---
title: "diffusers"
description: "Conventions and task skills for contributing to diffusers"
canonical: https://agentpluginsdirectory.com/plugins/diffusers
last-updated: 2026-10-13
---

# diffusers
Conventions and task skills for contributing to diffusers
- Slug: diffusers
- Publisher: The Hugging Face team
- Repository: https://github.com/huggingface/diffusers
- Manifest: .ai/plugin.json
- License: Apache-2.0
- Category (editorial): code-review
- Skills: 4 (custom-blocks, diffusers-cli, model-integration, self-review)
- MCP servers: 0
- Stars: 34708
- Repository created: 2022-05-30
- Repository last pushed: 2026-10-13
- Publisher type: Organization
- Listing: https://agentpluginsdirectory.com/plugins/diffusers
- Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json

## What diffusers does, in the publisher's words

🤗 Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, 🤗 Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions.

🤗 Diffusers offers three core components:

- State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code.
- Interchangeable noise schedulers for different diffusion speeds and output quality.
- Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.

From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/huggingface/diffusers/HEAD/README.md

## Skills

- custom-blocks: 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_p…
- diffusers-cli: Use when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Pyt…
- model-integration: Use when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.
- self-review: Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity, flagging what to fix before submitting (blocki…

Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.