Blending Concepts with Text-to-Image Diffusion Models
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
Diffusion models have dramatically advanced text-to-image generation in
recent years, translating abstract concepts into high-fidelity images with
remarkable ease. In this work, we examine whether they can also blend distinct
concepts, ranging from concrete objects to intangible ideas, into coherent new
visual entities under a zero-shot framework. Specifically, concept blending
merges the key attributes of multiple concepts (expressed as textual prompts)
into a single, novel image that captures the essence of each concept. We
investigate four blending methods, each exploiting different aspects of the
diffusion pipeline (e.g., prompt scheduling, embedding interpolation, or
layer-wise conditioning). Through systematic experimentation across diverse
concept categories, such as merging concrete concepts, synthesizing compound
words, transferring artistic styles, and blending architectural landmarks, we
show that modern diffusion models indeed exhibit creative blending capabilities
without further training or fine-tuning. Our extensive user study, involving
100 participants, reveals that no single approach dominates in all scenarios:
each blending technique excels under certain conditions, with factors like
prompt ordering, conceptual distance, and random seed affecting the outcome.
These findings highlight the remarkable compositional potential of diffusion
models while exposing their sensitivity to seemingly minor input variations.
