app.py: fixed controlnet options.

app.py

@@ -136,7 +136,7 @@ CONTROLNET_DICT = dict(
 		'preprocessor': 'canny',
 	},
 	lineart={
-		'model_name': 'controlnet-softedge-dexined-sdxl-1.0',
+		'model_name': 'controlnet-softedge-sdxl-1.0',
 		'strength': 1,
 		'preprocessor': 'lineart',
 	},
@@ -269,7 +269,7 @@ def generate_image (
 	num_steps,
 	identitynet_strength_ratio,
 	adapter_strength_ratio,
-	controlnet_strengths,
+	controlnet_strength_1, controlnet_strength_2, controlnet_strength_3, controlnet_strength_4,
 	controlnet_selection,
 	guidance_scale,
 	seed,
@@ -313,7 +313,7 @@ def generate_image (
 			CONTROLNET_DICT['canny']['strength'],
 			CONTROLNET_DICT['depth']['strength'],
 			CONTROLNET_DICT['lineart']['strength'],
-		) = controlnet_strengths
+		) = [controlnet_strength_1, controlnet_strength_2, controlnet_strength_3, controlnet_strength_4]
 
 		face_image_uploaded, ref_image_uploaded = upload_assets_with_cache(client, [face_image_path, ref_image_path])
 
@@ -474,7 +474,7 @@ with gr.Blocks(css=css) as demo:
 			)
 			with gr.Accordion('Controlnet'):
 				controlnet_selection = gr.CheckboxGroup(
-					['pose', 'canny', 'depth'], label='Controlnet', value=['pose'],
+					CONTROLNET_DICT.keys(), label='Controlnet', value=['pose'],
 					info='Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process'
 				)
 				pose_strength = gr.Slider(
@@ -580,12 +580,12 @@ with gr.Blocks(css=css) as demo:
 				num_steps,
 				identitynet_strength_ratio,
 				adapter_strength_ratio,
-				[
+				#[
 					pose_strength,
 					canny_strength,
 					depth_strength,
 					lineart_strength,
-				],
+				#],
 				controlnet_selection,
 				guidance_scale,
 				seed,