Update app.py

resize to 1024x1024
conditioning scale to 1.0

app.py

@@ -5,13 +5,14 @@ import torch
 import numpy as np
 import cv2
 import gradio as gr
+from torchvision import transforms 
 # from huggingface_hub import login
 # login()
 
-controlnet_conditioning_scale = 0.5  # recommended for good generalization
+controlnet_conditioning_scale = 1.0 
 
 controlnet = ControlNetModel.from_pretrained(
-    "briaai/ControlNet-Canny", # "diffusers/controlnet-canny-sdxl-1.0", # 
+    "briaai/ControlNet-Canny",
     torch_dtype=torch.float16
 )
 
@@ -25,6 +26,15 @@ pipe.enable_model_cpu_offload()
 low_threshold = 100
 high_threshold = 200
 
+def resize_image(image):
+    current_size = image.size
+    if current_size[0] > current_size[1]:
+        center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
+    else:
+        center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
+    resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
+    return resized_image
+
 def get_canny_filter(image):
     
     if not isinstance(image, np.ndarray):
@@ -37,6 +47,9 @@ def get_canny_filter(image):
     return canny_image
 
 def process(input_image, prompt):
+    # resize input_image to 1024x1024
+    input_image = resize_image(input_image)
+    
     canny_image = get_canny_filter(input_image)
     images = pipe(
         prompt,image=canny_image, controlnet_conditioning_scale=controlnet_conditioning_scale,