| import gradio as gr | |
| import numpy as np | |
| import time | |
| def fake_diffusion(input_img, steps): | |
| for i 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.Image(=type="filepath"), gr.Slider(1, 10, 3)], outputs="image") | |
| if __name__ == "__main__": | |
| demo.launch() |