Update app.py

app.py

@@ -14,8 +14,8 @@ sys.path.append("diffusion-point-cloud")
 from models.vae_gaussian import *
 from models.vae_flow import *
 
-airplane=network_pkl=hf_hub_download("SerdarHelli/diffusion-point-cloud", filename="GEN_airplane.pt",revision="main")
-chair=network_pkl=hf_hub_download("SerdarHelli/diffusion-point-cloud", filename="GEN_chair.pt",revision="main")
+airplane=hf_hub_download("SerdarHelli/diffusion-point-cloud", filename="GEN_airplane.pt",revision="main")
+chair="./GEN_chair.pt"
 
 device='cuda' if torch.cuda.is_available() else 'cpu'
 
@@ -99,10 +99,21 @@ def generate(seed,value):
     
 markdown=f'''
   # Diffusion Probabilistic Models for 3D Point Cloud Generation
-  [[The Paper](https://arxiv.org/abs/2103.01458)] [[Original Code](https://github.com/luost26/diffusion-point-cloud)]
-  The space demo for our CVPR 2021 paper "Diffusion Probabilistic Models for 3D Point Cloud Generation".
+  [The space demo for the CVPR 2021 paper "Diffusion Probabilistic Models for 3D Point Cloud Generation".](https://arxiv.org/abs/2103.01458)
+  [For the official implementation.](https://github.com/luost26/diffusion-point-cloud)
 
   It is running on {device}
+  
+  ### Citation By
+  
+  @inproceedings{luo2021diffusion,
+  author = {Luo, Shitong and Hu, Wei},
+  title = {Diffusion Probabilistic Models for 3D Point Cloud Generation},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  month = {June},
+  year = {2021}
+    }
+   
 '''
 with gr.Blocks() as demo:
     with gr.Column():