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-  <title>Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps</title>
+  <title>Invertible Consistency Distillation for <br> Text-Guided Image Editing in Around 7 Steps</title>
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-            <h1 class="title is-1 publication-title">Invertible Consistency Distillation for
+            <h1 class="title is-1 publication-title">Invertible Consistency Distillation for <br>
               Text-Guided Image Editing in Around 7 Steps</h1>
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         <h2 class="title is-3">Abstract</h2>
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+            Diffusion distillation represents a highly promising direction for achieving faithful text-to-image generation in a few sampling steps.
+  However, despite recent successes, existing distilled models still do not provide the full spectrum of diffusion abilities, such as real image inversion, which enables many precise image manipulation methods.
+  This work aims to enrich distilled text-to-image diffusion models with the ability to effectively encode real images into their latent space.
+  To this end, we introduce <i>invertible Consistency Distillation</i> (iCD), a generalized consistency distillation framework that facilitates both high-quality image synthesis and accurate image encoding in only 3-4 inference steps. 
+  Though the inversion problem for text-to-image diffusion models gets exacerbated by high classifier-free guidance scales, we notice that <i>dynamic guidance</i> significantly reduces reconstruction errors without noticeable degradation in generation performance.
+  As a result, we demonstrate that iCD equipped with dynamic guidance may serve as a highly effective tool for zero-shot text-guided image editing, competing with more expensive state-of-the-art alternatives.
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