Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import torch
-from torchvision.transforms import ToPILImage, ToTensor
+from PIL import Image
+from io import BytesIO
 from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionUpscalePipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -19,16 +20,18 @@ output_image = gr.outputs.Image(type="filepath")
 # Define the function that will be called when the interface is used
 def upscale_image(model, image):
     # Convert the image to a PyTorch tensor
-    image_tensor = ToTensor()(image)
+    generator = torch.manual_seed(999999)
+    low_res_img = Image.open(low_res_img).convert("RGB")
+    low_res_latents = low_res_img
     
     # Upscale the image using the selected model
     if model == "SD 2.0 2x Latent Upscaler":
-        upscaled_tensor = sd_2_0_2x(image_tensor)
+        upscaled_image = sd_2_0_2x(image)
     else:
-        upscaled_tensor = sd_2_1_4x(image_tensor)
+        upscaled_image = sd_2_1_4x(image)
     
     # Convert the upscaled tensor back to a PIL image
-    upscaled_image = ToPILImage()(upscaled_tensor)
+     
     
     # Return the upscaled image
     return upscaled_image