Kandinsky V2.2

Kandinsky V2.2 is a text-to-image latent diffusion model. RBLN NPUs can accelerate Kandinsky V2.2 pipelines using Optimum RBLN.

Supported Pipelines

Optimum RBLN supports several Kandinsky V2.2 pipelines:

• Text-to-Image: Generate images from text prompts (using Prior + Decoder)
• Image-to-Image: Modify existing images based on text prompts (using Prior + Img2Img Decoder)
• Inpainting: Fill masked regions of an image guided by text prompts (using Prior + Inpaint Decoder)

Important: Batch Size Configuration for Guidance Scale

Batch Size and Guidance Scale

When using Kandinsky V2.2 with a guidance scale > 1.0 (the default), both the UNet's and Prior's effective batch sizes are doubled during runtime because of the classifier-free guidance technique.

Since RBLN NPU uses static graph compilation, these components' batch sizes at compilation time must match their runtime batch sizes, or you'll encounter errors during inference.

Default Behavior

By default, if you don't explicitly specify the UNet's or Prior's batch size, Optimum RBLN will:

• Assume you'll use the default guidance scale (which is > 1.0)
• Automatically set the UNet's and Prior's batch sizes to 2× your pipeline's batch size

If you plan to use the default guidance scale, this automatic configuration will work correctly. However, if you plan to use a different guidance scale or want more control, you should explicitly configure the batch sizes.

Example: Explicitly Setting Batch Sizes (Guidance Scale = 1.0)

If you plan to use a guidance scale of exactly 1.0 (which doesn't use classifier-free guidance), you should explicitly set the batch sizes to match your inference batch size:

Usage Examples

Option 1: Using Separate Prior and Decoder Pipelines

This approach gives you more control over the intermediate image embeddings:

import torch
from optimum.rbln import RBLNKandinskyV22PriorPipeline, RBLNKandinskyV22Pipeline

# Load and compile the Prior pipeline
prior_pipe = RBLNKandinskyV22PriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-prior",
    export=True  # Set to True for first-time compilation
)

# Load and compile the Decoder pipeline with specific image dimensions
pipe = RBLNKandinskyV22Pipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-decoder",
    export=True,
    rbln_img_height=768,
    rbln_img_width=768
)

# Generate image embeddings from text prompt using the Prior
prompt = "a cute cat sitting on a beach, 4k photo"
generator = torch.manual_seed(42)  # For reproducible results
prior_output = prior_pipe(prompt, generator=generator)
image_emb = prior_output.image_embeds
negative_image_emb = prior_output.negative_image_embeds

# Generate the final image using the Decoder and the image embeddings
output = pipe(
    image_embeds=image_emb,
    negative_image_embeds=negative_image_emb,
    height=768,
    width=768,
    num_inference_steps=50,
    generator=generator
)

# Save the generated image
image = output.images[0]
image.save("generated_cat.png")

Option 2: Using Combined Pipeline

The combined pipeline integrates both Prior and Decoder into a single seamless workflow:

import torch
from optimum.rbln import RBLNKandinskyV22CombinedPipeline

# Load and compile the Combined pipeline
pipe = RBLNKandinskyV22CombinedPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-decoder",  # Main decoder model
    export=True,  # Set to True for first-time compilation
    rbln_img_height=768,
    rbln_img_width=768
)

# Generate and save an image
prompt = "a cute cat sitting on a beach, 4k photo"
generator = torch.manual_seed(42)  # For reproducible results
image = pipe(
    prompt, 
    height=768, 
    width=768, 
    num_inference_steps=50, 
    generator=generator
).images[0]

image.save("generated_cat.png")

API Reference

Classes

RBLNKandinskyV22Pipeline

Bases: RBLNDiffusionMixin, KandinskyV22Pipeline

RBLN-accelerated implementation of Kandinsky 2.2 pipeline for text-to-image generation.

This pipeline compiles Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for generating images with distinctive artistic style and enhanced visual quality.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

Classes

RBLNKandinskyV22PriorPipeline

Bases: RBLNDiffusionMixin, KandinskyV22PriorPipeline

RBLN-accelerated implementation of Kandinsky 2.2 prior pipeline for text and image embedding generation.

