app.py

import gradio as gr
import torch
import numpy as np
from PIL import Image
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
def resize(height, width, img):
    img = Image.open(img)
    img = img.resize((height, width))
    return img
def infer(source_img, prompt, negative_prompt, height, width, guide, steps, seed, strength):
    generator = torch.Generator(device).manual_seed(seed)     
    source_image = resize(height, width, source_img)
    source_image.save('source.png')
    model_url = "stabilityai/stable-diffusion-xl-base-1.0"
    pipe = DiffusionPipeline.from_pretrained(model_url, torch_dtype=torch.float16) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
    pipe = pipe.to(device)
    image = pipe(prompt, negative_prompt=negative_prompt, image=source_image, strength=strength, guidance_scale=guide, num_inference_steps=steps).images[0]
    return image
gr.Interface(
    fn=infer,
    inputs=[
        gr.Image(source="upload", type="filepath", label="Raw Image. Must Be .png"),
        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=1, label='Ширина картинки'),
        gr.Slider(512, 1024, 768, step=1, label='Высота картинки'),                          
        gr.Slider(2, 15, value=7, label='Шкала расхождения'),
        gr.Slider(1, 25, value=10, 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 - Dreamlike-Photoreal-2.0",
    article=""
).launch(debug=True, max_threads=80)