ControlNet

ControlNet models add extra conditions (like edge maps, depth maps, human poses) to Stable Diffusion models, allowing for more precise control over the generated image structure. RBLN NPUs can accelerate ControlNet model inference using Optimum RBLN.

Key Classes

• RBLNControlNetModel: The main model class for running ControlNetModel on RBLN NPU.
• RBLNControlNetModelConfig: Configuration class specifically for RBLNControlNetModel.

Usage within Pipelines

Typically, you don't interact with RBLNControlNetModel directly. Instead, it's automatically loaded and managed as part of an RBLN ControlNet pipeline, such as RBLNStableDiffusionControlNetPipeline or RBLNStableDiffusionXLControlNetPipeline.

When configuring an RBLN ControlNet pipeline, you can pass specific settings for the ControlNet model via the controlnet argument in the pipeline's configuration object:

from optimum.rbln import RBLNStableDiffusionControlNetPipelineConfig

# Example: Configure pipeline with specific batch size for the ControlNet
config = RBLNStableDiffusionControlNetPipelineConfig(
    batch_size=1, # Pipeline inference batch size
    # ... other SD pipeline settings ...
    controlnet=dict(
        batch_size=2 # ControlNet batch size (e.g., doubled for CFG)
    )
)

# ... load pipeline using this config ...

Refer to the ControlNet Pipeline documentation (Standard, SDXL) for details on pipeline usage and configuration, including handling guidance scale effects on the ControlNet's batch size.

API Reference

Classes

RBLNControlNetModel

Bases: RBLNModel

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

RBLNControlNetModelConfig

Bases: RBLNModelConfig

Configuration class for RBLN ControlNet models.

Functions

__init__(batch_size=None, max_seq_len=None, unet_sample_size=None, vae_sample_size=None, text_model_hidden_size=None, **kwargs)

Parameters:

Name	Type	Description	Default
batch_size	Optional[int]	The batch size for inference. Defaults to 1.	None
max_seq_len	Optional[int]	Maximum sequence length for text inputs when used with cross-attention.	None
unet_sample_size	Optional[Tuple[int, int]]	The spatial dimensions (height, width) of the UNet input latents this ControlNet works with.	None
vae_sample_size	Optional[Tuple[int, int]]	The spatial dimensions (height, width) of the original image input to the ControlNet condition.	None
text_model_hidden_size	Optional[int]	Hidden size of the text encoder model used for conditioning (relevant for SDXL ControlNets).	None
**kwargs	Dict[str, Any]	Additional arguments passed to the parent RBLNModelConfig.	{}