# SPDX-License-Identifier: Apache-2.0 from dataclasses import dataclass from typing import Any from fastvideo.logger import init_logger logger = init_logger(__name__) @dataclass class SamplingParam: """ Sampling parameters for video generation. """ # All fields below are copied from ForwardBatch data_type: str = "video" # Image inputs image_path: str | None = None # Text inputs prompt: str | list[str] | None = None negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" prompt_path: str | None = None output_path: str = "outputs/" output_video_name: str | None = None # Batch info num_videos_per_prompt: int = 1 seed: int = 1024 # Original dimensions (before VAE scaling) num_frames: int = 125 num_frames_round_down: bool = False # Whether to round down num_frames if it's not divisible by num_gpus height: int = 720 width: int = 1280 fps: int = 24 # Denoising parameters num_inference_steps: int = 50 guidance_scale: float = 1.0 guidance_rescale: float = 0.0 boundary_ratio: float | None = None # TeaCache parameters enable_teacache: bool = False # Misc save_video: bool = True return_frames: bool = False return_trajectory_latents: bool = False # returns all latents for each timestep return_trajectory_decoded: bool = False # returns decoded latents for each timestep def __post_init__(self) -> None: self.data_type = "video" if self.num_frames > 1 else "image" def check_sampling_param(self): if self.prompt_path and not self.prompt_path.endswith(".txt"): raise ValueError("prompt_path must be a txt file") def update(self, source_dict: dict[str, Any]) -> None: for key, value in source_dict.items(): if hasattr(self, key): setattr(self, key, value) else: logger.exception("%s has no attribute %s", type(self).__name__, key) self.__post_init__() @classmethod def from_pretrained(cls, model_path: str) -> "SamplingParam": from fastvideo.configs.sample.registry import ( get_sampling_param_cls_for_name) sampling_cls = get_sampling_param_cls_for_name(model_path) if sampling_cls is not None: sampling_param: SamplingParam = sampling_cls() else: logger.warning( "Couldn't find an optimal sampling param for %s. Using the default sampling param.", model_path) sampling_param = cls() return sampling_param @staticmethod def add_cli_args(parser: Any) -> Any: """Add CLI arguments for SamplingParam fields""" parser.add_argument( "--prompt", type=str, default=SamplingParam.prompt, help="Text prompt for video generation", ) parser.add_argument( "--negative-prompt", type=str, default=SamplingParam.negative_prompt, help="Negative text prompt for video generation", ) parser.add_argument( "--prompt-path", type=str, default=SamplingParam.prompt_path, help="Path to a text file containing the prompt", ) parser.add_argument( "--output-path", type=str, default=SamplingParam.output_path, help="Path to save the generated video", ) parser.add_argument( "--output-video-name", type=str, default=SamplingParam.output_video_name, help="Name of the output video", ) parser.add_argument( "--num-videos-per-prompt", type=int, default=SamplingParam.num_videos_per_prompt, help="Number of videos to generate per prompt", ) parser.add_argument( "--seed", type=int, default=SamplingParam.seed, help="Random seed for generation", ) parser.add_argument( "--num-frames", type=int, default=SamplingParam.num_frames, help="Number of frames to generate", ) parser.add_argument( "--height", type=int, default=SamplingParam.height, help="Height of generated video", ) parser.add_argument( "--width", type=int, default=SamplingParam.width, help="Width of generated video", ) parser.add_argument( "--fps", type=int, default=SamplingParam.fps, help="Frames per second for saved video", ) parser.add_argument( "--num-inference-steps", type=int, default=SamplingParam.num_inference_steps, help="Number of denoising steps", ) parser.add_argument( "--guidance-scale", type=float, default=SamplingParam.guidance_scale, help="Classifier-free guidance scale", ) parser.add_argument( "--guidance-rescale", type=float, default=SamplingParam.guidance_rescale, help="Guidance rescale factor", ) parser.add_argument( "--boundary-ratio", type=float, default=SamplingParam.boundary_ratio, help="Boundary timestep ratio", ) parser.add_argument( "--save-video", action="store_true", default=SamplingParam.save_video, help="Whether to save the video to disk", ) parser.add_argument( "--no-save-video", action="store_false", dest="save_video", help="Don't save the video to disk", ) parser.add_argument( "--return-frames", action="store_true", default=SamplingParam.return_frames, help="Whether to return the raw frames", ) parser.add_argument( "--image-path", type=str, default=SamplingParam.image_path, help="Path to input image for image-to-video generation", ) parser.add_argument( "--moba-config-path", type=str, default=None, help= "Path to a JSON file containing V-MoBA specific configurations.", ) parser.add_argument( "--return-trajectory-latents", action="store_true", default=SamplingParam.return_trajectory_latents, help="Whether to return the trajectory", ) parser.add_argument( "--return-trajectory-decoded", action="store_true", default=SamplingParam.return_trajectory_decoded, help="Whether to return the decoded trajectory", ) return parser @dataclass class CacheParams: cache_type: str = "none"