Update app.py

app.py

@@ -33,42 +33,6 @@ css = """
     }
 """
 
-controlnet_depth= ControlNetModel.from_pretrained(
-    "controlnet_depth", torch_dtype=dtype, use_safetensors=True)
-controlnet_seg = ControlNetModel.from_pretrained(
-    "own_controlnet", torch_dtype=dtype, use_safetensors=True)
-
-pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-    "SG161222/Realistic_Vision_V5.1_noVAE",
-    #"models/runwayml--stable-diffusion-inpainting",
-    controlnet=[controlnet_depth, controlnet_seg],
-    safety_checker=None,
-    torch_dtype=dtype
-)
-
-pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models",
-                     weight_name="ip-adapter_sd15.bin")
-pipe.set_ip_adapter_scale(0.4)
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to(device)
-guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B",
-                                                       torch_dtype=dtype, use_safetensors=True, variant="fp16")
-guide_pipe = guide_pipe.to(device)
-   
-seg_image_processor, image_segmentor = get_segmentation_pipeline()
-depth_feature_extractor, depth_estimator = get_depth_pipeline()
-depth_estimator = depth_estimator.to(device)
-
-# ---------------------------------------
-# Load Detr Model
-# ---------------------------------------
-detr_model = DetrForObjectDetection.from_pretrained("facebook-detr-50", 
-                                                    # id2label={v:k for k,v in CLASS2ID.items()}, 
-                                                    use_safetensors=True)
-detr_processor = DetrImageProcessor.from_pretrained("facebook-detr-50", 
-                                                    use_safetensors=True)
-
-
 def filter_items(
     colors_list: Union[List, np.ndarray],
     items_list: Union[List, np.ndarray],
@@ -422,6 +386,41 @@ def create_demo(model):
                            inputs=[input_image, input_text], cache_examples=False)
 
 
+controlnet_depth= ControlNetModel.from_pretrained(
+    "controlnet_depth", torch_dtype=dtype, use_safetensors=True)
+controlnet_seg = ControlNetModel.from_pretrained(
+    "own_controlnet", torch_dtype=dtype, use_safetensors=True)
+
+pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+    "SG161222/Realistic_Vision_V5.1_noVAE",
+    #"models/runwayml--stable-diffusion-inpainting",
+    controlnet=[controlnet_depth, controlnet_seg],
+    safety_checker=None,
+    torch_dtype=dtype
+)
+
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models",
+                     weight_name="ip-adapter_sd15.bin")
+pipe.set_ip_adapter_scale(0.4)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to(device)
+guide_pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B",
+                                                       torch_dtype=dtype, use_safetensors=True, variant="fp16")
+guide_pipe = guide_pipe.to(device)
+   
+seg_image_processor, image_segmentor = get_segmentation_pipeline()
+depth_feature_extractor, depth_estimator = get_depth_pipeline()
+depth_estimator = depth_estimator.to(device)
+
+# ---------------------------------------
+# Load Detr Model
+# ---------------------------------------
+detr_model = DetrForObjectDetection.from_pretrained("facebook-detr-50", 
+                                                    # id2label={v:k for k,v in CLASS2ID.items()}, 
+                                                    use_safetensors=True)
+detr_processor = DetrImageProcessor.from_pretrained("facebook-detr-50", 
+                                                    use_safetensors=True)
+
 def main():
     model = ControlNetDepthDesignModelMulti()
     print('Models uploaded successfully')
@@ -438,7 +437,7 @@ def main():
         gr.HTML('''<br><br><br><center>You can duplicate this Space to skip the queue:<a href="https://huggingface.co/spaces/MykolaL/StableDesign?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a><br>
                 <p><img src="https://visitor-badge.glitch.me/badge?page_id=MykolaL/StableDesign" alt="visitors"></p></center>''')
 
-    demo.queue().launch(share=False, show_error=True)
+    demo.queue().launch(share=False)
 
 
 if __name__ == '__main__':