Preliminaries on Diffusion Types. Diffusion probabilistic styles ho2020denoising define a ahead–reverse method that transforms thoroughly clean details samples 0mathbf x _ 0 into Gaussian sound via a Markov chain and learns to invert this noising procedure. In latent diffusion designs rombach2022high, this process operates in a very compressed latent Place as an alternative to in pixel House; an encoder EE to start with maps photographs into the latent area where by diffusion takes place, as well as a decoder DD afterwards reconstructs them.
Diffusion types can unintentionally reproduce training illustrations, increasing privateness and copyright considerations as these systems are increasingly deployed at scale. Current inference-time mitigation approaches generally manipulate classifier-no cost steering (CFG) or perturb prompt embeddings; nonetheless, they often struggle to lower memorization without compromising alignment with the conditioning prompt. We introduce CAPTAIN, a education-cost-free framework that mitigates memorization by right modifying latent characteristics all through denoising. CAPTAIN first applies frequency-dependent sounds initialization to decrease the tendency to replicate memorized patterns early from the denoising approach.
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Initialization by way of Frequency Decomposition. Drawing inspiration from modern works demonstrating that initialization influences memorization conduct hanadjusting; wu2024freeinit, we adopt a frequency decomposed initialization system that blends the lower-frequency factors of the reference impression with large-frequency Gaussian sounds.
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Furthermore, we compute a perceptual uniqueness term h3h_ 3 from 64-little bit perceptual hashes (pHash) since the normalized minimal Hamming distance concerning Every single prospect picture and hashes with the LAION subset, gratifying photos that are visually unique from education samples.
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