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from diffusers import DiffusionPipeline |
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import gradio as gr |
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import numpy as np |
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import imageio |
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from PIL import Image |
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import torch |
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import modin.pandas as pd |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0") |
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pipe = pipe.to(device) |
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def resize(value,img): |
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img = Image.open(img) |
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img = img.resize((value,value)) |
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return img |
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def predict(source_img, prompt, negative_prompt): |
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imageio.imwrite("data.png", source_img["image"]) |
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imageio.imwrite("data_mask.png", source_img["mask"]) |
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src = resize(768, "data.png") |
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src.save("src.png") |
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mask = resize(768, "data_mask.png") |
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mask.save("mask.png") |
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image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=src, mask_image=mask, num_inference_steps=20).images[0] |
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return image |
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title="Stable Diffusion 2.0 Inpainting CPU" |
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description="Inpainting with Stable Diffusion 2.0 <br />Warning: Slow process... ~10 min inference time.<br> <b>Please use 512x512 or 768x768 square .png image as input to avoid memory error!!!</b>" |
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gr.Interface(fn=predict, inputs=[gr.Image(source="upload", type="numpy", tool="sketch", elem_id="source_container"), 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')], outputs='image', title=title, description=description, article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(max_threads=True, debug=True) |
