app.py

import torch, os, gc, random
import gradio as gr
from PIL import Image
from diffusers.utils import load_image
from accelerate import Accelerator
from diffusers import StableDiffusionXLPipeline
accelerator = Accelerator(cpu=True)

pipe = accelerator.prepare(StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16"))
pipe.unet.to(memory_format=torch.channels_last)
pipe.to("cpu")

def plex(prompt,neg_prompt,stips):
    apol=[]
    nm = random.randint(1, 4836928)
    while nm % 32 != 0:
        nm = random.randint(1, 4836928)
    generator = torch.Generator(device="cpu").manual_seed(nm)
    image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, num_inference_steps=stips, output_type="pil",generator=generator)
    for i, imge in enumerate(image["images"]):
        apol.append(imge)
    return apol

iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=4)], outputs=gr.Gallery(label="out", columns=2))
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1)