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| #!/usr/bin/env python | |
| import gradio as gr | |
| from settings import ( | |
| DEFAULT_IMAGE_RESOLUTION, | |
| DEFAULT_NUM_IMAGES, | |
| MAX_IMAGE_RESOLUTION, | |
| MAX_NUM_IMAGES, | |
| MAX_SEED, | |
| ) | |
| from utils import randomize_seed_fn | |
| def create_demo(process): | |
| 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_NUM_IMAGES, value=DEFAULT_NUM_IMAGES, step=1 | |
| ) | |
| image_resolution = gr.Slider( | |
| label="Image resolution", | |
| minimum=256, | |
| maximum=MAX_IMAGE_RESOLUTION, | |
| value=DEFAULT_IMAGE_RESOLUTION, | |
| 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=MAX_SEED, step=1, value=0) | |
| 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, | |
| api_name=False, | |
| ).then( | |
| fn=process, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name=False, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=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() | |