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from __future__ import annotations | |
import math | |
import random | |
import gradio as gr | |
import torch | |
from PIL import Image, ImageOps | |
from diffusers import StableDiffusionSAGPipeline | |
help_text = """ | |
""" | |
examples = [ | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
8367, | |
3.0, | |
1.0, | |
], | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
65911, | |
3.0, | |
1.0, | |
], | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
98184, | |
3.0, | |
1.0, | |
], | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
33784, | |
3.0, | |
1.0, | |
], | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
74545, | |
3.0, | |
1.0, | |
], | |
[ | |
' ', | |
50, | |
"Fix Seed", | |
8393, | |
3.0, | |
1.0, | |
], | |
[ | |
'.', | |
50, | |
"Fix Seed", | |
24865, | |
3.0, | |
1.0, | |
], | |
[ | |
'A poster', | |
50, | |
"Fix Seed", | |
37956, | |
3.0, | |
1.0, | |
], | |
[ | |
'A high-quality living room', | |
50, | |
"Fix Seed", | |
78710, | |
3.0, | |
1.0, | |
], | |
[ | |
'A Scottish Fold playing with a ball', | |
50, | |
"Fix Seed", | |
11511, | |
3.0, | |
1.0, | |
], | |
] | |
model_id = "runwayml/stable-diffusion-v1-5" | |
def main(): | |
pipe = StableDiffusionSAGPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to('cuda') | |
def generate( | |
prompt: str, | |
steps: int, | |
randomize_seed: bool, | |
seed: int, | |
cfg_scale: float, | |
sag_scale: float, | |
): | |
seed = random.randint(0, 100000) if randomize_seed else seed | |
generator = torch.manual_seed(seed) | |
ori_image = pipe(prompt, generator=generator, num_inference_steps=steps, guidance_scale=cfg_scale, sag_scale=0.0).images[0] | |
generator = torch.manual_seed(seed) | |
sag_image = pipe(prompt, generator=generator, num_inference_steps=steps, guidance_scale=cfg_scale, sag_scale=sag_scale).images[0] | |
return [ori_image, sag_image, seed] | |
def reset(): | |
return [50, "Randomize Seed", 90061, 3.0, 1.0, None, None] | |
with gr.Blocks() as demo: | |
gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 10px;"> | |
Self-Attention Guidance Demo | |
</h1> | |
<p>Condition-agnostic diffusion guidance using the internal self-attention by Susung Hong.<p> | |
<p>SAG also produces fine <b>unconditional</b> results. Just leave the prompt blank for the unconditional sampling of Stable Diffusion.<p> | |
<a href="https://huggingface.co/spaces/susunghong/Self-Attention-Guidance?duplicate=true"> | |
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> | |
""") | |
with gr.Row(): | |
with gr.Column(scale=5): | |
prompt = gr.Textbox(lines=1, label="Enter your prompt", interactive=True) | |
with gr.Column(scale=1, min_width=60): | |
generate_button = gr.Button("Generate") | |
with gr.Column(scale=1, min_width=60): | |
reset_button = gr.Button("Reset") | |
with gr.Row(): | |
steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) | |
randomize_seed = gr.Radio( | |
["Fix Seed", "Randomize Seed"], | |
label="Seed Type", | |
value="Fix Seed", | |
type="index", | |
show_label=False, | |
interactive=True, | |
) | |
seed = gr.Number(value=90061, precision=0, label="Seed", interactive=True) | |
with gr.Row(): | |
cfg_scale = gr.Slider( | |
label="Text Guidance Scale", minimum=0, maximum=10, value=3.0, step=0.1 | |
) | |
sag_scale = gr.Slider( | |
label="Self-Attention Guidance Scale", minimum=0, maximum=1.0, value=1.0, step=0.05 | |
) | |
with gr.Row(): | |
ori_image = gr.Image(label="CFG", type="pil", interactive=False) | |
sag_image = gr.Image(label="SAG + CFG", type="pil", interactive=False) | |
ori_image.style(height=512, width=512) | |
sag_image.style(height=512, width=512) | |
ex = gr.Examples( | |
examples=examples, | |
fn=generate, | |
inputs=[ | |
prompt, | |
steps, | |
randomize_seed, | |
seed, | |
cfg_scale, | |
sag_scale, | |
], | |
outputs=[ori_image, sag_image, seed], | |
cache_examples=False, | |
) | |
gr.Markdown(help_text) | |
generate_button.click( | |
fn=generate, | |
inputs=[ | |
prompt, | |
steps, | |
randomize_seed, | |
seed, | |
cfg_scale, | |
sag_scale, | |
], | |
outputs=[ori_image, sag_image, seed], | |
) | |
reset_button.click( | |
fn=reset, | |
inputs=[], | |
outputs=[steps, randomize_seed, seed, cfg_scale, sag_scale, ori_image, sag_image], | |
) | |
demo.queue(concurrency_count=1) | |
demo.launch(share=False) | |
if __name__ == "__main__": | |
main() |