| # Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE | |
| # | |
| # Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any | |
| # output and results therefrom are provided "AS IS" without any express or implied warranties of | |
| # any kind including any warranties of title, merchantability, noninfringement, course of dealing, | |
| # usage of trade, or fitness for a particular purpose. You are solely responsible for determining the | |
| # appropriateness of using, reproducing, modifying, performing, displaying or distributing any of | |
| # the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a | |
| # third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise | |
| # of rights and permissions under this agreement. | |
| # See the License for the specific language governing permissions and limitations under the License. | |
| import torch | |
| from typing import List | |
| import numpy as np | |
| from PIL import Image | |
| def numpy_to_pil(images: np.ndarray) -> List[Image.Image]: | |
| """ | |
| Convert a numpy image or a batch of images to a PIL image. | |
| Args: | |
| images (np.ndarray): The image array to convert to PIL format. | |
| Returns: | |
| List[Image.Image]: A list of PIL images. | |
| """ | |
| if images.ndim == 3: | |
| images = images[None, ...] | |
| images = (images * 255).round().astype("uint8") | |
| if images.shape[-1] == 1: | |
| # special case for grayscale (single channel) images | |
| pil_images = [Image.fromarray(image.squeeze(), mode="L") for image in images] | |
| else: | |
| pil_images = [Image.fromarray(image) for image in images] | |
| return pil_images | |
| def merge_tensor_by_mask(tensor_1, tensor_2, mask, dim): | |
| assert tensor_1.shape == tensor_2.shape | |
| # Mask is a 0/1 vector. Choose tensor_2 when the value is 1; otherwise, tensor_1 | |
| masked_indices = torch.nonzero(mask).squeeze(1) | |
| tmp = tensor_1.clone() | |
| if dim == 0: | |
| tmp[masked_indices] = tensor_2[masked_indices] | |
| elif dim == 1: | |
| tmp[:, masked_indices] = tensor_2[:, masked_indices] | |
| elif dim == 2: | |
| tmp[:, :, masked_indices] = tensor_2[:, :, masked_indices] | |
| return tmp | |