Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization

ArXi:2604.23540v1 Announce Type: new Text-to-image diffusion models have achieved remarkable generative capabilities, yet accurately aligning complex textual prompts with synthesized layouts remains an ongoing challenge. In these models, the initial Gaussian noise acts as a critical structural seed dictating the macroscopic layout. Recent online optimization and search methods attempt to refine this noise to enhance text-image alignment.