ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints

TL;DR

This paper introduces ConceptLab, a novel method for generating unique, imaginary concepts in text-to-image models by optimizing diffusion priors with adaptive constraints and hybridization techniques, enhancing creativity and diversity.

Contribution

It formulates creative concept generation as an optimization over diffusion priors, integrating a vision-language model for adaptive constraints and hybrid concept creation.

Findings

Generated highly unique and diverse concepts.

Enabled hybridization of different generated ideas.

Improved control over creativity in text-to-image synthesis.

Abstract

Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery. The surge of personalization techniques that has followed has also allowed us to imagine unique concepts in new scenes. However, an intriguing question remains: How can we generate a new, imaginary concept that has never been seen before? In this paper, we present the task of creative text-to-image generation, where we seek to generate new members of a broad category (e.g., generating a pet that differs from all existing pets). We leverage the under-studied Diffusion Prior models and show that the creative generation problem can be formulated as an optimization process over the output space of the diffusion prior, resulting in a set of "prior constraints". To keep our generated concept from converging into existing members, we incorporate a question-answering Vision-Language Model (VLM) that adaptively adds new constraints to the optimization problem, encouraging the model to discover increasingly more unique creations. Finally, we show that our prior constraints can also serve as a strong mixing mechanism allowing us to create hybrids between generated concepts, introducing even more flexibility into the creative process.