Enhancing Creative Generation on Stable Diffusion-based Models

Recent text-to-image generative models, particularly Stable Diffusion and its distilled variants, have achieved impressive fidelity and strong text-image alignment. However, their creative generation capacity remains limited, as simply adding the term ``creative" to prompts often fails to yield genuinely creative results. In this paper, we introduce C3 (Creative Concept Catalyst), a training-free approach designed to enhance creativity in Stable Diffusion-based models. C3 selectively amplifies features during the denoising process to foster more creative outputs. We offer practical guidelines for choosing amplification factors based on two main aspects of creativity. C3 allows user-friendly creativity control in image generation and is the first study to enhance creativity in diffusion models without extensive computational costs. We demonstrate its effectiveness across various Stable Diffusion-based models. Source codes will be publicly available.