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UNet2DCondition

UNet2DCondition 모델은 Stable Diffusion과 같은 확산 기반 이미지 생성 모델의 핵심 구성 요소입니다. 각 디노이징 단계에서 노이즈가 있는 이미지에서 제거해야 할 노이즈를 예측합니다. RBLN Optimum을 사용하면 RBLN NPU에서 UNet2DCondition 모델 추론을 가속할 수 있습니다.

지원 NPU

Diffusers 파이프라인은 ATOM™에서만 실행됩니다. Rebel100™은 아직 지원하지 않습니다.

API 참조

RBLNUNet2DConditionModel

Bases: RBLNModel

RBLN implementation of UNet2DConditionModel for diffusion models.

This model is used to accelerate UNet2DCondition models from diffusers library on RBLN NPUs. It is a key component in diffusion-based image generation models like Stable Diffusion.

This class inherits from [RBLNModel]. Check the superclass documentation for the generic methods the library implements for all its models.

forward(sample, timestep, encoder_hidden_states, class_labels=None, timestep_cond=None, attention_mask=None, cross_attention_kwargs=None, added_cond_kwargs=None, down_block_additional_residuals=None, mid_block_additional_residual=None, down_intrablock_additional_residuals=None, encoder_attention_mask=None, return_dict=True, **kwargs)

Forward pass for the RBLN-optimized UNet2DConditionModel.

Parameters:

Name Type Description Default
sample Tensor

The noisy input tensor with the following shape (batch, channel, height, width).

 required
timestep Tensor | float | int

The number of timesteps to denoise an input.

 required
encoder_hidden_states Tensor

The encoder hidden states.

 required
added_cond_kwargs dict[str, Tensor]

A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks.

 None
down_block_additional_residuals tuple[Tensor] | None

A tuple of tensors that if specified are added to the residuals of down unet blocks.

 None
mid_block_additional_residual Tensor | None

A tensor that if specified is added to the residual of the middle unet block.

 None
return_dict bool

Whether or not to return a [~diffusers.models.unets.unet_2d_condition.UNet2DConditionOutput] instead of a plain tuple.

 True

Returns:

Type Description
UNet2DConditionOutput | tuple

(~diffusers.models.unets.unet_2d_condition.UNet2DConditionOutput, Tuple)

from_pretrained(model_id, export=None, rbln_config=None, **kwargs) classmethod

The from_pretrained() function is utilized in its standard form as in the HuggingFace transformers library. User can use this function to load a pre-trained model from the HuggingFace library and convert it to a RBLN model to be run on RBLN NPUs.

Parameters:

Name Type Description Default
model_id str | Path

The model id of the pre-trained model to be loaded. It can be downloaded from the HuggingFace model hub or a local path, or a model id of a compiled model using the RBLN Compiler.

 required
export bool | None

A boolean flag to indicate whether the model should be compiled. If None, it will be determined based on the existence of the compiled model files in the model_id.

 None
rbln_config dict | RBLNModelConfig | None

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

 None
kwargs Any

Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

 {}

Returns:

Type Description
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

get_hf_class() classmethod

Lazily load and cache the HuggingFace class this model wraps.

The class is looked up in hf_library_name under the name without the RBLN prefix (e.g. RBLNLlamaForCausalLM -> LlamaForCausalLM).

save_pretrained(save_directory, push_to_hub=False, **kwargs)

Saves a model and its configuration file to a directory, so that it can be re-loaded using the [~optimum.rbln.modeling_base.RBLNBaseModel.from_pretrained] class method.

Parameters:

Name Type Description Default
save_directory str | Path

Directory where to save the model file.

 required
push_to_hub bool

Whether or not to push your model to the HuggingFace model hub after saving it.

 False

from_model(model, config=None, rbln_config=None, model_save_dir=None, subfolder='', **kwargs) classmethod

Converts and compiles a pre-trained HuggingFace library model into a RBLN model. This method performs the actual model conversion and compilation process.

Parameters:

Name Type Description Default
model PreTrainedModel | ModelMixin

The PyTorch model to be compiled. The object must be a HuggingFace transformers PreTrainedModel or a Diffusers component based on diffusers.ModelMixin, such as a VAE, UNet or ControlNet.

 required
config PretrainedConfig | None

The configuration object associated with the model.

 None
rbln_config RBLNModelConfig | dict | None

Configuration for RBLN model compilation and runtime. This can be provided as a dictionary or an instance of the model's configuration class (e.g., RBLNLlamaForCausalLMConfig for Llama models). For detailed configuration options, see the specific model's configuration class documentation.

 None
kwargs Any

Additional keyword arguments. Arguments with the prefix rbln_ are passed to rbln_config, while the remaining arguments are passed to the HuggingFace library.

 {}

The method performs the following steps:

  1. Compiles the PyTorch model into an optimized RBLN graph
  2. Configures the model for the specified NPU device
  3. Creates the necessary runtime objects if requested
  4. Saves the compiled model and configurations

Returns:

Type Description
RBLNModel

A RBLN model instance ready for inference on RBLN NPU devices.

RBLNUNet2DConditionModelConfig

Bases: RBLNModelConfig

Configuration class for RBLN UNet2DCondition models.

This class inherits from RBLNModelConfig and provides specific configuration options for UNet2DCondition models used in diffusion-based image generation.

__init__(batch_size=None, sample_size=None, in_channels=None, cross_attention_dim=None, use_additional_residuals=None, max_seq_len=None, in_features=None, text_model_hidden_size=None, image_model_hidden_size=None, **kwargs)

Parameters:

Name Type Description Default
batch_size int | None

The batch size for inference. Defaults to 1.

 None
sample_size tuple[int, int] | None

The spatial dimensions (height, width) of the generated samples. If an integer is provided, it's used for both height and width.

 None
in_channels int | None

Number of input channels for the UNet.

 None
cross_attention_dim int | None

Dimension of the cross-attention features.

 None
use_additional_residuals bool | None

Whether to use additional residual connections in the model.

 None
max_seq_len int | None

Maximum sequence length for text inputs when used with cross-attention.

 None
in_features int | None

Number of input features for the model.

 None
text_model_hidden_size int | None

Hidden size of the text encoder model.

 None
image_model_hidden_size int | None

Hidden size of the image encoder model.

 None
kwargs Any

Additional arguments passed to the parent RBLNModelConfig.

 {}

Raises:

Type Description
ValueError

If batch_size is not a positive integer.

from_pretrained(path, rbln_config=None, return_unused_kwargs=False, **kwargs) classmethod

Load a RBLNModelConfig from a path.

Parameters:

Name Type Description Default
path str

Path to the RBLNModelConfig file or directory containing the config file.

 required
rbln_config dict[str, Any] | None

Additional configuration to override.

 None
return_unused_kwargs bool

Whether to return unused kwargs.

 False
kwargs Any

Additional keyword arguments to override configuration values. Keys starting with 'rbln_' will have the prefix removed and be used to update the configuration.

 {}

Returns:

Name Type Description
RBLNModelConfig Self | tuple[Self, dict[str, Any]]

The loaded configuration instance.
Note

This method loads the configuration from the specified path and applies any provided overrides. If the loaded configuration class doesn't match the expected class, a warning will be logged.

Examples:

1
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.