Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization
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. However, relying on unconstrained Euclidean gradient ascent mathematically inflates the latent norm and destroys the standard Gaussian prior, causing severe visual artifacts like color over-saturation. Furthermore, these methods suffer from inefficient semantic routing and easily fall into t...
| Authors | Haosen Li, Wenshuo Chen, Lei Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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