: An Image Set Variability Metric

As proposed in our paper, the "Words of a Thousand Pictures" metric (W1KP) measures perceptual variability for sets of images in text-to-image generation, bootstrapped from existing perceptual distances such as DreamSim.

Getting Started

Installation

1. Install PyTorch for your Python 3.10+ environment.

2. Install W1KP: pip install w1kp

3. Download the calibration data file.

4. You're done!

Sample Library Usage

We recommend $\text{DreamSim}_{\ell_2}$, the best-performing perceptual distance backbone in our paper.

import asyncio

import torch
from w1kp import StableDiffusionXLImageGenerator, DreamSimDistanceMeasure, query_inverted_cdf


async def amain():
  # Generate 10 SDXL images for a prompt
  prompt = 'cat'
  images = []
  image_gen = StableDiffusionXLImageGenerator()

  for seed in range(10):
    ret = await image_gen.generate_image(prompt, seed=seed)
    images.append(ret['image'])

  # Compute and normalize the W1KP score
  dreamsim_l2 = DreamSimDistanceMeasure().to_listwise()
  w1kp_score = dreamsim_l2.measure(images)
  cdf_x, cdf_y = torch.load('cdf-xy.pt')  # download this data file from the repo

  dist = dreamsim_l2.measure(prompt, images)
  dist = query_inverted_cdf(cdf_x, cdf_y, dist)  # normalize to U[0, 1]
  w1kp_score = 1 - dist  # invert for the W1KP score

  for im in images:
    im.show()

  print(f'The W1KP score for the images are {w1kp_score}')
  

if __name__ == '__main__':
  asyncio.run(amain())

Citation

@article{tang2024w1kp,
  title={Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation},
  author={Tang, Raphael and Zhang, Xinyu and Xu, Lixinyu and Lu, Yao and Li, Wenyan and Stenetorp, Pontus and Lin, Jimmy and Ture, Ferhan},
  journal={arXiv:2210.04885},
  year={2024}
}