Update app.py

app.py

@@ -54,17 +54,7 @@ controlnet = ControlNetModel.from_pretrained(
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
 )
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 
-# This command loads the individual model components on GPU on-demand. So, we don't
-# need to explicitly call pipe.to("cuda").
-pipe.enable_model_cpu_offload()
-
-# xformers
-pipe.enable_xformers_memory_efficient_attention()
-
-# Generator seed,
-generator = torch.manual_seed(0)
 
 def get_pose(image):
     return pose_model(image) 
@@ -74,29 +64,21 @@ def get_pose(image):
 def generate_an_image_from_text(text, text_size_, width, lenght):
   # Create a blank image
   image = Image.new('RGB', (width, lenght), color = (255, 255, 255))
-
   # Create a drawing object
   draw = ImageDraw.Draw(image)
-
   # font def 
   font_dir = '/usr/share/fonts/truetype/liberation/'
-
   # Get a list of all the font files in the directory
   font_files = [os.path.join(font_dir, f) for f in os.listdir(font_dir) if os.path.isfile(os.path.join(font_dir, f))]
-  
   # Select a random font
   font_path = random.choice(font_files)
-  print(font_path)
-
+  #print(font_path)
   font = ImageFont.truetype(font_path, text_size_)
-
   # Get the text size
   text_size = draw.textsize(text, font)
-
   # Calculate the x and y positions for the text
   x = (image.width - text_size[0]) / 2
   y = (image.height - text_size[1]) / 2
-
   # Draw the text on the image
   draw.text((x, y), text, fill=(0, 0, 0), font=font)
   return image
@@ -115,17 +97,20 @@ def to_Canny(image):
   return canny_image
 
 def inference(prompt,canny_image,number,seed ):
-  pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-  pipe.to("cuda")
-  #pipe.enable_model_cpu_offload()
-  pipe.enable_xformers_memory_efficient_attention()
-  generator = torch.manual_seed(seed)
-  
-  image_ = canny_image
-  prompt = prompt
-  out_image = pipe(
+    
+    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+    # This command loads the individual model components on GPU on-demand. So, we don't
+    # need to explicitly call pipe.to("cuda").
+    pipe.enable_model_cpu_offload()
+    # xformers
+    pipe.enable_xformers_memory_efficient_attention()
+    # Generator seed,
+    generator = torch.manual_seed(seed)
+    image_ = canny_image
+    prompt = prompt
+    out_image = pipe(
       prompt, num_inference_steps=20, generator=generator, image=image_, num_images_per_prompt=number)
-  return out_image
+    return out_image
     
 def generate_images(image, prompt):
     pose = get_pose(image)