We present FreeMorph, the first tuning-free method for image morphing that
accommodates inputs with different semantics or layouts. Unlike existing
methods that rely on finetuning pre-trained diffusion models and are limited by
time constraints and semantic/layout discrepancies, FreeMorph delivers
high-fidelity image morphing without requiring per-instance training. Despite
their efficiency and potential, tuning-free methods face challenges in
maintaining high-quality results due to the non-linear nature of the multi-step
denoising process and biases inherited from the pre-trained diffusion model. In
this paper, we introduce FreeMorph to address these challenges by integrating
two key innovations. 1) We first propose a guidance-aware spherical
interpolation design that incorporates explicit guidance from the input images
by modifying the self-attention modules, thereby addressing identity loss and
ensuring directional transitions throughout the generated sequence. 2) We
further introduce a step-oriented variation trend that blends self-attention
modules derived from each input image to achieve controlled and consistent
transitions that respect both inputs. Our extensive evaluations demonstrate
that FreeMorph outperforms existing methods, being 10x ~ 50x faster and
establishing a new state-of-the-art for image morphing.
