| | |
| |
|
| | 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): |
| | preprocessor_name = gr.Radio(label='Preprocessor', |
| | choices=[ |
| | 'HED', |
| | 'PidiNet', |
| | 'HED safe', |
| | 'PidiNet safe', |
| | 'None', |
| | ], |
| | type='value', |
| | value='PidiNet') |
| | 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) |
| | 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, |
| | preprocessor_name, |
| | ] |
| | 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='softedge', |
| | ) |
| | return demo |
| |
|
| |
|
| | if __name__ == '__main__': |
| | from model import Model |
| | model = Model(task_name='softedge') |
| | demo = create_demo(model.process_softedge) |
| | demo.queue().launch() |
| |
|
