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EasyAnimate
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model:
  base_learning_rate: 1.0e-04
  target: easyanimate.vae.ldm.models.omnigen_casual3dcnn.AutoencoderKLMagvit_fromOmnigen
  params:
    slice_compression_vae: true
    mini_batch_encoder: 8
    mini_batch_decoder: 2
    monitor: train/rec_loss
    ckpt_path: models/Diffusion_Transformer/EasyAnimateV2-XL-2-512x512/vae/diffusion_pytorch_model.safetensors
    down_block_types: ("SpatialDownBlock3D", "SpatialTemporalDownBlock3D", "SpatialTemporalDownBlock3D",
      "SpatialTemporalDownBlock3D",)
    up_block_types: ("SpatialUpBlock3D", "SpatialTemporalUpBlock3D", "SpatialTemporalUpBlock3D",
      "SpatialTemporalUpBlock3D",)
    lossconfig:
      target: easyanimate.vae.ldm.modules.losses.LPIPSWithDiscriminator
      params:
        disc_start: 50001
        kl_weight: 1.0e-06
        disc_weight: 0.5
        l2_loss_weight: 0.0
        l1_loss_weight: 1.0
        perceptual_weight: 1.0

data:
  target: train_vae.DataModuleFromConfig

  params:
    batch_size: 1
    wrap: true
    num_workers: 8
    train:
      target: easyanimate.vae.ldm.data.dataset_image_video.CustomSRTrain
      params:
        data_json_path: pretrain.json
        data_root: /your_data_root # This is used in relative path
        size: 256
        degradation: pil_nearest
        video_size: 256
        video_len: 25
        slice_interval: 1
    validation:
      target: easyanimate.vae.ldm.data.dataset_image_video.CustomSRValidation
      params:
        data_json_path: pretrain.json
        data_root: /your_data_root # This is used in relative path
        size: 256
        degradation: pil_nearest
        video_size: 256
        video_len: 25
        slice_interval: 1

lightning:
  callbacks:
    image_logger:
      target: train_vae.ImageLogger
      params:
        batch_frequency: 5000
        max_images: 8
        increase_log_steps: True

  trainer:
    benchmark: True
    accumulate_grad_batches: 1
    gpus: "0"
    num_nodes: 1