args:
  checkpoint_activations: True ## using gradient checkpointing
  model_parallel_size: 1
  experiment_name: lora-disney
  mode: finetune
  load: "CogVideoX-2b-sat/transformer"
  no_load_rng: True
  train_iters: 1000
  eval_iters: 1
  eval_interval: 100
  eval_batch_size: 1
  save: ckpts
  save_interval: 100
  log_interval: 20
  train_data: ["disney"]
  valid_data: ["disney"]
  split: 1,0,0
  num_workers: 8
  force_train: True
  only_log_video_latents: True

data:
    target: data_video.SFTDataset
    params:
      video_size: [480, 720]
      fps: 8
      max_num_frames: 49
      skip_frms_num: 3.

deepspeed:
  train_micro_batch_size_per_gpu: 1
  gradient_accumulation_steps: 1
  steps_per_print: 50
  gradient_clipping: 0.1
  zero_optimization:
    stage: 2
    cpu_offload: false
    contiguous_gradients: false
    overlap_comm: true
    reduce_scatter: true
    reduce_bucket_size: 1000000000
    allgather_bucket_size: 1000000000
    load_from_fp32_weights: false
  zero_allow_untested_optimizer: true
  bf16:
    enabled: False
  fp16:
    enabled: True
    loss_scale: 0
    loss_scale_window: 400
    hysteresis: 2
    min_loss_scale: 1
  optimizer:
    type: sat.ops.FusedEmaAdam
    params:
      lr: 0.0002
      betas: [0.9, 0.95]
      eps: 1e-8
      weight_decay: 1e-4
  activation_checkpointing:
    partition_activations: false
    contiguous_memory_optimization: false
  wall_clock_breakdown: false


model:
  scale_factor: 1.15258426
  disable_first_stage_autocast: true
  not_trainable_prefixes: ['all'] ## Using Lora
  log_keys:
    - txt
  
  denoiser_config:
    target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
    params:
      num_idx: 1000
      quantize_c_noise: False

      weighting_config:
        target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
      scaling_config:
        target: sgm.modules.diffusionmodules.denoiser_scaling.VideoScaling
      discretization_config:
        target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
        params:
          shift_scale: 3.0

  network_config:
    target: dit_video_concat.DiffusionTransformer
    params:
      time_embed_dim: 512
      elementwise_affine: True
      num_frames: 49
      time_compressed_rate: 4
      latent_width: 90
      latent_height: 60
      num_layers: 30
      patch_size: 2
      in_channels: 16
      out_channels: 16
      hidden_size: 1920
      adm_in_channels: 256
      num_attention_heads: 30

      transformer_args:
        checkpoint_activations: True ## using gradient checkpointing
        vocab_size: 1
        max_sequence_length: 64
        layernorm_order: pre
        skip_init: false
        model_parallel_size: 1
        is_decoder: false

      modules:
        pos_embed_config:
          target: dit_video_concat.Basic3DPositionEmbeddingMixin
          params:
            text_length: 226
            height_interpolation: 1.875
            width_interpolation: 1.875

        lora_config: ## Using Lora
          target: sat.model.finetune.lora2.LoraMixin
          params:
            r: 128

        patch_embed_config:
          target: dit_video_concat.ImagePatchEmbeddingMixin
          params:
            text_hidden_size: 4096

        adaln_layer_config:
          target: dit_video_concat.AdaLNMixin
          params:
            qk_ln: True

        final_layer_config:
          target: dit_video_concat.FinalLayerMixin

  conditioner_config:
    target: sgm.modules.GeneralConditioner
    params:
      emb_models:
        - is_trainable: false
          input_key: txt
          ucg_rate: 0.1
          target: sgm.modules.encoders.modules.FrozenT5Embedder
          params:
            model_dir: "google/t5-v1_1-xxl"
            max_length: 226

  first_stage_config:
    target: vae_modules.autoencoder.VideoAutoencoderInferenceWrapper
    params:
      cp_size: 1
      ckpt_path: "CogVideoX-2b-sat/vae/3d-vae.pt"
      ignore_keys: [ 'loss' ]

      loss_config:
        target: torch.nn.Identity

      regularizer_config:
        target: vae_modules.regularizers.DiagonalGaussianRegularizer

      encoder_config:
        target: vae_modules.cp_enc_dec.ContextParallelEncoder3D
        params:
          double_z: true
          z_channels: 16
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult: [ 1, 2, 2, 4 ]
          attn_resolutions: [ ]
          num_res_blocks: 3
          dropout: 0.0
          gather_norm: True

      decoder_config:
        target: vae_modules.cp_enc_dec.ContextParallelDecoder3D
        params:
          double_z: True
          z_channels: 16
          resolution: 256
          in_channels: 3
          out_ch: 3
          ch: 128
          ch_mult: [ 1, 2, 2, 4 ]
          attn_resolutions: [ ]
          num_res_blocks: 3
          dropout: 0.0
          gather_norm: false

  loss_fn_config:
    target: sgm.modules.diffusionmodules.loss.VideoDiffusionLoss
    params:
      offset_noise_level: 0
      sigma_sampler_config:
        target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling
        params:
          uniform_sampling: True
          num_idx: 1000
          discretization_config:
            target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
            params:
              shift_scale: 3.0

  sampler_config:
    target: sgm.modules.diffusionmodules.sampling.VPSDEDPMPP2MSampler
    params:
      num_steps: 50
      verbose: True

      discretization_config:
        target: sgm.modules.diffusionmodules.discretizer.ZeroSNRDDPMDiscretization
        params:
          shift_scale: 3.0

      guider_config:
        target: sgm.modules.diffusionmodules.guiders.DynamicCFG
        params:
          scale: 6
          exp: 5
          num_steps: 50