Spaces:
Sleeping
Sleeping
File size: 3,216 Bytes
2ec2ebd d415ad5 fe9c201 d415ad5 740a0ae c3eb335 d415ad5 740a0ae 2ec2ebd e18f8f6 d415ad5 ba74c9e d415ad5 c3eb335 d415ad5 c3eb335 d415ad5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import gradio as gr
from diffusion_lens import get_images
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>Based on the paper <a href='https://arxiv.org/abs/2403.05846' target='_blank'>InstantStyle: 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):
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):
# 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
gallery = gr.Gallery(label="Generated Images", columns=6, rows=2, 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 cat, masterpiece, best quality, high quality",
)
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]
gr.on(
triggers=[
prompt.input,
generate_button.click,
guidance_scale.input,
scale.input,
control_scale.input,
seed.input,
],
fn=generate_images,
inputs=inputs,
outputs=outputs,
show_progress="full",
show_api=False,
trigger_mode="always_last",
)
gr.Markdown(article)
block.queue(api_open=False)
block.launch(show_api=False)
|