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wan

Compatibility exports for the family-local Wan pipeline configuration.

Classes

fastvideo.configs.pipelines.wan.DiTConfig dataclass 

DiTConfig(arch_config: DiTArchConfig = DiTArchConfig(), prefix: str = '', quant_config: QuantizationConfig | None = None, *, _resolved_attention_backend: AttentionBackendEnum | None = None)

Bases: ModelConfig

Methods:

fastvideo.configs.pipelines.wan.DiTConfig.add_cli_args staticmethod 
add_cli_args(parser: Any, prefix: str = 'dit-config') -> Any

Add CLI arguments for DiTConfig fields

Source code in fastvideo/configs/models/dits/base.py 
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@staticmethod
def add_cli_args(parser: Any, prefix: str = "dit-config") -> Any:
    """Add CLI arguments for DiTConfig fields"""
    parser.add_argument(
        f"--{prefix}.prefix",
        type=str,
        dest=f"{prefix.replace('-', '_')}.prefix",
        default=DiTConfig.prefix,
        help="Prefix for the DiT model",
    )

    parser.add_argument(
        f"--{prefix}.quant-config",
        type=str,
        dest=f"{prefix.replace('-', '_')}.quant_config",
        default=None,
        help="Quantization configuration for the DiT model",
    )

    return parser

fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config dataclass 

FastWan2_1_T2V_480P_Config(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 8.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = (lambda: [1000, 757, 522])(), ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanT2V480PConfig

Base configuration for FastWan T2V 1.3B 480P pipeline architecture with DMD

fastvideo.configs.pipelines.wan.LucyEditDevConfig dataclass 

LucyEditDevConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = (lambda: WanVideoConfig(arch_config=WanVideoArchConfig(num_attention_heads=24, in_channels=96, out_channels=48, ffn_dim=14336, num_layers=30)))(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = (lambda: WanVAEConfig(arch_config=WanVAEArchConfig(base_dim=160, decoder_base_dim=256, z_dim=48, in_channels=12, out_channels=12, scale_factor_spatial=16, patch_size=2, is_residual=True, clip_output=False, latents_mean=(-0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, -0.1382, 0.0542, 0.2813, 0.0891, 0.157, -0.0098, 0.0375, -0.1825, -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.123, -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.052, 0.3748, 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667), latents_std=(0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.499, 0.4818, 0.5013, 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, 0.3971, 1.06, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744))))(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = True, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True, expand_timesteps: bool = True)

Bases: Wan2_2_TI2V_5B_Config

Configuration for Decart Lucy Edit Dev video editing.

fastvideo.configs.pipelines.wan.PipelineConfig dataclass 

PipelineConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = None, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = DiTConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = VAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str | None = None, vae_tiling: bool = True, vae_sp: bool = True, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (EncoderConfig(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], tensor], ...] = (lambda: (postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None)

Base configuration for all pipeline architectures.

Methods:

fastvideo.configs.pipelines.wan.PipelineConfig.from_kwargs classmethod 
from_kwargs(kwargs: dict[str, Any], config_cli_prefix: str = '') -> PipelineConfig

Load PipelineConfig from kwargs Dictionary. kwargs: dictionary of kwargs config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance

Source code in fastvideo/configs/pipelines/base.py 
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@classmethod
def from_kwargs(cls, kwargs: dict[str, Any], config_cli_prefix: str = "") -> "PipelineConfig":
    """
    Load PipelineConfig from kwargs Dictionary.
    kwargs: dictionary of kwargs
    config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
    """
    from fastvideo.registry import get_pipeline_config_cls_from_name

    prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip() != "") else ""
    model_path: str | None = kwargs.get(prefix_with_dot + 'model_path', None) or kwargs.get('model_path')
    pipeline_config_or_path: str | PipelineConfig | dict[str, Any] | None = kwargs.get(
        prefix_with_dot + 'pipeline_config', None) or kwargs.get('pipeline_config')
    if model_path is None:
        raise ValueError("model_path is required in kwargs")

    # 1. Get the pipeline config class from the registry
    pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

    # 2. Instantiate PipelineConfig
    if pipeline_config_cls is None:
        logger.warning("Couldn't find pipeline config for %s. Using the default pipeline config.", model_path)
        pipeline_config = cls()
    else:
        pipeline_config = pipeline_config_cls()

    # 3. Load PipelineConfig from a json file or a PipelineConfig object if provided
    if isinstance(pipeline_config_or_path, str):
        pipeline_config.load_from_json(pipeline_config_or_path)
        kwargs[prefix_with_dot + 'pipeline_config_path'] = pipeline_config_or_path
    elif isinstance(pipeline_config_or_path, PipelineConfig):
        pipeline_config = pipeline_config_or_path
    elif isinstance(pipeline_config_or_path, dict):
        pipeline_config.update_pipeline_config(pipeline_config_or_path)

