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Stable Diffusion 3

Stable Diffusion 3 (SD3) is a text-to-image model from Stability AI built on a Multimodal Diffusion Transformer (MMDiT) architecture. It excels at handling complex prompts involving multiple subjects, spatial relationships, and diverse text styles. SD3 uses three distinct text encoders (CLIP-L, OpenCLIP-G, T5-XXL) for enhanced prompt interpretation. RBLN NPUs can accelerate Stable Diffusion 3 pipelines using RBLN Optimum.

Supported NPU

Diffusers pipelines run on ATOM™ only. Rebel100™ does not support them yet.

Supported Pipelines

RBLN Optimum supports several Stable Diffusion 3 pipelines:

Pipeline Class Description
Text-to-Image RBLNStableDiffusion3Pipeline Generate images from text prompts.
Image-to-Image RBLNStableDiffusion3Img2ImgPipeline Modify existing images based on text prompts.
Inpainting RBLNStableDiffusion3InpaintPipeline Fill masked regions of an image guided by text prompts.

Important: Batch Size Configuration for Guidance Scale

Batch Size and Guidance Scale

When using Stable Diffusion 3 with a guidance scale > 1.0 (the default is 7.0), the MMDiT Transformer's effective batch size is doubled during runtime because of the classifier-free guidance technique.

Because RBLN NPUs use static graph compilation, the Transformer's batch size at compilation time must match its runtime batch size. Otherwise, the compiled graph cannot execute and inference fails.

Default Behavior

By default, if you do not explicitly specify the Transformer's batch size, RBLN Optimum does the following:

  • Assumes you'll use a guidance scale > 1.0.
  • Automatically sets the Transformer's batch size to 2× your pipeline's batch size.

If you plan to use the default guidance scale (which is > 1.0), this automatic configuration works correctly. However, if you plan to use a different guidance scale or want more control, explicitly configure the Transformer's batch size.

Example: Explicitly Setting the Transformer Batch Size (guidance_scale > 1.0)

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import torch
from optimum.rbln import (
    RBLNStableDiffusion3PipelineConfig,
    RBLNStableDiffusion3Pipeline,
)

# For inference with guidance_scale > 1.0 (e.g., default 7.0)
# Set Transformer's batch size to 2 * your desired inference batch size
config = RBLNStableDiffusion3PipelineConfig(
    # text_encoder_3 (~8.7 GiB) does not fit on the same ATOM™ as the rest; give it a second device
    text_encoder={"device": 0},
    text_encoder_2={"device": 0},
    text_encoder_3={"device": 1},
    vae={"device": 0},
    batch_size=1,  # Inference batch size
    height=1024,
    width=1024,
    # Configure Transformer with doubled batch size
    transformer=dict(batch_size=2, device=0),  # Double the batch size for Transformer
)

pipe = RBLNStableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers",
    torch_dtype=torch.float16,
    export=True,
    rbln_config=config,
)

# Normal inference with default guidance_scale (e.g., 7.0)
prompt = "A cat riding a bike through a field of sunflowers, realistic photo."
image = pipe(prompt).images[0]  # Batch size of 1

# Save the generated image
image.save("sd3_generated_image.png")
print("Image saved as sd3_generated_image.png")

Example: Disabling Guidance (guidance_scale = 0.0)

If you plan to use a guidance scale of exactly 0.0 (disabling classifier-free guidance), explicitly set the Transformer batch size to match your inference batch size:

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import torch
from optimum.rbln import (
    RBLNStableDiffusion3PipelineConfig,
    RBLNStableDiffusion3Pipeline,
)

config = RBLNStableDiffusion3PipelineConfig(
    # text_encoder_3 (~8.7 GiB) does not fit on the same ATOM™ as the rest; give it a second device
    text_encoder={"device": 0},
    text_encoder_2={"device": 0},
    text_encoder_3={"device": 1},
    vae={"device": 0},
    height=1024,
    width=1024,
    # Transformer batch size matches the inference batch size
    transformer=dict(batch_size=1, device=0),
)

pipe = RBLNStableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers",
    torch_dtype=torch.float16,
    export=True,
    rbln_config=config,
)

# Must use guidance_scale=0.0 during inference
prompt = "A cat riding a bike through a field of sunflowers, realistic photo."
image = pipe(prompt, guidance_scale=0.0).images[0]

# Save the generated image
image.save("sd3_no_guidance_image.png")
print("Image saved as sd3_no_guidance_image.png")

Usage Example (Text-to-Image)

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import torch
from optimum.rbln import (
    RBLNStableDiffusion3Pipeline,
    RBLNStableDiffusion3PipelineConfig,
)

# Create a configuration object (optional, can use defaults)
config = RBLNStableDiffusion3PipelineConfig(
    height=1024,
    width=1024,
    # text_encoder_3 (~8.7 GiB) does not fit on the same ATOM™ as the rest; give it a second device
    text_encoder={"device": 0},
    text_encoder_2={"device": 0},
    text_encoder_3={"device": 1},
    vae={"device": 0},
    transformer={"device": 0},
)

# Load and compile the SD3 model for RBLN NPU
pipe = RBLNStableDiffusion3Pipeline.from_pretrained(
    "stabilityai/stable-diffusion-3-medium-diffusers",
    torch_dtype=torch.float16,
    export=True,
    rbln_config=config,
)

# Generate an image
prompt = "A cat riding a bike through a field of sunflowers, realistic photo."
image = pipe(prompt).images[0]

# Save the generated image
image.save("sd3_cat_bike.png")
print("Image saved as sd3_cat_bike.png")

API Reference

RBLNStableDiffusion3Pipeline

Bases: RBLNDiffusionMixin, StableDiffusion3Pipeline

RBLN-accelerated implementation of Stable Diffusion 3 pipeline for advanced text-to-image generation.

