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 


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

if torch.cuda.is_available():
    PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
    torch.cuda.max_memory_allocated(device=device)
    torch.cuda.empty_cache()
    
    pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
    torch.cuda.empty_cache()
    
    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16")
    refiner.enable_xformers_memory_efficient_attention()
    refiner = refiner.to(device)
    torch.cuda.empty_cache()
    
    upscaler = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True)
    upscaler.enable_xformers_memory_efficient_attention()
    upscaler = upscaler.to(device)
    torch.cuda.empty_cache()
else: 
    pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", use_safetensors=True)
    pipe = pipe.to(device)
    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
    refiner = refiner.to(device)
    
def genie (prompt, negative_prompt, height, width, scale, steps, seed, upscaling):
    generator = torch.Generator(device=device).manual_seed(seed)
    int_image = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=steps, height=height, width=width, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images
    if upscaling == 'Yes':
        image = refiner(prompt=prompt, image=int_image).images[0]
        upscaled = upscaler(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=5, guidance_scale=0).images[0]
        torch.cuda.empty_cache()
        return (image, upscaled)
    else:
        image = refiner(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]   
        torch.cuda.empty_cache()
    return (image, image)
   
gr.Interface(fn=genie, inputs=[gr.Textbox(label='Что вы хотите, чтобы ИИ генерировал'), 
    gr.Textbox(label='Что вы не хотите, чтобы ИИ генерировал'), 
    gr.Slider(512, 1024, 768, step=128, label='Высота картинки'),
    gr.Slider(512, 1024, 768, step=128, label='Ширина картинки'),
    gr.Slider(1, 15, 10, step=.25, label='Шкала расхождения'), 
    gr.Slider(25, maximum=100, value=50, step=25, label='Количество итераций'), 
    gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True, label='Зерно'),
    gr.Radio(['Да', 'Нет'], label='Ремастеринг?')], 
    outputs=['image', 'image'],
    title="Стабильная Диффузия - SDXL - Upscaler", 
    description="", 
    article = "<br><br><br><br><br><br><br><br><br><br>").launch(debug=True, max_threads=80)