Update app.py

app.py

@@ -16,19 +16,25 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, MAX_SEED)
     return seed
 
-def pixelate(input_file_path, pixel_size):
-    image = Image.open(input_file_path)
-    image = image.resize(
-        (image.size[0] // pixel_size, image.size[1] // pixel_size),
-        Image.NEAREST
-    )
-    image = image.resize(
-        (image.size[0] * pixel_size, image.size[1] * pixel_size),
-        Image.NEAREST
-    )
-    return image
+def generate_normal_map(image):
+    # Convert image to grayscale
+    grayscale = image.convert("L")
+    grayscale_np = np.array(grayscale)
+    
+    # Compute gradients
+    grad_x, grad_y = np.gradient(grayscale_np.astype(float))
+    
+    # Normalize gradients
+    grad_x = (grad_x - grad_x.min()) / (grad_x.max() - grad_x.min())
+    grad_y = (grad_y - grad_y.min()) / (grad_y.max() - grad_y.min())
+    
+    # Create normal map
+    normal_map = np.dstack((grad_x, grad_y, np.ones_like(grad_x)))
+    normal_map = (normal_map * 255).astype(np.uint8)
+    
+    return Image.fromarray(normal_map)
 
-def generate(
+def generate_image(
     prompt: str,
     negative_prompt: str = "",
     prompt_2: str = "",
@@ -47,7 +53,7 @@ def generate(
     pixel_size: int = 16
 ):
     client = Client("hysts/SDXL")
-    result = client.predict(
+    image = client.predict(
         prompt="((Seamless texture)), versatile pattern, high resolution, detailed design, subtle patterns, non-repetitive, smooth edges, square, "+prompt,
         negative_prompt=negative_prompt,
         prompt_2=prompt_2,
@@ -65,30 +71,8 @@ def generate(
         apply_refiner=apply_refiner,
         api_name="/run",
     )
-    image = pixelate(result, pixel_size)
-    return image
-
-def generate_normal_map(image):
-    # Convert image to grayscale
-    grayscale = image.convert("L")
-    grayscale_np = np.array(grayscale)
-    
-    # Compute gradients
-    grad_x, grad_y = np.gradient(grayscale_np.astype(float))
-    
-    # Normalize gradients
-    grad_x = (grad_x - grad_x.min()) / (grad_x.max() - grad_x.min())
-    grad_y = (grad_y - grad_y.min()) / (grad_y.max() - grad_y.min())
-    
-    # Create normal map
-    normal_map = np.dstack((grad_x, grad_y, np.ones_like(grad_x)))
-    normal_map = (normal_map * 255).astype(np.uint8)
-    
-    return Image.fromarray(normal_map)
-
-def compose(image):
     normal_map = generate_normal_map(image)
-    return normal_map
+    return image, normal_map
 
 examples = [
     "A texture of wooden planks, grey wood, high contrast",
@@ -198,8 +182,8 @@ with gr.Blocks(css="style.css") as demo:
     gr.Examples(
         examples=examples,
         inputs=prompt,
-        outputs=result_image,
-        fn=generate,
+        outputs=[result_image, result_normal],
+        fn=generate_image,
     )
 
     use_negative_prompt.change(
@@ -245,7 +229,7 @@ with gr.Blocks(css="style.css") as demo:
         queue=False,
         api_name=False,
     ).then(
-        fn=generate,
+        fn=generate_image,
         inputs=[
             prompt,
             negative_prompt,
@@ -264,13 +248,8 @@ with gr.Blocks(css="style.css") as demo:
             apply_refiner,
             pixel_size
         ],
-        outputs=result_image,
-        api_name="generate",
-    ).then(
-        fn=compose,
-        inputs=[result_image],
-        outputs=[result_normal],
-        api_name="compose",
+        outputs=[result_image, result_normal],
+        api_name="run",
     )
 
 if __name__ == "__main__":