Create app.py

app.py

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+
+import gradio as gr
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+import re
+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", subfolder="scheduler", prediction_type="v_prediction")
+pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
+pipe = pipe.to(device)
+
+def genie (prompt, scale, steps, seed):
+     generator = torch.Generator(device=device).manual_seed(seed)
+     images = pipe(prompt, width=768, height=768, num_inference_steps=steps, guidance_scale=scale, num_images_per_prompt=1, generator=generator).images
+     return images[0]
+gr.Interface(fn=genie, inputs=['text', gr.Slider(1, 10, 20), gr.Slider(), gr.Slider(maximum=987654321)], outputs='image').launch(debug=True)