AuraSR-v2 / app.py
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import gradio as gr
from gradio_imageslider import ImageSlider
from PIL import Image
import numpy as np
from aura_sr import AuraSR
import torch
import spaces
# Force CPU usage
torch.set_default_tensor_type(torch.FloatTensor)
# Override torch.load to always use CPU
original_load = torch.load
torch.load = lambda *args, **kwargs: original_load(*args, **kwargs, map_location=torch.device('cpu'))
# Initialize the AuraSR model
aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR")
# Restore original torch.load
torch.load = original_load
@spaces.GPU
def process_image(input_image):
if input_image is None:
return None
# Convert to PIL Image for resizing
pil_image = Image.fromarray(input_image)
# Upscale the image using AuraSR
with torch.no_grad():
upscaled_image = aura_sr.upscale_4x(pil_image)
# Convert result to numpy array if it's not already
result_array = np.array(upscaled_image)
return [input_image, result_array]
title = """<h1 align="center">AuraSR - An open reproduction of the GigaGAN Upscaler from fal.ai</h1>
<p><center>
<a href="https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/" target="_blank">[Blog Post]</a>
<a href="https://huggingface.co/fal-ai/AuraSR" target="_blank">[Model Page]</a>
</center></p>
"""
with gr.Blocks() as demo:
gr.HTML(title)
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(label="Input Image", type="numpy")
process_btn = gr.Button("Upscale Image")
with gr.Column(scale=1):
output_slider = ImageSlider(label="Before / After", type="numpy")
process_btn.click(
fn=process_image,
inputs=[input_image],
outputs=output_slider
)
# Add examples
gr.Examples(
examples=[
"image1.png",
"image3.png"
],
inputs=input_image,
outputs=output_slider,
fn=process_image,
cache_examples=True
)
demo.launch(debug=True)