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Kandinsky V2.2

Kandinsky V2.2는 텍스트-이미지 잠재 확산 모델입니다. RBLN NPU는 Optimum RBLN을 사용하여 Kandinsky V2.2 파이프라인을 가속화할 수 있습니다.

지원하는 파이프라인

Optimum RBLN은 여러 Kandinsky V2.2 파이프라인을 지원합니다:

  • 텍스트-이미지 변환(Text-to-Image): 텍스트 프롬프트에서 이미지 생성 (Prior + Decoder 사용)
  • 이미지-이미지 변환(Image-to-Image): 텍스트 프롬프트를 기반으로 기존 이미지 수정 (Prior + Img2Img Decoder 사용)
  • 인페인팅(Inpainting): 텍스트 프롬프트에 따라 이미지의 마스킹된 영역 채우기 (Prior + Inpaint Decoder 사용)

주요 클래스

중요: 가이던스 스케일을 위한 배치 크기 설정

배치 크기와 가이던스 스케일

Kandinsky V2.2를 가이던스 스케일 > 1.0(기본값)으로 사용할 때, UNet과 Prior 두 구성 요소의 실제 배치 크기는 클래스 없는 가이던스 기법으로 인해 런타임 중에 두 배가 됩니다.

RBLN NPU는 정적 그래프 컴파일을 사용하기 때문에, 이러한 구성 요소의 컴파일 시 배치 크기가 런타임 배치 크기와 일치해야 합니다. 그렇지 않으면 추론 중에 오류가 발생합니다.

기본 동작

UNet이나 Prior의 배치 크기를 명시적으로 지정하지 않으면 Optimum RBLN은 다음과 같이 동작합니다:

  • 기본 가이던스 스케일(1.0보다 큼)을 사용할 것으로 가정
  • UNet과 Prior의 배치 크기를 파이프라인 배치 크기의 2배로 자동 설정

기본 가이던스 스케일을 사용할 계획이라면 이 자동 설정이 정상적으로 작동합니다. 그러나 다른 가이던스 스케일을 사용하거나 더 많은 제어가 필요한 경우, 배치 크기를 명시적으로 설정해야 합니다.

예제: 명시적으로 배치 크기 설정하기 (가이던스 스케일 = 1.0)

정확히 가이던스 스케일 1.0을 사용할 계획이라면(클래스 없는 가이던스를 사용하지 않음), 배치 크기를 추론 배치 크기와 일치하도록 명시적으로 설정해야 합니다:

사용 예제

방법 1: 개별 Prior 및 Decoder 파이프라인 사용

이 방식은 중간 이미지 임베딩에 대한 더 많은 제어가 가능합니다:

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import torch
from optimum.rbln import RBLNKandinskyV22PriorPipeline, RBLNKandinskyV22Pipeline

# Prior 파이프라인 로드 및 컴파일
prior_pipe = RBLNKandinskyV22PriorPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-prior",
    export=True  # 첫 번째 컴파일 시 True로 설정
)

# 특정 이미지 크기로 Decoder 파이프라인 로드 및 컴파일
pipe = RBLNKandinskyV22Pipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-decoder",
    export=True,
    rbln_img_height=768,
    rbln_img_width=768
)

# Prior를 사용하여 텍스트 프롬프트에서 이미지 임베딩 생성
prompt = "a cute cat sitting on a beach, 4k photo"
generator = torch.manual_seed(42)  # 재현 가능한 결과를 위해
prior_output = prior_pipe(prompt, generator=generator)
image_emb = prior_output.image_embeds
negative_image_emb = prior_output.negative_image_embeds

# Decoder와 이미지 임베딩을 사용하여 최종 이미지 생성
output = pipe(
    image_embeds=image_emb,
    negative_image_embeds=negative_image_emb,
    height=768,
    width=768,
    num_inference_steps=50,
    generator=generator
)

# 생성된 이미지 저장
image = output.images[0]
image.save("generated_cat.png")

방법 2: 결합 파이프라인 사용

결합 파이프라인은 Prior와 Decoder를 하나의 원활한 워크플로우로 통합합니다:

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import torch
from optimum.rbln import RBLNKandinskyV22CombinedPipeline

# 결합 파이프라인 로드 및 컴파일
pipe = RBLNKandinskyV22CombinedPipeline.from_pretrained(
    "kandinsky-community/kandinsky-2-2-decoder",  # 주 디코더 모델
    export=True,  # 첫 번째 컴파일 시 True로 설정
    rbln_img_height=768,
    rbln_img_width=768
)

# 이미지 생성 및 저장
prompt = "a cute cat sitting on a beach, 4k photo"
generator = torch.manual_seed(42)  # 재현 가능한 결과를 위해
image = pipe(
    prompt, 
    height=768, 
    width=768, 
    num_inference_steps=50, 
    generator=generator
).images[0]

image.save("generated_cat.png")

API 참조

Classes

RBLNKandinskyV22PriorPipeline

Bases: RBLNDiffusionMixin, KandinskyV22PriorPipeline

RBLN wrapper for Kandinsky V2.2 Prior pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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.

RBLNKandinskyV22Pipeline

Bases: RBLNDiffusionMixin, KandinskyV22Pipeline

RBLN wrapper for Kandinsky V2.2 text-to-image pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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.

RBLNKandinskyV22Img2ImgPipeline

Bases: RBLNDiffusionMixin, KandinskyV22Img2ImgPipeline

RBLN wrapper for Kandinsky V2.2 image-to-image pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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.

RBLNKandinskyV22InpaintPipeline

Bases: RBLNDiffusionMixin, KandinskyV22InpaintPipeline

RBLN wrapper for Kandinsky V2.2 inpainting pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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.

RBLNKandinskyV22CombinedPipeline

Bases: RBLNDiffusionMixin, KandinskyV22CombinedPipeline

RBLN wrapper for Kandinsky V2.2 Combined (Prior + Text-to-Image Decoder) pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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 wrapper for Kandinsky V2.2 Combined (Prior + Image-to-Image Decoder) pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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 wrapper for Kandinsky V2.2 Combined (Prior + Inpainting Decoder) pipeline.

Functions

from_pretrained(model_id, *, export=False, 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 Hugging Face 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.

 False
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 Dict[str, 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
Self

A compiled 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

Base configuration class for Kandinsky V2.2 decoder pipelines.

Functions

__init__(unet=None, movq=None, *, sample_size=None, batch_size=None, guidance_scale=None, image_size=None, img_height=None, img_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
**kwargs Dict[str, 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.

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, **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
**kwargs Dict[str, 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.

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, **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
**kwargs Dict[str, 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.

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, **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
**kwargs Dict[str, 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.

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)

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 Dict[str, 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.

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, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

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
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 Dict[str, Any]

Additional arguments passed to the parent RBLNModelConfig.

 {}

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, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

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
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 Dict[str, Any]

Additional arguments passed to the parent RBLNModelConfig.

 {}

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, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

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
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 Dict[str, Any]

Additional arguments passed to the parent RBLNModelConfig.

 {}

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, guidance_scale=None, prior_prior=None, prior_image_encoder=None, prior_text_encoder=None, unet=None, movq=None, **kwargs)

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
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 Dict[str, Any]

Additional arguments passed to the parent RBLNModelConfig.

 {}