README.md

VAE Training

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After completing data preprocessing, we can obtain the following dataset:

📦 project/
├── 📂 datasets/
│   ├── 📂 internal_datasets/
│       ├── 📂 videos/
│       │   ├── 📄 00000001.mp4
│       │   ├── 📄 00000001.jpg
│       │   └── 📄 .....
│       └── 📄 json_of_internal_datasets.json

The json_of_internal_datasets.json is a standard JSON file. The file_path in the json can to be set as relative path, as shown in below:

[
    {
      "file_path": "videos/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "train/00000001.jpg",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "image"
    },
    .....
]

You can also set the path as absolute path as follow:

[
    {
      "file_path": "/mnt/data/videos/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "/mnt/data/train/00000001.jpg",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "image"
    },
    .....
]

Train Video VAE

We need to set config in easyanimate/vae/configs/autoencoder at first. The default config is autoencoder_kl_32x32x4_slice.yaml. We need to set the some params in yaml file.

• data_json_path corresponds to the JSON file of the dataset.
• data_root corresponds to the root path of the dataset. If you want to use absolute path in json file, please delete this line.
• ckpt_path corresponds to the pretrained weights of the vae.
• gpus and num_nodes need to be set as the actual situation of your machine.

The we run shell file as follow:

sh scripts/train_vae.sh