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import spaces
import gradio as gr
from inference_utils import inference
@spaces.GPU
def send_to_model(id_image, makeup_image, guidance_scale):
if guidance_scale is None:
# when creating example caches.
guidance_scale = 1.6
return inference(id_image, makeup_image, guidance_scale, size=512)
if __name__ == "__main__":
with gr.Blocks() as demo:
gr.Interface(
fn=send_to_model,
inputs=[
gr.Image(type="pil", label="id_image", height=512, width=512),
gr.Image(type="pil", label="makeup_image", height=512, width=512),
gr.Slider(minimum=1.01, maximum=3, value=1.6, step=0.05, label="guidance_scale", info="1.05-1.15 is suggested for light makeup and 2 for heavy makeup."),
],
outputs="image",
allow_flagging="never",
description="FACE: AI Makeup",
)
demo.queue(max_size=10).launch()
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