Stable Video Diffusion

Stable Video Diffusion은 입력 이미지로부터 비디오를 생성할 수 있는 이미지-비디오 잠재 확산 모델입니다. RBLN NPU는 Optimum RBLN을 사용하여 Stable Video Diffusion 파이프라인을 가속화할 수 있습니다.

지원하는 파이프라인

Optimum RBLN은 아래 Stable Video Diffusion 파이프라인을 지원합니다:

• 이미지-비디오 변환(Image-to-Video): 입력 이미지에서 비디오 생성

중요: Guidance Scale에 따른 배치 크기 설정

배치 크기와 Guidance Scale

Stable Video Diffusion을 max guidance scale > 1.0으로 사용할 때(기본값은 3.0), classifier-free guidance 기법으로 인해 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 추론 중에 오류가 발생합니다.

기본 동작

UNet의 배치 크기를 명시적으로 지정하지 않는 경우, Optimum RBLN은 다음과 같이 동작합니다:

• 기본 max guidance scale(3.0)을 사용한다고 가정합니다
• 자동으로 UNet의 배치 크기를 파이프라인 배치 크기의 2배로 설정합니다

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

예시: UNet 배치 크기 명시적 설정

from optimum.rbln import (
    RBLNStableVideoDiffusionPipelineConfig,
    RBLNStableVideoDiffusionPipeline,
)
from diffusers.utils import export_to_video, load_image

# max_guidance_scale > 1.0인 경우 (기본값은 3.0)
# UNet의 배치 크기를 원하는 추론 배치 크기의 2배로 설정
config = RBLNStableVideoDiffusionPipelineConfig(
    batch_size=2,  # 추론 배치 크기
    height=576,
    width=1024,
    # UNet의 배치 크기를 2배로 구성
    unet=dict(batch_size=4, device=1),  # UNet 배치 크기를 2배로 설정
)

pipe = RBLNStableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt-1-1", export=True, rbln_config=config
)

# 기본 max_guidance_scale=3.0로 일반 추론
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"
image = load_image(url).resize((1024, 576))
generator = torch.manual_seed(42)
frames = pipe(image=[image, image], generator=generator).frames[0]

# 생성된 비디오 저장
export_to_video(frames, "svd_guided.mp4", fps=7)
print("svd_guided.mp4로 비디오가 저장되었습니다")

예시: Max Guidance Scale 1.0 사용

from optimum.rbln import (
    RBLNStableVideoDiffusionPipelineConfig,
    RBLNStableVideoDiffusionPipeline,
)
from diffusers.utils import export_to_video, load_image

config = RBLNStableVideoDiffusionPipelineConfig(
    height=576,
    width=1024,
    # UNet 배치 크기가 추론 배치 크기와 일치
    unet=dict(batch_size=1),
)

pipe = RBLNStableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt-1-1", export=True, rbln_config=config
)

# 추론 시 반드시 guidance_scale=1.0 사용
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"
image = load_image(url).resize((1024, 576))
generator = torch.manual_seed(42)
frames = pipe(image=image, generator=generator, max_guidance_scale=1.0).frames[0]

# 생성된 비디오 저장
export_to_video(frames, "가이던스_없는_비디오.mp4", fps=7)
print("비디오가 가이던스_없는_비디오.mp4로 저장되었습니다")

사용 예제

from optimum.rbln import (
    RBLNStableVideoDiffusionPipelineConfig,
    RBLNStableVideoDiffusionPipeline,
)
from diffusers.utils import export_to_video, load_image

# 특정 설정으로 구성 객체 생성
config = RBLNStableVideoDiffusionPipelineConfig(
    height=576,
    width=1024,
)

# RBLN NPU용 모델 로드 및 컴파일
pipe = RBLNStableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt-1-1", export=True, rbln_config=config
)

# 비디오 생성
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png"
image = load_image(url).resize((1024, 576))
generator = torch.manual_seed(42)
frames = pipe(image=image, generator=generator).frames[0]

# 생성된 비디오 저장
export_to_video(frames, "로켓_비디오.mp4", fps=7)
print("비디오가 로켓_비디오.mp4로 저장되었습니다")

API 참조

Classes

RBLNStableVideoDiffusionPipeline

Bases: RBLNDiffusionMixin, StableVideoDiffusionPipeline

RBLN-accelerated implementation of Stable Video Diffusion pipeline for image-to-video generation.

This pipeline compiles Stable Video Diffusion models to run efficiently on RBLN NPUs, enabling high-performance inference for generating videos from images with optimized memory usage and throughput.

Methods:

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

Functions:

Classes

RBLNStableVideoDiffusionPipelineConfig

Bases: RBLNModelConfig

Methods:

__init__(image_encoder=None, unet=None, vae=None, *, batch_size=None, height=None, width=None, num_frames=None, decode_chunk_size=None, guidance_scale=None, **kwargs)

Parameters:

Name	Type	Description	Default
image_encoder	RBLNCLIPVisionModelWithProjectionConfig | None	Configuration for the image encoder component. Initialized as RBLNCLIPVisionModelWithProjectionConfig if not provided.	None
unet	RBLNUNetSpatioTemporalConditionModelConfig | None	Configuration for the UNet model component. Initialized as RBLNUNetSpatioTemporalConditionModelConfig if not provided.	None
vae	RBLNAutoencoderKLTemporalDecoderConfig | None	Configuration for the VAE model component. Initialized as RBLNAutoencoderKLTemporalDecoderConfig if not provided.	None
batch_size	int | None	Batch size for inference, applied to all submodules.	None
height	int | None	Height of the generated images.	None
width	int | None	Width of the generated images.	None
num_frames	int | None	The number of frames in the generated video.	None
decode_chunk_size	int | None	The number of frames to decode at once during VAE decoding. Useful for managing memory usage during video generation.	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 height/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	dict[str, Any] | None	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	Union[RBLNModelConfig, tuple[RBLNModelConfig, 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:

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