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import gradio as gr
from PIL import Image
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
import io
pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
def generate_image(uploaded_image, surgery_option, additional_info):
if uploaded_image is not None:
pil_image = uploaded_image.convert("RGB")
init_image = load_image(pil_image).convert("RGB")
prompt = f"generate image of how this person would look after {surgery_option} also use this additional information {additional_info}"
image = pipeline(prompt, image=init_image).images[0]
return image
else:
return "No image uploaded."
surgery_options = ["Facelift", "Rhinoplasty", "Chin Augmentation"]
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Image(type="pil", label="Upload an Image"),
gr.Dropdown(surgery_options, label="Select Surgery Option"),
gr.Textbox(label="Additional Information")
],
outputs=gr.Image(type="pil", label="Generated Image"),
title="AI.HAIR - Version of Plastic Surgery by Bulut",
description="Visualize the results of a surgical procedure before it happens."
)
iface.launch()
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