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