Model Settings

This page maps key configuration flags to their runtime behavior in code.

Primary config files used in current EO setup: - configs/data_config_eo_4band.yaml - configs/model_config_eo_4band.yaml - configs/training_config_eo_4band.yaml

Legacy single-band configs: - configs/older_configs/data_config.yaml - configs/older_configs/model_config.yaml - configs/older_configs/training_config.yaml

Major Settings

Conditioning channels

Config (model_config_*): - model.generated_channels - model.condition_channels - model.condition_mask_channels - model.condition_include_eo - model.condition_use_valid_mask

Runtime effect: - controls how condition = [eo?, x, valid_mask?] is assembled - channel count is validated against expected condition_channels

Diffusion target parameterization

Config: - model.parameterization: epsilon or x0

Runtime effect: - defines target in diffusion loss and sampler conversions - current EO config uses x0

Masked loss

Config: - model.mask_loss_with_valid_pixels

Runtime effect: - if enabled, loss is computed on missing pixels (1 - valid_mask) - optionally gated by land_mask to focus on ocean pixels - mask alignment preserves per-band semantics (B x C x H x W) unless a single shared mask channel is explicitly used

Inference output composition

Runtime effect: - final output keeps observed pixels from x where valid_mask=1 - model predictions are used where valid_mask=0 - land_mask is then applied to zero land pixels

Known-pixel clamping during sampling

Config: - model.clamp_known_pixels

Runtime effect: - if enabled and known masks/values are available, known pixels are overwritten each reverse step - useful for inpainting-style stability

Illustration:

Coordinate/date FiLM conditioning

Config: - data: dataset.return_coords - model: - coord_conditioning.enabled - coord_conditioning.encoding - coord_conditioning.include_date - coord_conditioning.date_encoding - coord_conditioning.embed_dim

Runtime effect: - creates a coordinate/date embedding and injects it via FiLM in ConvNeXt blocks - details: Date + Coordination Injection

Training and Optimization Settings

Noise schedule and diffusion steps

Config (training.noise): - num_timesteps - schedule: linear, cosine, quadratic, sigmoid - beta_start, beta_end

Validation sampling mode

Config (training.validation_sampling): - sampler: ddpm or ddim - ddim_num_timesteps, ddim_eta - log_intermediates

Runtime effect: - training loss still uses forward noising objective - full reverse sampling diagnostics use chosen validation sampler

Learning-rate warmup and plateau scheduler

Config (scheduler): - warmup.enabled, warmup.steps, warmup.start_ratio - reduce_on_plateau.enabled - reduce_on_plateau.monitor, mode, factor, patience, threshold, cooldown

Runtime effect: - warmup is applied per optimizer step in optimizer_step - plateau scheduler is applied on epoch-level monitored metric

Trainer/Runtime Controls

Config (trainer): - hardware/precision: accelerator, devices, optional num_gpus, precision - logging/checkpoint cadence: log_every_n_steps, ckpt_monitor, lr_logging_interval - validation load: val_batches_per_epoch or limit_val_batches - stability knobs: gradient_clip_val, warning suppressions

Dataloader Settings

Config (dataloader): - batch_size, val_batch_size - num_workers, val_num_workers - shuffle, val_shuffle - pin_memory, persistent_workers, prefetch_factor

Runtime notes: - prefetch_factor is only applied when num_workers > 0 - validation shuffle defaults to true in DataModule unless explicitly changed

Logging Settings (W&B)

Config (wandb): - project/entity/run naming - model logging policy - watch toggles (watch_gradients, watch_parameters) - scalar/image logging intervals

Runtime notes: - watch mode is resolved from explicit gradient/parameter toggles - config files used in the run are uploaded to W&B run files when possible