import torch
import gradio as gr
from PIL import Image
import PIL.Image
from diffusers import StableDiffusionPipeline
from diffusers.utils import load_image
from accelerate import Accelerator
from optimum.onnxruntime import ORTStableDiffusionPipeline
accelerator = Accelerator(cpu=True)
pipe = accelerator.prepare(ORTStableDiffusionPipeline.from_pretrained("JoPmt/AbsoluteReality-onnx"))
pipe.to("cpu")
def plex(prompt,neg_prompt,stips,scaly):
    apol=[]
    image = pipe(prompt=prompt, negative_prompt=neg_prompt, num_inference_steps=stips, guidance_scale=scaly)
    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="num inference steps",minimum=1,step=1,maximum=30,value=25), gr.Slider(label="guidance_scale",minimum=1,step=1,maximum=10,value=7)],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)