In this initialization, the high-frequency components of Tmathbf x _ T are drawn within the sound ϵepsilon, encouraging stochasticity. The lower-frequency parts through the reference graphic latent rmathbf x _ r introduce novel structural patterns. By decoupling broad spatial framework, which tends being memorized, from semantics with the sound degree, we lessen the design’s reliance on memorized spatial priors throughout early denoising actions.
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In addition, we compute a perceptual uniqueness time period h3h_ 3 from 64-bit perceptual hashes (pHash) as the normalized minimal Hamming length among each candidate graphic and hashes through the LAION subset, rewarding images that are visually unique from training samples.
In lieu of manipulating CFG or perturbing prompt embeddings to control generation, we tutorial the denoising trajectory far from memorized outputs by instantly modifying latent attributes. The contributions are as follows:
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(iii) Semantic aspect injection for memorization mitigation. We propose a localized function injection system that replaces memorized latent locations with semantically aligned features extracted from non-memorized reference photos, steering generation from memorized outputs although preserving prompt fidelity and visual high quality.
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