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Calligrapher
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<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1">
  <title>Calligrapher: Freestyle - Text Image Customization</title>
  <link rel="icon" href="./static/images/icon.jpg">

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  <link rel="stylesheet" href="./static/css/bulma.min.css">
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  <link rel="stylesheet" href="./static/css/index.css">

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  <script defer src="./static/js/fontawesome.all.min.js"></script>
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  <script src="./static/js/bulma-slider.min.js"></script>
  <script src="./static/js/index.js"></script>
</head>
<body>


<section class="hero">
  <div class="hero-body">
    <div class="container is-max-desktop">
      <div class="columns is-centered">
        <div class="column has-text-centered">
          <h1 class="title is-1 publication-title">Calligrapher: </h1>
          <h1 class="title is-1 publication-title">Freestyle Text Image Customization</h1>

        <div class="column has-text-centered">
          <div class="publication-links">
            <!-- Video Link. -->
            <span class="link-block">
              <a href="https://youtu.be/FLSPphkylQE"
                 class="external-link button is-normal is-rounded is-dark">
                <span class="icon">
                    <i class="fab fa-youtube"></i>
                </span>
                <span>Video</span>
              </a>
            </span>
            <!-- Code Link. -->
            <span class="link-block">
              <a href="https://github.com/Calligrapher2025/Calligrapher"
                 class="external-link button is-normal is-rounded is-dark">
                <span class="icon">
                    <i class="fab fa-github"></i>
                </span>
                <span>Code</span>
                </a>
            </span>
            <!-- Dataset Link. -->
            <span class="link-block">
              <a href="https://huggingface.co/Calligrapher2025/Calligrapher"
                 class="external-link button is-normal is-rounded is-dark">
                <span class="icon">
                    <i class="far fa-images"></i>
                </span>
                <span>Model & Data</span>
                </a>
            </span>

          </div>
        </div>
        </div>
      </div>
    </div>
  </div>


  
</section>

<section class="hero teaser">
  <div class="container is-max-desktop">
    <div class="hero-body">
      <img src="./static/images/teaser.jpg" alt="Teaser Image" style="width: 100%;" />
      <h2 class="subtitle has-text-centered teaser-subtitle">
        Photorealistic text image customization results produced by our proposed
        <span><strong>Calligrapher</strong>,</span> which allows users to perform customization with
        diverse stylized images and text prompts.
      </h2>
    </div>
  </div>
</section>



<section class="section">
  <div class="container is-max-desktop">
    <!-- Abstract. -->
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Abstract</h2>
        <div class="content has-text-justified">

          <p>
            We introduce Calligrapher, a novel diffusion-based framework that innovatively integrates advanced text customization with artistic typography for digital calligraphy and design applications. Addressing the challenges of precise style control and data dependency in typographic customization, our framework incorporates three key technical contributions. First, we develop a self-distillation mechanism that leverages the pre-trained text-to-image generative model itself alongside the large language model to automatically construct a style-centric typography benchmark. Second, we introduce a localized style injection framework via a trainable style encoder, which comprises both Qformer and linear layers, to extract robust style features from reference images. An in-context generation mechanism is also employed to directly embed reference images into the denoising process, further enhancing the refined alignment of target styles. Extensive quantitative and qualitative evaluations across diverse fonts and design contexts confirm Calligrapher's accurate reproduction of intricate stylistic details and precise glyph positioning. By automating high-quality, visually consistent typography, Calligrapher surpasses traditional models, empowering creative practitioners in digital art, branding, and contextual typographic design.
          </p>

        </div>
      </div>
    </div>
    <!--/ Abstract. -->

    <!-- Paper video. -->
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3" style="margin-bottom: 1.5rem;">Demo Video</h2>
        <div class="publication-video">
          <iframe src="https://www.youtube.com/embed/FLSPphkylQE"
              frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
        </div>
      </div>
    </div>
    <!--/ Paper video. -->
  </div>
</section>

<section class="hero framework">
  <div class="container is-max-desktop">
    <div class="hero-body">
      <h1 class="title is-2 has-text-centered" style="margin-bottom: 2rem;">
        Framework
      </h1>
      <div style="overflow: hidden; border-radius: 10px;">
        <img src="./static/images/framework.jpg"
             alt="Framework Diagram"
             style="width: 100%; height: auto; object-fit: cover;" />
      </div>


      <div class="content has-text-justified">

        <p>
          Training framework of <strong>Calligrapher</strong>, demonstrating the integration of localized style injection and diffusion-based learning. The framework
processes masked images through a Variational Auto-Encoder (VAE) to obtain latent representations, concatenated with mask and noise latents. A style
encoder comprising a visual encoder, Qformer, and linear layers is designed to extract style-related features from the reference style image, while text
embeddings (e.g., "gic" in the case) modulate the denoising transformer. In the denoising block, style attention predicted from the style features replaces the
original cross-attention, injecting style embeddings with the denoiser's query to enable granular typographic control in the latent space. The
model is optimized under the flow-matching learning objective with the self-distillation typography dataset.
        </p>

      </div>
    </div>
  </div>
</section>


<section class="section">
  <div class="container is-max-desktop">

    <div class="columns is-centered">
      <div class="column is-full-width">
         <h2 class="title is-3">Application</h2>
        <div class="content has-text-justified">
          <p>
            Qualitative results of Calligrapher under various settings. We demonstrate text customization results respectively under settings of (a) self-reference, (b) cross-reference, and (c) non-text reference. Reference-based image generation results are also incorporated in (d).
          </p>
        </div>
        <div style="overflow: hidden; border-radius: 10px;" class="content has-text-centered">
          <img src="./static/images/application.jpg"
               alt="Application Results"
               style="width: 100%; height: auto; object-fit: cover;" />
        </div>

        <h2 class="title is-3">Multilingual Samples</h2>
        <div style="overflow: hidden; border-radius: 10px;" class="content has-text-centered">
          <img src="./static/images/multilingual_samples.png"
               alt="Multilingual Results"
               style="width: 100%; height: auto; object-fit: cover;" />
               <p class="image-caption">
                Multilingual freestyle text customization results. Tested languages and text: Chinese (你好朋友/夏天来了), Korean (서예가), and Japanese (ナルト).
              </p>
        </div>

        <h2 class="title is-3">Gallery</h2>

        <div style="overflow: hidden; border-radius: 10px;" class="content has-text-centered">
          <img src="./static/images/self_custom.jpg"
               alt="Self-reference Results"
               style="width: 100%; height: auto; object-fit: cover;" />
          <p class="image-caption">
            Self-reference text image customization results.
          </p>
        </div>

        <div style="overflow: hidden; border-radius: 10px;" class="content has-text-centered">
          <img src="./static/images/cross_custom.jpg"
               alt="Cross-reference Results"
               style="width: 100%; height: auto; object-fit: cover;" />
          <p class="image-caption">
            Cross-reference text image customization results.
          </p>
        </div>

        <div style="overflow: hidden; border-radius: 10px;" class="content has-text-centered">
          <img src="./static/images/non-text.jpg"
               alt="Non-text reference Results"
               style="width: 100%; height: auto; object-fit: cover;" />
          <p class="image-caption">
            Non-text reference text image customization results.
          </p>
        </div>


      </div>
</div>

  </div>
</section>


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