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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 NPU는 Optimum RBLN을 사용하여 UNet2DCondition 모델 추론을 가속화할 수 있습니다.

API 참조

Classes

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.

Functions

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

The PyTorch model to be compiled. The object must be an instance of the HuggingFace transformers PreTrainedModel class.

required
config Optional[PretrainedConfig]

The configuration object associated with the model.

None
rbln_config Optional[Union[RBLNModelConfig, Dict]]

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.

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 Union[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 Optional[bool]

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 Optional[Union[Dict, RBLNModelConfig]]

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.

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 Union[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

Functions

Classes

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.

Functions

__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 Optional[int]

The batch size for inference. Defaults to 1.

None
sample_size Optional[Tuple[int, int]]

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 Optional[int]

Number of input channels for the UNet.

None
cross_attention_dim Optional[int]

Dimension of the cross-attention features.

None
use_additional_residuals Optional[bool]

Whether to use additional residual connections in the model.

None
max_seq_len Optional[int]

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

None
in_features Optional[int]

Number of input features for the model.

None
text_model_hidden_size Optional[int]

Hidden size of the text encoder model.

None
image_model_hidden_size Optional[int]

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.

load(path, **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
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 RBLNModelConfig

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.