Optimizing noise latents in diffusion models is powerful for controllable generation, reward-guided sampling, and latent inversion, but the process is notoriously unstable. Without a principled regularizer, optimized latents drift away from the Gaussian prior, collapsing out of the typical set and producing severe artifacts. Existing constraints like norm-matching or simple KL divergence losses are often insufficient, as they fail to capture the full statistical properties of true Gaussian noise. We propose a principled, differentiable regularizer that correctly targets the high-mass typical set rather than the high-probability mode. Our energy function tractably approximates the KL divergence by matching low-order statistics. It combines a 1D marginal term to match the pixel-value histogram and a 2D spatial term to enforce decorrelation. By applying this in a multi-scale pyramid, our method penalizes correlations at all ranges, effectively projecting samples closer onto the true Gaussian typical set. We demonstrate its effectiveness for robust, artifact-free reward-guided generation and model-free latent inversion.

Anamorphosis refers to a category of images that are intentionally distorted, making them unrecognizable when viewed directly. Their true form only reveals itself when seen from a specific viewpoint, which can be through some catadioptric device like a mirror or a lens. While the construction of these mathematical devices can be traced back to as early as the 17th century [28], they are only interpretable when viewed from a specific vantage point and tend to lose meaning when seen normally. In this paper, we revisit these famous optical illusions with a generative twist. With the help of latent rectified flow models, we propose a method to create anamorphic images that still retain a valid interpretation when viewed directly. To this end, we introduce Laplacian Pyramid Warping, a frequency-aware image warping technique key to generating high-quality visuals. Our work extends Visual Anagrams [17] to latent space models and to a wider range of spatial transforms, enabling the creation of novel generative perceptual illusions.