From 7184903bdba97f567e57233ff6dee9e54ce8075f Mon Sep 17 00:00:00 2001
From: HannahBenita <77296142+HannahBenita@users.noreply.github.com>
Date: Fri, 1 Dec 2023 16:42:43 +0100
Subject: [PATCH] Update index.html

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 1 file changed, 2 insertions(+), 2 deletions(-)

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@@ -251,9 +251,9 @@ <h4>Image Fidelity and Text-to-Image Alignment</h4>
 		<p>First we meassure image fidelity and image-text-alignment using the standard metrics FID-30K and Clip Scores. We find that MultiFusion prompted with text only performs on par with Stable Diffusion despite extension of the Encoder to support multiple languages and modalities.</p>
 		<image src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/evaluation.png" class="img-responsive"><br>
 		<h4>Compositional Robustness</h4>
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 		<div float="left">
-			<image margin-bottom="2cm" height="150px" src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/compositional_robustness.png" alt="method"><br>
+			<image  height="150px" src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/compositional_robustness.png" alt="method"><br>
 		</div>
 		<div float="left">
 			<p>Image Composition is a known limitation of Diffusion Models. Through evaluation of our new benchmark <a href="https://huggingface.co/datasets/AIML-TUDA/MCC-250">MCC-250</a> we show that multimodal prompting leads to more compositional robustness as judged by humans. Each prompt is a complex conjunction of two different objects with different