FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation
Olivia Markham Yuhao Chen Chi-en Amy Tai Alexander Wong
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.