ACEdit：基于非对称条件的稳定多轮图像编辑
ACEdit: Stable Multi-Turn Image Editing via Asymmetric Conditioning
AI总结：
ACEdit通过将自强迫原则应用于图像编辑，结合非对称条件方案与蒸馏目标，解决多轮图像编辑中质量累积下降问题，在长序列编辑中保持质量与指令 adherence。
摘要：
图像编辑模型在单次编辑中能生成令人印象深刻的结果，但其质量往往在多轮编辑中下降。每一轮编辑都会引入小的分布偏移，这些偏移会在后续编辑中累积，在编辑器原始训练时遇到的输入分布与多轮推理时的输入分布之间形成差距。我们提出一种训练方案，使多轮图像编辑在这种累积分布偏移下保持稳定，关键在于迫使编辑器在训练过程中基于自身前几轮编辑的输出进行操作，将自强迫原则应用于图像编辑。主要挑战在于自生成的编辑轨迹偏离真实轨迹，导致直接监督不可靠。我们通过一种蒸馏目标解决该挑战，当模型基于自身前几轮输出操作时，利用教师图像编辑器提供可靠的训练信号。我们引入非对称条件方案：学生模型在自身输出上依次执行编辑，而教师模型则锚定在原始清晰图像上并接收复合编辑指令。实验表明，该方案在长多轮序列中大幅降低了编辑质量的下降，同时保持了编辑质量和指令 adherence。
英文摘要：
Image editing models produce impressive results for individual edits, but their quality often deteriorates over multiple editing turns. Each editing turn introduces small distribution shifts that accumulate over successive edits, creating a gap between the input distributions encountered during the editor's original training and multi-turn inference. We introduce a training scheme that makes multi-turn image editing stable under this accumulating distribution shift. A key is to force the editor to operate on its own outputs from previous editing turns during training, bringing the principle of Self-Forcing to image editing. The main challenge is that self-generated editing trajectories deviate from ground-truth trajectories, making direct supervision unreliable. We address this challenge through a distillation objective that leverages a teacher image editor to provide a reliable training signal when the model operates on its own previous outputs. We introduce an asymmetric conditioning scheme: while the student performs edits sequentially on its own outputs, the teacher remains anchored to the original clean image and receives a compound editing instruction. Experiments show substantially reduced degradation over long multi-turn sequences while maintaining editing quality and instruction adherence.
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。
