V1 API

V1 API#

FastVideo’s V1 API provides a streamlined interface for video generation tasks with powerful customization options. This page documents the primary components of the API.

Video Generator#

This class will be the primary Python API for generating videos and images.

fastvideo.VideoGenerator

A unified class for generating videos using diffusion models.
class VideoGenerator(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.v1.worker.executor.Executor], log_stats: bool)#

A unified class for generating videos using diffusion models.

This class provides a simple interface for video generation with rich customization options, similar to popular frameworks like HF Diffusers.

VideoGenerator.from_pretrained() should be the primary way of creating a new video generator.

classmethod from_pretrained(model_path: str, device: Optional[str] = None, torch_dtype: Optional[torch.dtype] = None, pipeline_config: Optional[Union[str | fastvideo.v1.configs.pipelines.PipelineConfig]] = None, **kwargs) → fastvideo.v1.entrypoints.video_generator.VideoGenerator#

Create a video generator from a pretrained model.

Parameters:

  • model_path – Path or identifier for the pretrained model

  • device – Device to load the model on (e.g., “cuda”, “cuda:0”, “cpu”)

  • torch_dtype – Data type for model weights (e.g., torch.float16)

  • **kwargs – Additional arguments to customize model loading

Returns:

The created video generator

Priority level: Default pipeline config < User’s pipeline config < User’s kwargs

Configuring FastVideo#

The follow two classes PipelineConfig and SamplingParam are used to configure initialization and sampling parameters, respectively.

PipelineConfig#

class PipelineConfig#

Base configuration for all pipeline architectures.

classmethod from_pretrained(model_path: str) → fastvideo.v1.configs.pipelines.base.PipelineConfig#
dump_to_json(file_path: str)#

SamplingParam#

class SamplingParam#

Sampling parameters for video generation.

classmethod from_pretrained(model_path: str) → fastvideo.v1.configs.sample.base.SamplingParam#
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