无需训练的文本到图像组合食品生成 via 提示嫁接
Training-Free Text-to-Image Compositional Food Generation via Prompt Grafting
AI总结：
无需训练的文本到图像组合食品生成方法通过提示嫁接实现对多食物图像生成的可控分离。
摘要：
现实中的餐食图像往往包含多个食物项，这使得可靠的组合食品图像生成对于图像基于的饮食评估等应用至关重要，需要多食物数据增强和食谱可视化。然而，现代文本到图像扩散模型在生成准确的多食物图像时面临困难，因为物体纠缠，相邻的食物（例如米饭和汤）会融合在一起，因为许多食物没有清晰的边界。为了解决这一挑战，我们引入了提示嫁接（PG），一种无需训练的框架，它结合了文本中的显式空间提示和采样期间的隐式布局指导。PG运行一个两阶段过程，其中布局提示首先建立不同的区域，然后在布局形成稳定后将目标提示嫁接。该框架使食品纠缠控制成为可能：用户可以通过编辑布局的安排来指定哪些食物项应保持分离或有意混合。在两个食物数据集中，我们的方法显著提高了目标对象的出现程度，并提供了可控分离的定性证据。
英文摘要：
Real-world meal images often contain multiple food items, making reliable compositional food image generation important for applications such as image-based dietary assessment, where multi-food data augmentation is needed, and recipe visualization. However, modern text-to-image diffusion models struggle to generate accurate multi-food images due to object entanglement, where adjacent foods (e.g., rice and soup) fuse together because many foods do not have clear boundaries. To address this challenge, we introduce Prompt Grafting (PG), a training-free framework that combines explicit spatial cues in text with implicit layout guidance during sampling. PG runs a two-stage process where a layout prompt first establishes distinct regions and the target prompt is grafted once layout formation stabilizes. The framework enables food entanglement control: users can specify which food items should remain separated or be intentionally mixed by editing the arrangement of layouts. Across two food datasets, our method significantly improves the presence of target objects and provides qualitative evidence of controllable separation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
