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  <title>Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation</title>
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            <h1 class="title is-1 publication-title">Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
                <a href="http://ralphtang.com" target="_blank">Raphael Tang</a>,<sup>1,2</sup></span>
                <span class="author-block">
                  <a href="https://crystina-z.github.io/" target="_blank">Xinyu Zhang</a>,<sup>2</sup></span>
                  <span class="author-block">
                    <a href="https://www.linkedin.com/in/ulie-xu/" target="_blank">Lixinyu Xu</a>,<sup>1</sup>
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                    <a href="https://yaolu.github.io/" target="_blank">Yao Lu</a>,<sup>3</sup>
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                    <a href="https://wenyanli.org/" target="_blank">Wenyan Li</a>,<sup>4</sup>
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                  <span class="author-block">
                    <a href="https://pontus.stenetorp.se/" target="_blank">Pontus Stenetorp</a>,<sup>3</sup>
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                  <span class="author-block">
                    <a href="https://cs.uwaterloo.ca/~jimmylin/" target="_blank">Jimmy Lin</a>,<sup>2</sup>
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                  <span class="author-block">
                    <a href="https://ferhanture.com/" target="_blank">Ferhan Ture</a><sup>1</sup>
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                    <span class="author-block"><sup>1</sup>Comcast AI Technologies, <sup>2</sup>University of Waterloo, <sup>3</sup>University College London, <sup>4</sup>University of Copenhagen<br></span>
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      <h3><span class="highlight">tl;dr:</span>  We propose W1KP, a human-calibrated measure of variability in a set of images. We apply it to study prompt reusability and linguistic feature salience for image generation.</h3>
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        Above, we visualize the overlap between the two most similar images (on average) as we generate more images for a given prompt. We remove the green channel for one image (magenta) and keep only the green for the other, then stack the two. Imagen is reusable up to 10-50 images while the others 50-200 images. 
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          <p>
Diffusion models are the state of the art in text-to-image generation, but their perceptual variability remains understudied.
In this paper, we examine how prompts affect image variability in black-box diffusion-based models.
We propose W1KP, a human-calibrated measure of variability in a set of images, bootstrapped from existing image-pair perceptual distances.
Current datasets do not cover recent diffusion models, thus we curate three test sets for evaluation.
Our best perceptual distance outperforms nine baselines by up to 18 points in accuracy, and our calibration matches graded human judgements 78% of the time.
Using W1KP, we study prompt reusability and show that Imagen prompts can be reused for 10-50 random seeds before new images become too similar to already generated images, while Stable Diffusion XL and DALL-E 3 can be reused 50-200 times.
Lastly, we analyze 56 linguistic features of real prompts, finding that the prompt's length, CLIP embedding norm, concreteness, and word senses influence variability most.
As far as we are aware, we are the first to analyze diffusion variability from a visuolinguistic perspective.          
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           Image pairs from SDXL, ordered column-wise by calibrated W1KP scores. From left to right, the image pairs correspond to high (0.85-1.0), medium (0.4-0.85), low (0.2-0.4), and no similarity (0.0-0.2).
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           A plot of W1KP similarity score against word frequency, CLIP embedding norm, concreteness, and word senses for single-word prompts. These linguistic features consistently and significantly affect the perceptual variability.
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          Dimensionality reduction using W1KP score and multidimensional scaling. Imagen has six distinct clusters of high similarity.
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      <pre><code>@inproceedings{tang2024words,
    title = "Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation",
    author = "Tang, Raphael  and
      Zhang, Crystina  and
      Xu, Lixinyu  and
      Lu, Yao  and
      Li, Wenyan  and
      Stenetorp, Pontus  and
      Lin, Jimmy  and
      Ture, Ferhan",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    year = "2024",
    url = "https://aclanthology.org/2024.emnlp-main.311",
    pages = "5441--5454",
}</code></pre>
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<!--End BibTex citation -->


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