fix bugs

app.py

@@ -36,16 +36,16 @@ with gr.Blocks(css="style.css") as demo:
     )
 
     with gr.Tabs():
-        with gr.TabItem("Canny"):
-            create_demo_canny(model.process_canny)
-        with gr.TabItem("SoftEdge"):
-            create_demo_softedge(model.process_softedge)
-        with gr.TabItem("Segmentation"):
-            create_demo_segmentation(model.process_segmentation)
-        with gr.TabItem("Depth"):
-            create_demo_depth(model.process_depth)
         with gr.TabItem("Lineart"):
             create_demo_lineart(model.process_lineart)
+        with gr.TabItem("Depth"):
+            create_demo_depth(model.process_depth)
+        with gr.TabItem("Segmentation"):
+            create_demo_segmentation(model.process_segmentation)
+        with gr.TabItem("SoftEdge"):
+            create_demo_softedge(model.process_softedge)
+        with gr.TabItem("Canny"):
+            create_demo_canny(model.process_canny)
 
     with gr.Accordion(label="Base model", open=False):
         with gr.Row():

app_canny.py

@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_depth.py

@@ -16,8 +16,8 @@ def create_demo(process):
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
+                image = gr.Image(value='images/depth_demo.png')
+                prompt = gr.Textbox(label="Prompt", value='heart, mountains, and nature image')
                 run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
                     preprocessor_name = gr.Radio(
@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_ip2p.py

@@ -34,7 +34,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_lineart.py

@@ -16,8 +16,8 @@ def create_demo(process):
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
+                image = gr.Image(value='images/lineart_demo.jpg')
+                prompt = gr.Textbox(label="Prompt", value='Picture Of Looking Through A View Finder')
                 run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
                     preprocessor_name = gr.Radio(
@@ -50,7 +50,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_mlsd.py

@@ -43,7 +43,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_normal.py

@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_openpose.py

@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_scribble.py

@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_scribble_interactive.py

@@ -54,7 +54,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_segmentation.py

@@ -16,12 +16,12 @@ def create_demo(process):
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
+                image = gr.Image(value='images/seg_demo.png')
+                prompt = gr.Textbox(label="Prompt", value='A large building with a pointed roof and several chimneys.')
                 run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
                     preprocessor_name = gr.Radio(
-                        label="Preprocessor", choices=["UPerNet", "None"], type="value", value="UPerNet"
+                        label="Preprocessor", choices=["UPerNet", "None"], type="value", value="None"
                     )
                     num_samples = gr.Slider(
                         label="Number of images", minimum=1, maximum=MAX_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1
@@ -40,7 +40,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_shuffle.py

@@ -37,7 +37,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

app_softedge.py

@@ -16,8 +16,8 @@ def create_demo(process):
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label="Prompt")
+                image = gr.Image(value='images/hed_demo.jpeg')
+                prompt = gr.Textbox(label="Prompt", value='Language trip to Laon')
                 run_button = gr.Button("Run")
                 with gr.Accordion("Advanced options", open=False):
                     preprocessor_name = gr.Radio(
@@ -49,7 +49,7 @@ def create_demo(process):
                     guidance_scale = gr.Slider(label="Guidance scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    a_prompt = gr.Textbox(label="Additional prompt", value="best quality, extremely detailed")
+                    a_prompt = gr.Textbox(label="Additional prompt", value="high-quality, extremely detailed, 4K")
                     n_prompt = gr.Textbox(
                         label="Negative prompt",
                         value="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",

model.py

@@ -159,7 +159,12 @@ class Model:
             guidance_scale=guidance_scale,
             seed=seed,
         )
-        return [control_image] + results
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x, low_threshold=low_threshold, high_threshold=high_threshold, detect_resolution=image_resolution
+            ) for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
 
     @torch.inference_mode()
     def process_mlsd(
@@ -351,7 +356,15 @@ class Model:
             guidance_scale=guidance_scale,
             seed=seed,
         )
-        return [control_image] + results
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+                scribble=safe,
+            ) for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
 
     @torch.inference_mode()
     def process_openpose(
@@ -442,7 +455,15 @@ class Model:
             guidance_scale=guidance_scale,
             seed=seed,
         )
-        return [control_image] + results
+        self.preprocessor.load('UPerNet')
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=np.array(x),
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            ) for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
 
     @torch.inference_mode()
     def process_depth(
@@ -487,7 +508,14 @@ class Model:
             guidance_scale=guidance_scale,
             seed=seed,
         )
-        return [control_image] + results
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            ) for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
 
     @torch.inference_mode()
     def process_normal(
@@ -590,7 +618,14 @@ class Model:
             guidance_scale=guidance_scale,
             seed=seed,
         )
-        return [control_image] + results
+        conditions_of_generated_imgs = [
+            self.preprocessor(
+                image=x,
+                image_resolution=image_resolution,
+                detect_resolution=preprocess_resolution,
+            ) for x in results
+        ]
+        return [control_image] * num_images + results + conditions_of_generated_imgs
 
     @torch.inference_mode()
     def process_shuffle(