To address memorization in text-to-picture diffusion models, we propose CAPTAIN, a training-no cost framework that steers the denoising trajectory toward non-memorized outputs by injecting capabilities straight in to the latent denoising procedure with frequency decomposed sound initialization.
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We Appraise the computational overhead of our method towards baseline versions in Desk 2, which include BE chen2025exploring, PRSS chen2025enhancing, and hanadjusting. All methods are in contrast in opposition to the vanilla Steady Diffusion v1.four, whose common inference time two.06 is used to compute the for every-technique overhead. As documented in Area 4, our strategy maintains computational efficiency with regards to the two wall-clock time and FLOPs. BE and PRSS exhibit similar performance with nominal overhead. On the other hand, the for each-sample mitigation variant of hanadjusting exhibits noticeably better overhead than other procedures resulting from its optimization-based mostly noise initialization course of action, which incurs further computational fees that differ throughout circumstances.
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By injecting semantically aligned options from non-memorized reference images, CAPTAIN suppresses memorized material without sacrificing visual high quality or alignment with the conditioning prompt. Substantial experiments exhibit that CAPTAIN achieves stronger memorization mitigation than existing inference-time baselines although preserving competitive semantic fidelity.
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Existing mitigation strategies is usually broadly classified into education-time and inference-time methods. Coaching-time methods ordinarily have to have entry to the first dataset and huge computational resources, restricting their practicality wen2024detecting.
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It then identifies the optimum denoising timesteps for function injection and localizes memorized locations. Lastly, CAPTAIN injects semantically aligned features from non-memorized reference illustrations or photos into localized latent areas, suppressing memorization while preserving prompt fidelity and visual good quality. Our experiments display that CAPTAIN achieves considerable reductions in memorization when compared to CFG-primarily based baselines although keeping strong alignment with the supposed prompt.
More lately, MoCA-Video zhang2025motion extends idea-level alignment to the video area by leveraging motion-conscious semantic function fusion, ensuring temporal regularity across edited sequences. In distinction to these techniques, which inject or modify capabilities to improve editability and temporal coherence, our system initializes with non-memorized information and explicitly injects localized capabilities to even more suppress memorized material, integrating semantic cues to preserve strategy fidelity although stopping overfitting into the teaching info.