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Stable Diffusion XL

Stable Diffusion XL(SDXL)은 이전 Stable Diffusion 버전에 비해 더 높은 해상도의 이미지를 더 세밀하고 사실적으로 생성하는 것으로 알려진 고급 텍스트-이미지 잠재 확산 모델입니다. 향상된 프롬프트 이해를 위해 두 개의 텍스트 인코더를 활용합니다. RBLN Optimum을 사용하면 RBLN NPU에서 Stable Diffusion XL 파이프라인을 가속할 수 있습니다.

지원 NPU

Diffusers 파이프라인은 ATOM™에서만 실행됩니다. Rebel100™은 아직 지원하지 않습니다.

지원하는 파이프라인

RBLN Optimum은 여러 Stable Diffusion XL 파이프라인을 지원합니다:

파이프라인 클래스 설명
텍스트-이미지 변환(Text-to-Image) RBLNStableDiffusionXLPipeline 텍스트 프롬프트에서 고해상도 이미지 생성.
이미지-이미지 변환(Image-to-Image) RBLNStableDiffusionXLImg2ImgPipeline 텍스트 프롬프트를 기반으로 기존 이미지 수정.
인페인팅(Inpainting) RBLNStableDiffusionXLInpaintPipeline 텍스트 프롬프트에 따라 이미지의 마스킹된 영역 채우기.

중요: Guidance Scale에 따른 배치 크기 설정

배치 크기와 Guidance Scale

Stable Diffusion XL을 guidance scale > 1.0으로 사용할 때(기본값은 text-to-image와 image-to-image가 5.0, inpainting이 7.5), classifier-free guidance가 적용되어 UNet의 실제 배치 크기가 실행 시 2배가 됩니다.

RBLN NPU는 정적 그래프 컴파일을 사용하므로, 컴파일 시 UNet의 배치 크기가 실행 시 배치 크기와 일치해야 합니다. 그렇지 않으면 컴파일된 그래프를 실행할 수 없어 추론이 실패합니다.

기본 동작

UNet의 배치 크기를 명시적으로 지정하지 않는 경우, RBLN Optimum은 다음과 같이 동작합니다:

  • 파이프라인의 기본 guidance scale(text-to-image와 image-to-image는 5.0, inpainting은 7.5)을 사용한다고 가정합니다.
  • 자동으로 UNet의 배치 크기를 파이프라인 배치 크기의 2배로 설정합니다.

기본 guidance scale(1.0보다 큰 값)을 사용할 계획이라면, 이 자동 구성이 올바르게 작동합니다. 그러나 다른 guidance scale을 사용하거나 더 많은 제어가 필요한 경우에는 UNet의 배치 크기를 명시적으로 구성해야 합니다.

예시: UNet 배치 크기 명시적 설정

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import torch
from optimum.rbln import (
    RBLNStableDiffusionXLPipelineConfig,
    RBLNStableDiffusionXLPipeline,
)

# guidance_scale > 1.0인 경우 (기본값은 5.0)
# UNet의 배치 크기를 원하는 추론 배치 크기의 2배로 설정
config = RBLNStableDiffusionXLPipelineConfig(
    batch_size=2,  # 추론 배치 크기
    height=1024,
    width=1024,
    # UNet의 배치 크기를 2배로 구성
    unet=dict(batch_size=4),  # UNet 배치 크기를 2배로 설정
)

pipe = RBLNStableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    export=True,
    rbln_config=config,
)

# text-to-image 기본 guidance_scale=5.0으로 일반 추론
prompts = [
    "A cinematic shot of a baby raccoon wearing an intricate italian priest robe",
    "A photo of a Corgi dog surfing a wave",
]
images = pipe(prompts).images  # 배치 크기 2

# 생성된 이미지 저장
for i, image in enumerate(images):
    image.save(f"sdxl_generated_image_{i}.png")
    print(f"이미지 {i}가 sdxl_generated_image_{i}.png로 저장되었습니다")

예시: Guidance 비활성화(guidance_scale = 0.0)

SDXL-Turbo와 같은 모델이나 classifier-free guidance를 비활성화하는 경우(guidance_scale=0.0 설정), UNet 배치 크기를 추론 배치 크기와 일치하도록 명시적으로 설정합니다:

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import torch
from optimum.rbln import (
    RBLNStableDiffusionXLPipelineConfig,
    RBLNStableDiffusionXLPipeline,
)

config = RBLNStableDiffusionXLPipelineConfig(
    height=1024,
    width=1024,
    # UNet 배치 크기가 추론 배치 크기와 일치
    unet=dict(batch_size=1),
)

pipe = RBLNStableDiffusionXLPipeline.from_pretrained(
    "stabilityai/sdxl-turbo",
    torch_dtype=torch.float16,
    variant="fp16",
    export=True,
    rbln_config=config,
)

