# NYU Depth Training Configuration - Native Resolution (Variable Resolution with FluxResizer)
# Uses FLUX.1-dev and FluxResizer for optimal native resolutions (no padding)

# Experiment settings
experiment:
  name: "nyu_depth_native_resolution"
  description: "NYU Depth with FLUX.1-dev at native resolutions using FluxResizer"

# Concept configurations (same as original)
concepts:
  basic: ["depth", "surface", "object", "background"]
  basic_2: ["depth", "surface", "object", "near", "far"]
  indoor: ["wall", "floor", "furniture", "object", "depth"]
  spatial: ["near", "far", "depth", "distance", "surface"]
  detailed: ["wall", "floor", "ceiling", "furniture", "object", "depth", "surface"]
  comprehensive: ["wall", "floor", "ceiling", "furniture", "object", "depth", "near", "far", "surface", "indoor", "outdoor", "structure"]
  semantic: ["bedroom", "kitchen", "bathroom", "living_room", "depth", "furniture", "wall", "floor"]
  geometric: ["depth", "surface", "edge", "plane", "corner", "boundary", "gradient", "distance"]

# Training settings
training:
  concept_config: "basic_2"  # Which concept configuration to use
  epochs: 20
  learning_rate: 1e-4
  batch_size: 1  # REQUIRED: Must be 1 for variable resolution
  accumulate_grad_batches: 4  # Effective batch size = 4
  gradient_clip_val: 1.0
  precision: "16-mixed"
  
# Data settings
data:
  dataset: "nyu_depth_v2"
  # NYU Depth V2 standard train/test split. Point the trainer at it via CLI args
  # (--data_path / --filenames_path); see shell_scripts/train_nyu.sh.
  num_classes: 1  # Depth is regression, but num_classes used for decoder
  min_depth: 0.1  # Minimum valid depth (meters)
  max_depth: 10.0  # Maximum valid depth (meters)
  # NOTE: No target_size! FluxResizer selects optimal resolution per image
  
# Model settings
model:
  flux_model: "black-forest-labs/FLUX.1-dev"  # Changed from schnell to dev
  dtype: "float16"
  dino_model: "dinov3_vitb16"  # DINOv3 base model
  feature_locations:
    transformer_blocks: [4, 9, 13, 18]
    single_transformer_blocks: [4, 15, 26, 37]
  decoder:
    features: 256
  hyperfeature_fusion:
    num_timesteps: 4
    fusion_type: "transformer"
    hidden_dim: 768
    num_transformer_layers: 3
    layer_scale_init: 1e-4
    
# FLUX settings
flux:
  timesteps: 28
  guidance_scale: 3.5
  num_inference_steps: 1
  concept_timesteps: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]
  concept_layers: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]

# Hardware settings
hardware:
  devices: 1  # Number of GPUs (DDP for multi-GPU)
  #strategy: "ddp_find_unused_parameters_true"  # Required for DDP with conditional logic
  num_sanity_val_steps: 1
  detect_anomaly: true
  accelerator: "gpu"
  num_workers: 8

# Paths (timestamps will be automatically added)
paths:
  # All outputs live under ${MMDIFF_OUTPUT} (set it to a writable dir, e.g. /work/<user>/mmdiff_out).
  cache_base_dir: "${MMDIFF_OUTPUT}/cache"
  log_base_dir: "${MMDIFF_OUTPUT}/logs"
  checkpoint_base_dir: "${MMDIFF_OUTPUT}/checkpoints"
  use_timestamp: true
  permanent_cache_dir: "${MMDIFF_OUTPUT}/cache/nyu_native_feature_cache"  # Native resolution feature cache
  
# Logging
logging:
  log_every_n_steps: 10
  save_top_k: 3
  monitor: "val_loss"
  mode: "min"

# HuggingFace
huggingface:
  token: ""  # Leave empty and authenticate via `huggingface-cli login` or the HF_TOKEN env var