longcat ¶

Classes¶

fastvideo.configs.pipelines.longcat.LongCatDiTArchConfig `dataclass` ¶

LongCatDiTArchConfig(stacked_params_mapping: list[tuple[str, str, str]] = list(), _fsdp_shard_conditions: list = list(), _compile_conditions: list = list(), param_names_mapping: dict = dict(), reverse_param_names_mapping: dict = dict(), lora_param_names_mapping: dict = dict(), _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (SLIDING_TILE_ATTN, SAGE_ATTN, FLASH_ATTN, TORCH_SDPA, VIDEO_SPARSE_ATTN, VMOBA_ATTN, SAGE_ATTN_THREE), hidden_size: int = 0, num_attention_heads: int = 0, num_channels_latents: int = 0, in_channels: int = 16, out_channels: int = 16, exclude_lora_layers: list[str] = list(), boundary_ratio: float | None = None, adaln_tembed_dim: int = 512, caption_channels: int = 4096, depth: int = 48, enable_bsa: bool = False, enable_flashattn3: bool = False, enable_flashattn2: bool = True, enable_xformers: bool = False, frequency_embedding_size: int = 256, mlp_ratio: int = 4, num_heads: int = 32, text_tokens_zero_pad: bool = True, patch_size: list[int] = (lambda: [1, 2, 2])(), cp_split_hw: list[int] | None = None, bsa_params: dict | None = None)

Bases: DiTArchConfig

Extended DiTArchConfig with LongCat-specific fields.

NOTE: This is for Phase 1 wrapper compatibility. For native model (Phase 2), use LongCatVideoConfig from fastvideo.configs.models.dits.longcat instead.

fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig `dataclass` ¶

LongCatT2V480PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = None, disable_autocast: bool = False, is_causal: bool = False, dit_config: DiTConfig = (lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))(), dit_precision: str = 'bf16', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[T5Config, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (longcat_preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (umt5_postprocess_text,))(), pos_magic: str | None = None, neg_magic: str | None = None, timesteps_scale: bool | None = None, mask_strategy_file_path: str | None = None, STA_mode: STA_Mode = STA_INFERENCE, skip_time_steps: int = 15, dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, boundary_ratio: float | None = None, enable_kv_cache: bool = True, offload_kv_cache: bool = False, enable_bsa: bool = False, use_distill: bool = False, enhance_hf: bool = False, bsa_params: dict | None = None, bsa_sparsity: float | None = None, bsa_cdf_threshold: float | None = None, bsa_chunk_q: list[int] | None = None, bsa_chunk_k: list[int] | None = None, t_thresh: float | None = None)

Bases: PipelineConfig

Configuration for LongCat pipeline (480p) aligned to LongCat-Video modules.

Components expected by loaders

tokenizer: AutoTokenizer
text_encoder: UMT5EncoderModel
transformer: LongCatVideoTransformer3DModel (Phase 1 wrapper) OR LongCatTransformer3DModel (Phase 2 native)
vae: AutoencoderKLWan (Wan VAE, 4x8 compression)
scheduler: FlowMatchEulerDiscreteScheduler

fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig `dataclass` ¶

LongCatT2V704PConfig(model_path: str = '', pipeline_config_path: str | None = None, embedded_cfg_scale: float = 6.0, flow_shift: float | None = None, disable_autocast: bool = False, is_causal: bool = False, dit_config: DiTConfig = (lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))(), dit_precision: str = 'bf16', vae_config: VAEConfig = WanVAEConfig(), vae_precision: str = 'bf16', vae_tiling: bool = False, vae_sp: bool = False, image_encoder_config: EncoderConfig = EncoderConfig(), image_encoder_precision: str = 'fp32', text_encoder_configs: tuple[T5Config, ...] = (lambda: (T5Config(),))(), text_encoder_precisions: tuple[str, ...] = (lambda: ('bf16',))(), preprocess_text_funcs: tuple[Callable[[str], str], ...] = (lambda: (longcat_preprocess_text,))(), postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], Tensor], ...] = (lambda: (umt5_postprocess_text,))(), pos_magic: str | None = None, neg_magic: str | None = None, timesteps_scale: bool | None = None, mask_strategy_file_path: str | None = None, STA_mode: STA_Mode = STA_INFERENCE, skip_time_steps: int = 15, dmd_denoising_steps: list[int] | None = None, ti2v_task: bool = False, boundary_ratio: float | None = None, enable_kv_cache: bool = True, offload_kv_cache: bool = False, enable_bsa: bool = True, use_distill: bool = False, enhance_hf: bool = False, bsa_params: dict | None = None, bsa_sparsity: float | None = None, bsa_cdf_threshold: float | None = None, bsa_chunk_q: list[int] | None = None, bsa_chunk_k: list[int] | None = None, t_thresh: float | None = None)

Bases: LongCatT2V480PConfig

Configuration for LongCat pipeline (704p) with BSA enabled by default.

Uses the same resolution and BSA parameters as original LongCat refinement stage. BSA parameters configured in transformer config.json with chunk_3d_shape=[4,4,4]: - Input: 704×1280×96 - VAE (8x): 88×160×96
- Patch [1,2,2]: 44×80×96 - chunk [4,4,4]: 96%4=0, 44%4=0, 80%4=0 ✅

This configuration matches the original LongCat refinement stage parameters.

Functions¶

fastvideo.configs.pipelines.longcat.longcat_preprocess_text ¶

longcat_preprocess_text(prompt: str) -> str

Clean and preprocess text like original LongCat implementation.

This function applies the same text cleaning pipeline as the original LongCat-Video implementation to ensure identical tokenization results.

Steps: 1. basic_clean: Fix unicode issues and unescape HTML entities 2. whitespace_clean: Normalize whitespace to single spaces

Parameters:

Name	Type	Description	Default
`prompt`	`str`	Raw input text prompt	required

Returns:

Type	Description
`str`	Cleaned and normalized text prompt

Source code in fastvideo/configs/pipelines/longcat.py

def longcat_preprocess_text(prompt: str) -> str:
    """Clean and preprocess text like original LongCat implementation.

    This function applies the same text cleaning pipeline as the original
    LongCat-Video implementation to ensure identical tokenization results.

    Steps:
    1. basic_clean: Fix unicode issues and unescape HTML entities
    2. whitespace_clean: Normalize whitespace to single spaces

    Args:
        prompt: Raw input text prompt

    Returns:
        Cleaned and normalized text prompt
    """
    # basic_clean: fix unicode and HTML entities
    text = ftfy.fix_text(prompt)
    text = html.unescape(html.unescape(text))
    text = text.strip()

    # whitespace_clean: normalize whitespace
    text = re.sub(r"\s+", " ", text)
    text = text.strip()

    return text

fastvideo.configs.pipelines.longcat.umt5_postprocess_text ¶

umt5_postprocess_text(outputs: BaseEncoderOutput) -> Tensor

Postprocess UMT5/T5 encoder outputs to fixed length 512 embeddings.