Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG

2024-12-12

arXiv.org

AI Summary

Existing text-to-image (T2I) diffusion models suffer from limited training data coverage, hindering accurate generation of rare, complex, or culturally specific subjects. To address this, we propose a knowledge graph–driven retrieval-augmented generation (RAG) framework—the first to integrate graph-structured RAG into T2I modeling. Our approach dynamically retrieves fine-grained character attributes and relational context via a knowledge graph, employs graph neural networks for semantic alignment, and introduces a knowledge-guided self-correction mechanism to ensure visual consistency and semantic fidelity. Additionally, we incorporate ControlNet for precise spatial control and dynamic prompt engineering for adaptive textual conditioning. Extensive experiments on Flux, Stable Diffusion, and DALL-E demonstrate substantial improvements over baselines across multiple evaluation dimensions—particularly in cultural sensitivity, compositional accuracy, and fine-grained controllable editing.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We introduce a novel approach to enhance the capabilities of text-to-image models by incorporating a graph-based RAG. Our system dynamically retrieves detailed character information and relational data from the knowledge graph, enabling the generation of visually accurate and contextually rich images. This capability significantly improves upon the limitations of existing T2I models, which often struggle with the accurate depiction of complex or culturally specific subjects due to dataset constraints. Furthermore, we propose a novel self-correcting mechanism for text-to-image models to ensure consistency and fidelity in visual outputs, leveraging the rich context from the graph to guide corrections. Our qualitative and quantitative experiments demonstrate that Context Canvas significantly enhances the capabilities of popular models such as Flux, Stable Diffusion, and DALL-E, and improves the functionality of ControlNet for fine-grained image editing tasks. To our knowledge, Context Canvas represents the first application of graph-based RAG in enhancing T2I models, representing a significant advancement for producing high-fidelity, context-aware multi-faceted images.

Problem

• Generates rare concepts lacking in training data via graph-based retrieval.
• Enables context-driven image editing without visual exemplars using knowledge graphs.
• Improves generation quality through iterative self-correction for semantic fidelity.

Innovation

• Uses knowledge graphs for relational guidance in diffusion models
• Introduces self-correction module for iterative prompt refinement
• Is model-agnostic and compatible with leading diffusion frameworks