import gradio as gr import torch import numpy as np import modin.pandas as pd from PIL import Image from datasets import load_dataset from diffusers import DiffusionPipeline, EulerDiscreteScheduler device = "cuda" if torch.cuda.is_available() else "cpu" scheduler = EulerDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-2-1", subfolder="scheduler", prediction_type="v_prediction") pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", scheduler=scheduler) pipe = pipe.to(device) def genie (prompt, negative_prompt, scale, steps, seed): generator = torch.Generator(device=device).manual_seed(seed) images = pipe(prompt, negative_prompt=negative_prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, 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.Textbox(label='What you Do Not want the AI to generate.'), gr.Slider(1, 25, 10), gr.Slider(1, maximum=20, value=10, step=1), gr.Slider(minimum=1, step=1, maximum=999999999999999999, randomize=True)], outputs='image', title="Stable Diffusion 2.1 CPU", description="SD 2.1 CPU. WARNING: Extremely Slow. 130s/Iteration. Expect 25-50mins an image for 10-20 iterations respectively.", article = "Code Monkey: Manjushri").launch()