app.py

import torch
from diffusers import AmusedPipeline
from transformers import pipeline
import PIL.Image
from diffusers.utils import load_image
import gradio as gr
from PIL import Image
import cv2
import os, random, gc
import numpy as np
from accelerate import Accelerator
accelerator = Accelerator(cpu=True)
pipe = accelerator.prepare(AmusedPipeline.from_pretrained("amused/amused-512", variant="fp32", torch_dtype=torch.float32))
pipe.vqvae.to(torch.float32)
pipe = pipe.to("cpu")
def plex(prompt):
    prompt = prompt
    image = pipe(prompt, generator=generator)
    for a, imze in enumerate(image["images"]):
        apol.append(imze)
    return apol

iface = gr.Interface(fn=plex, inputs=gr.Textbox(label="prompt"), outputs=gr.Gallery(label="out", columns=2),description="Running on cpu, very slow! by JoPmt.")
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)