hao-ai-lab-FastVideo/fastvideo/v1/configs/sample/base.py

from dataclasses import dataclass
from typing import Any

from fastvideo.v1.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 | None = None
    prompt_path: str | None = None
    output_path: str = "outputs/"

    # 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

    # TeaCache parameters
    enable_teacache: bool = False

    # Misc
    save_video: bool = True
    return_frames: bool = False

    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.v1.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(
            "--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(
            "--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",
        )
        return parser


@dataclass
class CacheParams:
    cache_type: str = "none"