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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 <a href="https://arxiv.org/abs/2210.00939">Susung Hong et al.</a> This space uses <a href="https://huggingface.co/docs/diffusers/api/pipelines/self_attention_guidance">StableDiffusionSAGPipeline</a> in Diffusers.<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() |