This pipeline compiles Kandinsky 2.2 prior models to run efficiently on RBLN NPUs, enabling high-performance inference for generating image embeddings from text prompts and image inputs for downstream generation tasks.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

Classes

RBLNKandinskyV22Img2ImgPipeline

Bases: RBLNDiffusionMixin, KandinskyV22Img2ImgPipeline

RBLN-accelerated implementation of Kandinsky 2.2 pipeline for image-to-image generation.

This pipeline compiles Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for transforming input images with distinctive artistic style and enhanced visual fidelity.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

Classes

RBLNKandinskyV22InpaintPipeline

Bases: RBLNDiffusionMixin, KandinskyV22InpaintPipeline

RBLN-accelerated implementation of Kandinsky 2.2 pipeline for image inpainting.

This pipeline compiles Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for filling masked regions with distinctive artistic style and seamless content integration.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

Classes

RBLNKandinskyV22CombinedPipeline

Bases: RBLNDiffusionMixin, KandinskyV22CombinedPipeline

RBLN-accelerated implementation of Kandinsky 2.2 combined pipeline for end-to-end text-to-image generation.

This pipeline compiles both prior and decoder Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for complete text-to-image generation with distinctive artistic style.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

RBLNKandinskyV22Img2ImgCombinedPipeline

Bases: RBLNDiffusionMixin, KandinskyV22Img2ImgCombinedPipeline

RBLN-accelerated implementation of Kandinsky 2.2 combined pipeline for end-to-end image-to-image generation.

This pipeline compiles both prior and decoder Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for complete image-to-image transformation with distinctive artistic style.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

RBLNKandinskyV22InpaintCombinedPipeline

Bases: RBLNDiffusionMixin, KandinskyV22InpaintCombinedPipeline

RBLN-accelerated implementation of Kandinsky 2.2 combined pipeline for end-to-end image inpainting.

This pipeline compiles both prior and decoder Kandinsky 2.2 models to run efficiently on RBLN NPUs, enabling high-performance inference for complete image inpainting with distinctive artistic style and seamless integration.

Functions

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config={}, lora_ids=None, lora_weights_names=None, lora_scales=None, **kwargs) classmethod

Load a pretrained diffusion pipeline from a model checkpoint, with optional compilation for RBLN NPUs.

This method has two distinct operating modes

• When export=True: Takes a PyTorch-based diffusion model, compiles it for RBLN NPUs, and loads the compiled model
• When export=False: Loads an already compiled RBLN model from model_id without recompilation

It supports various diffusion pipelines including Stable Diffusion, Kandinsky, ControlNet, and other diffusers-based models.

Parameters:

Name	Type	Description	Default
model_id	`str`	The model ID or path to the pretrained model to load. Can be either: A model ID from the HuggingFace Hub A local path to a saved model directory	required
export	bool	If True, takes a PyTorch model from model_id and compiles it for RBLN NPU execution. If False, loads an already compiled RBLN model from model_id without recompilation.	None
model_save_dir	Optional[PathLike]	Directory to save the compiled model artifacts. Only used when export=True. If not provided and export=True, a temporary directory is used.	None
rbln_config	Dict[str, Any]	Configuration options for RBLN compilation. Can include settings for specific submodules such as text_encoder, unet, and vae. Configuration can be tailored to the specific pipeline being compiled.	{}
lora_ids	Optional[Union[str, List[str]]]	LoRA adapter ID(s) to load and apply before compilation. LoRA weights are fused into the model weights during compilation. Only used when export=True.	None
lora_weights_names	Optional[Union[str, List[str]]]	Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.	None
lora_scales	Optional[Union[float, List[float]]]	Scaling factor(s) to apply to the LoRA adapter(s). Only used when export=True.	None
kwargs	Any	Additional arguments to pass to the underlying diffusion pipeline constructor or the RBLN compilation process. These may include parameters specific to individual submodules or the particular diffusion pipeline being used.	{}

Returns:

Type	Description
RBLNDiffusionMixin	A compiled or loaded diffusion pipeline that can be used for inference on RBLN NPU. The returned object is an instance of the class that called this method, inheriting from RBLNDiffusionMixin.

