Paper Image

Diverse image generation from text

Published on:

19 October 2023

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Mariia Zameshina,

Olivier Teytaud,

Laurent Najman


Key Details

Proposes Diverse Diffusion to boost image diversity in text-to-image models

Method selects distant latent vectors to create varied batches

Evaluates color, content, and demographic diversity benefits

Shows potential to improve realism, creativity, and fairness

Applicable as general technique for existing models like Stable Diffusion

AI generated summary

Diverse image generation from text

This paper introduces Diverse Diffusion, a method to generate more varied and inclusive images from text prompts using latent diffusion models like Stable Diffusion. The approach focuses on finding distant points in the latent space to produce batches of images with more diversity in color, content, and representation of people. Experiments highlight the benefits for realism, creativity, and fairness.

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