train_low_memory.yaml

defaults:
  - train
  - _self_

# Low memory configuration for EDGS
# This config reduces memory usage while maintaining quality

# Disable densification to avoid memory spikes
train:
  no_densify: True  # EDGS works well without densification
  gs_epochs: 60000  # Keep full training iterations for quality

# Override Gaussian Splatting optimization settings for lower memory
gs:
  opt:
    # Reduce batch size to use less memory per iteration
    batch_size: 16  # Reduced from 64
    
    # Increase opacity reset interval to avoid frequent memory allocations
    opacity_reset_interval: 1_000_000  # Effectively disable opacity reset
    
    # Keep densification settings but they won't be used due to no_densify
    densify_from_iter: 500
    densify_until_iter: 15000
    densify_grad_threshold: 0.0002
    
# Keep high quality initialization settings
init_wC:
  matches_per_ref: 15000  # Slightly reduced from 20000 for memory
  num_refs: 180  # Reduced from 360 but still high quality
  nns_per_ref: 3  # Keep nearest neighbors for good initialization