L2/config.yaml

model:
  base_learning_rate: 1.0e-05
  adjust_learning_rate: false
  find_unused_parameters: false
  gradient_clip_algorithm: norm
  precision: bf16-mixed
  grad_clip: 1.0
  grad_acc_steps: 4
  target: models.second_stage.cd_model_v2.ConsistencyDistillPredictorModule
  params:
    # INFERENCE-ONLY: self-reference, so the ~9 GB distillation teacher does NOT
    # have to be shipped.
    #
    # ConsistencyDistillPredictorModule.__init__ requires this file to exist and uses
    # its `ema_vit.*` weights to initialise vit/ema_vit -- but _build_l2_predictor
    # immediately overwrites both by loading this checkpoint (the distilled student)
    # on top. So the teacher's weights are dead at inference; only the file's
    # existence is required. Pointing at the student's own last.ckpt satisfies that
    # (it has the ema_vit.* keys) and is a no-op.
    #
    # For TRAINING/distillation, restore the real teacher:
    teacher_ckpt_path: $ORBIS2_MODELS_DIR/L2/checkpoints/last.ckpt
    cd_ema_mu: 0.999
    cd_weight_decay: 0.0
    warmup_steps: 500
    min_lr_multiplier: 0.1
    enc_scale: 4.0
    enc_scale_dino: 4.0
    adjust_lr_to_batch_size: false
    num_pred_frames: 1
    tokenizer_config:
      # Same tokenizer as the L1 model -- shipped under models/tok, not duplicated.
      # (Originally $TK_WORK_DIR/<training-run>.)
      folder: $ORBIS2_MODELS_DIR/tok
      ckpt_path: checkpoints/last.ckpt
    condition_preprocessor_config:
      target: models.second_stage.fm_model_v2.SpeedYawMovingGoalConditionPreprocessor
      params:
        goal_offsets:
        - 1
        - 2
        - 3
        - 4
        - 5
        context_frame_anchor_index: -1
        odometry_steps_per_image_frame: 5
    objective_config:
      target: models.second_stage.cd_model_v2.ConsistencyDistillationObjective
      params:
        sigma_min: 1.0e-05
        cd_num_timesteps: 48
        cd_t_min: 0.01
        cd_t_max: 0.99
        cd_grid: uniform
        cd_loss: l2
        cd_pseudo_huber_c: 0.00054
    sampler_config:
      target: modules.fm_samplers.FlowMatchingSamplerEuler
      params:
        timescale: 1.0
        timestep_conditioning: per_frame
    first_stage_handler_config:
      target: models.second_stage.fm_model_v2.IFSelectBranchFirstStageHandler
      params:
        encoder_branch: sem
    generator_config:
      target: networks.DiT.dit_v2.DiT
      params:
        max_num_frames: 64
        hidden_size: 768
        depth: 24
        num_heads: 16
        mlp_ratio: 4
        input_size:
        - 18
        - 32
        patch_size: 1
        in_channels: 16
        dropout: 0.0
        mlp_block: swiglu
        conditioner_config:
          target: networks.DiT.dit_v2.PerFrameSequenceGlobalAdditiveConditioner
          params:
            aggregation: mean
            add_positional_embedding: true
            sequence_length: 5
            condition_configs:
              steering:
                target: modules.steering.LinearSteeringEmbedder
                params:
                  num_input_features: 2
                  hidden_size: 768
        block_config:
          target: networks.DiT.dit.STDiTBlock
          params:
            causal_time_attn: true
            modulate_time_attn: true
# Training datasets stripped for release; only `model` is read at inference.