app.py

import gradio as gr
import torch
import modin.pandas as pd
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("stabilityai/stable-diffusion-xl-base-1.0", 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-refiner-1.0", use_safetensors=True, torch_dtype=torch.float16, variant="fp16")
    refiner.enable_xformers_memory_efficient_attention()
    refiner.enable_sequential_cpu_offload()
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
    pipe = pipe.to(device)
    refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", use_safetensors=True)
    refiner = refiner.to(device)

def genie (prompt, negative_prompt, height, width, scale, steps, seed, strength):
    generator = torch.Generator(device=device).manual_seed(seed)
    int_image = pipe(prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator, output_type="latent").images 
    image = refiner(prompt=prompt, negative_prompt=negative_prompt, image=int_image).images[0]
    return 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'),
    gr.Slider(512, 1024, 768, step=128, label='Ширина'),
    gr.Slider(1, 15, 10, label='Шкала навигации'), 
    gr.Slider(25, maximum=50, value=25, step=1, label='Количество итераций'), 
    gr.Slider(label="Зерно", minimum=0, maximum=987654321987654321, step=1, randomize=True), 
        gr.Slider(label='Сила', minimum=0, maximum=1, step=.05, value=.5)], 
    outputs='image', 
    title="Стабильная Диффузия - SDXL - txt2img", 
        article = "<br><br><br><br><br>").launch(debug=True, max_threads=80)