Endpoint-Calibrated Pullback Interpolation for Latent Image Diffusion

Diffusion image interpolation can lose texture and edge definition between otherwise detailed endpoints. Geometric and density-based routing criteria guide intermediate latents, but do not directly target such losses in the rendered images. We propose Endpoint-Calibrated Pullback Interpolation (ECPI), a plug-in correction for existing diffusion interpolation paths. ECPI derives position-dependent detail references from the frequency-band statistics of reconstructed endpoints. Responses below these references define the spectral deficits to correct. We differentiate the rendered responses through denoising and decoding to obtain updates to the noisy intermediate latents, allowing the generative process to realize the correction. On MorphBench and our 100-pair Animals-and-Humans evaluation set (AH100), ECPI improves no-reference image quality and reduces FID against endpoint reference sets across SLERP, Tangent, and GeoDiff routes. These gains are achieved while constraining each correction to have a non-negative first-order change in conditional log density at the correction state. This shows that detail recovery is compatible with locally probability-supported correction directions.