Preliminaries on Diffusion Models. Diffusion probabilistic styles ho2020denoising define a ahead–reverse approach that transforms clean up details samples 0mathbf x _ 0 into Gaussian noise by way of a Markov chain and learns to invert this noising course of action. In latent diffusion designs rombach2022high, this method operates within a compressed latent Place instead of in pixel Place; an encoder EE very first maps visuals into the latent domain the place diffusion takes place, plus a decoder DD later on reconstructs them.
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We provide added qualitative leads to fig.˜7 and fig.˜8, together with the corresponding prompts outlined in desk˜three. Table˜three also includes the prompts Employed in fig.˜5 of the key paper. As proven during the figures, our method achieves structural alterations though sustaining strong semantic alignment, a property that strategies like BE chen2025exploring often struggle with. PRSS chen2025enhancing performs much better On this regard but can deviate from your intended prompt due to the semantic prompt replacement. hanadjusting can also induce structural variations via its initialization method, which aligns with our frequency-decomposed sounds initialization, enabling early latent actions to include external structural bias prior to memorization takes place.
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four rombach2022high. We use 500 prompts extracted with the LAION dataset schuhmann2022laion that are known to result in memorization in SD v1.four to evaluate the performance of memorization mitigation. These prompts, provided by webster2023reproducible, are related with instances where the model reproduces near-equivalent education images.
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To recognize the optimum injection window, we analyze how graphic–textual content alignment modifications more than the denoising trajectory. At each timestep tt, the partly denoised latent tmathbf x _ t is decoded to impression Area and compared with the conditioning prompt mathbf p working with CLIP similarity:
The framework integrates detection and mitigation within a unified pipeline that adaptively decides the semantic content, spatial areas, and intermediate timesteps for characteristic injection, effectively mitigating memorization when preserving prompt regularity.
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