Update app.py

app.py

@@ -11,9 +11,6 @@ from gradio_imageslider import ImageSlider
 from pathlib import Path
 from gradio.utils import get_cache_folder
 
-# Constants
-DEFAULT_SHARPNESS = 2
-
 class Examples(gr.helpers.Examples):
     def __init__(self, *args, directory_name=None, **kwargs):
         super().__init__(*args, **kwargs, _initiated_directly=False)
@@ -24,13 +21,12 @@ class Examples(gr.helpers.Examples):
 
 def load_predictor():
     """Load model predictor using torch.hub"""
-    predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal", trust_repo=True, yoso_version='yoso-normal-v1-8-1')
+    predictor = torch.hub.load("hugoycj/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-8-1')
     return predictor
 
 def process_image(
     predictor,
     path_input: str,
-    sharpness: int = DEFAULT_SHARPNESS,
     data_type: str = "object"
 ) -> tuple:
     """Process single image"""
@@ -42,8 +38,7 @@ def process_image(
 
     # Load and process image
     input_image = Image.open(path_input)
-    normal_image = predictor(input_image, num_inference_steps=sharpness, 
-                             match_input_resolution=False, data_type=data_type)
+    normal_image = predictor(input_image, match_input_resolution=False, data_type=data_type)
     normal_image.save(out_path)
 
     yield [input_image, out_path]
@@ -57,23 +52,8 @@ def create_demo():
 
     # Define markdown content
     HEADER_MD = """
-    # 🎪 StableNormal Turbo Beta
-    
-    ### ✨ What's Cooking in Our Beta Kitchen? ✨
-    - **Zoom Zoom**: 2x faster - because waiting is boring!
-    - **Sharp as a Tack**: Better quality for those pixel-perfect folks
-    - **Your Way**: Tweak the sharpness slider and watch the magic happen
-    - **Pick Your Fighter**: Objects, Scenes, or Humans - we've got you covered!
-    
-    ### 🎯 Pro Tips
-    - Start with lower sharpness for a quick, stable result
-    - Want more details? Crank it up, but watch out for those floating bits!
-    - Sweet spot is usually around 2-3 for most images 😉
-    - If you get a flat result, try:
-      * Different sharpness
-      * Another image crop
-      * Another mode
-    
+    # 🎪 StableNormal Turbo
+
     <p align="center">
     <a title="Website" href="https://stable-x.github.io/StableNormal/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
         <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
@@ -111,14 +91,6 @@ def create_demo():
                 with gr.Row():
                     with gr.Column():
                         object_input = gr.Image(label="Input Object Image", type="filepath")
-                        object_sharpness = gr.Slider(
-                            minimum=1,
-                            maximum=10,
-                            value=DEFAULT_SHARPNESS,
-                            step=1,
-                            label="Sharpness (inference steps)",
-                            info="Higher values produce sharper results but take longer"
-                        )
                         with gr.Row():
                             object_submit_btn = gr.Button("Compute Normal", variant="primary")
                             object_reset_btn = gr.Button("Reset")
@@ -150,19 +122,19 @@ def create_demo():
         # Event Handlers for Object Tab
         object_submit_btn.click(
             fn=lambda x, _: None if x else gr.Error("Please upload an image"),
-            inputs=[object_input, object_sharpness],
+            inputs=object_input,
             outputs=None,
             queue=False,
         ).success(
             fn=process_object,
-            inputs=[object_input, object_sharpness],
+            inputs=object_input,
             outputs=[object_output_slider],
         )
 
         object_reset_btn.click(
             fn=lambda: (None, DEFAULT_SHARPNESS, None),
             inputs=[],
-            outputs=[object_input, object_sharpness, object_output_slider],
+            outputs=[object_input, object_output_slider],
             queue=False,
         )