With the development of AI models such as ChatGPT, artificial intelligence has become deeply integrated into our daily lives. Additionally, the growing focus on health and wellness has accelerated the development of AI applications in healthcare. In the realm of dietary management, some smartphone applications offer automated calorie calculation and nutrient tracking features. However, these systems often rely on nutrition facts labels, making it challenging for users to visually comprehend the portion sizes corresponding to their desired caloric intake. To address this limitation, this paper proposes a novel food image editing model that incorporates image generation AI to adjust the caloric content of food images. Our model begins by extracting features, such as estimated current calories, food regions, and visual attributes, from input food images. Subsequently, a conditional edge image is generated based on the desired caloric value and food regions. By providing these features into an image generation model, our model produces a new food image that aligns with the specified caloric target. This approach enables accurate and visually intuitive dietary management.