Update app.py

app.py

@@ -114,8 +114,8 @@ download_file(
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Load ControlNet model
-controlnet = ControlNetModel.from_pretrained(
-    "lllyasviel/control_v11f1e_sd15_tile", torch_dtype=torch.float16
+controlnet = ControlNetModel.from_single_file(
+    "models/ControlNet/control_v11f1e_sd15_tile.pth", torch_dtype=torch.float16
 )
 
 # Load the Stable Diffusion pipeline with Juggernaut Reborn model
@@ -226,15 +226,14 @@ def process_image(input_image, prompt, negative_prompt, resolution=2048, num_inf
     return result
 
 
-
 # Simple options
 simple_options = [
     gr.Image(type="pil", label="Input Image"),
-    gr.Slider(minimum=2048, maximum=3072, step=512, default=2048, label="Resolution"),
-    gr.Slider(minimum=10, maximum=100, step=10, default=20, label="Inference Steps"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, default=0.35, label="Strength"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.1, default=0, label="HDR"),
-    gr.Slider(minimum=1, maximum=10, step=0.1, default=3, label="Guidance Scale")
+    gr.Slider(minimum=2048, maximum=3072, step=512, value=2048, label="Resolution"),
+    gr.Slider(minimum=10, maximum=100, step=10, value=20, label="Inference Steps"),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, value=0.35, label="Strength"),
+    gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="HDR"),
+    gr.Slider(minimum=1, maximum=10, step=0.1, value=3, label="Guidance Scale")
 ]
 
 # Create the Gradio interface
@@ -246,4 +245,5 @@ iface = gr.Interface(
     description="Upload an image and adjust the settings to process it using Stable Diffusion."
 )
 
+
 iface.launch()