In this post, we'll walk through how Modular Diffusers works — from the familiar API to run a modular pipeline, to building fully custom blocks and composing them into your own workflow. We'll also show how it integrates with Mellon, a node-based visual workflow interface that you can use to wire Modular Diffusers blocks together.

Here is a simple example of how to run inference with FLUX.2 Klein 4B using pre-built blocks:

You get the same results as with a standard DiffusionPipeline, but the pipeline is very different under the hood: it's composed of flexible blocks — text encoding, image encoding, denoising, and decoding — that you can inspect directly:

Each block is self-contained with its own inputs and outputs. You can run any block independently as its own pipeline, or add, remove, and swap blocks freely — they dynamically recompose to work with whatever blocks remain. Use .init_pipeline() to convert blocks into a runnable pipeline, and .load_components() to load the model weights.

For more on block types, composition patterns, lazy loading, and memory management with ComponentsManager, check out the Modular Diffusers documentation.

Modular Diffusers really shines when creating your own blocks. A custom block is a Python class that defines its components, inputs, outputs, and computation logic — and once defined, you can plug it into any workflow.

Here's an example block that extracts depth maps from images using Depth Anything V2.

Let's use this block with Qwen's ControlNet workflow.

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