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| import sys | |
| from pathlib import Path | |
| from typing import Optional | |
| import gradio as gr | |
| from PIL import Image | |
| from diffusers.training_utils import set_seed | |
| 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 | |
| ) | |
| print(config) | |
| set_seed(config.seed) | |
| 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.queue(max_size=50).launch(share=False) |