# Guidance 없이 추론하려면 guidance_scale=0.0 사용
# 이는 sdxl-turbo와 같은 모델에서 자주 사용되며 더 적은 추론 단계가 필요할 수 있습니다.
prompt = "A cinematic shot of a baby raccoon wearing an intricate italian priest robe"
image = pipe(prompt, guidance_scale=0.0, num_inference_steps=1).images[
    0
]  # guidance_scale=0.0 사용

# 생성된 이미지 저장
image.save("sdxl_no_guidance_image.png")

사용 예제

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import torch
from optimum.rbln import (
    RBLNStableDiffusionXLPipeline,
    RBLNStableDiffusionXLPipelineConfig,
)

# 특정 설정(예: 1024x1024)으로 구성 객체 생성
config = RBLNStableDiffusionXLPipelineConfig(height=1024, width=1024)

# RBLN NPU용 SDXL 모델 로드 및 컴파일
pipe = RBLNStableDiffusionXLPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype=torch.float16,
    variant="fp16",
    export=True,
    rbln_config=config,
)

# 이미지 생성
prompt = "A cinematic shot of a baby raccoon wearing an intricate italian priest robe"
image = pipe(prompt).images[0]

# 생성된 이미지 저장
image.save("sdxl_raccoon_priest.png")
print("이미지가 sdxl_raccoon_priest.png로 저장되었습니다")

API 참조

RBLNStableDiffusionXLPipeline

Bases: RBLNDiffusionMixin, StableDiffusionXLPipeline

RBLN-accelerated implementation of Stable Diffusion XL pipeline for high-resolution text-to-image generation.

This pipeline compiles Stable Diffusion XL models to run efficiently on RBLN NPUs, enabling high-performance inference for generating high-quality images with enhanced detail and improved prompt adherence.

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config=None, 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 | None

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 str | PathLike[str] | None

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] | RBLNModelConfig | None

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.

 None
lora_ids str | list[str] | None

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 str | list[str] | None

Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.

 None
lora_scales float | list[float] | None

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
Self

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.

RBLNStableDiffusionXLImg2ImgPipeline

Bases: RBLNDiffusionMixin, StableDiffusionXLImg2ImgPipeline

RBLN-accelerated implementation of Stable Diffusion XL pipeline for high-resolution image-to-image generation.

This pipeline compiles Stable Diffusion XL models to run efficiently on RBLN NPUs, enabling high-performance inference for transforming input images with enhanced quality and detail preservation.

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config=None, 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 | None

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 str | PathLike[str] | None

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] | RBLNModelConfig | None

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.

 None
lora_ids str | list[str] | None

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 str | list[str] | None

Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.

 None
lora_scales float | list[float] | None

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
Self

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.

RBLNStableDiffusionXLInpaintPipeline

Bases: RBLNDiffusionMixin, StableDiffusionXLInpaintPipeline

RBLN-accelerated implementation of Stable Diffusion XL pipeline for high-resolution image inpainting.

This pipeline compiles Stable Diffusion XL models to run efficiently on RBLN NPUs, enabling high-performance inference for filling masked regions with enhanced quality and seamless blending capabilities.

from_pretrained(model_id, *, export=None, model_save_dir=None, rbln_config=None, 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 | None

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 str | PathLike[str] | None

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] | RBLNModelConfig | None

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.

 None
lora_ids str | list[str] | None

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 str | list[str] | None

Names of specific LoRA weight files to load, corresponding to lora_ids. Only used when export=True.

 None
lora_scales float | list[float] | None

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
Self

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.

RBLNStableDiffusionXLPipelineBaseConfig

Bases: RBLNModelConfig

__init__(text_encoder=None, text_encoder_2=None, unet=None, vae=None, *, batch_size=None, img_height=None, img_width=None, height=None, width=None, sample_size=None, image_size=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
text_encoder RBLNCLIPTextModelConfig | None

Configuration for the primary text encoder component. Initialized as RBLNCLIPTextModelConfig if not provided.

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

Configuration for the secondary text encoder component. Initialized as RBLNCLIPTextModelWithProjectionConfig if not provided.

 None
unet RBLNUNet2DConditionModelConfig | None

Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

Configuration for the VAE model component. Initialized as RBLNAutoencoderKLConfig if not provided.

 None
batch_size int | None

Batch size for inference, applied to all submodules.

 None
img_height int | None

Height of the generated images.

 None
img_width int | None

Width of the generated images.

 None
height int | None

Height of the generated images.

 None
width int | None

Width of the generated images.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the UNet model.

 None
image_size tuple[int, int] | None

Alternative way to specify image dimensions. Cannot be used together with img_height/img_width.

 None
guidance_scale float | None

Scale for classifier-free guidance.

 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.

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 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 Self | tuple[Self, 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:

1
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.

