#!/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): preprocessor_name = gr.Radio( label="Preprocessor", choices=["ContentShuffle", "None"], type="value", value="ContentShuffle" ) 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, ) 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, 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="content-shuffle", ) return demo if __name__ == "__main__": from model import Model model = Model(task_name="shuffle") demo = create_demo(model.process_shuffle) demo.queue().launch()