Upload folder using huggingface_hub

.idea/workspace.xml

@@ -54,7 +54,7 @@
       <option name="number" value="Default" />
       <option name="presentableId" value="Default" />
       <updated>1760056680813</updated>
-      <workItem from="1760056681869" duration="3537000" />
+      <workItem from="1760056681869" duration="4202000" />
     </task>
     <servers />
   </component>

app.py

@@ -19,22 +19,6 @@ from scripts.camera.visualization.visualize_batch import make_perspective_figure
 
 from huggingface_hub import snapshot_download
 import os
-_ = snapshot_download(
-    repo_id="KangLiao/Puffin",
-    repo_type="model",       
-    #filename="Puffin-Base.pth",         
-    local_dir="checkpoints/",  
-    local_dir_use_symlinks=False,     
-    revision="main",                  
-)
-_ = snapshot_download(
-    repo_id="wusize/Puffin",
-    repo_type="model",
-    #filename="Puffin-Base.pth",
-    local_dir="checkpoints/",
-    local_dir_use_symlinks=False,
-    revision="main",
-)
 
 NUM = r"[+-]?(?:\d+(?:\.\d+)?|\.\d+)(?:[eE][+-]?\d+)?"
 CAM_PATTERN = re.compile(r"(?:camera parameters.*?:|roll.*?:)\s*("+NUM+r")\s*,\s*("+NUM+r")\s*,\s*("+NUM+r")", re.IGNORECASE|re.DOTALL)
@@ -51,10 +35,29 @@ def center_crop(image):
 config = "configs/pipelines/stage_2_base.py"
 config = Config.fromfile(config)
 model = BUILDER.build(config.model).eval()
+_ = snapshot_download(
+    repo_id="KangLiao/Puffin",
+    repo_type="model",
+    allow_patterns="Puffin-Base.pth",
+    local_dir="checkpoints/",
+    local_dir_use_symlinks=False,
+    revision="main",
+)
 checkpoint_path = "checkpoints/Puffin-Base.pth"
 checkpoint = torch.load(checkpoint_path, map_location='cpu')
 _ = model.load_state_dict(checkpoint, strict=False)
+
+os.remove(checkpoint)
+
+_ = snapshot_download(
+    repo_id="wusize/Puffin",
+    repo_type="model",
+    local_dir="checkpoints/",
+    local_dir_use_symlinks=False,
+    revision="main",
+)
 _ = model.vae.load_state_dict(torch.load('checkpoints/vae.pth', map_location='cpu'), strict=True)
+os.remove('checkpoints/vae.pth')
 
 
 if torch.cuda.is_available():