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Python module

max.pipelines.architectures.ideogram4

Ideogram 4 flow-matching text-to-image architecture.

Ideogram4ArchConfig

class max.pipelines.architectures.ideogram4.Ideogram4ArchConfig(*, pipeline_config: 'PipelineConfig', quantization_encoding: 'SupportedEncoding | None' = None)

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Bases: ArchConfig

Parameters:

• pipeline_config (PipelineConfig)
• quantization_encoding (SupportedEncoding | None)

DEFAULT_ENCODING

DEFAULT_ENCODING: ClassVar[SupportedEncoding] = 'bfloat16'

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SUPPORTED_ENCODINGS

SUPPORTED_ENCODINGS: ClassVar[set[SupportedEncoding]] = {'bfloat16'}

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get_max_seq_len()

get_max_seq_len()

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Returns the default maximum sequence length for the model.

Subclasses should determine whether this value can be overridden by setting the --max-length (pipeline_config.model.max_length) flag.

Return type:

int

initialize()

classmethod initialize(pipeline_config, model_config=None)

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Initialize the config from a PipelineConfig.

Parameters:

• pipeline_config (PipelineConfig) – The pipeline configuration.
• model_config (MAXModelConfig | None) – The model configuration to read from. When None (the default), pipeline_config.model is used. Pass an explicit config (e.g. pipeline_config.draft_model) to initialize the arch config for a different model.

Return type:

Self

pipeline_config

pipeline_config: PipelineConfig

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quantization_encoding

quantization_encoding: SupportedEncoding | None = None

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Ideogram4Config

class max.pipelines.architectures.ideogram4.Ideogram4Config(*, config_file=None, section_name=None, emb_dim=4608, num_layers=34, num_heads=18, intermediate_size=12288, adaln_dim=512, norm_eps=1e-05, in_channels=128, llm_features_dim=53248, rope_theta=5000000.0, mrope_section=(24, 20, 20), dtype=bfloat16, device=<factory>)

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Bases: MAXModelConfigBase

Architecture parameters for Ideogram4Transformer2DModel.

Defaults mirror ideogram-ai/ideogram-4-fp8 transformer/config.json and the reference ideogram4.modeling_ideogram4.Ideogram4Config.

Parameters:

• config_file (str | None)
• section_name (str | None)
• emb_dim (int)
• num_layers (int)
• num_heads (int)
• intermediate_size (int)
• adaln_dim (int)
• norm_eps (float)
• in_channels (int)
• llm_features_dim (int)
• rope_theta (float)
• mrope_section (tuple[int, ...])
• dtype (DType)
• device (DeviceRef)

adaln_dim

adaln_dim: int

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device

device: DeviceRef

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dtype

dtype: DType

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emb_dim

emb_dim: int

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head_dim

property head_dim: int

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in_channels

in_channels: int

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initialize_from_config()

classmethod initialize_from_config(config_dict, encoding, devices)

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Parameters:

• config_dict (dict[str, Any])
• encoding (Literal['float32', 'float16', 'bfloat16', 'q4_k', 'q4_0', 'q6_k', 'float8_e4m3fn', 'float4_e2m1fnx2', 'gptq'])
• devices (list[Device])

Return type:

Self

intermediate_size

intermediate_size: int

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llm_features_dim

llm_features_dim: int

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model_config

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'strict': False}

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Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mrope_section

mrope_section: tuple[int, ...]

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norm_eps

norm_eps: float

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num_heads

num_heads: int

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num_layers

num_layers: int

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rope_theta

rope_theta: float

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Ideogram4Transformer2DModel

class max.pipelines.architectures.ideogram4.Ideogram4Transformer2DModel(config)

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Bases: Module

Ideogram 4 flow-matching transformer producing velocity predictions.

Parameters:

config (Ideogram4Config)

forward()

forward(*args)

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Defines the computation performed by the module.

Users must override this method in their subclass to define the module’s computation.

Parameters:

• *args (Tensor) – Positional arguments for the computation.
• **kwargs – Keyword arguments for the computation.

Returns:

The result of applying the module to the input.

Raises:

NotImplementedError – If the subclass does not override this method.

Return type:

tuple[Tensor, …]

input_types()

input_types()

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Return type:

tuple[TensorType, …]

Ideogram4TransformerModel

class max.pipelines.architectures.ideogram4.Ideogram4TransformerModel(config, encoding, devices, weights)

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Bases: ComponentModel

Loads + adapts one Ideogram 4 DiT checkpoint (cond or uncond branch).

Parameters:

• config (dict[str, Any])
• encoding (SupportedEncoding)
• devices (list[Device])
• weights (Weights)

load_model()

load_model()

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Adapt the checkpoint into a bf16 state dict (no standalone compile).

The actual graph compilation happens once in the pipeline, where both branches are fused into a single denoise-step graph.

Return type:

None