modular pipeline composition. Once you have one defined locally, you almost always want to publish it as a small Hub repo so others can from_pretrained it. diffusers-cli custom_blocks automates the packaging step: it parses your Python file, instantiates the chosen block class, and writes a save_pretrained-style directory in your cwd that's ready to push to the Hub.

Use this skill when:

• The user is writing a custom modular block and asks "how do I publish this?" or "package this for the Hub".
• The user has a block.py (or similar) file with one or more ModularPipelineBlocks subclasses.
• You're scaffolding a new modular pipeline repo and need the on-disk layout that ModularPipelineBlocks.from_pretrained expects.

Don't use this skill for: running an existing modular pipeline (diffusers-cli run), introspecting one (diffusers-cli schema), or writing the block class itself — this skill packages an already-written block.

The end-to-end workflow

[you: write block.py] → diffusers-cli custom_blocks → [packaged dir in cwd]
                                                            ↓
                                            hf upload <repo> .
                                                            ↓
                                consumers: ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)
                                           diffusers-cli schema --model <repo> --trust-remote-code
                                           diffusers-cli run --model <repo> --trust-remote-code ...

The skill covers the middle box. The bookends (writing the block and uploading) are out of scope.

Command surface

diffusers-cli custom_blocks [--block_module_name <file.py>] [--block_class_name <ClassName>]

Flags

• --block_module_name <file> — Python file containing the block class. Defaults to block.py in the cwd.
• --block_class_name <name> — Which class in the file to package. Optional: if omitted, the CLI parses the file with ast, finds every class that inherits from ModularPipelineBlocks, and uses the first one (with an info log naming the others). Specify explicitly when the file defines more than one block and you want a specific one.

What it does

1. AST scan: parses <file> without executing it, walks top-level ClassDef nodes, and collects every class whose bases include ModularPipelineBlocks.
2. Pick a class: uses --block_class_name if given, else the first found. Errors with the list of available classes if your name doesn't match.
3. Load and save: imports the file via importlib.util.spec_from_file_location (this does execute the module — make sure your block.py is something you trust to run), instantiates the chosen class with no constructor args, and calls .save_pretrained(os.getcwd()).

The result is a Hub-uploadable directory laid out the way ModularPipelineBlocks.from_pretrained expects: your block source, an auto_map in the config so consumers know to load it with trust_remote_code=True, and any artifacts save_pretrained writes for that block class.

End-to-end example

Given a block.py like:

from diffusers.modular_pipelines import ModularPipelineBlocks, InputParam, OutputParam

class MyDenoiseBlock(ModularPipelineBlocks):
    model_name = "my-denoise"

    @property
    def inputs(self):
        return [
            InputParam("latents", type_hint="torch.Tensor", required=True, description="Noisy latents."),
            InputParam("guidance_scale", type_hint="float", default=7.5),
        ]

    @property
    def intermediate_outputs(self):
        return [OutputParam("latents", type_hint="torch.Tensor")]

    def __call__(self, components, state):
        # ... denoising logic ...
        return components, state

Package it:

diffusers-cli custom_blocks --block_module_name block.py

Output in cwd:

./
├── block.py
├── modular_config.json  # contains auto_map → MyDenoiseBlock
└── (any state files MyDenoiseBlock.save_pretrained writes)

Upload to the Hub:

hf upload my-user/my-denoise-block .

Consumers can now use it:

from diffusers import ModularPipeline
pipe = ModularPipeline.from_pretrained("my-user/my-denoise-block", trust_remote_code=True)

Or via CLI:

diffusers-cli schema --model my-user/my-denoise-block --trust-remote-code
diffusers-cli run --model my-user/my-denoise-block --trust-remote-code \
    --pipeline-kwargs '{"latents": "...", "guidance_scale": 7.5}'

Common errors

• Could not parse '<file>': SyntaxError — the file isn't valid Python. Fix the syntax; the AST step runs before any execution.
• block_class_name could not be retrieved. Available classes from <file>: [ClassA, ClassB] — your --block_class_name doesn't match any ModularPipelineBlocks subclass found. Pick from the list shown.
• No classes found: silent — the command will try to use the first entry in an empty list and raise IndexError. If you hit that, double-check your class actually inherits from ModularPipelineBlocks (the AST scan looks for that literal base-class name; aliased imports like from diffusers import ... as MPB won't be picked up).
• Block requires constructor args: the command calls <ClassName>() with no args. If your block needs __init__ parameters, refactor to take them from state/components at __call__ time instead, or hardcode defaults in __init__.

Verifying the install

If diffusers-cli isn't on PATH, see the install verification section of ../diffusers-cli/SKILL.md.

Related

• diffusers-cli skill — once your block is uploaded, schema/run let you call it from the terminal without writing Python.
• diffusers' modular pipelines docs — for writing the block class itself.