通过自适应起源引导实现编辑模型的连续控制
Continuous Control of Editing Models via Adaptive-Origin Guidance
AI总结：
本文提出AdaOr方法，通过自适应调整指导起源实现编辑模型的连续强度控制，提升编辑过程的平滑性和一致性。
摘要：
基于扩散的编辑模型已经 emerged 作为一种强大的工具，用于语义图像和视频操纵。然而，现有模型缺乏一种机制，用于平滑地控制文本引导编辑的强度。在标准文本条件生成中，分类器自由引导（CFG）影响提示的遵循性，建议其作为编辑模型中编辑强度的潜在控制手段。然而，我们证明在这些模型中扩大 CFG 并不能产生输入与编辑结果之间的平滑过渡。我们将这种行为归因于无条件预测，它作为指导的起源并主导低引导尺度下的生成，同时代表了对输入内容的任意操纵。为了实现连续控制，我们引入了自适应起源引导（AdaOr），一种方法，通过使用与身份操纵相对应的身份指令来调整这种标准指导起源。通过根据编辑强度插值此身份预测与标准无条件预测，我们确保了从输入到编辑结果的连续过渡。我们在图像和视频编辑任务上评估了我们的方法，证明其比当前滑动条编辑方法提供了更平滑和一致的控制。我们的方法将身份指令纳入标准训练框架中，在推理时实现了细粒度控制，而无需每编辑操作的流程或依赖专门的数据集。
英文摘要：
Diffusion-based editing models have emerged as a powerful tool for semantic image and video manipulation. However, existing models lack a mechanism for smoothly controlling the intensity of text-guided edits. In standard text-conditioned generation, Classifier-Free Guidance (CFG) impacts prompt adherence, suggesting it as a potential control for edit intensity in editing models. However, we show that scaling CFG in these models does not produce a smooth transition between the input and the edited result. We attribute this behavior to the unconditional prediction, which serves as the guidance origin and dominates the generation at low guidance scales, while representing an arbitrary manipulation of the input content. To enable continuous control, we introduce Adaptive-Origin Guidance (AdaOr), a method that adjusts this standard guidance origin with an identity-conditioned adaptive origin, using an identity instruction corresponding to the identity manipulation. By interpolating this identity prediction with the standard unconditional prediction according to the edit strength, we ensure a continuous transition from the input to the edited result. We evaluate our method on image and video editing tasks, demonstrating that it provides smoother and more consistent control compared to current slider-based editing approaches. Our method incorporates an identity instruction into the standard training framework, enabling fine-grained control at inference time without per-edit procedure or reliance on specialized datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
