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Configuration error
Configuration error
| import os | |
| import sys | |
| from img_processing import custom_to_pil, preprocess, preprocess_vqgan | |
| sys.path.append("taming-transformers") | |
| import glob | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import PIL | |
| import taming | |
| import torch | |
| from loaders import load_config, load_default | |
| from utils import get_device | |
| def get_embedding(model, path=None, img=None, device="cpu"): | |
| assert path or img, "Input either path or tensor" | |
| if img is not None: | |
| raise NotImplementedError | |
| x = preprocess(PIL.Image.open(path), target_image_size=256).to(device) | |
| x_processed = preprocess_vqgan(x) | |
| z, _, [_, _, indices] = model.encode(x_processed) | |
| return z | |
| def blend_paths( | |
| model, path1, path2, quantize=False, weight=0.5, show=True, device="cuda" | |
| ): | |
| x = preprocess(PIL.Image.open(path1), target_image_size=256).to(device) | |
| y = preprocess(PIL.Image.open(path2), target_image_size=256).to(device) | |
| x_latent = get_embedding(model, path=path1, device=device) | |
| y_latent = get_embedding(model, path=path2, device=device) | |
| z = torch.lerp(x_latent, y_latent, weight) | |
| if quantize: | |
| z = model.quantize(z)[0] | |
| decoded = model.decode(z)[0] | |
| if show: | |
| plt.figure(figsize=(10, 20)) | |
| plt.subplot(1, 3, 1) | |
| plt.imshow(x.cpu().permute(0, 2, 3, 1)[0]) | |
| plt.subplot(1, 3, 2) | |
| plt.imshow(custom_to_pil(decoded)) | |
| plt.subplot(1, 3, 3) | |
| plt.imshow(y.cpu().permute(0, 2, 3, 1)[0]) | |
| plt.show() | |
| return custom_to_pil(decoded), z | |
| if __name__ == "__main__": | |
| device = get_device() | |
| model = load_default(device) | |
| model.to(device) | |
| blend_paths( | |
| model, | |
| "./test_pics/face.jpeg", | |
| "./test_pics/face2.jpeg", | |
| quantize=False, | |
| weight=0.5, | |
| ) | |
| plt.show() | |