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import gradio as gr
import torch
import numpy as np
import modin.pandas as pd
from PIL import Image
from diffusers import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("ductridev/uber-realistic-porn-merge-urpm", torch_dtype=torch.float16, safety_checker=None)
pipe = pipe.to(device)
def genie (prompt, scale, steps, Seed):
generator = torch.Generator(device=device).manual_seed(Seed)
images = pipe(prompt, num_inference_steps=steps, guidance_scale=scale, generator=generator).images[0]
return images
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
gr.Slider(1, maximum=25, value=10, step=.25, label='Prompt Guidance Scale:', interactive=True),
gr.Slider(1, maximum=200, value=100, step=1, label='Number of Iterations: 50 is typically fine.'),
gr.Slider(minimum=1, step=10, maximum=999999999999999999, randomize=True, interactive=True)],
outputs=gr.Image(label='512x512 Generated Image'),
title="OpenJourney V4 GPU",
description="OJ V4 GPU. Ultra Fast, now running on a T4 <br><br><b/>Warning: This Demo is capable of producing NSFW content.",
article = "Code Monkey: <a href=\"https://huggingface.co./Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True) |