DreamRenderer/index.html at main · limuloo/DreamRenderer · GitHub

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<!DOCTYPE html>
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  <title>DreamRenderer</title>
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  <section class="hero">
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            <h1 class="title is-1 publication-title">
            DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models</h1>
            <div class="is-size-5 publication-authors">
              <!-- Paper authors -->
              <span class="author-block">
		      <a href="https://scholar.google.com/citations?user=4C_OwWMAAAAJ&hl=en&oi=ao" target="_blank">Dewei Zhou</a><sup>1</sup>,</span>
              <span class="author-block">
                <a href="https://scholar.google.com/citations?user=EQMVZh4AAAAJ&hl=en&oi=ao" target="_blank">Mingwei Li</a><sup>1</sup>,</span>
                <span class="author-block">
                  <a href="https://scholar.google.com/citations?user=8IE0CfwAAAAJ&hl=en&oi=ao" target="_blank">Zongxin Yang</a><sup>2</sup>,</span>
			  <a href="https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=en" target="_blank">Yi Yang</a><sup>1</sup><sup>*</sup>
                  </span>
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                  <div class="is-size-5 publication-authors">
			  <span class="author-block"><sup>1</sup>ReLER, CCAI, Zhejiang University <sup>2</sup>DBMI, HMS, Harvard University
				  <br>Arxiv 2025</span>
                    <span class="eql-cntrb"><small><br><sup>*</sup>Corresponding Author</small></span>
                  </div>

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                        <a href="https://github.com/limuloo/DreamRenderer" target="_blank"
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                        <span>Arxiv</span>
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                    <span>Code</span>
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                  <a href="https://drive.google.com/file/d/1MNaKZmIyBXT7Ia_6DJ56vJ2TeB5o8m6c/view?usp=sharing" target="_blank"
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                  <span>Supplement Material</span>
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            </div>
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</section>

<!-- Teaser video-->
<section class="hero teaser">
    <div class="container is-max-desktop has-text-centered">
      <div class="hero-body">
          <img src="static/images/teaser.png" alt="MY ALT TEXT"/>
      <p>DreamRenderer is a plug-and-play controller that grants users fine-grained control over the content of each region and instance during depth- or canny-conditioned generation without any training.</p>
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  </section>
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          <h2 class="title is-3">Abstract</h2>
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            <p>
              Image-conditioned generation methods, such as depth- and canny-conditioned approaches, have demonstrated remarkable abilities for precise image synthesis. However, existing models still struggle to accurately control the content of multiple instances (or regions). Even state-of-the-art models like FLUX and 3DIS face challenges, such as attribute leakage between instances, which limits user control. To address these issues, we introduce DreamRenderer, a training-free approach built upon the FLUX model. DreamRenderer enables users to control the content of each instance via bounding boxes or masks, while ensuring overall visual harmony. We propose two key innovations: 1) Bridge Image Tokens for Hard Text Attribute Binding, which uses replicated image tokens as bridge tokens to ensure that T5 text embeddings, pre-trained solely on text data, bind the correct visual attributes for each instance during Joint Attention; 2) Hard Image Attribute Binding applied only to vital layers. Through our analysis of FLUX, we identify the critical layers responsible for instance attribute rendering and apply Hard Image Attribute Binding only in these layers, using soft binding in the others. This approach ensures precise control while preserving image quality. Evaluations on the COCO-POS and COCO-MIG benchmarks demonstrate that DreamRenderer improves the Image Success Ratio by 17.7% over FLUX and enhances the performance of layout-to-image models like GLIGEN and 3DIS by up to 26.8%.
            </p>
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        <h2 class="title is-3">Method</h2>
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          <img src="static/images/framework.png" alt="MY ALT TEXT"/>
      <h2 class="content has-text-justified">
          <p>
              We propose two key innovations: 1) Bridge Image Tokens for Hard Text Attribute Binding, which uses replicated image tokens as bridge tokens to ensure that T5 text embeddings, pre-trained solely on text data, bind the correct visual attributes for each instance during Joint Attention; 2) Hard Image Attribute Binding applied only to vital layers. Through our analysis of FLUX, we identify the critical layers responsible for instance attribute rendering and apply Hard Image Attribute Binding only in these layers, using soft binding in the others.
          </p>
        </h2>
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        <h2 class="title is-3">Adding region/instance control on FLUX</h2>
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         <div class="item">
          <!-- Your image here -->
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  <!-- End image carousel -->

    <!-- Image carousel -->
<section class="hero is-small">
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        <h2 class="title is-3">Rerendering on Layout-to-Image Models</h2>
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      <h2 class="title is-3">COCO-Position Results</h2>
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        <h2 class="title is-3">COCO-MIG Results</h2>
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          <img src="static/images/cocomig.png" alt="MY ALT TEXT"/>
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