Title: SPICE: A Synergistic, Precise, Iterative, and Customizable Image Editing Workflow Show Chinese title

Title: SPICE：一种协同、精确、迭代和可定制的图像编辑工作流程

Abstract: Recent prompt-based image editing models have demonstrated impressive prompt-following capability at structural editing tasks. However, existing models still fail to perform local edits, follow detailed editing prompts, or maintain global image quality beyond a single editing step. To address these challenges, we introduce SPICE, a training-free workflow that accepts arbitrary resolutions and aspect ratios, accurately follows user requirements, and improves image quality consistently during more than 100 editing steps. By synergizing the strengths of a base diffusion model and a Canny edge ControlNet model, SPICE robustly handles free-form editing instructions from the user. SPICE outperforms state-of-the-art baselines on a challenging realistic image-editing dataset consisting of semantic editing (object addition, removal, replacement, and background change), stylistic editing (texture changes), and structural editing (action change) tasks. Not only does SPICE achieve the highest quantitative performance according to standard evaluation metrics, but it is also consistently preferred by users over existing image-editing methods. We release the workflow implementation for popular diffusion model Web UIs to support further research and artistic exploration. Show Chinese abstract

Abstract: 基于提示的图像编辑模型在结构编辑任务中展示了出色的提示遵循能力。 然而，现有模型仍然无法执行局部编辑、遵循详细编辑提示或在单次编辑步骤之外保持全局图像质量。 为解决这些挑战，我们引入了SPICE，一种无需训练的工作流程，可接受任意分辨率和宽高比，准确遵循用户需求，并在超过100次编辑步骤中持续提升图像质量。 通过结合基础扩散模型和Canny边缘ControlNet模型的优势，SPICE能够稳健地处理用户提供的自由格式编辑指令。 SPICE在由语义编辑（对象添加、删除、替换和背景更改）、风格编辑（纹理变化）和结构编辑（动作更改）任务组成的具有挑战性的现实图像编辑数据集上优于最先进的基线方法。 SPICE不仅根据标准评估指标实现了最高的定量性能，而且在用户中也始终优于现有的图像编辑方法。 我们发布了适用于流行扩散模型Web UI的工作流程实现，以支持进一步的研究和艺术探索。