Classes

RBLNKandinskyV22PipelineBaseConfig

Bases: RBLNModelConfig

Functions

__init__(unet=None, movq=None, *, sample_size=None, batch_size=None, guidance_scale=None, image_size=None, img_height=None, img_width=None, height=None, width=None, **kwargs)

Parameters:

Name	Type	Description	Default
unet	Optional[RBLNUNet2DConditionModelConfig]	Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.	None
movq	Optional[RBLNVQModelConfig]	Configuration for the MoVQ (VQ-GAN) model component. Initialized as RBLNVQModelConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If both image_size and img_height/img_width are provided.

Note

When guidance_scale > 1.0, the UNet batch size is automatically doubled to accommodate classifier-free guidance.

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.

RBLNKandinskyV22PipelineConfig

Bases: RBLNKandinskyV22PipelineBaseConfig

Configuration class for the Kandinsky V2.2 text-to-image decoder pipeline.

Functions

__init__(unet=None, movq=None, *, sample_size=None, batch_size=None, guidance_scale=None, image_size=None, img_height=None, img_width=None, height=None, width=None, **kwargs)

Parameters:

Name	Type	Description	Default
unet	Optional[RBLNUNet2DConditionModelConfig]	Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.	None
movq	Optional[RBLNVQModelConfig]	Configuration for the MoVQ (VQ-GAN) model component. Initialized as RBLNVQModelConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If both image_size and img_height/img_width are provided.

Note

When guidance_scale > 1.0, the UNet batch size is automatically doubled to accommodate classifier-free guidance.

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.

RBLNKandinskyV22Img2ImgPipelineConfig

Bases: RBLNKandinskyV22PipelineBaseConfig

Configuration class for the Kandinsky V2.2 image-to-image decoder pipeline.

Functions

__init__(unet=None, movq=None, *, sample_size=None, batch_size=None, guidance_scale=None, image_size=None, img_height=None, img_width=None, height=None, width=None, **kwargs)

Parameters:

Name	Type	Description	Default
unet	Optional[RBLNUNet2DConditionModelConfig]	Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.	None
movq	Optional[RBLNVQModelConfig]	Configuration for the MoVQ (VQ-GAN) model component. Initialized as RBLNVQModelConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If both image_size and img_height/img_width are provided.

Note

When guidance_scale > 1.0, the UNet batch size is automatically doubled to accommodate classifier-free guidance.

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.

RBLNKandinskyV22InpaintPipelineConfig

Bases: RBLNKandinskyV22PipelineBaseConfig

Configuration class for the Kandinsky V2.2 inpainting decoder pipeline.

Functions

__init__(unet=None, movq=None, *, sample_size=None, batch_size=None, guidance_scale=None, image_size=None, img_height=None, img_width=None, height=None, width=None, **kwargs)

Parameters:

Name	Type	Description	Default
unet	Optional[RBLNUNet2DConditionModelConfig]	Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.	None
movq	Optional[RBLNVQModelConfig]	Configuration for the MoVQ (VQ-GAN) model component. Initialized as RBLNVQModelConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Raises:

Type	Description
ValueError	If both image_size and img_height/img_width are provided.

Note

When guidance_scale > 1.0, the UNet batch size is automatically doubled to accommodate classifier-free guidance.

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.

RBLNKandinskyV22PriorPipelineConfig

Bases: RBLNModelConfig

Configuration class for the Kandinsky V2.2 Prior pipeline.

Functions

__init__(text_encoder=None, image_encoder=None, prior=None, *, batch_size=None, guidance_scale=None, **kwargs)

Initialize a configuration for Kandinsky 2.2 prior pipeline optimized for RBLN NPU.

This configuration sets up the prior components of the Kandinsky 2.2 architecture, which includes text and image encoders along with a prior transformer that maps text/image embeddings to latent representations used to condition the diffusion process.

