init

app.py

@@ -64,10 +64,10 @@ def get_segmentation_pipeline(
         Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]: segmentation pipeline
     """
     image_processor = AutoImageProcessor.from_pretrained(
-        "models/openmmlab--upernet-convnext-small"
+        "openmmlab/upernet-convnext-small"
     )
     image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
-        "models/openmmlab--upernet-convnext-small"
+        "openmmlab/upernet-convnext-small"
     )
     return image_processor, image_segmentor
 
@@ -109,9 +109,9 @@ def segment_image(
 
 
 def get_depth_pipeline():
-    feature_extractor = AutoImageProcessor.from_pretrained("models/models--LiheYoung--depth-anything-large-hf",
+    feature_extractor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf",
                                                            torch_dtype=dtype)
-    depth_estimator = AutoModelForDepthEstimation.from_pretrained("models/models--LiheYoung--depth-anything-large-hf",
+    depth_estimator = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf",
                                                                   torch_dtype=dtype)
     return feature_extractor, depth_estimator
 
@@ -174,9 +174,9 @@ class ControlNetDepthDesignModelMulti:
 
         #os.environ['HF_HUB_OFFLINE'] = "True"
         controlnet_depth= ControlNetModel.from_pretrained(
-            "models/controlnet_depth", torch_dtype=dtype, use_safetensors=True)
+            "controlnet_depth", torch_dtype=dtype, use_safetensors=True)
         controlnet_seg = ControlNetModel.from_pretrained(
-            "models/own_controlnet", torch_dtype=dtype, use_safetensors=True)
+            "own_controlnet", torch_dtype=dtype, use_safetensors=True)
 
         self.pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
             "SG161222/Realistic_Vision_V5.1_noVAE",