fastvideo.v1.entrypoints.video_generator
#
VideoGenerator module for FastVideo.
This module provides a consolidated interface for generating videos using diffusion models.
Module Contents#
Classes#
A unified class for generating videos using diffusion models. |
Data#
API#
- class fastvideo.v1.entrypoints.video_generator.VideoGenerator(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs, executor_class: type[fastvideo.v1.worker.executor.Executor], log_stats: bool)[source]#
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.
Initialization
Initialize the video generator.
- Parameters:
pipeline β The pipeline to use for inference
fastvideo_args β The inference arguments
- classmethod from_fastvideo_args(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs) fastvideo.v1.entrypoints.video_generator.VideoGenerator [source]#
Create a video generator with the specified arguments.
- Parameters:
fastvideo_args β The inference arguments
- Returns:
The created 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 [source]#
Create a video generator from a pretrained model.
- Parameters:
model_path β Path or identifier for the pretrained model
device β Device to load the model on (e.g., βcudaβ, βcuda:0β, βcpuβ)
torch_dtype β Data type for model weights (e.g., torch.float16)
**kwargs β Additional arguments to customize model loading
- Returns:
The created video generator
Priority level: Default pipeline config < Userβs pipeline config < Userβs kwargs
- generate_video(prompt: str, sampling_param: Optional[fastvideo.v1.configs.sample.SamplingParam] = None, **kwargs) Union[Dict[str, Any], List[numpy.ndarray]] [source]#
Generate a video based on the given prompt.
- Parameters:
prompt β The prompt to use for generation
negative_prompt β The negative prompt to use (overrides the one in fastvideo_args)
output_path β Path to save the video (overrides the one in fastvideo_args)
save_video β Whether to save the video to disk
return_frames β Whether to return the raw frames
num_inference_steps β Number of denoising steps (overrides fastvideo_args)
guidance_scale β Classifier-free guidance scale (overrides fastvideo_args)
num_frames β Number of frames to generate (overrides fastvideo_args)
height β Height of generated video (overrides fastvideo_args)
width β Width of generated video (overrides fastvideo_args)
fps β Frames per second for saved video (overrides fastvideo_args)
seed β Random seed for generation (overrides fastvideo_args)
callback β Callback function called after each step
callback_steps β Number of steps between each callback
- Returns:
Either the output dictionary or the list of frames depending on return_frames