From e647e082a75d4b4e273b4e55bf79657c55c4ef5e Mon Sep 17 00:00:00 2001
From: HannahBenita <77296142+HannahBenita@users.noreply.github.com>
Date: Fri, 1 Dec 2023 12:54:25 +0100
Subject: [PATCH] Update index.html

---
 index.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/index.html b/index.html
index c379c40..8ef3210 100644
--- a/index.html
+++ b/index.html
@@ -246,7 +246,7 @@ <h4>Image Fidelity and Text-to-Image Alignment</h4>
 		<image src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/evaluation.png" class="img-responsive"><br>
 		<h4>Compositional Robustness</h4>
 		<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.</p>
-		<image height="100px" 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>
 		<h4>Multilinguality</h4>
 		<image src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/multilingual_alignment.png" class="img-responsive" alt="method"><br>	
 		<p>Evaluating the alignment of prompt embeddings as well as generated images across multiple languagese we show that well aligned embeddings enable the transfer of multiligualism to downstream tasks even for task-specific monolingual training data.</p>