High-Dimensional Latents Inhibit Diffusion Optimization via Orthogonal Noise

Research shows that fine-tuning visual encoders for reconstruction reduces effective dimensionality, causing flow matching models to waste optimization effort on orthogonal noise directions. The study suggests that standard velocity prediction in high-dimensional spaces is inherently inefficient due to these manifold geometry shifts.