Create app.py

app.py

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+from PIL import Image
+import gradio as gr
+import numpy as np
+import random, os, gc, base64, io
+import cv2
+import torch
+from accelerate import Accelerator
+from transformers import pipeline, DiffusionModel
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler
+from gradio_client import Client
+
+accelerator = Accelerator(cpu=True)
+pipe = accelerator.prepare(DiffusionModel.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float32, use_safetensors=True, safety_checker=None))
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = accelerator.prepare(pipe.to("cpu"))
+generator = torch.Generator("cpu").manual_seed(random.randint(1, 867346))
+apol=[]
+
+def plex(prompt):
+    gc.collect()
+    apol=[]
+    imags = pipe(prompt=prompt,negative_prompt="bad quality",scheduler=pipe.scheduler,num_inference_steps=5,width=512,height=512,generator=generator).images[0]
+    apol.append(imags)
+    return apol
+
+iface = gr.Interface(fn=plex,inputs=[gr.Image(type="filepath"),gr.Textbox()], outputs=gr.Gallery(columns=2), title="Img2Img_SkyV22CntrlNet_CPU", description="Running on CPU, very slow!")
+iface.queue(max_size=1)
+iface.launch(max_threads=1)