Variational Autoencoder (VAE)

The Variational Autoencoder (VAE) is a key component in diffusion models like Stable Diffusion, responsible for encoding images into latent space and decoding latent representations back into images. RBLN NPUs can accelerate VAE inference using Optimum RBLN.

Usage in diffusion models

In diffusion-based image generation models like Stable Diffusion, the VAE performs two primary functions:

1. Encoder: Converts an input image into a latent representation for tasks such as image-to-image translation or inpainting.
2. Decoder: Converts the denoised latent representation back into pixel space at the final stage of generation.

API Reference

Classes

RBLNAutoencoderKL

Bases: RBLNModel

RBLN implementation of AutoencoderKL (VAE) for diffusion models.

This model is used to accelerate AutoencoderKL (VAE) models from diffusers library on RBLN NPUs. It can be configured to include both encoder and decoder, or just the decoder part for latent-to-image conversion.

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

Methods:

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	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.

encode(x, return_dict=True, **kwargs)

Encode an input image into a latent representation.

Parameters:

Name	Type	Description	Default
x	FloatTensor	The input image to encode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True
kwargs	dict[str, Any]	Additional arguments to pass to the encoder.	{}

Returns:

Type	Description
FloatTensor | AutoencoderKLOutput	The latent representation or AutoencoderKLOutput if return_dict=True

decode(z, return_dict=True, **kwargs)

Decode a latent representation into an image.

Parameters:

Name	Type	Description	Default
z	FloatTensor	The latent representation to decode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True
kwargs	dict[str, Any]	Additional arguments to pass to the decoder.	{}

Returns:

Type	Description
FloatTensor | DecoderOutput	The decoded image or DecoderOutput if return_dict=True

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.

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

Functions:

Classes

RBLNAutoencoderKLConfig

Bases: RBLNModelConfig

Configuration class for RBLN Variational Autoencoder (VAE) models.

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

Methods:

__init__(batch_size=None, sample_size=None, uses_encoder=None, vae_scale_factor=None, in_channels=None, latent_channels=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 input/output images. If an integer is provided, it's used for both height and width.	None
uses_encoder	bool | None	Whether to include the encoder part of the VAE in the model. When False, only the decoder is used (for latent-to-image conversion).	None
vae_scale_factor	float | None	The scaling factor between pixel space and latent space. Determines how much smaller the latent representations are compared to the original images.	None
in_channels	int | None	Number of input channels for the model.	None
latent_channels	int | None	Number of channels in the latent space.	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	dict[str, Any] | None	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	Union[RBLNModelConfig, tuple[RBLNModelConfig, 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:

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

Classes

RBLNAutoencoderKLCosmos

Bases: RBLNModel

RBLN implementation of AutoencoderKLCosmos for diffusion models.

This model is used to accelerate AutoencoderKLCosmos models from diffusers library on RBLN NPUs. It can be configured to include both encoder and decoder, or just the decoder part for latent-to-video conversion.

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

Methods:

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	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.

encode(x, return_dict=True, **kwargs)

Encode an input video into a latent representation.

Parameters:

Name	Type	Description	Default
x	FloatTensor	The input video to encode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True
kwargs	dict[str, Any]	Additional arguments to pass to the encoder.	{}

Returns:

Type	Description
FloatTensor | AutoencoderKLOutput	The latent representation or AutoencoderKLOutput if return_dict=True

decode(z, return_dict=True)

Decode a latent representation into a video.

Parameters:

Name	Type	Description	Default
z	FloatTensor	The latent representation to decode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True

Returns:

Type	Description
FloatTensor | DecoderOutput	The decoded video or DecoderOutput if return_dict=True

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.

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

Functions:

Classes

RBLNAutoencoderKLCosmosConfig

Bases: RBLNModelConfig

Configuration class for RBLN Cosmos Variational Autoencoder (VAE) models.

Methods:

__init__(batch_size=None, uses_encoder=None, num_frames=None, height=None, width=None, num_channels_latents=None, vae_scale_factor_temporal=None, vae_scale_factor_spatial=None, use_slicing=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	int | None	The batch size for inference. Defaults to 1.	None
uses_encoder	bool | None	Whether to include the encoder part of the VAE in the model. When False, only the decoder is used (for latent-to-video conversion).	None
num_frames	int | None	The number of frames in the generated video. Defaults to 121.	None
height	int | None	The height in pixels of the generated video. Defaults to 704.	None
width	int | None	The width in pixels of the generated video. Defaults to 1280.	None
num_channels_latents	int | None	The number of channels in latent space.	None
vae_scale_factor_temporal	int | None	The scaling factor between time space and latent space. Determines how much shorter the latent representations are compared to the original videos.	None
vae_scale_factor_spatial	int | None	The scaling factor between pixel space and latent space. Determines how much smaller the latent representations are compared to the original videos.	None
use_slicing	bool | None	Enable sliced VAE encoding and decoding. If True, the VAE will split the input tensor in slices to compute encoding or decoding in several steps.	None
kwargs	dict[str, 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	dict[str, Any] | None	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	Union[RBLNModelConfig, tuple[RBLNModelConfig, 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:

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

Functions:

Classes

RBLNAutoencoderKLTemporalDecoder

Bases: RBLNModel

Methods:

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	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.

encode(x, return_dict=True)

Encode an input image into a latent representation.

Parameters:

Name	Type	Description	Default
x	FloatTensor	The input image to encode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True

Returns:

Type	Description
AutoencoderKLOutput | tuple[DiagonalGaussianDistribution]	The latent representation or AutoencoderKLOutput if return_dict=True

decode(z, return_dict=True)

Decode a latent representation into a video.

Parameters:

Name	Type	Description	Default
z	FloatTensor	The latent representation to decode.	required
return_dict	bool	Whether to return output as a dictionary. Defaults to True.	True

Returns:

Type	Description
FloatTensor	The decoded video or DecoderOutput if return_dict=True

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.

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

Functions:

Classes

RBLNAutoencoderKLTemporalDecoderConfig

Bases: RBLNModelConfig

Methods:

__init__(batch_size=None, sample_size=None, uses_encoder=None, num_frames=None, decode_chunk_size=None, vae_scale_factor=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 input/output images. If an integer is provided, it's used for both height and width.	None
uses_encoder	bool | None	Whether to include the encoder part of the VAE in the model. When False, only the decoder is used (for latent-to-image conversion).	None
num_frames	int | None	The number of frames in the generated video.	None
decode_chunk_size	int | None	The number of frames to decode at once during VAE decoding. Useful for managing memory usage during video generation.	None
vae_scale_factor	float | None	The scaling factor between pixel space and latent space. Determines how much smaller the latent representations are compared to the original images.	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	dict[str, Any] | None	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	Union[RBLNModelConfig, tuple[RBLNModelConfig, 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:

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