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 		<h4>Image Fidelity and Text-to-Image Alignment</h4>
 		<p>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>
+		<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="200" src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/compositional_robustness.png" class="img-responsive" alt="method"><br>
+		<image height="100" src="https://Aleph-Alpha.github.io/MultiFusion/src/imgs/compositional_robustness.png" class="img-responsive" 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>