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longcat

LongCat pipeline module.

Classes

fastvideo.pipelines.basic.longcat.LongCatImageToVideoPipeline 

LongCatImageToVideoPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat Image-to-Video pipeline.

Generates video from a single input image using Tier 3 I2V conditioning: - Per-frame timestep masking (timestep[:, 0] = 0) - num_cond_latents parameter to transformer - RoPE skipping for conditioning frames - Selective denoising (skip first frame in scheduler)

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore

Functions

fastvideo.pipelines.basic.longcat.LongCatImageToVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up I2V-specific pipeline stages.

Source code in fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """Set up I2V-specific pipeline stages."""

    # 1. Input validation
    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    # 2. Text encoding (same as T2V)
    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # 3. Image VAE encoding (for I2V - skipped in refinement mode)
    self.add_stage(
        stage_name="image_vae_encoding_stage",
        stage=LongCatImageVAEEncodingStage(vae=self.get_module("vae")))

    # 4. Refinement initialization (skipped if not refining)
    self.add_stage(stage_name="longcat_refine_init_stage",
                   stage=LongCatRefineInitStage(vae=self.get_module("vae")))

    # 5. Timestep preparation (generic)
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # 6. Refinement timestep override (skipped if not refining)
    self.add_stage(stage_name="longcat_refine_timestep_stage",
                   stage=LongCatRefineTimestepStage(
                       scheduler=self.get_module("scheduler")))

    # 7. Latent preparation with I2V conditioning
    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LongCatI2VLatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer")))

    # 8. Denoising with I2V support
    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatI2VDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    # 9. Decoding
    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.LongCatImageToVideoPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""
    # Same BSA initialization as base LongCat pipeline
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        return

    # Enable BSA if configured
    if pipeline_config.enable_bsa:
        bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
        sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
        cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
        chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
        chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k

        # Provide defaults
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info("Enabling BSA for LongCat I2V transformer")
            transformer.enable_bsa()
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning("Failed to set BSA params: %s", e)
            logger.info("BSA parameters: %s", effective_bsa_params)
    else:
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

fastvideo.pipelines.basic.longcat.LongCatPipeline 

LongCatPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat video diffusion pipeline with LoRA support.

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore

Functions

fastvideo.pipelines.basic.longcat.LongCatPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/longcat/longcat_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
    """Set up pipeline stages with proper dependency injection."""

    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # Add refine initialization stage (will be skipped if not refining)
    self.add_stage(stage_name="longcat_refine_init_stage",
                   stage=LongCatRefineInitStage(vae=self.get_module("vae")))

    # First prepare generic timesteps (for non-refine paths)
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # Then override timesteps for refinement (will be a no-op if not refining),
    # matching LongCat's generate_refine schedule.
    self.add_stage(stage_name="longcat_refine_timestep_stage",
                   stage=LongCatRefineTimestepStage(
                       scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer", None)))

    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.LongCatPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""

    # Enable BSA (Block Sparse Attention) if configured
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        raise RuntimeError(
            "Transformer module not found during initializing LongCat pipeline."
        )
    # If user toggles BSA via CLI/config
    if pipeline_config.enable_bsa:
        # Build effective BSA params:
        # 1) from explicit CLI overrides if provided
        # 2) else from pipeline_config.bsa_params
        # 3) else fall back to reasonable defaults
        bsa_params_cfg = pipeline_config.bsa_params
        sparsity = pipeline_config.bsa_sparsity
        cdf_threshold = pipeline_config.bsa_cdf_threshold
        chunk_q = pipeline_config.bsa_chunk_q
        chunk_k = pipeline_config.bsa_chunk_k

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k
        # Provide defaults if still missing
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info(
                "Enabling Block Sparse Attention (BSA) for LongCat transformer"
            )
            transformer.enable_bsa()
            # Propagate params to all attention modules
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning(
                        "Failed to set BSA params on all blocks: %s", e)
            logger.info("BSA parameters in effect: %s",
                        effective_bsa_params)
        else:
            logger.warning(
                "BSA is enabled in config but transformer does not support it"
            )
    else:
        # Explicitly disable if present
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

fastvideo.pipelines.basic.longcat.LongCatVideoContinuationPipeline 

LongCatVideoContinuationPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat Video Continuation pipeline.

