To address memorization in text-to-graphic diffusion versions, we suggest CAPTAIN, a instruction-totally free framework that steers the denoising trajectory toward non-memorized outputs by injecting characteristics right into your latent denoising approach with frequency decomposed sounds initialization.
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As noticed, our method successfully suppresses memorized content whilst manufacturing visually coherent and prompt-devoted results throughout distinctive random seeds (rightmost columns). Illustrations include eradicating the unique facial attributes of coaching portraits, altering animal poses and appearances, and generating novel object designs whilst preserving classification-level semantics. Supplemental qualitative examples and prompt lists are presented within the supplementary substance.
To obtain seamless repetition, divide your pattern into quadrants. This system will help keep alignment and continuity across repetitions, making sure a professional finish.
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In Just about every pair, the remaining image exhibits the memorized education case in point, while the correct impression demonstrates the corresponding mitigated final result generated by our method.
Current mitigation approaches is often broadly categorized into schooling-time and inference-time strategies. Schooling-time procedures commonly call for access to the first dataset and large computational means, limiting their practicality wen2024detecting.
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Much more not long ago, MoCA-Movie zhang2025motion extends thought-amount alignment into the online video area by leveraging movement-mindful semantic attribute fusion, guaranteeing temporal consistency throughout edited sequences. In distinction to these techniques, which inject or modify capabilities to boost editability and temporal coherence, our strategy initializes with non-memorized articles and explicitly injects localized characteristics to even more suppress memorized material, integrating semantic cues to preserve thought fidelity when protecting against overfitting for the coaching data.