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Advancements in generative models have sparked significant interest ingenerating images while adhering to specific structural guidelines. Scene graphto image generation is one such task of generating images which are consistentwith the given scene graph. However, the complexity of visual scenes poses achallenge in accurately aligning objects based on specified relations withinthe scene graph. Existing methods approach this task by first predicting ascene layout and generating images from these layouts using adversarialtraining. In this work, we introduce a novel approach to generate images fromscene graphs which eliminates the need of predicting intermediate layouts. Weleverage pre-trained text-to-image diffusion models and CLIP guidance totranslate graph knowledge into images. Towards this, we first pre-train ourgraph encoder to align graph features with CLIP features of correspondingimages using a GAN based training. Further, we fuse the graph features withCLIP embedding of object labels present in the given scene graph to create agraph consistent CLIP guided conditioning signal. In the conditioning input,object embeddings provide coarse structure of the image and graph featuresprovide structural alignment based on relationships among objects. Finally, wefine tune a pre-trained diffusion model with the graph consistent conditioningsignal with reconstruction and CLIP alignment loss. Elaborate experimentsreveal that our method outperforms existing methods on standard benchmarks ofCOCO-stuff and Visual Genome dataset.

Scene Graph to Image Synthesis: Integrating CLIP Guidance with Graph Conditioning in Diffusion Models