updated new model

app.py

@@ -83,8 +83,8 @@ def ui():
                     )
                 with gr.Accordion(label='Preprocess', open=True):
                     with gr.Row():
-                        threshold1 = gr.Slider(minimum=-1, maximum=255, step=1, value=100, label='Threshold 1', info='-1 for auto')
-                        threshold2 = gr.Slider(minimum=-1, maximum=255, step=1, value=200, label='Threshold 2', info='-1 for auto')
+                        threshold1 = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label='Threshold 1', info='-1 for auto')
+                        threshold2 = gr.Slider(minimum=-1, maximum=255, step=1, value=-1, label='Threshold 2', info='-1 for auto')
                         process_button = gr.Button("Process", variant='primary', min_width=96, scale=0)
                 with gr.Row():
                     scheduler = gr.Dropdown(
@@ -98,7 +98,7 @@ def ui():
                     num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, value=28, label='Steps')
                 with gr.Row():
                     cfg_scale = gr.Slider(minimum=1, maximum=30, step=1, value=7.5, label='CFG Scale')
-                    controlnet_scale = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.35, label='ControlNet Scale')
+                    controlnet_scale = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label='ControlNet Scale')
                 with gr.Row():
                     seed = gr.Number(label='Seed', step=1, precision=0, value=-1)
             with gr.Column(scale=1):

models/controlnet.py

@@ -353,10 +353,10 @@ class ControlNetModel(ModelMixin, ConfigMixin):
             nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1),
             nn.GroupNorm(2, 64),
             nn.ReLU(),
-            nn.Conv2d(64, 64, kernel_size=3),
+            nn.Conv2d(64, 64, kernel_size=3, padding=1),
             nn.GroupNorm(2, 64),
             nn.ReLU(),
-            nn.Conv2d(64, 128, kernel_size=3),
+            nn.Conv2d(64, 128, kernel_size=3, padding=1),
             nn.GroupNorm(2, 128),
             nn.ReLU(),
         )