电商和广告领域的开发者终于有了一个标准化的产品网页生成评测工具——ProductWebGen 帮你快速对比不同多模态模型在可控生成上的真实表现，做营销自动化或电商页面生成的团队值得关注。
ProductWebGen 是一个用于评估多模态生成模型在电商产品网页生成任务上能力的基准测试。它包含 500 个测试样本，覆盖 13 个产品类别，每个样本提供源图像、视觉内容指令和网页布局指令，要求模型生成包含多张一致图像的 HTML 网页。研究对比了两种工作流：基于图像编辑模型和语言模型的编辑式方法，以及基于统一多模态模型的端到端方法。实验表明编辑式方法在网页指令遵循和内容吸引力上领先，而统一模型在视觉内容指令执行上更有优势。团队还构建了包含 1000 组真实产品图像和 LLM 生成 HTML 代码的微调数据集 ProductWebGen-1k，并在开源模型 BAGEL 上验证了其有效性。
ProductWebGen: Benchmarking Multimodal Product Webpage Generation
Crafting a product display webpage from a source product image, along with layout and visual content instructions, holds significant practical value for domains such as marketing, advertising, and E-commerce. Intuitively, this task demands strict visual consistency across product displays and high-fidelity instruction following to jointly generate renderable HTML code. These requirements on controllability and instruction-following are closely aligned with the core features of advanced multimodal generative models, such as image editing models and unified models. To this end, this paper introduces ProductWebGen to systematically benchmark the product webpage generation capacities of these models. We organize ProductWebGen with 500 test samples covering 13 product categories; each sample consists of a source image, a visual content instruction, and a webpage instruction. The task is to generate a product showcase webpage including multiple consistent images in accordance with the source image and instructions. Given the mixed-modality input-output nature of the task, we design and systematically compare two workflows for evaluation -- one uses large language models and image editing models to separately generate HTML code and images (editing-based), while the other relies on a single UM to generate both, with image generation conditioned on the preceding multimodal context (UM-based). Empirical results show that editing-based approaches achieve leading results in webpage instruction following and content appeal, while UM-based ones may display more advantages in fulfilling visual content instructions. We also construct a supervised fine-tuning dataset, ProductWebGen-1k, with 1,000 groups of real product images and LLM-generated HTML code. We verify its effectiveness on the open-source UM BAGEL. The data and code are available at https://github.com/SJTU-DENG-Lab/ProductWebGen.