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from diffusers import AutoPipelineForText2Image
import torch
import gradio as gr
from PIL import Image
import os, random
import PIL.Image
from transformers import pipeline
from diffusers.utils import load_image
from accelerate import Accelerator
accelerator = Accelerator()
def plex(prompt,neg_prompt):
apol=[]
pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, variant=None, use_safetensors=False, safety_checker=None))
pipe = accelerator.prepare(pipe.to("cpu"))
image = pipe(prompt=prompt, negative_prompt=neg_prompt,num_inference_steps=10)
for a, imze in enumerate(image["images"]):
apol.append(imze)
return apol
iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality")],outputs=gr.Gallery(label="Generated Output Image", columns=1), title="Txt2Img_Overall_v1_SD",description="Running on cpu, very slow!")
iface.queue(max_size=1,api_open=False)
iface.launch(max_threads=1) |