Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Faye, Bilal, Azzag, Hanane, Lebbah, Mustapha
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891830934
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