Update app.py

app.py

@@ -1,10 +1,8 @@
 import gradio as gr
 import torch
 from diffusers import AutoPipelineForImage2Image
-from diffusers.utils import load_image, make_image_grid
+from diffusers.utils import make_image_grid
 from PIL import Image
-import requests
-from io import BytesIO
 
 # Load the pipeline
 pipeline = AutoPipelineForImage2Image.from_pretrained(
@@ -17,14 +15,8 @@ pipeline = AutoPipelineForImage2Image.from_pretrained(
 # Offload model to reduce memory usage
 pipeline.enable_model_cpu_offload()
 
-# Function to load the initial image from a URL
-def load_init_image(url):
-    response = requests.get(url)
-    return Image.open(BytesIO(response.content))
-
 # Gradio function for image generation
-def generate_image(prompt, image_url, strength):
-    init_image = load_init_image(image_url)  # Load the initial image from the URL
+def generate_image(prompt, init_image, strength):
     result_image = pipeline(prompt, image=init_image, strength=strength).images[0]
     
     # Display both the initial and result images side by side
@@ -36,10 +28,10 @@ gr.Interface(
     fn=generate_image,
     inputs=[
         gr.Textbox(lines=1, label="Prompt", placeholder="Enter the image description prompt"),
-        gr.Textbox(lines=1, label="Image URL", placeholder="Enter the URL of the initial image"),
+        gr.Image(type="pil", label="Upload Initial Image"),  # Image input instead of URL
         gr.Slider(0.0, 1.0, value=0.5, label="Strength"),
     ],
     outputs=gr.Image(label="Image Comparison"),
     title="Stable Diffusion XL Refiner - Image to Image",
-    description="Generate an image transformation from an initial image and a text prompt using the Stable Diffusion XL Refiner model.",
+    description="Upload an initial image and provide a text prompt to generate a new image using the Stable Diffusion XL Refiner model.",
 ).launch()