Update app.py

app.py

@@ -7,12 +7,10 @@ import random
 
 import gradio as gr
 import numpy as np
+import PIL.Image
 import spaces
-import requests
 import torch
-import PIL.Image
-from io import BytesIO
-from diffusers import StableDiffusionImg2ImgPipeline, AutoencoderKL, DiffusionPipeline
+from diffusers import AutoencoderKL, DiffusionPipeline
 
 DESCRIPTION = "# SDXL"
 if not torch.cuda.is_available():
@@ -58,12 +56,11 @@ def generate(
     vaecall = 'stabilityai/sd-vae-ft-mse',
     lora = 'amazonaws-la/juliette',
     lora_scale: float = 0.7,
-    url = "https://m.media-amazon.com/images/I/81zPcrN6m+L.jpg",
 ) -> PIL.Image.Image:
     if torch.cuda.is_available():
-        
-        if not use_vae:
-            pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model, torch_dtype=torch.float16)
+
+        if not use_vae:  
+            pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
             
         if use_vae:
             vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
@@ -72,7 +69,7 @@ def generate(
         if use_lora:
             pipe.load_lora_weights(lora)
             pipe.fuse_lora(lora_scale=0.7)
-
+                                   
         if ENABLE_CPU_OFFLOAD:
             pipe.enable_model_cpu_offload()
             
@@ -102,7 +99,6 @@ def generate(
             guidance_scale=guidance_scale_base,
             num_inference_steps=num_inference_steps_base,
             generator=generator,
-            image=url,
             output_type="pil",
         ).images[0]
     else: