gensbi.experimental.models.fielddit

FieldDiT: conditional flow-matching for 2D field-level inference (Phase 1).

Submodules

• gensbi.experimental.models.fielddit.blocks
• gensbi.experimental.models.fielddit.codec
• gensbi.experimental.models.fielddit.cond
• gensbi.experimental.models.fielddit.core
• gensbi.experimental.models.fielddit.model

Classes

FieldDiT	Conditional flow-matching network for 2D fields (pixel space).
FieldDiTParams	Configuration for FieldDiT (mirrors the style of Flux1Params).
RopeIds	Integer rope/positional id buffers.

Package Contents

class gensbi.experimental.models.fielddit.FieldDiT(params)

Bases: flax.nnx.Module

Conditional flow-matching network for 2D fields (pixel space).

Forward: (t, obs=field, cond) -> velocity field of the same shape. The conv encoder is modulated by time only (or by the full vec when cond_modulates_encoder=True); the decoder and the MMDiT core are modulated by vec = time (+ cond summary if flagged-C) (+ guidance). The meeting-grid rope2d obs ids and absolute cond ids are built internally.

Parameters:

params (FieldDiTParams)

__call__(t, obs, cond, obs_ids=None, cond_ids=None, conditioned=True, guidance=None)

cond_dim

cond_embedder

cond_ids

cond_modulates_encoder

core

decoder

encoder

field_shape

guidance_embed

in_channels

obs_ids

param_dtype

time_in

token_grid

tokenizer

untokenizer

use_cond_summary_in_vec

class gensbi.experimental.models.fielddit.FieldDiTParams

Configuration for FieldDiT (mirrors the style of Flux1Params).

The meeting-grid token count is derived from field_shape, encoder_widths (depth) and patch_size — it is not prescribed: tokens = (H / (2**D * p)) * (W / (2**D * p)) with D = len(encoder_widths) - 1.

Note: rngs is a live nnx.Rngs stream (mirrors Flux1Params). Constructing two models from the same params object yields different weights, because the stream advances; build a fresh FieldDiTParams (or a fresh nnx.Rngs(seed)) per model for reproducibility.

__post_init__()

axes_dim: List[int] | None = None

cond_dim: int

cond_in_channels: int = 1

cond_modulates_encoder: bool = False

depth: int = 2

depth_single_blocks: int = 2

encoder_widths: Tuple[int, Ellipsis]

field_shape: Tuple[int, int]

guidance_embed: bool = False

in_channels: int

mlp_ratio: float = 4.0

norm_groups: int = 8

num_heads: int = 12

param_dtype: jax.typing.DTypeLike

patch_size: int = 2

qkv_bias: bool = False

res_blocks_down: int = 2

res_blocks_up: int = 2

rngs: flax.nnx.Rngs

theta: int | None = None

use_cond_summary_in_vec: bool = True

vec_in_dim: int | None = None

class gensbi.experimental.models.fielddit.RopeIds(value, *, hijax=None, ref=None, eager_sharding=None, **metadata)

Bases: flax.nnx.Variable

Integer rope/positional id buffers.

A dedicated Variable type so the ids are (a) filterable with nnx.state(model, RopeIds), (b) excluded from nnx.Param state, and (c) safe from blanket float-dtype casts applied to the parameter state.

Parameters:

• value (A | VariableMetadata[A])

• hijax (bool | None)

• ref (bool | None)

• eager_sharding (bool | None)

• metadata (Any)