app

app.py

@@ -39,7 +39,7 @@ import tempfile
 from PIL import Image
 import glob
 from src.data import DemoData
-from src.models import LiNo_UniPS
+from src.models import LINO_UniPS
 from torch.utils.data import DataLoader
 import pytorch_lightning as pl
 import spaces
@@ -206,7 +206,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
     with gr.Row():
        gr.Markdown(
         """    
-        LiNo-UniPS is a method for Univeral Photometric Stereo. It predicts the normal map from a given set of images. Key features include:
+        LINO-UniPS is a method for Univeral Photometric Stereo. It predicts the normal map from a given set of images. Key features include:
         
         * **Light-Agnostic:** Does not require specific lighting parameters as input.
         * **Arbitrary-Resolution:** Supports inputs of any resolution.
@@ -222,7 +222,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
                 
                 2.  **Upload Your Mask (Optional)**: A mask is not required for scene reconstruction. However, to reconstruct the normal map for a specific **object**, providing a mask is highly recommended. Use the "Mask" button on the left.
                 
-                3.  **Reconstruct**: Click the "Run" button to start the reconstruction process. You can use the slider in "Advanced Settings" to control the number of multi-light images used by LiNo-UniPS. Note: If the selected number exceeds the total number of uploaded images, the maximum available number will be used instead.
+                3.  **Reconstruct**: Click the "Run" button to start the reconstruction process. You can use the slider in "Advanced Settings" to control the number of multi-light images used by LINO-UniPS. Note: If the selected number exceeds the total number of uploaded images, the maximum available number will be used instead.
                 
                 4.  **Visualize**: The result will appear in the "Normal Output" viewer on the right. If you use one of our provided examples that includes a ground truth normal map, it will be displayed in the "Ground Truth" viewer for comparison.
                 
@@ -271,7 +271,7 @@ with gr.Blocks(css="footer {visibility: hidden}") as demo:
 
             with gr.Column(scale=2):
                 with gr.Tabs():
-                    with gr.Tab("LiNo-UniPS Output"):
+                    with gr.Tab("LINO-UniPS Output"):
                         with gr.Row(scale=3):
                             normal_output = gr.Image(label="Normal Output",height=700,)
                             normal_gt = gr.Image(label="Ground Truth",height=700)
@@ -378,8 +378,9 @@ if __name__ == "__main__":
 
     hi3dgen_pipeline = Hi3DGenPipeline.from_pretrained("weights/trellis-normal-v0-1")
     hi3dgen_pipeline.cuda()
-    lino = LiNo_UniPS()
+    lino = LINO_UniPS()
     lino.from_pretrained("weights/lino/lino.pth")
   
     demo.launch(share=False, server_name="0.0.0.0")
 
+