app.py

import torch
import gradio as gr
from diffusers import StableDiffusionXLPipeline
from utils import (
    cross_attn_init,
    register_cross_attention_hook,
    attn_maps,
    get_net_attn_map,
    resize_net_attn_map,
    return_net_attn_map,
)

cross_attn_init()
pipe = StableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
)
pipe.unet = register_cross_attention_hook(pipe.unet)
pipe = pipe.to("cuda")


def inference(prompt):
    image = pipe(prompt).images[0]
    net_attn_maps = get_net_attn_map(image.size)
    net_attn_maps = resize_net_attn_map(net_attn_maps, image.size)
    net_attn_maps = return_net_attn_map(net_attn_maps, pipe.tokenizer, prompt)
    
    return image, net_attn_maps


with gr.Blocks() as demo:
    gr.Markdown(
    """
    🚀 Text-to-Image Cross Attention Map for 🧨 Diffusers ⚡
    """)
    prompt = gr.Textbox(value="A photo of a black puppy, christmas atmosphere", label="Prompt", lines=2)
    btn = gr.Button("Generate images", scale=0)
    
    with gr.Row():
        image = gr.Image(height=512,width=512,type="pil")
        gallery = gr.Gallery(
        value=None,
        label="Generated images", show_label=False, elem_id="gallery",
        object_fit="contain", height="auto")
    

    btn.click(inference, prompt, [image, gallery])

if __name__ == "__main__":
    demo.launch(share=True)