Update app.py

app.py

@@ -10,6 +10,27 @@ import gradio as gr
 from torchvision import transforms 
 from controlnet_aux import OpenposeDetector
 
+ratios_map =  {
+    0.5:{"width":704,"height":1408},
+    0.57:{"width":768,"height":1344},
+    0.68:{"width":832,"height":1216},
+    0.72:{"width":832,"height":1152},
+    0.78:{"width":896,"height":1152},
+    0.82:{"width":896,"height":1088},
+    0.88:{"width":960,"height":1088},
+    0.94:{"width":960,"height":1024},
+    1.00:{"width":1024,"height":1024},
+    1.13:{"width":1088,"height":960},
+    1.21:{"width":1088,"height":896},
+    1.29:{"width":1152,"height":896},
+    1.38:{"width":1152,"height":832},
+    1.46:{"width":1216,"height":832},
+    1.67:{"width":1280,"height":768},
+    1.75:{"width":1344,"height":768},
+    2.00:{"width":1408,"height":704}
+}
+ratios = np.array(list(ratios_map.keys()))
+
 
 openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
 
@@ -37,7 +58,22 @@ pipe.scheduler = EulerAncestralDiscreteScheduler(
 # pipe.enable_xformers_memory_efficient_attention()
 pipe.force_zeros_for_empty_prompt = False
 
+def get_size(init_image):
+    w,h=init_image.size
+    curr_ratio = w/h
+    ind = np.argmin(np.abs(curr_ratio-ratios))
+    ratio = ratios[ind]
+    chosen_ratio  = ratios_map[ratio]
+    w,h = chosen_ratio['width'], chosen_ratio['height']
+    return w,h
+
 def resize_image(image):
+    image = image.convert('RGB')
+    w,h = get_size(image)
+    resized_image = image.resize((w, h))
+    return resized_image
+    
+def resize_image_old(image):
     image = image.convert('RGB')
     current_size = image.size
     if current_size[0] > current_size[1]: