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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 = "https://i.picsum.photos/id/867/600/600.jpg?hmac=qE7QFJwLmlE_WKI7zMH6SgH5iY5fx8ec6ZJQBwKRT44" | |
| yield image | |
| demo = gr.Interface(fake_diffusion, | |
| inputs=gr.Slider(1, 10, 3), | |
| outputs="image") | |
| # define queue - required for generators | |
| demo.queue() | |
| demo.launch() |