UNetSpatioTemporalCondition

UNetSpatioTemporalCondition 모델은 Stable Video Diffusion과 같은 확산 기반 비디오 생성 모델의 핵심 구성 요소입니다. 각 디노이징 단계에서 노이즈가 있는 비디오 프레임에서 제거해야 할 노이즈를 예측합니다. RBLN NPU는 Optimum RBLN을 사용하여 UNetSpatioTemporalCondition 모델 추론을 가속화할 수 있습니다.

API 참조

Classes

RBLNUNetSpatioTemporalConditionModel

Bases: RBLNModel

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.

forward(sample, timestep, encoder_hidden_states, added_time_ids, return_dict=True, **kwargs)

Forward pass for the RBLN-optimized UNetSpatioTemporalConditionModel.

Parameters:

Name	Type	Description	Default
sample	Tensor	The noisy input tensor with the following shape (batch, channel, height, width).	required
timestep	Union[Tensor, float, int]	The number of timesteps to denoise an input.	required
encoder_hidden_states	Tensor	The encoder hidden states.	required
added_time_ids	Tensor	A tensor containing additional sinusoidal embeddings and added to the time embeddings.	required
return_dict	bool	Whether or not to return a [~diffusers.models.unets.unet_spatio_temporal_condition.UNetSpatioTemporalConditionOutput] instead of a plain tuple.	True

Returns:

Type	Description
Union[UNetSpatioTemporalConditionOutput, Tuple]	(Union[~diffusers.models.unets.unet_spatio_temporal_condition.UNetSpatioTemporalConditionOutput], 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	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

RBLNUNetSpatioTemporalConditionModelConfig

Bases: RBLNModelConfig

Functions

__init__(batch_size=None, sample_size=None, in_features=None, num_frames=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_features	Optional[int]	Number of input features for the model.	None
num_frames	Optional[int]	The number of frames in the generated video.	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.