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import os
import gradio as gr
from gradio_client import Client, handle_file
from gradio_imageslider import ImageSlider
tile_upscaler_url = "https://gokaygokay-tileupscalerv2.hf.space"
client_tile_upscaler = Client(tile_upscaler_url)
def upscale_image(image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name):
result = client_tile_upscaler.predict(
param_0=handle_file(image),
param_1=resolution,
param_2=num_inference_steps,
param_3=strength,
param_4=hdr,
param_5=guidance_scale,
param_6=controlnet_strength,
param_7=scheduler_name,
api_name="/wrapper"
)
return result
def clear_output(image_slider):
image_slider[0] = None
image_slider[1] = None
return image_slider
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image", type="filepath")
run_button = gr.Button("Enhance Image")
with gr.Column():
# output_image = gr.Image(label="Output Image")
output_slider = ImageSlider(label="Before / After")
with gr.Accordion("Advanced Options", open=False):
resolution = gr.Slider(minimum=128, maximum=2048, value=1024, step=128, label="Resolution")
num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
strength = gr.Slider(minimum=0, maximum=1, value=0.2, step=0.01, label="Strength")
hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
guidance_scale = gr.Slider(minimum=0, maximum=20, value=6, step=0.5, label="Guidance Scale")
controlnet_strength = gr.Slider(minimum=0.0, maximum=2.0, value=0.75, step=0.05, label="ControlNet Strength")
scheduler_name = gr.Dropdown(
choices=["DDIM", "DPM++ 3M SDE Karras", "DPM++ 3M Karras"],
value="DDIM",
label="Scheduler"
)
run_button.click(
fn=clear_output,
inputs=[output_slider],
outputs=[output_slider]
).then(
upscale_image,
[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale, controlnet_strength, scheduler_name],
output_slider
)
demo.launch()
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