From 2dda7b29436536a0253ff6e83a9e4cd7555d9946 Mon Sep 17 00:00:00 2001 From: bubbliiiing <3323290568@qq.com> Date: Wed, 22 Jan 2025 07:26:08 +0000 Subject: [PATCH] Update Training Readme --- scripts/README_TRAIN.md | 2 +- scripts/README_TRAIN_CONTROL.md | 2 +- scripts/README_TRAIN_LORA.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) mode change 100644 => 100755 scripts/README_TRAIN.md mode change 100644 => 100755 scripts/README_TRAIN_LORA.md diff --git a/scripts/README_TRAIN.md b/scripts/README_TRAIN.md old mode 100644 new mode 100755 index 45d19db..edecd5d --- a/scripts/README_TRAIN.md +++ b/scripts/README_TRAIN.md @@ -8,7 +8,7 @@ Some parameters in the sh file can be confusing, and they are explained in this - `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution. - `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts. -- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `video_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. +- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the shape of video inputs for training is `512x512x49` to `1024x1024x49`. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the shape of video inputs for training is `256x256x49`. - `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum. diff --git a/scripts/README_TRAIN_CONTROL.md b/scripts/README_TRAIN_CONTROL.md index e4a3de4..ca4828c 100755 --- a/scripts/README_TRAIN_CONTROL.md +++ b/scripts/README_TRAIN_CONTROL.md @@ -28,7 +28,7 @@ Some parameters in the sh file can be confusing, and they are explained in this - `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution. - `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts. -- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `video_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. +- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`. - `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum. diff --git a/scripts/README_TRAIN_LORA.md b/scripts/README_TRAIN_LORA.md old mode 100644 new mode 100755 index 1d87d19..c66ba80 --- a/scripts/README_TRAIN_LORA.md +++ b/scripts/README_TRAIN_LORA.md @@ -8,7 +8,7 @@ Some parameters in the sh file can be confusing, and they are explained in this - `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution. - `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts. -- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `video_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. +- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`. - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`. - `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum.