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Running on Zero
| # 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 numpy as np | |
| from PIL import Image | |
| def resize_and_center_crop(image, target_width, target_height): | |
| if target_height == image.shape[0] and target_width == image.shape[1]: | |
| return image | |
| pil_image = Image.fromarray(image) | |
| original_width, original_height = pil_image.size | |
| scale_factor = max(target_width / original_width, target_height / original_height) | |
| resized_width = int(round(original_width * scale_factor)) | |
| resized_height = int(round(original_height * scale_factor)) | |
| resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS) | |
| left = (resized_width - target_width) / 2 | |
| top = (resized_height - target_height) / 2 | |
| right = (resized_width + target_width) / 2 | |
| bottom = (resized_height + target_height) / 2 | |
| cropped_image = resized_image.crop((left, top, right, bottom)) | |
| return np.array(cropped_image) | |
| def get_closest_ratio(height: float, width: float, ratios: list, buckets: list): | |
| """ | |
| Get the closest ratio in the buckets. | |
| Args: | |
| height (float): video height | |
| width (float): video width | |
| ratios (list): video aspect ratio | |
| buckets (list): buckets generated by `generate_crop_size_list` | |
| Returns: | |
| the closest size in the buckets and the corresponding ratio | |
| """ | |
| aspect_ratio = float(height) / float(width) | |
| ratios_array = np.array(ratios) | |
| closest_ratio_id = np.abs(ratios_array - aspect_ratio).argmin() | |
| closest_size = buckets[closest_ratio_id] | |
| closest_ratio = ratios_array[closest_ratio_id] | |
| return closest_size, closest_ratio | |
| def generate_crop_size_list(base_size=256, patch_size=16, max_ratio=4.0): | |
| num_patches = round((base_size / patch_size) ** 2) | |
| assert max_ratio >= 1.0 | |
| crop_size_list = [] | |
| wp, hp = num_patches, 1 | |
| while wp > 0: | |
| if max(wp, hp) / min(wp, hp) <= max_ratio: | |
| crop_size_list.append((wp * patch_size, hp * patch_size)) | |
| if (hp + 1) * wp <= num_patches: | |
| hp += 1 | |
| else: | |
| wp -= 1 | |
| return crop_size_list | |