RBLNStableDiffusionXLPipelineConfig

Bases: RBLNStableDiffusionXLPipelineBaseConfig

Config for SDXL Text2Img Pipeline

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

__init__(text_encoder=None, text_encoder_2=None, unet=None, vae=None, *, batch_size=None, img_height=None, img_width=None, height=None, width=None, sample_size=None, image_size=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
text_encoder RBLNCLIPTextModelConfig | None

Configuration for the primary text encoder component. Initialized as RBLNCLIPTextModelConfig if not provided.

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

Configuration for the secondary text encoder component. Initialized as RBLNCLIPTextModelWithProjectionConfig if not provided.

 None
unet RBLNUNet2DConditionModelConfig | None

Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

Configuration for the VAE model component. Initialized as RBLNAutoencoderKLConfig if not provided.

 None
batch_size int | None

Batch size for inference, applied to all submodules.

 None
img_height int | None

Height of the generated images.

 None
img_width int | None

Width of the generated images.

 None
height int | None

Height of the generated images.

 None
width int | None

Width of the generated images.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the UNet model.

 None
image_size tuple[int, int] | None

Alternative way to specify image dimensions. Cannot be used together with img_height/img_width.

 None
guidance_scale float | None

Scale for classifier-free guidance.

 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.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.

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 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 Self | tuple[Self, 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:

1
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

RBLNStableDiffusionXLImg2ImgPipelineConfig

Bases: RBLNStableDiffusionXLPipelineBaseConfig

Config for SDXL Img2Img Pipeline

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

__init__(text_encoder=None, text_encoder_2=None, unet=None, vae=None, *, batch_size=None, img_height=None, img_width=None, height=None, width=None, sample_size=None, image_size=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
text_encoder RBLNCLIPTextModelConfig | None

Configuration for the primary text encoder component. Initialized as RBLNCLIPTextModelConfig if not provided.

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

Configuration for the secondary text encoder component. Initialized as RBLNCLIPTextModelWithProjectionConfig if not provided.

 None
unet RBLNUNet2DConditionModelConfig | None

Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

Configuration for the VAE model component. Initialized as RBLNAutoencoderKLConfig if not provided.

 None
batch_size int | None

Batch size for inference, applied to all submodules.

 None
img_height int | None

Height of the generated images.

 None
img_width int | None

Width of the generated images.

 None
height int | None

Height of the generated images.

 None
width int | None

Width of the generated images.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the UNet model.

 None
image_size tuple[int, int] | None

Alternative way to specify image dimensions. Cannot be used together with img_height/img_width.

 None
guidance_scale float | None

Scale for classifier-free guidance.

 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.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.

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 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 Self | tuple[Self, 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:

1
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")

RBLNStableDiffusionXLInpaintPipelineConfig

Bases: RBLNStableDiffusionXLPipelineBaseConfig

Config for SDXL Inpainting Pipeline

initialize_submodule_config(submodule_config=None, force_kwargs=False, **kwargs)

Build the config of a submodule from the parent's runtime options and kwargs.

Returns the submodule config instance when its class is known (an instance was passed, or cls_name was given), otherwise the merged kwargs dict for the model class to resolve later.

__init__(text_encoder=None, text_encoder_2=None, unet=None, vae=None, *, batch_size=None, img_height=None, img_width=None, height=None, width=None, sample_size=None, image_size=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
text_encoder RBLNCLIPTextModelConfig | None

Configuration for the primary text encoder component. Initialized as RBLNCLIPTextModelConfig if not provided.

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

Configuration for the secondary text encoder component. Initialized as RBLNCLIPTextModelWithProjectionConfig if not provided.

 None
unet RBLNUNet2DConditionModelConfig | None

Configuration for the UNet model component. Initialized as RBLNUNet2DConditionModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

Configuration for the VAE model component. Initialized as RBLNAutoencoderKLConfig if not provided.

 None
batch_size int | None

Batch size for inference, applied to all submodules.

 None
img_height int | None

Height of the generated images.

 None
img_width int | None

Width of the generated images.

 None
height int | None

Height of the generated images.

 None
width int | None

Width of the generated images.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the UNet model.

 None
image_size tuple[int, int] | None

Alternative way to specify image dimensions. Cannot be used together with img_height/img_width.

 None
guidance_scale float | None

Scale for classifier-free guidance.

 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.

get_load_overrides()

Extract the options that must cross the load boundary, for passing to from_pretrained.

Returns the explicitly-set runtime options (device, device_map, create_runtimes, activate_profiler, timeout) and the subclass-specific non-save attributes (load-behavior flags such as _load_visual_runtime, which are never serialized so they can only travel with the caller), recursively including submodules. Compile-time attributes are not included: when loading a compiled model they come from the artifact's rbln_config.json, and this dict is merged on top of it.

Returns:

Type Description
dict[str, Any]

Dictionary of load overrides, keyed like the rbln_config dict accepted by from_pretrained.

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 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 Self | tuple[Self, 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:

1
config = RBLNResNetForImageClassificationConfig.from_pretrained("/path/to/model")