Blending concepts with text-to-image diffusion models

Text-to-image diffusion models can generate high-quality images from individual prompts, yet it remains unclear how reliably they can fuse two distinct concepts into a single coherent image in a strictly zero-shot setting. Inspired by human combinational creativity, we operationalize visual concept blending here as conditioning a diffusion model on a pair of text prompts and ask whether the resulting image preserves recognizable traits of both concepts while remaining visually coherent. We study four inference-time blending strategies that intervene at different stages of the diffusion process, including embedding-space interpolation, mid-denoising prompt switching, timestep-wise prompt alternation, and layer-wise conditioning within the denoising network. We evaluate these strategies across diverse blend categories, spanning object–object blends, compound concepts, style–content combinations, and architectural landmarks, and we complement the analysis with a user study involving 100 participants. Results indicate that no single strategy dominates across all categories: timestep-scheduling approaches are often preferred for producing recognisable and well-integrated hybrids, while embedding- and layer-based interventions exhibit characteristic strengths and failure modes in specific settings. Finally, we analyse practical control factors such as prompt ordering, blend ratio, and random seed, showing how they can substantially alter outcomes and providing concrete levers for steering blends more predictably in creative applications.