app.py

import torch
import gradio as gr
from diffusers import DiffusionPipeline
from utils import (
    attn_maps,
    cross_attn_init,
    init_pipeline,
    save_attention_maps
)
# from transformers.utils.hub import move_cache

# move_cache()

cross_attn_init()

pipe = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
)

pipe = init_pipeline(pipe)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pipe = pipe.to(device)


def inference(prompt):
    image = pipe(
        [prompt],
        num_inference_steps=15,
    ).images[0]

    total_attn_maps = save_attention_maps(attn_maps, pipe.tokenizer, [prompt])
    
    return image, total_attn_maps


with gr.Blocks() as demo:
    gr.Markdown(
        """
        # 🚀 Text-to-Image Cross Attention Map for 🧨 Diffusers ⚡
        """
    )
    prompt = gr.Textbox(value="A portrait photo of a kangaroo wearing an orange hoodie and blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says 'SDXL'!.", 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)