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", 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, num_inference_steps=steps, guidance_scale=scale, generator=generator, output_type="latent").images upscaled_image = upscaler(prompt='', image=low_res_latents, num_inference_steps=5, guidance_scale=0, generator=generator).images[0] return 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', title = 'OpenJourney V4 with SD 2.1 2X Upscaler - CPU', description = "OJ V4 CPU. WARNING: Extremely Slow. 35s/Iteration. Expect 8-16mins an image for 15-30 iterations respectively. 50 iterations takes ~28mins.", article = "Code Monkey: Manjushri").launch(debug=True, max_threads=True)