app.py

#!/usr/bin/env python

from __future__ import annotations

import os

import gradio as gr
import PIL.Image

from model import Model

DESCRIPTION = '''# Attend-and-Excite
This is a demo for [Attend-and-Excite](https://arxiv.org/abs/2301.13826).
Attend-and-Excite performs attention-based generative semantic guidance to mitigate subject neglect in Stable Diffusion.
Select a prompt and a set of indices matching the subjects you wish to strengthen (the `Check token indices` cell can help map between a word and its index).
'''

model = Model()


def process_example(
    prompt: str,
    indices_to_alter_str: str,
    seed: int,
    apply_attend_and_excite: bool,
) -> tuple[list[tuple[int, str]], PIL.Image.Image]:
    num_steps = 50
    guidance_scale = 7.5

    token_table = model.get_token_table(prompt)
    result = model.run(prompt, indices_to_alter_str, seed,
                       apply_attend_and_excite, num_steps, guidance_scale)
    return token_table, result


with gr.Blocks(css='style.css') as demo:
    gr.Markdown(DESCRIPTION)

    with gr.Row():
        with gr.Column():
            prompt = gr.Text(
                label='Prompt',
                max_lines=1,
                placeholder=
                'A pod of dolphins leaping out of the water in an ocean with a ship on the background'
            )
            with gr.Accordion(label='Check token indices', open=False):
                show_token_indices_button = gr.Button('Show token indices')
                token_indices_table = gr.Dataframe(label='Token indices',
                                                   headers=['Index', 'Token'],
                                                   col_count=2)
            token_indices_str = gr.Text(
                label=
                'Token indices (a comma-separated list indices of the tokens you wish to alter)',
                max_lines=1,
                placeholder='4,16')
            seed = gr.Slider(label='Seed',
                             minimum=0,
                             maximum=100000,
                             value=0,
                             step=1)
            apply_attend_and_excite = gr.Checkbox(
                label='Apply Attend-and-Excite', value=True)
            num_steps = gr.Slider(label='Number of steps',
                                  minimum=0,
                                  maximum=100,
                                  step=1,
                                  value=50)
            guidance_scale = gr.Slider(label='CFG scale',
                                       minimum=0,
                                       maximum=50,
                                       step=0.1,
                                       value=7.5)
            run_button = gr.Button('Generate')
        with gr.Column():
            result = gr.Image(label='Result')

    with gr.Row():
        examples = [
            [
                'A mouse and a red car',
                '2,6',
                2098,
                True,
            ],
            [
                'A mouse and a red car',
                '2,6',
                2098,
                False,
            ],
            [
                'A horse and a dog',
                '2,5',
                123,
                True,
            ],
            [
                'A horse and a dog',
                '2,5',
                123,
                False,
            ],
            [
                'A painting of an elephant with glasses',
                '5,7',
                123,
                True,
            ],
            [
                'A painting of an elephant with glasses',
                '5,7',
                123,
                False,
            ],
            [
                'A playful kitten chasing a butterfly in a wildflower meadow',
                '3,6,10',
                123,
                True,
            ],
            [
                'A playful kitten chasing a butterfly in a wildflower meadow',
                '3,6,10',
                123,
                False,
            ],
            [
                'A grizzly bear catching a salmon in a crystal clear river surrounded by a forest',
                '2,6,15',
                123,
                True,
            ],
            [
                'A grizzly bear catching a salmon in a crystal clear river surrounded by a forest',
                '2,6,15',
                123,
                False,
            ],
            [
                'A pod of dolphins leaping out of the water in an ocean with a ship on the background',
                '4,16',
                123,
                True,
            ],
            [
                'A pod of dolphins leaping out of the water in an ocean with a ship on the background',
                '4,16',
                123,
                False,
            ],
        ]
        gr.Examples(examples=examples,
                    inputs=[
                        prompt,
                        token_indices_str,
                        seed,
                        apply_attend_and_excite,
                    ],
                    outputs=[
                        token_indices_table,
                        result,
                    ],
                    fn=process_example,
                    cache_examples=os.getenv('CACHE_EXAMPLES') == '1',
                    examples_per_page=20)

    show_token_indices_button.click(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
    )

    inputs = [
        prompt,
        token_indices_str,
        seed,
        apply_attend_and_excite,
        num_steps,
        guidance_scale,
    ]
    prompt.submit(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
    )
    token_indices_str.submit(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
    )
    run_button.click(
        fn=model.get_token_table,
        inputs=prompt,
        outputs=token_indices_table,
        queue=False,
    ).then(
        fn=model.run,
        inputs=inputs,
        outputs=result,
        api_name='run',
    )

demo.queue(max_size=10).launch()