For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).

Python module

max.pipelines.architectures.ideogram4

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

`Ideogram4ArchConfig`

class max.pipelines.architectures.ideogram4.Ideogram4ArchConfig(*, pipeline_config: 'PipelineConfig')

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Bases: ArchConfig

Parameters:: pipeline_config (PipelineConfig)

`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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`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', '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

Ideogram4ArchConfig​

get_max_seq_len()​

initialize()​

pipeline_config​

Ideogram4Config​

adaln_dim​

device​

dtype​

emb_dim​

head_dim​

in_channels​

initialize_from_config()​

intermediate_size​

llm_features_dim​

model_config​

mrope_section​

norm_eps​

num_heads​

num_layers​

rope_theta​

Ideogram4Transformer2DModel​

forward()​

input_types()​

Ideogram4TransformerModel​

load_model()​

`Ideogram4ArchConfig`

`get_max_seq_len()`

`initialize()`

`pipeline_config`

`Ideogram4Config`

`adaln_dim`

`device`

`dtype`

`emb_dim`

`head_dim`

`in_channels`

`initialize_from_config()`

`intermediate_size`

`llm_features_dim`

`model_config`

`mrope_section`

`norm_eps`

`num_heads`

`num_layers`

`rope_theta`

`Ideogram4Transformer2DModel`

`forward()`

`input_types()`

`Ideogram4TransformerModel`

`load_model()`