Generating a poster with a diffusion model takes seconds—but what you get is a flattened bitmap. Text is error-prone, and moving a logo or changing a price line means regenerating the whole image. A paper published on arXiv on Sep 3, "Editable Visual Design" (arXiv:2609.04034), takes this pain point head-on. The paper page is listed under the Tencent Hunyuan organization, with 12 authors led by Junyan Ye; it picked up 42 upvotes on the Hugging Face papers page within two days, and the GitHub repository has climbed to nearly 500 stars.

The dilemma: diffusion looks good but cannot be edited; pure code is controllable but lacks taste

The paper lays out the shortcomings of both existing routes. Diffusion base models such as GPT-Image-2 and Nano-Banana are visually expressive, but end-to-end generation inherently yields flattened bitmaps with error-prone text, precluding layer-wise post-editing. Conversely, having a coding agent write code to draw gives precise layout control and decoupled layers, yet suffers from a lack of global aesthetic intuition and the difficulty of coding complex visual assets. Beautiful and editable have always been an either-or choice.

The fix: the VLM as the creative brain, the image model demoted to an asset shop

The division of labor is the interesting part: the VLM does the thinking—requirement comprehension, task planning, and aesthetic judgment—while the image generation model only shows up on demand to synthesize standalone visual assets. The whole pipeline runs an "imagine first, then act" closed loop: the agent generates isolated assets, writes native HTML/CSS, and iteratively refines the design against visual rendering feedback. A key constraint is "visual prior, without pixel shipping"—the image model provides art direction such as composition, hierarchy, color, and spatial relationships, but its reference pixels never enter the delivered design; typography and layout are rebuilt in HTML by the agent. The final artifact carries real text and semantic layers, and users can adjust layouts by simply dragging in a graphical interface.

Beyond generation: replayable design

Another detail worth noting is Agent Design Replay: the system faithfully records the creative and reasoning trajectory of the agent, replayable like the working process of a professional human designer. The repository comparison table lists this explicitly—image generation has no creation path, direct coding is only partially replayable, while this approach turns "how the design was made step by step" into part of the artifact. The repo also ships deterministic checks covering the canvas, fonts, layer contracts, rendering, and editor round trips.

Shipping: two Codex skills, plus editable PPT export

On the engineering side the delivery is two independent Codex skills: editable-design generates polished designs from a brief, producing HTML, PNG, a mouse editor, an animated layer breakdown, and Agent Design Replay; html-to-pptx converts clean designs into editable PowerPoint files with independently selectable elements. The README recommends GPT-5.6 with high reasoning effort or above for the strongest results. The gallery provides 14 prompts across six visual-design categories—from a tea-launch campaign poster and a skincare e-commerce hero to an academic poster for the paper "Attention Is All You Need". The repository is open source under Apache 2.0, and the authors note it is a community project, not an official OpenAI project.

So what

The point worth remembering is not "prettier generated images" but a demotion of the generative model inside the visual pipeline: it no longer delivers; it only supplies assets and art direction. The deliverable becomes a software artifact with layer contracts—versionable, composable, replayable. Once a design draft turns from a bitmap into a code artifact, revision, reuse, and collaboration can finally be engineered. For teams building design tools and agent products, this division-of-labor paradigm is more instructive than any single benchmark score.

References: arxiv.org/abs/2609.04034; github.com/yejy53/Editable-Design