Update app.py

app.py

@@ -7,18 +7,18 @@ from huggingface_hub import hf_hub_download
 
 from lrm.inferrer import LRMInferrer
 
-def prepare_checkpoint(model_name: str):
+# def prepare_checkpoint(model_name: str):
 
-    REPO_ID = f"zxhezexin/OpenLRM"
-    FILE_NAME = f"{model_name}.pth"
-    CACHE_PATH = f".cache"
+#     REPO_ID = f"zxhezexin/OpenLRM"
+#     FILE_NAME = f"{model_name}.pth"
+#     CACHE_PATH = f".cache"
 
-    print(f"Downloading ckpt ...")
+#     print(f"Downloading ckpt ...")
 
-    ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=FILE_NAME, local_dir=CACHE_PATH)
-    print(f"checkpoint path is {ckpt_path}")
+#     ckpt_path = hf_hub_download(repo_id=REPO_ID, filename=FILE_NAME, local_dir=CACHE_PATH)
+#     print(f"checkpoint path is {ckpt_path}")
 
-    print(f"Downloaded ckpt into {CACHE_PATH}")
+#     print(f"Downloaded ckpt into {CACHE_PATH}")
 
 def assert_input_image(input_image):
     if input_image is None:
@@ -68,13 +68,13 @@ def demo_image_to_video(inferrer: LRMInferrer):
     _DESCRIPTION = '''
         <div>
             <a style="display:inline-block" href='https://github.com/3DTopia/OpenLRM'><img src='https://img.shields.io/github/stars/3DTopia/OpenLRM?style=social'/></a>
-            <a style="display:inline-block; margin-left: .5em" href="https://huggingface.co/zxhezexin/OpenLRM"><img src='https://img.shields.io/badge/Model-Weights-blue'/></a>
+            <a style="display:inline-block; margin-left: .5em" href="https://huggingface.co/zxhezexin"><img src='https://img.shields.io/badge/Model-Weights-blue'/></a>
         </div>
         OpenLRM is an open-source implementation of Large Reconstruction Models.
 
         <strong>Image-to-3D in 10 seconds!</strong>
 
-        <strong>Disclaimer:</strong> This demo uses `lrm-base-obj-v1` model trained on Objaverse only, which consists of synthetic data. Its performance may decrease on in-the-wild images. We use 194x194 rendering resolution here for a quick demonstration.
+        <strong>Disclaimer:</strong> This demo uses `openlrm-base-obj-1.0` model trained on Objaverse only, which consists of synthetic data. Its performance may decrease on in-the-wild images. We use 194x194 rendering resolution here for a quick demonstration.
     '''
 
     with gr.Blocks(analytics_enabled=False) as iface:
@@ -157,9 +157,9 @@ def demo_image_to_video(inferrer: LRMInferrer):
 
 if __name__ == "__main__":
 
-    model_name = "lrm-base-obj-v1"
+    model_name = "openlrm-base-obj-1.0"
 
-    prepare_checkpoint(model_name)
+    # prepare_checkpoint(model_name)
 
     with LRMInferrer(model_name) as inferrer:
         iface = demo_image_to_video(inferrer)