Prototype-Guided Diffusion: Visual Conditioning without External Memory

TL;DR

This paper introduces Prototype Diffusion Model (PDM), a novel diffusion-based image generation method that uses learned visual prototypes for adaptive, memory-free conditioning, reducing computational costs while maintaining high quality.

Contribution

PDM embeds prototype learning into diffusion models, enabling effective visual conditioning without external memory or retrieval, improving scalability and efficiency.

Findings

PDM achieves comparable image quality to retrieval-based methods.

PDM reduces computational and storage costs significantly.

PDM demonstrates scalability for large-scale image generation.

Abstract

Diffusion models achieve state-of-the-art image generation but remain computationally costly due to iterative denoising. Latent-space models like Stable Diffusion reduce overhead yet lose fine detail, while retrieval-augmented methods improve efficiency but rely on large memory banks, static similarity models, and rigid infrastructures. We introduce the Prototype Diffusion Model (PDM), which embeds prototype learning into the diffusion process to provide adaptive, memory-free conditioning. Instead of retrieving references, PDM learns compact visual prototypes from clean features via contrastive learning, then aligns noisy representations with semantically relevant patterns during denoising. Experiments demonstrate that PDM sustains high generation quality while lowering computational and storage costs, offering a scalable alternative to retrieval-based conditioning.