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.

本研究探讨了现有文本到图像生成模型在生成具有中国特色菜肴的图像时所面临的限制，提出了专门为中国菜肴设计的全新模型Omni-Dish。研究通过构建最大的菜肴数据集及采用精细的训练方案，显著提升了图像生成的真实感与细节捕捉，并在菜肴编辑任务中展示了新的思路和强大的应用潜力。

全景菜肴：针对任意中国菜肴的照片级真实与忠实图像生成与编辑