nemo_automodel.components.models.hunyuan_image3

Submodules

Package Contents

Classes

Name	Description
`HunyuanImage3Config`	Configuration of the HunyuanImage-3.0 multimodal MoE transformer.
`HunyuanImage3ForCausalMM`	HunyuanImage-3.0 transformer with the text-to-image flow-matching forward.

API

class nemo_automodel.components.models.hunyuan_image3.config.HunyuanImage3Config(
    vocab_size: int = 133120,
    hidden_size: int = 4096,
    intermediate_size: int = 3072,
    moe_intermediate_size: int | list[int] = 3072,
    num_hidden_layers: int = 32,
    num_attention_heads: int = 32,
    num_key_value_heads: int = 8,
    attention_head_dim: int = 128,
    num_experts: int | list[int] = 64,
    moe_topk: int | list[int] = 8,
    num_shared_expert: int | list[int] = 1,
    use_mixed_mlp_moe: bool = True,
    norm_topk_prob: bool = True,
    router_aux_loss_coef: float = 0.0,
    hidden_act: str = 'silu',
    rms_norm_eps: float = 1e-05,
    rope_theta: float = 10000.0,
    use_qk_norm: bool = True,
    attention_bias: bool = False,
    mlp_bias: bool = False,
    max_position_embeddings: int = 22800,
    img_proj_type: str = 'unet',
    patch_size: int = 1,
    patch_embed_hidden_dim: int = 1024,
    image_base_size: int = 1024,
    image_token_id: int = 128006,
    timestep_token_id: int = 128017,
    vae: dict[str, typing.Any] | None = None,
    pad_token_id: int | None = 128009,
    bos_token_id: int | None = 127958,
    eos_token_id: int | list[int] | None = 127957,
    tie_word_embeddings: bool = False,
    torch_dtype: str = 'bfloat16',
    kwargs: typing.Any = {}
)

Bases: PretrainedConfig

Configuration of the HunyuanImage-3.0 multimodal MoE transformer.

Architecture (tencent/HunyuanImage-3.0):

32 decoder layers, hidden size 4096, GQA with 32 query / 8 KV heads, head dim 128
Every layer is MoE: 64 routed experts (top-8, softmax, renormalized) plus one shared expert
Per-head QK RMSNorm applied after RoPE; 2D RoPE over the joint text / image sequence
Image tokens come from VAE latents through a UNet patch embedding, and the diffusion velocity is read out through a UNet final layer, both conditioned on the flow-matching timestep

head_dim

int

keys_to_ignore_at_inference

= ['past_key_values']

latent_channels

int

model_type

= 'hunyuan_image_3_moe'

vae

class nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM(
    config: nemo_automodel.components.models.hunyuan_image3.config.HunyuanImage3Config,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs: typing.Any = {}
)

Bases: HFCheckpointingMixin, Module, MoEFSDPSyncMixin

HunyuanImage-3.0 transformer with the text-to-image flow-matching forward.

forward predicts the flow velocity of noisy latents given the token sequence; forward_text returns next-token logits (the release’s gen_text mode) and is used for parity checks.

_keep_in_fp32_modules_strict

= ['mlp.gate']

backend

= backend or BackendConfig()

final_layer

lm_head

model

patch_embed

state_dict_adapter

tie_word_embeddings_support

TieSupport = TieSupport.UNTIED_ONLY

time_embed

= TimestepEmbedder(hidden, dtype=dtype)

time_embed_2

= TimestepEmbedder(hidden, dtype=dtype)

timestep_emb

= TimestepEmbedder(hidden, dtype=dtype)

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.forward(
    input_ids: torch.Tensor,
    latents: torch.Tensor,
    timestep: torch.Tensor,
    valid_lengths: torch.Tensor | None = None,
    return_dict: bool = False,
    kwargs: typing.Any = {}
) -> tuple[torch.Tensor] | dict[str, torch.Tensor]

Predict the flow velocity of the noisy latents.

Parameters:

input_ids

torch.Tensor

[batch, seq] token ids laid out as described in the module docstring, right-padded. Every row holds exactly h*w contiguous image tokens, preceded by the <timestep> token.

latents

torch.Tensor

[batch, channels, h, w] noisy VAE latents.

timestep

torch.Tensor

[batch] flow-matching timesteps in [0, 1000] (sigma * 1000).

valid_lengths

torch.Tensor | NoneDefaults to None

[batch] number of non-padding tokens per row; defaults to the full length.

return_dict

boolDefaults to False

Return {"sample": velocity} instead of (velocity,).

Returns: tuple[torch.Tensor] | dict[str, torch.Tensor]

[batch, channels, h, w] predicted velocity, as a one-element tuple or a dict.

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.forward_text(
    input_ids: torch.Tensor
) -> torch.Tensor

Next-token logits of a causal text-only sequence (the release’s gen_text mode).

Parameters:

input_ids

torch.Tensor

Long tensor of shape [batch, sequence].

Returns: torch.Tensor

fp32 tensor of shape [batch, sequence, vocab].

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.from_config(
    config: nemo_automodel.components.models.hunyuan_image3.config.HunyuanImage3Config,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs: typing.Any = {}
) -> 'HunyuanImage3ForCausalMM'

classmethod

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.from_pretrained(
    pretrained_model_name_or_path: str,
    model_args: typing.Any = (),
    kwargs: typing.Any = {}
) -> 'HunyuanImage3ForCausalMM'

classmethod

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.get_input_embeddings() -> torch.nn.Embedding

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.get_output_embeddings() -> torch.nn.Module

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.initialize_weights(
    buffer_device: torch.device | None = None,
    dtype: torch.dtype = torch.bfloat16
) -> None

nemo_automodel.components.models.hunyuan_image3.model.HunyuanImage3ForCausalMM.update_moe_gate_bias() -> None