Update app.py

app.py

@@ -34,13 +34,13 @@ pipe.load_lora_weights(custom_model, use_auth_token=True)
 pipe.to("cuda")
 #pipe.enable_model_cpu_offload()
 
-def infer(image_in, prompt):
+def infer(image_in, prompt, controlnet_conditioning_scale, guidance_scale):
     prompt = prompt
     negative_prompt = ""
 
     image = load_image(image_in)
 
-    controlnet_conditioning_scale = 0.25  # recommended for good generalization
+    #controlnet_conditioning_scale = 0.25  # recommended for good generalization
 
     image = np.array(image)
     image = cv2.Canny(image, 100, 200)
@@ -52,8 +52,8 @@ def infer(image_in, prompt):
         prompt, 
         negative_prompt=negative_prompt, 
         image=image, 
-        #controlnet_conditioning_scale=controlnet_conditioning_scale,
-        guidance_scale = 5.0,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        guidance_scale = guidance_scale,
         num_inference_steps=50
         ).images
 
@@ -65,12 +65,14 @@ with gr.Blocks() as demo:
     with gr.Column():
         image_in = gr.Image(source="upload", type="filepath")
         prompt = gr.Textbox(label="Prompt")
+        guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=5.0)
+        controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.0, maximum=1.0, step=0.01, value=0.5)
         submit_btn = gr.Button("Submit")
         result = gr.Image(label="Result")
 
     submit_btn.click(
         fn = infer,
-        inputs = [image_in, prompt],
+        inputs = [image_in, prompt, controlnet_conditioning_scale, guidance_scale ],
         outputs = [result]
     )