| from diffusers import StableDiffusionXLInpaintPipeline | |
| import gradio as gr | |
| import numpy as np | |
| import imageio | |
| from PIL import Image | |
| import torch | |
| import modin.pandas as pd | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = StableDiffusionXLInpaintPipeline.from_pretrained("stabilityai/sdxl-turbo", safety_checker=None) | |
| pipe = pipe.to(device) | |
| def resize(value,img): | |
| img = Image.open(img) | |
| img = img.resize((value,value)) | |
| return img | |
| def img2img(source_img, prompt, strength): | |
| imageio.imwrite("data.png", source_img['image']) | |
| imageio.imwrite("data_mask.png", source_img["mask"]) | |
| src = resize(768, "data.png") | |
| src.save("src.png") | |
| mask = resize(768, "data_mask.png") | |
| mask.save("mask.png") | |
| image = pipe(prompt=prompt, image=src, mask_image=mask, num_inference_steps=6, strength=strength, guidance_scale=0.0).images[0] | |
| return image | |
| title="SDXL Turbo Inpainting CPU" | |
| description="Inpainting with SDXL Turbo <br><br> <b>Please use square .png image as input, 512x512, 768x768, or 1024x1024</b>" | |
| gr.Interface(fn=img2img, inputs=[gr.Image(source="upload", tool="sketch", label="Source Image"), | |
| gr.Textbox(label='What you want the AI to Generate, 77 Token limit'), | |
| gr.Slider(minimum=.5, maximum=1, value=.75, step=.025, label='Strength')], | |
| outputs='image', | |
| title=title, | |
| description=description, | |
| article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(max_threads=True, debug=True) |