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Running
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Zero
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#!/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]:
model_id = 'CompVis/stable-diffusion-v1-4'
num_steps = 50
guidance_scale = 7.5
token_table = model.get_token_table(model_id, prompt)
result = model.run(model_id, 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():
model_id = gr.Text(label='Model ID',
value='CompVis/stable-diffusion-v1-4',
visible=False)
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=[model_id, prompt],
outputs=token_indices_table,
queue=False,
)
inputs = [
model_id,
prompt,
token_indices_str,
seed,
apply_attend_and_excite,
num_steps,
guidance_scale,
]
prompt.submit(
fn=model.get_token_table,
inputs=[model_id, 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=[model_id, prompt],
outputs=token_indices_table,
queue=False,
).then(
fn=model.run,
inputs=inputs,
outputs=result,
)
run_button.click(
fn=model.get_token_table,
inputs=[model_id, prompt],
outputs=token_indices_table,
queue=False,
).then(
fn=model.run,
inputs=inputs,
outputs=result,
api_name='run',
)
demo.queue(max_size=10).launch()
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