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Create app.py
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app.py
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import torch
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from diffusers import AmusedPipeline
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from transformers import pipeline
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import PIL.Image
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from diffusers.utils import load_image
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import gradio as gr
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from PIL import Image
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import cv2
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import os, random, gc
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import numpy as np
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from accelerate import Accelerator
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accelerator = Accelerator(cpu=True)
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pipe = accelerator.prepare(AmusedPipeline.from_pretrained("amused/amused-512", variant="fp32", torch_dtype=torch.float32))
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pipe.vqvae.to(torch.float32)
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pipe = pipe.to("cpu")
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def plex(prompt):
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prompt = prompt
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image = pipe(prompt, generator=generator)
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for a, imze in enumerate(image["images"]):
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apol.append(imze)
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return apol
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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.")
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iface.queue(max_size=1,api_open=False)
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iface.launch(max_threads=1)
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