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from __future__ import annotations
import gradio as gr
import nltk
import numpy as np
from PIL import Image
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
from main import LPMConfig, main, setup
DESCRIPTION = '''# Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
This is a demo for our ''Localizing Object-level Shape Variations with Text-to-Image Diffusion Models'' [paper](https://arxiv.org/abs/2303.11306). Code is available [here](https://github.com/orpatashnik/local-prompt-mixing)
We introduce a method that generates object-level shape variation for a given image.
This demo supports both generated images and real images. To modify a real image, please upload it to the input image block and provide a prompt that describes its contents.
'''
stable, stable_config = setup(LPMConfig())
def main_pipeline(
prompt: str,
object_of_interest: str,
proxy_words: str,
number_of_variations: int,
start_prompt_range: int,
end_prompt_range: int,
objects_to_preserve: str,
background_nouns: str,
seed: int,
input_image: str):
prompt = prompt.replace(object_of_interest, '{word}')
proxy_words = proxy_words.split(',') if proxy_words != '' else []
objects_to_preserve = objects_to_preserve.split(',') if objects_to_preserve != '' else []
background_nouns = background_nouns.split(',') if background_nouns != '' else []
args = LPMConfig(
seed=seed,
prompt=prompt,
object_of_interest=object_of_interest,
proxy_words=proxy_words,
number_of_variations=number_of_variations,
start_prompt_range=start_prompt_range,
end_prompt_range=end_prompt_range,
objects_to_preserve=objects_to_preserve,
background_nouns=background_nouns,
real_image_path="" if input_image is None else input_image
)
result_images, result_proxy_words = main(stable, stable_config, args)
result_images = [im.permute(1, 2, 0).cpu().numpy() for im in result_images]
result_images = [(im * 255).astype(np.uint8) for im in result_images]
result_images = [Image.fromarray(im) for im in result_images]
return result_images, ",".join(result_proxy_words)
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
gr.HTML(
'''<a href="https://huggingface.co/spaces/orpatashnik/local-prompt-mixing?duplicate=true">
<img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to run privately without waiting in queue''')
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Input image (optional)",
type="filepath"
)
prompt = gr.Text(
label='Prompt',
max_lines=1,
placeholder='A table below a lamp',
)
object_of_interest = gr.Text(
label='Object of interest',
max_lines=1,
placeholder='lamp',
)
proxy_words = gr.Text(
label='Proxy words - words used to obtain variations (a comma-separated list of words, can leave empty)',
max_lines=1,
placeholder=''
)
number_of_variations = gr.Slider(
label='Number of variations (used only for automatic proxy-words)',
minimum=2,
maximum=30,
value=7,
step=1
)
start_prompt_range = gr.Slider(
label='Number of steps before starting shape interval',
minimum=0,
maximum=50,
value=7,
step=1
)
end_prompt_range = gr.Slider(
label='Number of steps before ending shape interval',
minimum=1,
maximum=50,
value=17,
step=1
)
objects_to_preserve = gr.Text(
label='Words corresponding to objects to preserve (a comma-separated list of words, can leave empty)',
max_lines=1,
placeholder='table',
)
background_nouns = gr.Text(
label='Words corresponding to objects that should be copied from original image (a comma-separated list of words, can leave empty)',
max_lines=1,
placeholder='',
)
seed = gr.Slider(
label='Seed',
minimum=0,
maximum=100000,
value=0,
step=1
)
run_button = gr.Button('Generate')
with gr.Column():
result = gr.Gallery(label='Result').style(grid=4)
proxy_words_result = gr.Text(label='Used proxy words')
examples = [
[
"hamster eating watermelon on the beach",
"watermelon",
"",
7,
6,
16,
"",
"hamster,beach",
48,
None
],
[
"A decorated lamp in the livingroom",
"lamp",
"",
7,
4,
14,
"livingroom",
"",
42,
None
],
[
"a snake in the field eats an apple",
"snake",
"",
7,
7,
17,
"apple",
"apple,field",
10,
None
]
]
gr.Examples(examples=examples,
inputs=[
prompt,
object_of_interest,
proxy_words,
number_of_variations,
start_prompt_range,
end_prompt_range,
objects_to_preserve,
background_nouns,
seed,
input_image
],
outputs=[
result,
proxy_words_result
],
fn=main_pipeline,
cache_examples=True)
inputs = [
prompt,
object_of_interest,
proxy_words,
number_of_variations,
start_prompt_range,
end_prompt_range,
objects_to_preserve,
background_nouns,
seed,
input_image
]
outputs = [
result,
proxy_words_result
]
run_button.click(fn=main_pipeline, inputs=inputs, outputs=outputs)
demo.queue(max_size=50).launch(share=False) |