Prior Transformer

Prior Transformer 모델은 Kandinsky V2.2 아키텍처의 핵심 구성 요소입니다. RBLN NPU는 Optimum RBLN을 사용하여 Prior Transformer 추론을 가속화할 수 있습니다.

주요 클래스

• RBLNPriorTransformer: RBLN NPU에서 PriorTransformer를 실행하기 위한 주요 모델 클래스 (참고: 실제 구현은 독립형 모델 클래스가 아닌 RBLN 파이프라인 클래스 내에 있을 수 있습니다).
• RBLNPriorTransformerConfig: Prior Transformer 모델을 위한 설정 클래스입니다.

API 참조

Classes

RBLNPriorTransformer

Bases: RBLNModel

RBLN implementation of PriorTransformer for diffusion models like Kandinsky V2.2.

The Prior Transformer takes text and/or image embeddings from encoders (like CLIP) and maps them to a shared latent space that guides the diffusion process to generate the desired image.

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

Functions

from_pretrained(model_id, export=False, 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	bool	A boolean flag to indicate whether the model should be compiled.	False
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	Dict[str, 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
Self	A RBLN model instance ready for inference on RBLN NPU devices.

from_model(model, *, rbln_config=None, **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
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	Dict[str, 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
Self	A RBLN model instance ready for inference on RBLN NPU devices.

save_pretrained(save_directory)

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

Parameters:

Name	Type	Description	Default
save_directory	Union[str, PathLike]	The directory to save the model and its configuration files. Will be created if it doesn't exist.	required

Classes

RBLNPriorTransformerConfig

Bases: RBLNModelConfig

Configuration class for RBLN Prior Transformer models.

This class inherits from RBLNModelConfig and provides specific configuration options for Prior Transformer models used in diffusion models like Kandinsky V2.2.

Functions

__init__(batch_size=None, embedding_dim=None, num_embeddings=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
embedding_dim	Optional[int]	Dimension of the embedding vectors in the model.	None
num_embeddings	Optional[int]	Number of discrete embeddings in the codebook.	None
**kwargs	Dict[str, Any]	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If batch_size is not a positive integer.