探索用于零样本图像编辑的迭代流形约束
Exploring Iterative Manifold Constraint for Zero-shot Image Editing
AI总结：
针对文本驱动图像编辑中“先反转再编辑”流程用近高斯噪声导致保真度错误的问题，提出ZZEdit范式，通过定位中间反转潜变量并设计ZigZag交替去噪反转过程，实现可编辑性与保真度的更好权衡，实验验证其有效性。
摘要：
可编辑性和保真度是文本驱动图像编辑的两大核心需求，要求编辑区域与目标提示对齐，其余部分保持不变。当前前沿编辑方法通常遵循“先反转再编辑”流程：将输入图像反转得到近似高斯噪声${z}_T$，再基于此用目标提示进行采样。但我们认为，以近高斯噪声为后续编辑的支点并非良策，会引发大量保真度错误。通过初步分析验证这一点，发现中间反转潜变量能更好地权衡可编辑性与保真度。基于此，我们提出新型零样本编辑范式ZZEdit：先定位合格的中间反转潜变量${z}_p$作为更好的编辑支点，既满足编辑需求又保留结构；再设计ZigZag过程交替执行去噪和反转，逐步向${z}_p$注入目标引导并保留$p$步的结构信息；最后在目标提示下执行纯采样以平衡反转和去噪步数。本质上，ZZEdit在$M_{p}$和$M_{p-1}$流形间执行迭代流形约束，减少保真度错误。大量实验表明，ZZEdit在多种图像编辑场景中相比“先反转再编辑”流程更有效。
英文摘要：
Editability and fidelity are two essential demands for text-driven image editing, which expects that the editing area should align with the target prompt and the rest remain unchanged separately. The current cutting-edge editing methods usually obey an "inversion-then-editing" pipeline, where the input image is inverted to an approximate Gaussian noise ${z}_T$, based on which a sampling process is conducted using the target prompt. Nevertheless, we argue that it is not a good choice to use a near-Gaussian noise as a pivot for further editing since it would bring plentiful fidelity errors. We verify this by a pilot analysis, discovering that intermediate-inverted latents can achieve a better trade-off between editability and fidelity than the fully-inverted ${z}_T$. Based on this, we propose a novel zero-shot editing paradigm dubbed ZZEdit, which first locates a qualified intermediate-inverted latent marked as ${z}_p$ as a better editing pivot, which is sufficient-for-editing while structure-preserving. Then, a ZigZag process is designed to execute denoising and inversion alternately, which progressively inject target guidance to ${z}_p$ while preserving the structure information of $p$ step. Afterwards, to achieve the same step number of inversion and denoising, we execute a pure sampling process under the target prompt. Essentially, our ZZEdit performs iterative manifold constraint between the manifold of $M_{p}$ and $M_{p-1}$, leading to fewer fidelity errors. Extensive experiments highlight the effectiveness of ZZEdit in diverse image editing scenarios compared with the "inversion-then-editing" pipeline.
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