app.py

import gradio as gr
from diffusion_lens import get_images
import numpy as np

MAX_SEED = np.iinfo(np.int32).max

# Description
title = r"""
<h1 align="center">Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines</h1>
"""

description = r"""
<b>A demo for the paper <a href='https://arxiv.org/abs/2403.05846' target='_blank'>Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines</a>.<br>
"""

article = r"""
---
📝 **Citation**
<br>
If our work is helpful for your research or applications, please cite us via:
```bibtex
@article{toker2024diffusion,
  title={Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines},
  author={Toker, Michael and Orgad, Hadas and Ventura, Mor and Arad, Dana and Belinkov, Yonatan},
  journal={arXiv preprint arXiv:2403.05846},
  year={2024}
}
```
📧 **Contact**
<br>
If you have any questions, please feel free to open an issue or directly reach us out at <b>tok@cs.technuin.ac.il</b>.
"""


model_num_of_layers = {
    'Stable Diffusion 1.4': 12,
    'Stable Diffusion 2.1': 22,
}

def generate_images(prompt, model, seed):
    seed = random.randint(0, MAX_SEED) if seed == -1 else seed
    print('calling diffusion lens with model:', model, 'and seed:', seed)
    gr.Info('Generating images from intermediate layers..')
    all_images = []  # Initialize a list to store all images
    max_num_of_layers = model_num_of_layers[model]
    for skip_layers in range(max_num_of_layers - 1, -1, -1):
        # Pass the model and seed to the get_images function
        images = get_images(prompt, skip_layers=skip_layers, model=model, seed=seed)
        all_images.append((images[0], f'layer_{12 - skip_layers}'))
        yield all_images

with gr.Blocks() as demo:
    
    gr.Markdown(title)
    gr.Markdown(description)
    
    # text_input = gr.Textbox(label="Enter prompt")
    # model_select = gr.Dropdown(label="Select Model", choices=['sd1', 'sd2'])
    # seed_input = gr.Number(label="Enter Seed", value=0)  # Default seed set to 0
    # Update the submit function to include the new inputs

    
    # text_input.submit(fn=generate_images, inputs=[text_input, model_select, seed_input], outputs=gallery)

    with gr.Column():
        prompt = gr.Textbox(
            label="Prompt",
            value="A photo of Steve Jobs",
        )

    model = gr.Radio(
        [
            "Stable Diffusion 1.4",
            "Stable Diffusion 2.1",
        ],
        value="Stable Diffusion 1.4",
        label="Model",
    )
    
    seed = gr.Slider(
        minimum=-1,
        maximum=MAX_SEED,
        value=-1,
        step=1,
        label="Seed Value",
    )

    inputs = [
        prompt,
        model,
        seed,
    ]


    generate_button = gr.Button("Generate Image")

    with gr.Column():
        gallery = gr.Gallery(label="Generated Images", columns=4, rows=3, object_fit="contain", height="auto")

    outputs = [gallery]

    gr.on(
        triggers=[
            # prompt.submit,
            generate_button.click,
            # seed.input,
            # model.input
        ],
        fn=generate_images,
        inputs=inputs,
        outputs=outputs,
        show_progress="full",
        show_api=False,
        trigger_mode="always_last",
        )

    gr.Markdown(article)

demo.queue(api_open=False)
demo.launch(show_api=False)