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Poster

ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation

Jack Lu · Ryan Teehan · Mengye Ren

# 243
Strong blind review: This paper was not made available on public preprint services during the review process Strong Double Blind
[ ] [ Paper PDF ]
Thu 3 Oct 7:30 a.m. PDT — 9:30 a.m. PDT

Abstract:

In this paper we propose ProCreate, a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent training data reproduction. ProCreate operates on a set of reference images and actively propels the generated image embedding away from the reference embeddings during the generation process. We collected a few-shot creative generation benchmark on eight different categories---encompassing different concepts, styles, and settings---in which ProCreate achieves the highest sample diversity and fidelity. Furthermore, we show that ProCreate is effective at preventing replicating training data in a large-scale evaluation using training text prompts.

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