bug fix

app.py

@@ -2,7 +2,7 @@ import spaces
 import gradio as gr
 import numpy as np
 import random
-import PIL.Image
+from PIL import Image
 import torch
 from diffusers import (
     ControlNetModel,
@@ -58,13 +58,14 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
 
 
 def get_depth_map(image):
+    original_size = (image.size[1], image.size[0]) 
     image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
     with torch.no_grad(), torch.autocast("cuda"):
         depth_map = depth_estimator(image).predicted_depth
-    size = (image.shape[-2], image.shape[-1])
+    print("get_depth_map", original_size)
     depth_map = torch.nn.functional.interpolate(
         depth_map.unsqueeze(1),
-        size=size,
+        size=original_size,
         mode="bicubic",
         align_corners=False,
     )
@@ -79,18 +80,20 @@ def get_depth_map(image):
 
 
 @spaces.GPU(enable_queue=True)
-def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, control_strength, seed):
+def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, control_strength, seed, progress=gr.Progress()):
 
     if image_url:
         orginal_image = load_image(image_url)
     else:
-        orginal_image = PIL.Image.fromarray(image)
+        orginal_image = Image.fromarray(image)
 
     size = (orginal_image.size[0], orginal_image.size[1])
+    print(size)
     depth_image = get_depth_map(orginal_image)
     generator = torch.Generator().manual_seed(seed)
     generated_image = pipe(
         prompt=prompt,
+        image=orginal_image,
         negative_prompt=n_prompt,
         width=size[0],
         height=size[1],
@@ -98,7 +101,7 @@ def process(image, image_url, prompt, n_prompt, num_steps, guidance_scale, contr
         num_inference_steps=num_steps,
         strength=control_strength,
         generator=generator,
-        image=depth_image,
+        control_image=depth_image
     ).images[0]
     return [[depth_image, generated_image], "ok"]