Parameters:

Name	Type	Description	Default
text_encoder	Optional[RBLNCLIPTextModelWithProjectionConfig]	Configuration for the text encoder component. Initialized as RBLNCLIPTextModelWithProjectionConfig if not provided.	None
image_encoder	Optional[RBLNCLIPVisionModelWithProjectionConfig]	Configuration for the image encoder component. Initialized as RBLNCLIPVisionModelWithProjectionConfig if not provided.	None
prior	Optional[RBLNPriorTransformerConfig]	Configuration for the prior transformer component. Initialized as RBLNPriorTransformerConfig if not provided.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

Note

When guidance_scale > 1.0, the prior batch size is automatically doubled to accommodate classifier-free guidance.

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.

RBLNKandinskyV22CombinedPipelineBaseConfig

Bases: RBLNModelConfig

Base configuration class for Kandinsky V2.2 combined pipelines.

Functions

__init__(prior_pipe=None, decoder_pipe=None, *, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

Initialize a configuration for combined Kandinsky 2.2 pipelines optimized for RBLN NPU.

This configuration integrates both the prior and decoder components of Kandinsky 2.2 into a unified pipeline, allowing for end-to-end text-to-image generation in a single model. It combines the text/image encoding, prior mapping, and diffusion steps together.

Parameters:

Name	Type	Description	Default
prior_pipe	Optional[RBLNKandinskyV22PriorPipelineConfig]	Configuration for the prior pipeline. Initialized as RBLNKandinskyV22PriorPipelineConfig if not provided.	None
decoder_pipe	Optional[RBLNKandinskyV22PipelineConfig]	Configuration for the decoder pipeline. Initialized as RBLNKandinskyV22PipelineConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
prior_prior	Optional[RBLNPriorTransformerConfig]	Direct configuration for the prior transformer. Used if prior_pipe is not provided.	None
prior_image_encoder	Optional[RBLNCLIPVisionModelWithProjectionConfig]	Direct configuration for the image encoder. Used if prior_pipe is not provided.	None
prior_text_encoder	Optional[RBLNCLIPTextModelWithProjectionConfig]	Direct configuration for the text encoder. Used if prior_pipe is not provided.	None
unet	Optional[RBLNUNet2DConditionModelConfig]	Direct configuration for the UNet. Used if decoder_pipe is not provided.	None
movq	Optional[RBLNVQModelConfig]	Direct configuration for the MoVQ (VQ-GAN) model. Used if decoder_pipe is not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

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.

RBLNKandinskyV22CombinedPipelineConfig

Bases: RBLNKandinskyV22CombinedPipelineBaseConfig

Configuration class for the Kandinsky V2.2 combined text-to-image pipeline.

Functions

__init__(prior_pipe=None, decoder_pipe=None, *, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

Initialize a configuration for combined Kandinsky 2.2 pipelines optimized for RBLN NPU.

This configuration integrates both the prior and decoder components of Kandinsky 2.2 into a unified pipeline, allowing for end-to-end text-to-image generation in a single model. It combines the text/image encoding, prior mapping, and diffusion steps together.

Parameters:

Name	Type	Description	Default
prior_pipe	Optional[RBLNKandinskyV22PriorPipelineConfig]	Configuration for the prior pipeline. Initialized as RBLNKandinskyV22PriorPipelineConfig if not provided.	None
decoder_pipe	Optional[RBLNKandinskyV22PipelineConfig]	Configuration for the decoder pipeline. Initialized as RBLNKandinskyV22PipelineConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
prior_prior	Optional[RBLNPriorTransformerConfig]	Direct configuration for the prior transformer. Used if prior_pipe is not provided.	None
prior_image_encoder	Optional[RBLNCLIPVisionModelWithProjectionConfig]	Direct configuration for the image encoder. Used if prior_pipe is not provided.	None
prior_text_encoder	Optional[RBLNCLIPTextModelWithProjectionConfig]	Direct configuration for the text encoder. Used if prior_pipe is not provided.	None
unet	Optional[RBLNUNet2DConditionModelConfig]	Direct configuration for the UNet. Used if decoder_pipe is not provided.	None
movq	Optional[RBLNVQModelConfig]	Direct configuration for the MoVQ (VQ-GAN) model. Used if decoder_pipe is not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

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.

RBLNKandinskyV22InpaintCombinedPipelineConfig

Bases: RBLNKandinskyV22CombinedPipelineBaseConfig

Configuration class for the Kandinsky V2.2 combined inpainting pipeline.

