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VLMs struggle with procedural 3D modeling, often producing flawed outputs due to API mismatches and geometric disconnections, but performance can be significantly boosted through iterative refinement.
Ditch the handcrafted noise schedules: spectral analysis unlocks per-image diffusion schedules that boost generative quality, especially when you're racing against the clock with few steps.
By strategically applying differential privacy in the wavelet domain, this work achieves a significantly better privacy-utility tradeoff for image generation, suggesting that not all image frequencies are created equal when it comes to privacy risk.
