Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport

TL;DR

This paper introduces a novel method using differentiable optimal transport to improve text-to-image synthesis, enhancing diversity and fidelity in generated images guided by multiple prompts.

Contribution

It proposes a differentiable optimal transport-based approach for multi-prompt image generation, addressing mode collapse and improving diversity over traditional mean-distance methods.

Findings

OT-based method produces more diverse images

Improved fidelity to multiple prompts

Qualitative and quantitative performance gains

Abstract

Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits. Text-to-image synthesis approaches that operate by generating images from text cues provide a case in point. These images are generated with a latent vector that is progressively refined to agree with text cues. To do so, patches are sampled within the generated image, and compared with the text prompts in the common text-image embedding space; The latent vector is then updated, using gradient descent, to reduce the mean (average) distance between these patches and text cues. While this approach provides artists with ample freedom to customize the overall appearance of images, through their choice in generative models, the reliance on a simple criterion (mean of distances) often causes mode collapse: The entire image is drawn to the average of all text cues, thereby losing their diversity. To address this issue, we propose using matching techniques found in the optimal transport (OT) literature, resulting in images that are able to reflect faithfully a wide diversity of prompts. We provide numerous illustrations showing that OT avoids some of the pitfalls arising from estimating vectors with mean distances, and demonstrate the capacity of our proposed method to perform better in experiments, qualitatively and quantitatively.