    # 4. Update PipelineConfig from CLI arguments if provided
    kwargs[prefix_with_dot + 'model_path'] = model_path
    pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)

    return pipeline_config
fastvideo.configs.pipelines.wan.PipelineConfig.from_pretrained classmethod 
from_pretrained(model_path: str) -> PipelineConfig

use the pipeline class setting from model_path to match the pipeline config

Source code in fastvideo/configs/pipelines/base.py 
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@classmethod
def from_pretrained(cls, model_path: str) -> "PipelineConfig":
    """
    use the pipeline class setting from model_path to match the pipeline config
    """
    from fastvideo.registry import get_pipeline_config_cls_from_name
    pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

    return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))

fastvideo.configs.pipelines.wan.VAEConfig dataclass 

VAEConfig(arch_config: VAEArchConfig = VAEArchConfig(), load_encoder: bool = True, load_decoder: bool = True, tile_sample_min_height: int = 256, tile_sample_min_width: int = 256, tile_sample_min_num_frames: int = 16, tile_sample_stride_height: int = 192, tile_sample_stride_width: int = 192, tile_sample_stride_num_frames: int = 12, blend_num_frames: int = 0, use_tiling: bool = True, use_temporal_tiling: bool = True, use_parallel_tiling: bool = True, use_temporal_scaling_frames: bool = True, *, _resolved_attention_backend: AttentionBackendEnum | None = None)

Bases: ModelConfig

Methods:

fastvideo.configs.pipelines.wan.VAEConfig.add_cli_args staticmethod 
add_cli_args(parser: Any, prefix: str = 'vae-config') -> Any

Add CLI arguments for VAEConfig fields

Source code in fastvideo/configs/models/vaes/base.py 
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@staticmethod
def add_cli_args(parser: Any, prefix: str = "vae-config") -> Any:
    """Add CLI arguments for VAEConfig fields"""
    parser.add_argument(
        f"--{prefix}.load-encoder",
        action=StoreBoolean,
        dest=f"{prefix.replace('-', '_')}.load_encoder",
        default=VAEConfig.load_encoder,
        help="Whether to load the VAE encoder",
    )
    parser.add_argument(
        f"--{prefix}.load-decoder",
        action=StoreBoolean,
        dest=f"{prefix.replace('-', '_')}.load_decoder",
        default=VAEConfig.load_decoder,
        help="Whether to load the VAE decoder",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-min-height",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_min_height",
        default=VAEConfig.tile_sample_min_height,
        help="Minimum height for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-min-width",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_min_width",
        default=VAEConfig.tile_sample_min_width,
        help="Minimum width for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-min-num-frames",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_min_num_frames",
        default=VAEConfig.tile_sample_min_num_frames,
        help="Minimum number of frames for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-stride-height",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_stride_height",
        default=VAEConfig.tile_sample_stride_height,
        help="Stride height for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-stride-width",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_stride_width",
        default=VAEConfig.tile_sample_stride_width,
        help="Stride width for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.tile-sample-stride-num-frames",
        type=int,
        dest=f"{prefix.replace('-', '_')}.tile_sample_stride_num_frames",
        default=VAEConfig.tile_sample_stride_num_frames,
        help="Stride number of frames for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.blend-num-frames",
        type=int,
        dest=f"{prefix.replace('-', '_')}.blend_num_frames",
        default=VAEConfig.blend_num_frames,
        help="Number of frames to blend for VAE tile sampling",
    )
    parser.add_argument(
        f"--{prefix}.use-tiling",
        action=StoreBoolean,
        dest=f"{prefix.replace('-', '_')}.use_tiling",
        default=VAEConfig.use_tiling,
        help="Whether to use tiling for VAE",
    )
    parser.add_argument(
        f"--{prefix}.use-temporal-tiling",
        action=StoreBoolean,
        dest=f"{prefix.replace('-', '_')}.use_temporal_tiling",
        default=VAEConfig.use_temporal_tiling,
        help="Whether to use temporal tiling for VAE",
    )
    parser.add_argument(
        f"--{prefix}.use-parallel-tiling",
        action=StoreBoolean,
        dest=f"{prefix.replace('-', '_')}.use_parallel_tiling",
        default=VAEConfig.use_parallel_tiling,
        help="Whether to use parallel tiling for VAE",
    )

    return parser

fastvideo.configs.pipelines.wan.WANV2VConfig dataclass 

WANV2VConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = WAN2_1ControlCLIPVisionConfig(), image_encoder_precision: str = 'bf16', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanI2V480PConfig

Configuration for WAN2.1 1.3B Control pipeline.

fastvideo.configs.pipelines.wan.WanI2V480PConfig dataclass 

WanI2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = CLIPVisionConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanT2V480PConfig

Base configuration for Wan I2V 14B 480P pipeline architecture.

fastvideo.configs.pipelines.wan.WanI2V720PConfig dataclass 

WanI2V720PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = CLIPVisionConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanI2V480PConfig

Base configuration for Wan I2V 14B 720P pipeline architecture.

fastvideo.configs.pipelines.wan.WanT2V480PConfig dataclass 

WanT2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 3.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: PipelineConfig

Base configuration for Wan T2V 1.3B pipeline architecture.

fastvideo.configs.pipelines.wan.WanT2V720PConfig dataclass 

WanT2V720PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = 5.0, flow_shift_sr: float | None = None, disable_autocast: bool = False, scheduler_step_in_fp32: bool = False, is_causal: bool = False, dit_config: DiTConfig = WanVideoConfig(), dit_precision: str = 'bf16', upsampler_config: UpsamplerConfig = UpsamplerConfig(), upsampler_precision: str = 'fp32', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'fp32', vae_decode_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', image_encoder_configs: tuple[EncoderConfig, ...] | None = None, image_encoder_precisions: tuple[str, ...] | None = None, text_encoder_configs: tuple[EncoderConfig, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('fp32',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (t5_postprocess_text,))(), dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, lucy_edit_task: bool = False, boundary_ratio: float | None = None, precision: str = 'bf16', warp_denoising_step: bool = True)

Bases: WanT2V480PConfig

Base configuration for Wan T2V 14B 720P pipeline architecture.