Functions

__init__(prior_pipe=None, decoder_pipe=None, *, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

Initialize a configuration for combined Kandinsky 2.2 pipelines optimized for RBLN NPU.

This configuration integrates both the prior and decoder components of Kandinsky 2.2 into a unified pipeline, allowing for end-to-end text-to-image generation in a single model. It combines the text/image encoding, prior mapping, and diffusion steps together.

Parameters:

Name	Type	Description	Default
prior_pipe	Optional[RBLNKandinskyV22PriorPipelineConfig]	Configuration for the prior pipeline. Initialized as RBLNKandinskyV22PriorPipelineConfig if not provided.	None
decoder_pipe	Optional[RBLNKandinskyV22PipelineConfig]	Configuration for the decoder pipeline. Initialized as RBLNKandinskyV22PipelineConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
prior_prior	Optional[RBLNPriorTransformerConfig]	Direct configuration for the prior transformer. Used if prior_pipe is not provided.	None
prior_image_encoder	Optional[RBLNCLIPVisionModelWithProjectionConfig]	Direct configuration for the image encoder. Used if prior_pipe is not provided.	None
prior_text_encoder	Optional[RBLNCLIPTextModelWithProjectionConfig]	Direct configuration for the text encoder. Used if prior_pipe is not provided.	None
unet	Optional[RBLNUNet2DConditionModelConfig]	Direct configuration for the UNet. Used if decoder_pipe is not provided.	None
movq	Optional[RBLNVQModelConfig]	Direct configuration for the MoVQ (VQ-GAN) model. Used if decoder_pipe is not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

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.

RBLNKandinskyV22Img2ImgCombinedPipelineConfig

Bases: RBLNKandinskyV22CombinedPipelineBaseConfig

Configuration class for the Kandinsky V2.2 combined image-to-image pipeline.

Functions

__init__(prior_pipe=None, decoder_pipe=None, *, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

Initialize a configuration for combined Kandinsky 2.2 pipelines optimized for RBLN NPU.

This configuration integrates both the prior and decoder components of Kandinsky 2.2 into a unified pipeline, allowing for end-to-end text-to-image generation in a single model. It combines the text/image encoding, prior mapping, and diffusion steps together.

Parameters:

Name	Type	Description	Default
prior_pipe	Optional[RBLNKandinskyV22PriorPipelineConfig]	Configuration for the prior pipeline. Initialized as RBLNKandinskyV22PriorPipelineConfig if not provided.	None
decoder_pipe	Optional[RBLNKandinskyV22PipelineConfig]	Configuration for the decoder pipeline. Initialized as RBLNKandinskyV22PipelineConfig if not provided.	None
sample_size	Optional[Tuple[int, int]]	Spatial dimensions for the UNet model.	None
image_size	Optional[Tuple[int, int]]	Dimensions for the generated images. Cannot be used together with img_height/img_width.	None
batch_size	Optional[int]	Batch size for inference, applied to all submodules.	None
img_height	Optional[int]	Height of the generated images.	None
img_width	Optional[int]	Width of the generated images.	None
height	Optional[int]	Height of the generated images.	None
width	Optional[int]	Width of the generated images.	None
guidance_scale	Optional[float]	Scale for classifier-free guidance.	None
prior_prior	Optional[RBLNPriorTransformerConfig]	Direct configuration for the prior transformer. Used if prior_pipe is not provided.	None
prior_image_encoder	Optional[RBLNCLIPVisionModelWithProjectionConfig]	Direct configuration for the image encoder. Used if prior_pipe is not provided.	None
prior_text_encoder	Optional[RBLNCLIPTextModelWithProjectionConfig]	Direct configuration for the text encoder. Used if prior_pipe is not provided.	None
unet	Optional[RBLNUNet2DConditionModelConfig]	Direct configuration for the UNet. Used if decoder_pipe is not provided.	None
movq	Optional[RBLNVQModelConfig]	Direct configuration for the MoVQ (VQ-GAN) model. Used if decoder_pipe is not provided.	None
kwargs	Any	Additional arguments passed to the parent RBLNModelConfig.	{}

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.