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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
with gr.Column():
gallery = gr.Gallery(label="Generated Images", columns=4, rows=3, object_fit="contain", height="auto")
# 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,
]
outputs = [gallery]
generate_button = gr.Button("Generate Image")
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)
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