Update app.py

app.py

@@ -6,7 +6,7 @@ import random
 import spaces
 import gradio as gr
 import torch
-from PIL import Image, ImageOps
+from PIL import Image
 from diffusers import StableDiffusionInstructPix2PixPipeline
 from huggingface_hub import InferenceClient
 
@@ -45,31 +45,6 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
         seed = random.randint(0, 999999)
     return seed
 
-def resize_image(image, output_size=(512, 512)):
-    # Calculate aspect ratios
-    target_aspect = output_size[0] / output_size[1]  # Aspect ratio of the desired size
-    image_aspect = image.width / image.height  # Aspect ratio of the original image
-
-    # Resize then crop if the original image is larger
-    if image_aspect > target_aspect:
-        new_height = output_size[1]
-        new_width = int(new_height * image_aspect)
-        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        left = (new_width - output_size[0]) / 2
-        top = 0
-        right = (new_width + output_size[0]) / 2
-        bottom = output_size[1]
-    else:
-        new_width = output_size[0]
-        new_height = int(new_width / image_aspect)
-        resized_image = image.resize((new_width, new_height), Image.LANCZOS)
-        left = 0
-        top = (new_height - output_size[1]) / 2
-        right = output_size[0]
-        bottom = (new_height + output_size[1]) / 2
-    cropped_image = resized_image.crop((left, top, right, bottom))
-    return cropped_image
-
 pipe2 = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
 
 @spaces.GPU(duration=30, queue=False)