FoodDiff: A Collaborative Relationship Perception Framework for Food Image Synthesis Using Diffusion Models

Mengling Xu

doi:10.1109/tmm.2026.3654435

FoodDiff: A Collaborative Relationship Perception Framework for Food Image Synthesis Using Diffusion Models

2026-01-01

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DOI：10.1109/tmm.2026.3654435

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Food image generation is a typical application of text-to-image (T2I) models. The core difference between food image synthesis and other T2I tasks is that there exist complex collaborative relationships among ingredients, cooking actions, and food images, which determine the appearance of dishes. However, existing food image generation models generally ignore or fail to sufficiently utilize such collaborative relationships, which hinders the model from precisely perceiving the shapes and details of food. Furthermore, the pre-training distribution of T2I models is usually noisy and differs from the user-preferred food feature distributions, resulting in deviations from human aesthetics. To address the above issues, we propose FoodDiff, a collaborative relationship-aware diffusion model for food image generation, which consists of three key components: (1) To perceive collaborative relationships, we propose a collaborative relation module to extract these relations and inject them into the image generation process. (2) To sufficiently interact with the relationships between recipe semantics and food representations, we propose a recipe fusion fine-tuning module to precisely fuse recipe semantics with visual features and fine-tune the pre-trained model. (3) To make the pre-training feature distribution conform to human preference, we introduce an image reward feedback mechanism to optimize the aesthetics of food images. In addition, we propose a high-quality food dataset named Food-Aesthetic with exquisite plates and elaborate annotations. Extensive experiments and human evaluations show that FoodDiff has superior image aesthetics and semantic consistency.

Keywords:

Collaboration

Image synthesis

Diffusion models

Visualization

Semantics

Text to image

Noise reduction

Feature extraction

Telecommunications

Generators

Text-to-image generation

aesthetic food images

diffusion model

image reward feedback

FoodDiff: A Collaborative Relationship Perception Framework for Food Image Synthesis Using Diffusion Models

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