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HannahBenita authored Dec 1, 2023
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Expand Up @@ -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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<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>
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<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
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