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Diffusion types can unintentionally reproduce instruction examples, elevating privateness and copyright considerations as these programs are progressively deployed at scale. Present inference-time mitigation solutions commonly manipulate classifier-free steerage (CFG) or perturb prompt embeddings; nonetheless, they often struggle to lower memorization without compromising alignment with the conditioning prompt. We introduce CAPTAIN, a education-totally free framework that mitigates memorization by instantly modifying latent characteristics through denoising. CAPTAIN very first applies frequency-dependent noise initialization to decrease the tendency to duplicate memorized patterns early within the denoising approach.
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Determine 3: Figuring out the ideal injection location for semantic modifying. (Left) Average CLIP rating evolution across diffusion timesteps for all the sdv1-500 memorized dataset. The injection location is bounded with the higher Restrict, wherever the CLIP rating exceeds the typical (indicating strong semantic content material), and the lessen limit ahead of the sharp drop in the speed of transform.
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The framework integrates detection and mitigation within a unified pipeline that adaptively decides the semantic written content, spatial areas, and intermediate timesteps for element injection, proficiently mitigating memorization although maintaining prompt consistency.
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