Update app.py

app.py

@@ -4,7 +4,7 @@ import torch
 from diffusers import AutoPipelineForInpainting, UNet2DConditionModel
 import diffusers
 from share_btn import community_icon_html, loading_icon_html, share_js
-from PIL import Image
+
 
 pipe = AutoPipelineForInpainting.from_pretrained("SG161222/Realistic_Vision_V5.0_noVAE").to("cuda")
 
@@ -17,7 +17,6 @@ def read_content(file_path: str) -> str:
     return content
 
 def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, strength=1.0, scheduler="EulerDiscreteScheduler"):
-    original_width, original_height = Image.open(dict["image"]).shape[1:]
     if negative_prompt == "":
         negative_prompt = None
     scheduler_class_name = scheduler.split("-")[0]
@@ -36,7 +35,7 @@ def predict(dict, prompt="", negative_prompt="", guidance_scale=7.5, steps=20, s
     
     output = pipe(prompt = prompt, negative_prompt=negative_prompt, image=init_image, mask_image=mask, guidance_scale=guidance_scale, num_inference_steps=int(steps), strength=strength, clip_skip=1)
     output_image = output.images[0]  # Assuming the output dictionary has an 'images' key
-    output_image = F.interpolate(output_image.unsqueeze(0), size=(original_height, original_width))[0]
+    output_image = F.interpolate(output_image.unsqueeze(0), size=(1024,832))[0]
     return output.images, gr.update(visible=True)