Generating Intermediate Representations for Compositional Text-To-Image Generation

• URL: http://arxiv.org/abs/2410.09792v2
• Date: Sun, 20 Oct 2024 05:07:08 GMT
• Title: Generating Intermediate Representations for Compositional Text-To-Image Generation
• Authors: Ran Galun, Sagie Benaim,
• Abstract summary: We propose a compositional approach for text-to-image generation based on two stages. In the first stage, we design a diffusion-based generative model to produce one or more aligned intermediate representations conditioned on text. In the second stage, we map these representations, together with the text, to the final output image using a separate diffusion-based generative model.
• Score: 16.757550214291015
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image diffusion models have demonstrated an impressive ability to produce high-quality outputs. However, they often struggle to accurately follow fine-grained spatial information in an input text. To this end, we propose a compositional approach for text-to-image generation based on two stages. In the first stage, we design a diffusion-based generative model to produce one or more aligned intermediate representations (such as depth or segmentation maps) conditioned on text. In the second stage, we map these representations, together with the text, to the final output image using a separate diffusion-based generative model. Our findings indicate that such compositional approach can improve image generation, resulting in a notable improvement in FID score and a comparable CLIP score, when compared to the standard non-compositional baseline.

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