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@@ -238,7 +238,7 @@ <h3 id="2-Variable Resolutions">(2) Variable Resolutions</h3>
 <h3 id="2-Variable durations">(3) Variable Durations </h3>
 
 <!-- <p>In addition to Vicuna-1.5 (<a href="https://huggingface.co/lmsys/vicuna-7b-v1.5">7B</a> and <a href="https://huggingface.co/lmsys/vicuna-13b-v1.5">13B</a>), we consider more LLMs, including <a href="https://mistral.ai/news/announcing-mistral-7b/">Mistral-7B</a> and <a href="https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B">Nous-Hermes-2-Yi-34B</a>. These LLMs possess nice properties, flexible commercial use terms, strong bilingual support and larger language model capacity. It allows LLaVA to support a wider spectrum of users and more scenarios in the community. The LLaVA recipe works well with various LLMs, and scales up smoothly with the LLM up to 34B.</p> -->
-<p>We employ vidoe VQ-VAE in <a href="https://arxiv.org/abs/2104.10157">Videogpt</a> to compress videos into latents, enabling multi-duration generation. We extend the spatial Position Interpolation to spatio-temporal version for handling varaible duration videos.
+<p>We employ video VQ-VAE in <a href="https://arxiv.org/abs/2104.10157">VideoGPT</a> to compress videos into latents, enabling multi-duration generation. We extend the spatial Position Interpolation to spatio-temporal version for handling varaible duration videos.
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@@ -341,12 +341,12 @@ <h2 id="team">Team</h2>
 
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-    <h2 class="footer-heading">Open-Sora</h2>
+    <h2 class="footer-heading">Open-Sora Plan</h2>
 
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-          <li class="p-name">Open-Sora</li></ul>
+          <li class="p-name">Open-Sora Plan</li></ul>
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