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mixture-of-diffusers

albarji/mixture-of-diffusers

Mixture of Diffusers for scene composition and high resolution image generation

GraphCanon updated 3w · GitHub synced 3w

449 stars41 forksLast push 3y Python MIT

Decision brief

Mixture-of-Diffusers enhances scene composition and resolution through parallel diffusion processes.

Good fit when

  • When precise placement of objects within the image is critical and desired composition needs detailed control over specific regions
  • For generating high-resolution images where memory constraints are an issue, as it can distribute tasks across multiple diffusers to reduce GPU load

Avoid when

  • If a user-friendly graphical interface is preferred, since Mixture-of-Diffusers may require more hands-on configuration and lacks built-in GUI features compared to plugins like Tiled Diffusion & VAE
  • In scenarios where images with less intricate or complex composition are sufficient, as the overhead of managing multiple diffusers could be unnecessary

Observed Jul 17, 2026 · Source: enrich:decision_facts

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Maintenance and security

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Install

pip install mixture-of-diffusers
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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

This repository holds scripts and tools implementing a method to integrate various diffusion processes collaborating on generating a single composite image.

Capability facts

Languages
python

Source: github.language · Aug 1, 2026

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README

Mixture of Diffusers

This repository holds various scripts and tools implementing a method for integrating a mixture of different diffusion processes collaborating to generate a single image. Each diffuser focuses on a particular region on the image, taking into account boundary effects to promote a smooth blending.

If you prefer a more user friendly graphical interface to use this algorithm, I recommend trying the Tiled Diffusion & VAE plugin developed by pkuliyi2015 for AUTOMATIC1111's stable-diffusion-webui.

Motivation

Current image generation methods, such as Stable Diffusion, struggle to position objects at specific locations. While the content of the generated image (somewhat) reflects the objects present in the prompt, it is difficult to frame the prompt in a way that creates an specific composition. For instance, take a prompt expressing a complex composition such as

A charming house in the countryside on the left, in the center a dirt road in the countryside crossing pastures, on the right an old and rusty giant robot lying on a dirt road, by jakub rozalski, sunset lighting on the left and center, dark sunset lighting on the right elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece

Out of a sample of 20 Stable Diffusion generations with different seeds, the generated images that align best with the prompt are the following:

Badge imageBadge imageBadge image

The method proposed here strives to provide a better tool for image composition by using several diffusion processes in parallel, each configured with a specific prompt and settings, and focused on a particular region of the image. For example, the following are three outputs from this method, using the following prompts from left to right:

  • "A charming house in the countryside, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
  • "A dirt road in the countryside crossing pastures, by jakub rozalski, sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"
  • "An old and rusty giant robot lying on a dirt road, by jakub rozalski, dark sunset lighting, elegant, highly detailed, smooth, sharp focus, artstation, stunning masterpiece"

The mixture of diffusion processes is done in a way that harmonizes the generation process, preventing "seam" effects in the generated image.

Using several diffusion processes in parallel has also practical advantages when generating very large images, as the GPU memory requirements are similar to that of generating an image of the size of a single tile.

Usage

This repository provides two new pipelines, StableDiffusionTilingPipeline and StableDiffusionCanvasPipeline, that extend the standard Stable Diffusion pipeline from Diffusers. They feature new options that allow defining the mixture of diffusers, which are distributed as a number of "diffusion regions" over the image to be generated. StableDiffusionTilingPipeline is simpler to use and arranges the diffusion regions as a grid over the canvas, while StableDiffusionCanvasPipeline allows a more flexible placement and also features image2image capabilities.

Prerequisites

Since this work is based on Stable Diffusion models, you will need to [request access and accept the usage terms of Stable Diffusion](https:/

For agents

This page has a .md twin and JSON over the API.

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