Update app.py

app.py

@@ -31,14 +31,42 @@ pipe = StableDiffusion3CommonPipeline.from_pretrained(
 )
 pipe.to('cuda:0', torch.float16)
 
+def resize_image(input_path, output_path, target_height):
+    # Open the input image
+    img = Image.open(input_path)
+
+    # Calculate the aspect ratio of the original image
+    original_width, original_height = img.size
+    original_aspect_ratio = original_width / original_height
+
+    # Calculate the new width while maintaining the aspect ratio and the target height
+    new_width = int(target_height * original_aspect_ratio)
+
+    # Resize the image while maintaining the aspect ratio and fixing the height
+    img = img.resize((new_width, target_height), Image.LANCZOS)
+
+    # Save the resized image
+    img.save(output_path)
+
+    return output_path
+
 def infer(image_in, prompt):
     prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
     n_prompt = 'NSFW, nude, naked, porn, ugly'
+
+    image_to_canny = load_image(image_in)
+
+    image_to_canny = np.array(image_to_canny)
+    image_to_canny = cv2.Canny(image_to_canny, 100, 200)
+    image_to_canny = image_to_canny[:, :, None]
+    image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
+    image_to_canny = Image.fromarray(image_to_canny)
+    
     # controlnet config
     controlnet_conditioning = [
         dict(
             control_index=0,
-            control_image=image_in,
+            control_image=image_to_canny,
             control_weight=0.7,
             control_pooled_projections='zeros'
         )