InstructG2I: A Graph Context-Aware Stable Diffusion Model for Synthesizing Images from Graphs with Multimodal Attributes

Multimodal attribute graphs (MMAG) have received little attention despite their versatility in image generation. MMAGs represent relationships between entities with combinatorial complexity in a graph-structured manner. Graph nodes contain image and text information. Compared to text or image conditioning models, graphics could become better and more informative images. Graph2Image is an interesting challenge in this field that requires generative models to synthesize image conditioning into text descriptions and graph connections. While MMAGs are useful, they cannot be directly incorporated into image and text conditioning.

The following are the most relevant challenges in using MMAG for image synthesis:

1. Explosion in chart size.– This phenomenon occurs due to the combinatorial complexity of graphs, where the size grows exponentially as we introduce local subgraphs into the model, which encompass images and text.
2. Graphic entity dependencies – Nodal features are mutually dependent and therefore their proximity reflects the relationships between entities across text and image and their preference in image generation. To exemplify this, when generating a light-colored shirt you should have a preference for light tones such as pastels.
3. Need for controllability in graph condition – The interpretability of the generated images should be controlled to follow the desired patterns or characteristics defined by the connections between entities in the graph.

A team of researchers at the University of Illinois developed InstructG2I to solve this problem. This is a contextual graph diffusion model that uses multimodal graph information. This approach addresses graph space complexity by compressing graph contexts into fixed-capacity graph conditioning tokens enhanced with custom PageRank-based semantic graph sampling. The Graph-QFormer architecture further improves these graph tokens by solving the problem of graph entity dependency. Last but not least, InstructG2I guides image generation with adjustable edge lengths.

InstructG2I introduces graph conditions in stable diffusion with PPR-based neighbor sampling. PPR or Custom PageRank identifies related nodes of the graph structure. To ensure that the generated images are semantically related to the target node, a semantic-based similarity calculation function is used for reclassification. This study also proposes Graph-QFormer, which is a two-transformer module for capturing text- and image-based dependencies. Graph-QFormer employs multi-head self-attention for image-image dependencies and multi-head cross-attention for text-image dependencies. The cross-attention layer aligns image features with text cues. It uses hidden states from the self-attention layer as input and text embeddings as query to generate relevant images. The end result of the two Graph-QFormer transformers is the graph-conditioned warning tokens that guide the image generation process in the diffusion model. Finally, to control the generation process, a classifierless guide is used, which is basically a technique for adjusting the strength of graphs.

InstructG2I was tested on three datasets from different domains: ART500K, amazon, and Goodreads. For text-to-image methods, Stable Diffusion 1.5 was decided as the reference model, and for image-to-image methods, InstructPix2Pix and ControlNet were chosen for comparison; both were initialized with SD 1.5 and fitted on selected data sets. The results of the study showed impressive improvements over the reference models on both tasks. InstructG2I outperformed all benchmark models in CLIP and DINOv2 scores. For qualitative evaluation, InstructG2I generated images that best fit the semantics of the text message and the context of the graph, ensuring the generation of content and context as it learned from neighbors in the graph and accurately conveyed information.

InstructG2I effectively solved the important challenges of explosion, inter-entity dependency, and controllability in multimodal attribute graphs and replaced baseline in image generation. In the coming years, there will be many opportunities to work with and incorporate graphics into image generation, a large part of which includes handling complex and heterogeneous image-text relationships in MMAG.

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Adeeba Alam Ansari is currently pursuing her dual degree from the Indian Institute of technology (IIT) Kharagpur, where she earned a bachelor's degree in Industrial Engineering and a master's degree in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and curious person. Adeeba firmly believes in the power of technology to empower society and promote well-being through innovative solutions driven by empathy and a deep understanding of real-world challenges.