import gradio as gr import spaces from diffusers import StableDiffusionXLPipeline import numpy as np import math import torch 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=None, guidance_scale=7.0, pag_scale=3.0, pag_layers=["mid"], randomize_seed=True, seed=42, lora=None, progress=gr.Progress(track_tqdm=True)): prompt = prompt.strip() negative_prompt = negative_prompt.strip() if negative_prompt and negative_prompt.strip() else None print(f"Initial seed for prompt `{prompt}`", seed) if(randomize_seed): seed = random.randint(0, 9007199254740991) if not prompt and not negative_prompt: guidance_scale = 0.0 pipe.unfuse_lora() pipe.unload_lora_weights() if lora: pipe.load_lora_weights(lora) pipe.fuse_lora(lora_scale=0.9) print(f"Seed before sending to generator for prompt: `{prompt}`", seed) generator = torch.Generator(device="cuda").manual_seed(seed) image_pag = pipe(prompt, negative_prompt=negative_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, negative_prompt=negative_prompt, guidance_scale=guidance_scale, generator=generator, num_inference_steps=25).images[0] print(f"Seed at the end of generation for prompt: `{prompt}`", seed) 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="CFG Guidance Scale", info="The guidance scale for CFG, ignored if no prompt is entered (unconditional generation)", 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=9007199254740991, step=1, randomize=True) lora = gr.Textbox(label="Custom LoRA path", info="Load a custom LoRA from Hugging Face to use PAG with") 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="lazy") gr.on( triggers=[ button.click, prompt.submit ], fn=run, inputs=[prompt, negative_prompt, guidance_scale, pag_scale, pag_layers, randomize_seed, seed, lora], outputs=[output, seed], ) if __name__ == "__main__": demo.launch(share=True)