Current state-of-the-art image generation models such as Latent Diffusion Models (LDMs) have demonstrated the capacity to produce visually striking food-related images. However, these generated images often exhibit an artistic or surreal quality that diverges from the authenticity of real-world food representations. This inadequacy renders them impractical for applications requiring realistic food imagery, such as training models for image-based dietary assessment. To address these limitations, we introduce FoodFusion, a Latent Diffusion model engineered specifically for the faithful synthesis of realistic food images from textual descriptions. The development of the FoodFusion model involves harnessing an extensive array of open-source food datasets, resulting in over 300,000 curated image-caption pairs. Additionally, we propose and employ two distinct data cleaning methodologies to ensure that the resulting image-text pairs maintain both realism and accuracy. The FoodFusion model, thus trained, demonstrates a remarkable ability to generate food images that exhibit a significant improvement in terms of both realism and diversity over the publicly available image generation models. We openly share the dataset and fine-tuned models to support advancements in this critical field of food image synthesis at https://bit.ly/genai4good.

翻译：当前最先进的图像生成模型（如潜在扩散模型LDM）虽已展现出生成视觉震撼的食品相关图像的能力，但生成的图像常呈现艺术化或超现实特性，偏离了真实世界食品表征的可靠性。这种不足使其难以应用于需要真实食品图像的场景，例如基于图像的膳食评估模型训练。为解决上述局限，我们提出FoodFusion——一种专门针对从文本描述忠实合成真实食品图像的潜在扩散模型。该模型的开发整合了海量开源食品数据集，构建了超过30万组经人工筛选的图像-描述对。同时，我们提出并采用两种独特的数据清洗方法，确保生成的图文对兼具真实性与准确性。实验表明，经训练的FoodFusion模型在生成食品图像的真实性与多样性方面，较现有公开图像生成模型均实现显著提升。我们已在https://bit.ly/genai4good 开源共享该数据集及微调模型，以推动食品图像合成这一关键领域的发展。