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import sys
from pathlib import Path
from typing import Optional
import gradio as gr
from PIL import Image
from appearance_transfer_model import AppearanceTransferModel
from run import run_appearance_transfer
from utils.latent_utils import load_latents_or_invert_images
from utils.model_utils import get_stable_diffusion_model
sys.path.append(".")
sys.path.append("..")
from config import RunConfig
DESCRIPTION = '''
<h1 style="text-align: center;"> Cross-Image Attention for Zero-Shot Appearance Transfer </h1>
<p style="text-align: center;">
This is a demo for our <a href="https://arxiv.org/abs/2311.03335">paper</a>:
''Cross-Image Attention for Zero-Shot Appearance Transfer''.
<br>
Given two images depicting a source structure and a target appearance, our method generates an image merging
the structure of one image with the appearance of the other.
<br>
We do so in a zero-shot manner, with no optimization or model training required while supporting appearance
transfer across images that may differ in size and shape.
</p>
'''
pipe = get_stable_diffusion_model()
def main_pipeline(app_image_path: str,
struct_image_path: str,
domain_name: str,
seed: int,
prompt: Optional[str] = None) -> Image.Image:
if prompt == "":
prompt = None
config = RunConfig(
app_image_path=Path(app_image_path),
struct_image_path=Path(struct_image_path),
domain_name=domain_name,
prompt=prompt,
seed=seed,
load_latents=False
)
model = AppearanceTransferModel(config=config, pipe=pipe)
latents_app, latents_struct, noise_app, noise_struct = load_latents_or_invert_images(model=model, cfg=config)
model.set_latents(latents_app, latents_struct)
model.set_noise(noise_app, noise_struct)
print("Running appearance transfer...")
images = run_appearance_transfer(model=model, cfg=config)
print("Done.")
return [images[0]]
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
gr.HTML('''<a href="https://huggingface.co./spaces/yuvalalaluf/cross-image-attention?duplicate=true"><img src="https://bit.ly/3gLdBN6"
alt="Duplicate Space"></a>''')
with gr.Row():
with gr.Column():
app_image_path = gr.Image(label="Upload appearance image", type="filepath")
struct_image_path = gr.Image(label="Upload structure image", type="filepath")
domain_name = gr.Text(label="Domain name", max_lines=1,
info="Specifies the domain the objects are coming from (e.g., 'animal', 'building', etc).")
prompt = gr.Text(label="Prompt to use for inversion.", value='',
info='If this kept empty, we will use the domain name to define '
'the prompt as "A photo of a <domain_name>".')
random_seed = gr.Number(value=42, label="Random seed", precision=0)
run_button = gr.Button('Generate')
with gr.Column():
result = gr.Gallery(label='Result')
inputs = [app_image_path, struct_image_path, domain_name, random_seed, prompt]
outputs = [result]
run_button.click(fn=main_pipeline, inputs=inputs, outputs=outputs)
with gr.Row():
examples = [
['inputs/zebra.png', 'inputs/giraffe.png', 'animal', 20, None],
['inputs/taj_mahal.jpg', 'inputs/duomo.png', 'building', 42, None],
['inputs/red_velvet_cake.jpg', 'inputs/chocolate_cake.jpg', 'cake', 42, 'A photo of cake'],
]
gr.Examples(examples=examples,
inputs=[app_image_path, struct_image_path, domain_name, random_seed, prompt],
outputs=[result],
fn=main_pipeline,
cache_examples=True)
demo.launch(share=False, server_name="127.0.0.1", server_port=8888)
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