FreeBlend: Advancing Concept Blending with Staged Feedback-Driven Interpolation Diffusion

TL;DR

FreeBlend is a training-free framework that improves concept blending in generative models by using staged interpolation and feedback mechanisms to enhance semantic coherence and visual quality.

Contribution

It introduces a novel, training-free approach with a feedback-driven interpolation strategy for better concept blending in generative models.

Findings

Significantly improves semantic coherence in blended images.

Enhances visual quality and naturalness of generated images.

Effective without additional training or fine-tuning.

Abstract

Concept blending is a promising yet underexplored area in generative models. While recent approaches, such as embedding mixing and latent modification based on structural sketches, have been proposed, they often suffer from incompatible semantic information and discrepancies in shape and appearance. In this work, we introduce FreeBlend, an effective, training-free framework designed to address these challenges. To mitigate cross-modal loss and enhance feature detail, we leverage transferred image embeddings as conditional inputs. The framework employs a stepwise increasing interpolation strategy between latents, progressively adjusting the blending ratio to seamlessly integrate auxiliary features. Additionally, we introduce a feedback-driven mechanism that updates the auxiliary latents in reverse order, facilitating global blending and preventing rigid or unnatural outputs. Extensive experiments demonstrate that our method significantly improves both the semantic coherence and visual quality of blended images, yielding compelling and coherent results.