This pipeline compiles Stable Diffusion 3 models to run efficiently on RBLN NPUs, enabling high-performance inference with improved text understanding, enhanced image quality, and superior 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.

RBLNStableDiffusion3Img2ImgPipeline

Bases: RBLNDiffusionMixin, StableDiffusion3Img2ImgPipeline

RBLN-accelerated implementation of Stable Diffusion 3 pipeline for advanced image-to-image generation.

This pipeline compiles Stable Diffusion 3 models to run efficiently on RBLN NPUs, enabling high-performance inference for transforming input images with superior text understanding and enhanced visual quality.

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.

RBLNStableDiffusion3InpaintPipeline

Bases: RBLNDiffusionMixin, StableDiffusion3InpaintPipeline

RBLN-accelerated implementation of Stable Diffusion 3 pipeline for advanced image inpainting.

This pipeline compiles Stable Diffusion 3 models to run efficiently on RBLN NPUs, enabling high-performance inference for filling masked regions with superior text understanding and seamless content generation.

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.

RBLNStableDiffusion3PipelineBaseConfig

Bases: RBLNModelConfig

__init__(transformer=None, text_encoder=None, text_encoder_2=None, text_encoder_3=None, vae=None, *, max_seq_len=None, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
transformer RBLNSD3Transformer2DModelConfig | None

Configuration for the transformer model component. Initialized as RBLNSD3Transformer2DModelConfig if not provided.

 None
text_encoder RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_3 RBLNT5EncoderModelConfig | None

Configuration for the tertiary text encoder. Initialized as RBLNT5EncoderModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

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

 None
max_seq_len int | None

Maximum sequence length for text inputs. Defaults to 256.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the transformer model.

 None
image_size tuple[int, int] | None

Dimensions for the generated images. Cannot be used together with img_height/img_width.

 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
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 transformer 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.

RBLNStableDiffusion3PipelineConfig

Bases: RBLNStableDiffusion3PipelineBaseConfig

Config for SD3 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__(transformer=None, text_encoder=None, text_encoder_2=None, text_encoder_3=None, vae=None, *, max_seq_len=None, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
transformer RBLNSD3Transformer2DModelConfig | None

Configuration for the transformer model component. Initialized as RBLNSD3Transformer2DModelConfig if not provided.

 None
text_encoder RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_3 RBLNT5EncoderModelConfig | None

Configuration for the tertiary text encoder. Initialized as RBLNT5EncoderModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

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

 None
max_seq_len int | None

Maximum sequence length for text inputs. Defaults to 256.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the transformer model.

 None
image_size tuple[int, int] | None

Dimensions for the generated images. Cannot be used together with img_height/img_width.

 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
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 transformer 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")

RBLNStableDiffusion3Img2ImgPipelineConfig

Bases: RBLNStableDiffusion3PipelineBaseConfig

Config for SD3 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__(transformer=None, text_encoder=None, text_encoder_2=None, text_encoder_3=None, vae=None, *, max_seq_len=None, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
transformer RBLNSD3Transformer2DModelConfig | None

Configuration for the transformer model component. Initialized as RBLNSD3Transformer2DModelConfig if not provided.

 None
text_encoder RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_3 RBLNT5EncoderModelConfig | None

Configuration for the tertiary text encoder. Initialized as RBLNT5EncoderModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

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

 None
max_seq_len int | None

Maximum sequence length for text inputs. Defaults to 256.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the transformer model.

 None
image_size tuple[int, int] | None

Dimensions for the generated images. Cannot be used together with img_height/img_width.

 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
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 transformer 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")

RBLNStableDiffusion3InpaintPipelineConfig

Bases: RBLNStableDiffusion3PipelineBaseConfig

Config for SD3 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__(transformer=None, text_encoder=None, text_encoder_2=None, text_encoder_3=None, vae=None, *, max_seq_len=None, sample_size=None, image_size=None, batch_size=None, img_height=None, img_width=None, height=None, width=None, guidance_scale=None, **kwargs)

Parameters:

Name Type Description Default
transformer RBLNSD3Transformer2DModelConfig | None

Configuration for the transformer model component. Initialized as RBLNSD3Transformer2DModelConfig if not provided.

 None
text_encoder RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_2 RBLNCLIPTextModelWithProjectionConfig | None

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

 None
text_encoder_3 RBLNT5EncoderModelConfig | None

Configuration for the tertiary text encoder. Initialized as RBLNT5EncoderModelConfig if not provided.

 None
vae RBLNAutoencoderKLConfig | None

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

 None
max_seq_len int | None

Maximum sequence length for text inputs. Defaults to 256.

 None
sample_size tuple[int, int] | None

Spatial dimensions for the transformer model.

 None
image_size tuple[int, int] | None

Dimensions for the generated images. Cannot be used together with img_height/img_width.

 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
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 transformer 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")