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Running
on
T4
Running
on
T4
| #!/usr/bin/env python | |
| import gradio as gr | |
| from utils import randomize_seed_fn | |
| def create_demo(process, max_images=12, default_num_images=3): | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image() | |
| prompt = gr.Textbox(label='Prompt') | |
| run_button = gr.Button('Run') | |
| with gr.Accordion('Advanced options', open=False): | |
| num_samples = gr.Slider(label='Number of images', | |
| minimum=1, | |
| maximum=max_images, | |
| value=default_num_images, | |
| step=1) | |
| image_resolution = gr.Slider(label='Image resolution', | |
| minimum=256, | |
| maximum=512, | |
| value=512, | |
| step=256) | |
| preprocess_resolution = gr.Slider( | |
| label='Preprocess resolution', | |
| minimum=128, | |
| maximum=512, | |
| value=512, | |
| step=1) | |
| mlsd_value_threshold = gr.Slider( | |
| label='Hough value threshold (MLSD)', | |
| minimum=0.01, | |
| maximum=2.0, | |
| value=0.1, | |
| step=0.01) | |
| mlsd_distance_threshold = gr.Slider( | |
| label='Hough distance threshold (MLSD)', | |
| minimum=0.01, | |
| maximum=20.0, | |
| value=0.1, | |
| step=0.01) | |
| num_steps = gr.Slider(label='Number of steps', | |
| minimum=1, | |
| maximum=100, | |
| value=20, | |
| step=1) | |
| guidance_scale = gr.Slider(label='Guidance scale', | |
| minimum=0.1, | |
| maximum=30.0, | |
| value=9.0, | |
| step=0.1) | |
| seed = gr.Slider(label='Seed', | |
| minimum=0, | |
| maximum=1000000, | |
| step=1, | |
| value=0, | |
| randomize=True) | |
| randomize_seed = gr.Checkbox(label='Randomize seed', | |
| value=True) | |
| a_prompt = gr.Textbox( | |
| label='Additional prompt', | |
| value='best quality, extremely detailed') | |
| n_prompt = gr.Textbox( | |
| label='Negative prompt', | |
| value= | |
| 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality' | |
| ) | |
| with gr.Column(): | |
| result = gr.Gallery(label='Output', | |
| show_label=False, | |
| columns=2, | |
| object_fit='scale-down') | |
| inputs = [ | |
| image, | |
| prompt, | |
| a_prompt, | |
| n_prompt, | |
| num_samples, | |
| image_resolution, | |
| preprocess_resolution, | |
| num_steps, | |
| guidance_scale, | |
| seed, | |
| mlsd_value_threshold, | |
| mlsd_distance_threshold, | |
| ] | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| ).then( | |
| fn=process, | |
| inputs=inputs, | |
| outputs=result, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| ).then( | |
| fn=process, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name='mlsd', | |
| ) | |
| return demo | |
| if __name__ == '__main__': | |
| from model import Model | |
| model = Model(task_name='MLSD') | |
| demo = create_demo(model.process_mlsd) | |
| demo.queue().launch() | |