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| import torch | |
| import numpy as np | |
| from PIL import Image | |
| def latent2image(latents, model, return_type='np'): | |
| latents = latents.detach() / model.vae.config.scaling_factor | |
| image_1 = model.vae.decode(latents)['sample'].detach() | |
| image_2 = model.vae.decode(latents)['sample'].detach() | |
| if return_type == 'np': | |
| image = (image_1 / 2 + 0.5).clamp(0, 1) | |
| image = image.cpu().permute(0, 2, 3, 1).numpy() | |
| image = (image * 255).astype(np.uint8) | |
| return image, image_2 | |
| def image2latent(image, model): | |
| if type(image) is Image: | |
| image = np.array(image) | |
| if type(image) is torch.Tensor and image.dim() == 4: | |
| latents = image | |
| else: | |
| image = torch.from_numpy(image).float() / 127.5 - 1 | |
| image = image.permute(2, 0, 1).unsqueeze(0).to(model.device).to(model.unet.dtype) | |
| latents = model.vae.encode(image)['latent_dist'].mean | |
| latents = latents * model.vae.config.scaling_factor | |
| return latents | |