update button

app.py

@@ -163,6 +163,9 @@ css="""
     margin: 0 auto;
     max-width: 750px;
 }
+#button {
+    color: bule;
+}
 """
 
 def load_description(fp):
@@ -180,13 +183,6 @@ with gr.Blocks(css=css) as Kolors:
                     placeholder="Enter your prompt",
                     lines=2
                 )
-            with gr.Row():
-                controlnet_type = gr.Dropdown(
-                    ["Depth", "Canny"],
-                    label = "Controlnet",
-                    value="Depth",
-                    visible=False
-                )
             with gr.Row():
                 image = gr.Image(label="Image", type="pil")
             with gr.Accordion("Advanced Settings", open=False):
@@ -243,8 +239,8 @@ with gr.Blocks(css=css) as Kolors:
                         value=1.0,
                     )
             with gr.Row():
-                canny_button = gr.Button("Canny")
-                depth_button = gr.Button("Depth")
+                canny_button = gr.Button("Canny", elem_id="button")
+                depth_button = gr.Button("Depth", elem_id="button")
             
         with gr.Column(elem_id="col-right"):
             result = gr.Gallery(label="Result", show_label=False, columns=2)
@@ -266,16 +262,15 @@ with gr.Blocks(css=css) as Kolors:
                 label = "Depth"
             )
 
-
     canny_button.click(
         fn = infer_canny,
-        inputs = [prompt, image, controlnet_type, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
         outputs = [result]
     )
 
     depth_button.click(
         fn = infer_depth,
-        inputs = [prompt, image, controlnet_type, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
+        inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps, controlnet_conditioning_scale, control_guidance_end, strength],
         outputs = [result]
     )