AI修图终于学会听懂“把猫调暗一点”
现在的AI修图工具，你让它“把背景调暗”，它可能连“背景”和“暗”都分不清——因为训练时只学了“整张图变暗”这种粗指令。这篇论文把修图指令拆成1000多种细粒度概念，造了1200万对“指令+修好后的图”来训练，还让一张图同时包含多个互不干扰的修改点，模型一次学更多、学得更准。结果是：它终于能听懂“只把猫的毛色调深、别动背景”这种具体话。这不是你明天就能用的功能，但它指向一个趋势：AI修图正在从“整图滤镜”走向“指哪打哪”。
📄 原文摘要(英文)
Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inherent discrepancies, specifically, the insufficient attention to edit concept granularity and the training inefficiency caused by sparse supervision signals. To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high-quality editing pairs via an improved synthesis framework. This library-driven approach effectively rectifies the distribution collapse of generated data while ensuring high data fidelity. Furthermore, we propose a dense supervision training strategy that synthesizes multiple non-interfering concepts into single image pairs. By providing richer learning signals, this strategy significantly enhances both training efficiency and overall model performance. Training results validate our strategy, significantly outperforming prior works. Finally, we present ConceptEdit-Bench, a granular evaluation suite designed to diagnose model capabilities across a vast array of real-world scenarios.