app.py

import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline, StableDiffusionLatentUpscalePipeline

device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained("circulus/canvers-realistic-v3.6", torch_dtype=torch.float16, safety_checker=None)
pipe = pipe.to(device)
pipe.enable_xformers_memory_efficient_attention()
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16")
refiner.enable_xformers_memory_efficient_attention()
refiner = refiner.to(device)

def genie (Prompt, negative_prompt, height, width, scale, steps, seed, upscale):
    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
    if upscale == "Yes":
        n_steps = 30
        high_noise_frac = 0.95
        int_image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
        image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, num_inference_steps=n_steps, denoising_start=high_noise_frac).images[0]
        return (int_image, image)
    else:
        image = pipe(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
    return (image, image)
    
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                               gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
                               gr.Slider(512, 1024, 768, step=128, label='Height'),
                               gr.Slider(512, 1024, 768, step=128, label='Width'),
                               gr.Slider(1, maximum=15, value=7, step=.25, label='Guidance Scale'), 
                               gr.Slider(25, maximum=100, value=50, step=25, label='Number of Iterations'), 
                               gr.Slider(minimum=0, step=1, maximum=9999999999999999, randomize=True, label='Seed: 0 is Random'), 
                               gr.Radio(["Yes", "No"], label='SDXL 1.0 Refiner', value='No'),
                              ],
             outputs=[gr.Image(label='Generated Image'), gr.Image(label='Generated Image')], 
             title="PhotoReal V3.6 with SD x2 Upscaler - GPU", 
             description="<br><br><b/>Warning: This Demo is capable of producing NSFW content.", 
             article = "If You Enjoyed this Demo and would like to Donate, you can send to any of these Wallets. <br>BTC: bc1qzdm9j73mj8ucwwtsjx4x4ylyfvr6kp7svzjn84 <br>3LWRoKYx6bCLnUrKEdnPo3FCSPQUSFDjFP <br>DOGE: DK6LRc4gfefdCTRk9xPD239N31jh9GjKez <br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)