multibind-attribute-misbinding-benchmark

Evaluate multi-reference image generation fidelity using MultiBind's dimension-wise confusion framework. Detects cross-subject attribute errors that holistic metrics (FID, CLIP) miss, including drift (degradation), swap (permutation), dominance (interference), and blending (averaging). Protocol uses specialist models for face identity, appearance, pose, and…

Independent GitHub snapshot · Automated classification · Original documentation governs