app.py

from diffusers import AutoPipelineForText2Image, PNDMScheduler
import torch
import gradio as gr
from PIL import Image
import os, random
import PIL.Image
from transformers import pipeline
from diffusers.utils import load_image
from accelerate import Accelerator

accelerator = Accelerator(cpu=True)
apol=[]
pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, variant=None, use_safetensors=False, safety_checker=None))
pipe.scheduler = accelerator.prepare(PNDMScheduler.from_config(pipe.scheduler.config))
pipe.unet.to(memory_format=torch.channels_last)
pipe = accelerator.prepare(pipe.to("cpu"))
def plex(prompt,neg_prompt,nut):
    if nut == 0:
        nm = random.randint(1, 2147483616)
        while nm % 32 != 0:
            nm = random.randint(1, 2147483616)
    else:
        nm=nut
    generator = torch.Generator(device="cpu").manual_seed(nm)
    image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, num_inference_steps=15)
    for a, imze in enumerate(image["images"]):
        apol.append(imze)
    return apol

iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality"),gr.Slider(label="manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)],outputs=gr.Gallery(label="Generated Output Image", columns=1),description="Running on cpu, very slow! by JoPmt.")
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)