starling.inference.constraints.HelicityConstraint

class HelicityConstraint

Bases: Constraint

Methods

__init__	Initialize base constraint with common parameters.
apply	Apply the constraint to the given latents.
bell_shaped_schedule	Bell-shaped schedule for time-dependent guidance strength.
compute_loss	Compute the loss for this constraint without applying gradients.
cosine_weight	Cosine schedule for time-dependent guidance strength.
get_adaptive_clip_threshold	Get an adaptive clipping threshold that follows a cosine schedule.
get_time_scale	Get the time-dependent scaling factor.
initialize	Called by the sampler to set model parameters.
should_apply_guidance	Check if guidance should be applied at the current timestep.

__init__(resid_start, resid_end, tolerance=0.0, force_constant=2.0, **kwargs)

Initialize base constraint with common parameters.

Parameters:

• constraint_weight (float, default=1.0) – Weight factor for the constraint

• schedule (str, default="cosine") – Scheduling function for time-dependent guidance strength

• verbose (bool, default=True) – Whether to print debug information

• guidance_start (float, default=0.0) – Normalized timestep to start applying guidance (0.0 = beginning)

• guidance_end (float, default=1.0) – Normalized timestep to stop applying guidance (1.0 = end)

compute_loss(distance_maps: Tensor) → Tuple[Tensor, Tensor]

Compute the loss for this constraint without applying gradients.

Parameters:

distance_maps (torch.Tensor) – Pre-computed distance maps from the latents

Returns:

(per_batch_loss, total_loss) - Individual sample losses and mean loss

Return type:

tuple[torch.Tensor, torch.Tensor]