电商主图AI已能日产百万张，还保真商标和文字
电商主图是门大生意，但AI一直干不了：商品细节、商标、上面的文字，一生成就糊。这套系统把流程拆成三步——先用AI把商品信息提炼成卖点文案，再优化排版，最后用专门训练的扩散模型同时吃进白底商品图、商标图和版式，硬是把「商品长得像、商标不糊、文字不歪」三件事同时保住。它已经在真实电商里跑，每天生成百万张主图，单张只要几秒。这不是你明天能上手用的工具，但它说明「AI直接产出能上架的商用图」已经从demo走到了工厂级。
📄 原文摘要(英文)
We present CommerceGen-Studio, an end-to-end framework for generating commercial-grade main images for e-commerce Stock Keeping Units (SKUs). Our framework is organized along a three-stage pipeline of information extraction, semantic rewriting, and multi-condition generation: Prompt-Eval-Refine-Loop Agent (PERL-Agent) distills heterogeneous SKU metadata into concise selling-point text lists through an iterative closed loop of VLM and LLM roles scored by a reference-free rubric. Layout Thinker Optimization (LTO) aligns the rewriter with both caption and rendered-image quality via alternating domain-specific policy optimization. SKU-DiT, trained under Reinforce-Distill-Reinforce (RDR) progressive training, conditions jointly on the white-background product image, the trademark crop, and the structured layout to hold subject fidelity, trademark integrity, and character-level text accuracy together. The framework runs in production at a scale of millions of main images per day with a latency of a few seconds per image, which we believe makes it among the first systems of this kind to operate at commercial scale. We also release CommerceGen-Bench, a benchmark of hard cases collected from live traffic across all product categories. On it, our framework achieves the highest overall scores among the evaluated open-source baselines on both editing tracks, with and without prompt enhancement. It also matches much larger closed-source models, and beats several of them at Instruction Following and Product Integration - the two properties that determine whether an image can be published.