Conditional diffusion models can create unseen images in various settings,
aiding image interpolation. Interpolation in latent spaces is well-studied, but
interpolation with specific conditions like text or poses is less understood.
Simple approaches, such as linear interpolation in the space of conditions,
often result in images that lack consistency, smoothness, and fidelity. To that
end, we introduce a novel training-free technique named Attention Interpolation
via Diffusion (AID). Our key contributions include 1) proposing an inner/outer
interpolated attention layer; 2) fusing the interpolated attention with
self-attention to boost fidelity; and 3) applying beta distribution to
selection to increase smoothness. We also present a variant, Prompt-guided
Attention Interpolation via Diffusion (PAID), that considers interpolation as a
condition-dependent generative process. This method enables the creation of new
images with greater consistency, smoothness, and efficiency, and offers control
over the exact path of interpolation. Our approach demonstrates effectiveness
for conceptual and spatial interpolation. Code and demo are available at
this https URL

条件扩散模型通过注意力插值技术实现图像的无缝插值，可用于处理潜在空间的插值问题，同时解决了文本或姿势等特殊条件下的插值问题，并提供了更好的一致性、平滑性和效率性。