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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 = """
Self-Attention Guidance (SAG) - Condition-Agnostic Diffusion Guidance Using the Internal Self-Attention
"""
examples = [
[
' ',
50,
"Fix Seed",
123,
7.5,
1.0,
],
[
'.',
50,
"Fix Seed",
456,
7.5,
1.0,
],
[
'A cute Scottish Fold playing with a ball',
50,
"Fix Seed",
789,
5.0,
1.0,
],
[
'A person with a happy dog',
50,
"Fix Seed",
901,
5.0,
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, guidance_scale=cfg_scale, sag_scale=0.0).images[0]
generator = torch.manual_seed(seed)
sag_image = pipe(prompt, generator=generator, guidance_scale=cfg_scale, sag_scale=sag_scale).images[0]
return [ori_image, sag_image]
def reset():
return [0, "Randomize Seed", 1371, 5.0, 0.75, None, None]
with gr.Blocks() as demo:
gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 7px;">
Self-Attention Guidance (SAG) Demo
""")
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=8978, precision=0, label="Seed", interactive=True)
with gr.Row():
cfg_scale = gr.Slider(
label="Guidance Scale", minimum=0, maximum=10, value=5.0, step=0.1
)
sag_scale = gr.Slider(
label="Self-Attention Guidance Scale", minimum=0, maximum=1.0, value=0.75, 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],
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],
)
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() |