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import gradio as gr
import spaces
from gradio_imageslider import ImageSlider
from image_gen_aux import UpscaleWithModel
from image_gen_aux.utils import load_image
MODELS = {
"UltraSharp": "Kim2091/UltraSharp",
"DAT X4": "OzzyGT/DAT_X4",
"DAT X3": "OzzyGT/DAT_X3",
"DAT X2": "OzzyGT/DAT_X2",
"RealPLKSR X4": "OzzyGT/4xNomosWebPhoto_RealPLKSR",
"DAT-2 RealWebPhoto X4": "Phips/4xRealWebPhoto_v4_dat2",
}
@spaces.GPU
def upscale_image(image, model_selection):
original = load_image(image)
upscaler = UpscaleWithModel.from_pretrained(MODELS[model_selection]).to("cuda")
image = upscaler(original, tiling=True, tile_width=1024, tile_height=1024)
return original, image
def clear_result():
return gr.update(value=None)
title = """<h1 align="center">Image Upscaler</h1>
<div align="center">This space is a showcase of the different super resolution models you can use to upscale with the
<a href="https://github.com/asomoza/image_gen_aux">Image Generation Auxiliary Tools</a> library.</div>
"""
with gr.Blocks() as demo:
gr.HTML(title)
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Input Image")
model_selection = gr.Dropdown(
choices=list(MODELS.keys()),
value="UltraSharp",
label="Model",
)
run_button = gr.Button("Upscale")
with gr.Column():
result = ImageSlider(
interactive=False,
label="Generated Image",
)
run_button.click(
fn=clear_result,
inputs=None,
outputs=result,
).then(
fn=upscale_image,
inputs=[input_image, model_selection],
outputs=result,
)
demo.launch(share=False)
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