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import gradio as gr
from diffusers import StableDiffusionXLPipeline
import numpy as np
import math
import spaces
import torch
import sys
import random
from gradio_imageslider import ImageSlider
theme = gr.themes.Base(
font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
custom_pipeline="multimodalart/sdxl_perturbed_attention_guidance",
torch_dtype=torch.float16
)
device="cuda"
pipe = pipe.to(device)
@spaces.GPU
def run(prompt, negative_prompt="", guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, progress=gr.Progress(track_tqdm=True)):
prompt = prompt.strip()
negative_prompt = negative_prompt.strip()
if(randomize_seed):
seed = random.randint(0, sys.maxsize)
if(prompt == "" and negative_prompt == ""):
guidance_scale = 0.0
generator = torch.Generator(device="cuda").manual_seed(seed)
image_pag = pipe(prompt, guidance_scale=guidance_scale, pag_scale=pag_scale, pag_applied_layers=pag_layers, generator=generator, num_inference_steps=25).images[0]
generator = torch.Generator(device="cuda").manual_seed(seed)
image_normal = pipe(prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=25).images[0]
return (image_pag, image_normal), seed
css = '''
.gradio-container{
max-width: 768px !important;
margin: 0 auto;
}
'''
with gr.Blocks(css=css, theme=theme) as demo:
gr.Markdown('''# Perturbed-Attention Guidance SDXL
SDXL 🧨 [diffusers implementation](https://huggingface.co/multimodalart/sdxl_perturbed_attention_guidance) of [Perturbed-Attenton Guidance](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/)
''')
with gr.Group():
with gr.Row():
prompt = gr.Textbox(show_label=False, scale=4, placeholder="Your prompt", info="Leave blank to test unconditional generation")
button = gr.Button("Generate", min_width=120)
output = ImageSlider(label="Left: PAG, Right: No PAG", interactive=False)
with gr.Accordion("Advanced Settings", open=False):
guidance_scale = gr.Number(label="Guidance Scale", value=7.0)
negative_prompt = gr.Textbox(label="Negative prompt", info="Is only applied for the CFG part, leave blank for unconditional generation")
pag_scale = gr.Number(label="Pag Scale", value=3.0)
pag_layers = gr.Dropdown(label="Model layers to apply Pag to", info="mid is the one used on the paper, up and down blocks seem unstable", choices=["up", "mid", "down"], multiselect=True, value="mid")
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
seed = gr.Slider(minimum=1, maximum=18446744073709551615, step=1, randomize=True)
gr.Examples(fn=run, examples=[" ", "an insect robot preparing a delicious meal, anime style", "a photo of a group of friends at an amusement park"], inputs=prompt, outputs=[output, seed], cache_examples=True)
gr.on(
triggers=[
button.click,
prompt.submit
],
fn=run,
inputs=[prompt, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed],
outputs=[output, seed],
)
if __name__ == "__main__":
demo.launch(share=True) |