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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, StableDiffusionLatentUpscalePipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("prompthero/openjourney-v4", safety_checker=None)
upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, safety_checker=None)
upscaler = upscaler.to(device)
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]
low_res_latents = pipe(prompt, generator=generator, output_type="latent").images
upscaled_image = upscaler(prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator).images[0]
return (images, upscaled_image)
gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'),
gr.Slider(1, maximum=15, value=10, step=.25),
gr.Slider(1, maximum=50, value=25, step=1),
gr.Slider(minimum=1, step=1, maximum=987654321, randomize=True)],
outputs=['image', 'image'],
title = 'OpenJourney V4 CPU',
description = "OJ V4 CPU. <b>WARNING:</b> Extremely Slow. 35s/Iteration. Expect 8-16mins an image for 15-30 iterations respectively. 50 iterations takes ~28mins.",
article = "Code Monkey: <a href=\"https://huggingface.co./Manjushri\">Manjushri</a>").launch(debug=True, max_threads=True) |