Create app.py

app.py

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+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)