Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import torch
 from diffusers import StableDiffusionXLImg2ImgPipeline
 from diffusers.utils import load_image
+#from diffusers import DiffusionPipeline
 '''
 pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
@@ -13,11 +14,14 @@ def run_fn(img_url):
     prompt = "a photo of an astronaut riding a horse on mars"
     image = pipe(prompt, image=init_image).images
     return image
-
+'''
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+#pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+
 pipe = pipe.to(device)
-'''
+
+
 def resize(value,img):
     img = Image.open(img)
     img = img.resize((value,value))