| from diffusers import DiffusionPipeline | |
| import gradio as gr | |
| import numpy as np | |
| import imageio | |
| from PIL import Image | |
| import torch | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-inpainting") | |
| pipe.to(device) | |
| def resize(height,img): | |
| baseheight = height | |
| img = Image.open(img) | |
| hpercent = (baseheight/float(img.size[1])) | |
| wsize = int((float(img.size[0])*float(hpercent))) | |
| img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) | |
| return img | |
| def predict(source_img, prompt, negative_prompt, steps): | |
| imageio.imwrite("data.png", source_img["image"]) | |
| imageio.imwrite("data_mask.png", source_img["mask"]) | |
| src = resize(512, "data.png") | |
| src.save("src.png") | |
| mask = resize(512, "data_mask.png") | |
| mask.save("mask.png") | |
| image = pipe(prompt=prompt, negative_prompt=negative_prompt, image=src, mask_image=mask, num_inference_steps=steps).images[0] | |
| return image | |
| title="Stable Diffusion 2.0 Inpainting CPU" | |
| 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>" | |
| 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'), gr.Slider(5, 25, 10, step=1, label='Number of Iterations')], outputs='image', title=title, description=description, article = "Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(max_threads=True, debug=True) |