用文生图模型自动生成合规营销图像，效率与质量双提升。
From Prompt to Production:Automating Brand-Safe Marketing Imagery with Text-to-Image Models
- 构建全自动流程，结合文生图模型与人类反馈控制
- 图像保真度提升30.77%，人类偏好度提高52.00%
- 适合需要规模化生产合规营销视觉内容的团队
文生图模型在根据文本描述生成图像方面已取得显著进展。然而，将这些模型部署到生产环境并实现可扩展的流水线仍面临挑战。在自动化与人工反馈之间取得平衡，对保障规模与质量至关重要。尽管自动化可处理大量内容，但人工审核仍是确保生成图像符合预期标准和创意愿景的关键。本文提出一种全新流水线，实现使用文生图模型自动生成商业产品营销图像的全自动化、可扩展解决方案。该系统在保持图像质量与真实感的同时，引入足够创意变化以符合营销规范。通过优化流程，实现了效率与人工监督的无缝结合：相比基准，使用DINOV2时营销对象保真度提升30.77%，人类偏好度提升52.00%。
原文摘要 · Abstract (English)
Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still an essential component to ensure that the generated images meet the desired standards and are aligned with the creative vision. This paper presents a new pipeline that offers a fully automated, scalable solution for generating marketing images of commercial products using text-to-image models. The proposed system maintains the quality and fidelity of images, while also introducing sufficient creative variation to adhere to marketing guidelines. By streamlining this process, we ensure a seamless blend of efficiency and human oversight, achieving a $30.77\%$ increase in marketing object fidelity using DINOV2 and a $52.00\%$ increase in human preference over the generated outcome.
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