Omni-Dish：面向任意中国菜品的逼真且忠实的图像生成与编辑
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes
AI总结：
本文提出Omni-Dish，首个专为生成中国菜品定制的文本到图像模型，通过构建大规模数据集、重描述策略与由粗到细训练方案提升生成质量，并引入Concept-Enhanced P2P实现菜品编辑。
摘要：
菜品图像在数字时代扮演着至关重要的角色，随着食品行业和电子商务的数字化，对具有文化特色的菜品图像的需求持续增加。在一般情况下，现有的文本到图像生成模型擅长生成高质量图像；然而，它们难以捕捉特定领域（尤其是中国菜品）的多样特征与忠实细节。为了解决这一局限，我们提出了Omni-Dish，这是首个专门为中国菜品定制的文本到图像生成模型。我们开发了全面的菜品筛选流水线，构建了迄今为止最大的菜品数据集。此外，我们引入了重描述策略，并采用由粗到细的训练方案，帮助模型更好地学习细粒度的烹饪细节。在推理阶段，我们利用预构建的高质量描述库和大语言模型增强用户的文本输入，从而实现更逼真且忠实的图像生成。此外，为了扩展模型在菜品编辑任务上的能力，我们提出了Concept-Enhanced P2P。基于该方法，我们构建了菜品编辑数据集并训练了专门的编辑模型。大量实验证明了我们方法的优越性。
英文摘要：
Dish images play a crucial role in the digital era, with the demand for culturally distinctive dish images continuously increasing due to the digitization of the food industry and e-commerce. In general cases, existing text-to-image generation models excel in producing high-quality images; however, they struggle to capture diverse characteristics and faithful details of specific domains, particularly Chinese dishes. To address this limitation, we propose Omni-Dish, the first text-to-image generation model specifically tailored for Chinese dishes. We develop a comprehensive dish curation pipeline, building the largest dish dataset to date. Additionally, we introduce a recaption strategy and employ a coarse-to-fine training scheme to help the model better learn fine-grained culinary nuances. During inference, we enhance the user's textual input using a pre-constructed high-quality caption library and a large language model, enabling more photorealistic and faithful image generation. Furthermore, to extend our model's capability for dish editing tasks, we propose Concept-Enhanced P2P. Based on this approach, we build a dish editing dataset and train a specialized editing model. Extensive experiments demonstrate the superiority of our methods.
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。
