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", 35934, 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 [0, "Randomize Seed", 90061, 3.0, 0.75, None, None] with gr.Blocks() as demo: gr.HTML("""
Condition-Agnostic Diffusion Guidance Using the Internal Self-Attention by Susung Hong.
""")
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=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, 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()