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on
Zero
Running
on
Zero
| import argparse | |
| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from PIL import Image | |
| from omegaconf import OmegaConf | |
| from tokenizer.vqgan.model import VQModel | |
| from tokenizer.vqgan.model import VQGAN_FROM_TAMING | |
| # before running demo, make sure to: | |
| # (1) download all needed models from https://github.com/CompVis/taming-transformers and put in pretrained_models/ | |
| # (2) pip install pytorch_lightning | |
| # (3) python3 tools/convert_pytorch_lightning_to_torch.py | |
| # (4) pip uninstall pytorch_lightning | |
| def main(args): | |
| # Setup PyTorch: | |
| torch.manual_seed(args.seed) | |
| torch.set_grad_enabled(False) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # create and load model | |
| cfg, ckpt = VQGAN_FROM_TAMING[args.vqgan] | |
| config = OmegaConf.load(cfg) | |
| model = VQModel(**config.model.get("params", dict())) | |
| model.init_from_ckpt(ckpt) | |
| model.to(device) | |
| model.eval() | |
| # load image | |
| img_path = args.image_path | |
| out_path = args.image_path.replace('.jpg', '_vqgan.jpg').replace('.jpeg', '_vqgan.jpeg').replace('.png', '_vqgan.png') | |
| input_size = args.image_size | |
| img = Image.open(img_path).convert("RGB") | |
| # preprocess | |
| size_org = img.size | |
| img = img.resize((input_size, input_size)) | |
| img = np.array(img) / 255. | |
| x = 2.0 * img - 1.0 # x value is between [-1, 1] | |
| x = torch.tensor(x) | |
| x = x.unsqueeze(dim=0) | |
| x = torch.einsum('nhwc->nchw', x) | |
| x_input = x.float().to("cuda") | |
| # inference | |
| with torch.no_grad(): | |
| latent, _, [_, _, indices] = model.encode(x_input) | |
| output = model.decode_code(indices, latent.shape) # output value is between [-1, 1] | |
| # postprocess | |
| output = F.interpolate(output, size=[size_org[1], size_org[0]], mode='bilinear').permute(0, 2, 3, 1)[0] | |
| sample = torch.clamp(127.5 * output + 128.0, 0, 255).to("cpu", dtype=torch.uint8).numpy() | |
| # save | |
| Image.fromarray(sample).save(out_path) | |
| print("Reconstructed image is saved to {}".format(out_path)) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--image-path", type=str, default="assets/example.jpg") | |
| parser.add_argument("--vqgan", type=str, choices=list(VQGAN_FROM_TAMING.keys()), default="vqgan_openimage_f8_16384") | |
| parser.add_argument("--image-size", type=int, choices=[256, 512, 1024], default=512) | |
| parser.add_argument("--seed", type=int, default=0) | |
| args = parser.parse_args() | |
| main(args) | |