Update README.md

README.md

@@ -46,9 +46,9 @@ language:
     <br>
     <a href="https://x-dyna.github.io/xdyna.github.io/">Project Page</a> 
     ·  
-    <a href="https://github.com/Boese0601/X-Dyna">Code</a>    
+    <a href="https://github.com/bytedance/X-Dyna">Code</a>    
     ·    
-    <a href="https://arxiv.org">Paper</a> 
+    <a href="">Paper</a> 
 </p>
 
 
@@ -90,7 +90,7 @@ a) IP-Adapter encodes the reference image as an image CLIP embedding and injects
 
 
 ## 🧱 Download Pretrained Models
-Due to restrictions we are not able to release the model pretrained with in-house data. Instead, we re-train our model on public datasets, e.g. [TikTok](https://www.yasamin.page/hdnet_tiktok), and [HumanVid](https://github.com/zhenzhiwang/HumanVid), and other human video data for research use, e.g.[Pexels](https://www.pexels.com/). We follow the implementation details in our paper and release pretrained weights and other necessary network modules in this huggingface repository. The Stable Diffusion 1.5 UNet can be found [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) and place it under pretrained_weights/unet_initialization/SD. After downloading, please put all of them under the pretrained_weights folder. Your file structure should look like this:
+Due to restrictions we are not able to release the model pretrained with in-house data. Instead, we re-train our model on public datasets, e.g. [HumanVid](https://github.com/zhenzhiwang/HumanVid), and other human video data for research use, e.g.[Pexels](https://www.pexels.com/). We follow the implementation details in our paper and release pretrained weights and other necessary network modules in this huggingface repository. The Stable Diffusion 1.5 UNet can be found [here](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) and place it under pretrained_weights/unet_initialization/SD. After downloading, please put all of them under the pretrained_weights folder. Your file structure should look like this:
 
 ```bash
 X-Dyna