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import gradio as gr
import numpy as np
import time
# define core fn, which returns a generator {steps} times before returning the image
def fake_diffusion(steps):
for _ in range(steps):
time.sleep(1)
image = np.random.random((600, 600, 3))
yield image
image = np.ones((1000,1000,3), np.uint8)
image[:] = [255, 124, 0]
yield image
demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs="image")
# define queue - required for generators
demo.queue()
demo.launch()
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