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English(EN) DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

新研究为文本到图像 AI 提供高级控制和安全性

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文提出了增强文本到图像生成模型控制和安全性的新颖方法。第一种方法 DiSCO，通过对比评分优化提示，提供了一种黑盒防御来防止生成不安全内容（NSFW），在保持图像质量的同时显著减少了有害输出。第二种方法 Concept Guidance (CoG) 是一种无需训练的方法，通过强化扩散模型中与概念相关的层来精确控制图像生成，在不改变模型本身的情况下提高了美学吸引力和局部连贯性。 AI

影响 这些方法为文本到图像模型提供了更高的安全性和控制力，有望带来更可靠、更合乎道德的 AI 生成内容。

排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了文本到图像生成的新方法。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新研究为文本到图像 AI 提供高级控制和安全性

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两篇在 arXiv 上发表的学术论文，详细介绍了文本到图像生成的新方法。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Tong Zhang, Motasem Alfarra, Carlos Hinojosa, Christos Louizos, Bernard Ghanem ·

    DiSCO：通过分布引导的对比提示优化来防御文本到图像生成

    arXiv:2608.17067v1 Announce Type: new Abstract: As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, further exacerbated by red-teaming adversarial attacks. Ex…

  2. arXiv cs.AI TIER_1 English(EN) · Nikolai R\"ohrich, Isabell Hans, Felix Krause, Bj\"orn Ommer ·

    概念引导：文本到图像生成的精确、无需训练的潜在控制

    arXiv:2608.14172v1 Announce Type: cross Abstract: Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controlling how ae…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    DiSCO：通过分布引导的对比提示优化来防御文本到图像生成

    DiSCO is a black-box, zero-shot prompt-level defense that uses distribution-guided suffix expansion and contrastive scoring to reduce harmful image generation without altering the model.

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