AI 出图终于能改字了：从位图到可编辑图层
现在的 AI 画图，生成的是扁平位图，文字经常糊成一团，想改一个字都得整张重画。这篇换了个思路：让视觉语言模型当“创意大脑”负责想和判断，让图像生成模型当“视觉模拟器”只负责画单个素材，再由代码智能体把素材拼成真正的 HTML/CSS 页面。于是你拿到的不再是一张死图，而是图层分离、文字真实可编辑的设计稿，鼠标拖一拖就能改布局。它验证了海报、信息图等场景，兼顾了美感和可编辑性。对设计师和做营销物料的人来说，这是从“生成一张图”到“生成一个可改的工程文件”的实质转变。
📄 原文摘要(英文)
While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, their end-to-end generation inherently yields flattened bitmaps with error-prone text, precluding layer-wise post-editing. Conversely, code-based visual generation via Coding Agents provides precise layout control and decoupled layers, yet remains constrained by a lack of global aesthetic intuition and the difficulty of coding complex visual assets. To address this, we propose Editable Visual Design, a new paradigm driven by a Coding Agent. We designate the VLM as the ``creative brain'' for requirement comprehension, task planning, and aesthetic judgment, while utilizing the image generation model as an on-demand ``visual world simulator'' to synthesize standalone visual assets. Operating under an ``imagine first, then act'' closed-loop workflow, the agent generates isolated assets, writes native HTML/CSS, and iteratively refines the design against visual rendering feedback. Furthermore, Agent Design Replay faithfully reproduces the creative and reasoning trajectory akin to that of professional human designers. Ultimately, the system delivers editable artifacts with decoupled layers and real text, enabling users to perform intuitive mouse dragging and layout adjustments on a graphical user interface. Validations on posters, infographics, and other scenarios show that this paradigm successfully achieves both refined aesthetics and production-grade editability.