Create app.py

app.py

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+from PIL import Image
+import gradio as gr
+import random, os, gc
+import torch
+from accelerate import Accelerator
+from transformers import pipeline
+from diffusers.utils import load_image
+from diffusers import EulerDiscreteScheduler, DiffusionPipeline
+
+accelerator = Accelerator(cpu=True)
+pipe = accelerator.prepare(DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.bfloat16, use_safetensors=True, variant="fp16", safety_checker=None))
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe.unet.to(memory_format=torch.channels_last)
+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]*2,num_inference_steps=2,width=512,height=512,guidance_scale=0.0,generator=generator)
+    for i, igs in enumerate(imags["images"]):
+        apol.append(igs)
+    return apol
+
+iface = gr.Interface(fn=plex,inputs=gr.Textbox(), outputs=gr.Gallery(columns=2), title="Stabilityai SDXL-Turbo CPU", description="Running on CPU, very slow! by JoPmt")
+iface.queue(max_size=1)
+iface.launch(max_threads=1)