Generates video continuation from multiple conditioning frames using optional KV cache for 2-3x speedup.

Key features: - Takes video input (13+ frames typically) - Encodes conditioning frames via VAE - Optionally pre-computes KV cache for conditioning - Uses cached K/V during denoising for speedup - Concatenates conditioning back after denoising

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore

Functions

fastvideo.pipelines.basic.longcat.LongCatVideoContinuationPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up VC-specific pipeline stages.

Source code in fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """Set up VC-specific pipeline stages."""

    # 1. Input validation
    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    # 2. Text encoding
    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # 3. Video VAE encoding (encodes conditioning frames)
    self.add_stage(
        stage_name="video_vae_encoding_stage",
        stage=LongCatVideoVAEEncodingStage(vae=self.get_module("vae")))

    # 4. Timestep preparation
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # 5. Latent preparation (reuse I2V stage - it handles video_latent too)
    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LongCatVCLatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer")))

    # 6. KV cache initialization (optional, based on config)
    # This is always added but will skip if use_kv_cache=False
    self.add_stage(stage_name="kv_cache_init_stage",
                   stage=LongCatKVCacheInitStage(
                       transformer=self.get_module("transformer")))

    # 7. Denoising with VC and KV cache support
    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatVCDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    # 8. Decoding
    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.LongCatVideoContinuationPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        return

    # Enable BSA if configured (for VC, BSA may not be needed)
    if getattr(pipeline_config, 'enable_bsa', False):
        bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
        sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
        cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
        chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
        chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k

        # Provide defaults
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info("Enabling BSA for LongCat VC transformer")
            transformer.enable_bsa()
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning("Failed to set BSA params: %s", e)
            logger.info("BSA parameters: %s", effective_bsa_params)
    else:
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

Modules

fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline

LongCat Image-to-Video pipeline implementation.

This module implements I2V (Image-to-Video) generation for LongCat using Tier 3 conditioning with timestep masking, num_cond_latents support, and RoPE skipping.

Supports: - Basic I2V (50 steps, guidance_scale=4.0) - Distilled I2V with LoRA (16 steps, guidance_scale=1.0) - Refinement I2V for 720p upscaling (with refinement LoRA + BSA)

Classes

fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline.LongCatImageToVideoPipeline 
LongCatImageToVideoPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat Image-to-Video pipeline.

Generates video from a single input image using Tier 3 I2V conditioning: - Per-frame timestep masking (timestep[:, 0] = 0) - num_cond_latents parameter to transformer - RoPE skipping for conditioning frames - Selective denoising (skip first frame in scheduler)

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore
Functions
fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline.LongCatImageToVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up I2V-specific pipeline stages.

Source code in fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """Set up I2V-specific pipeline stages."""

    # 1. Input validation
    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    # 2. Text encoding (same as T2V)
    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # 3. Image VAE encoding (for I2V - skipped in refinement mode)
    self.add_stage(
        stage_name="image_vae_encoding_stage",
        stage=LongCatImageVAEEncodingStage(vae=self.get_module("vae")))

    # 4. Refinement initialization (skipped if not refining)
    self.add_stage(stage_name="longcat_refine_init_stage",
                   stage=LongCatRefineInitStage(vae=self.get_module("vae")))

    # 5. Timestep preparation (generic)
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # 6. Refinement timestep override (skipped if not refining)
    self.add_stage(stage_name="longcat_refine_timestep_stage",
                   stage=LongCatRefineTimestepStage(
                       scheduler=self.get_module("scheduler")))

    # 7. Latent preparation with I2V conditioning
    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LongCatI2VLatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer")))

    # 8. Denoising with I2V support
    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatI2VDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    # 9. Decoding
    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline.LongCatImageToVideoPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""
    # Same BSA initialization as base LongCat pipeline
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        return

    # Enable BSA if configured
    if pipeline_config.enable_bsa:
        bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
        sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
        cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
        chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
        chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k

        # Provide defaults
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info("Enabling BSA for LongCat I2V transformer")
            transformer.enable_bsa()
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning("Failed to set BSA params: %s", e)
            logger.info("BSA parameters: %s", effective_bsa_params)
    else:
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

Functions

fastvideo.pipelines.basic.longcat.longcat_pipeline

LongCat video diffusion pipeline implementation.

This module implements the LongCat video diffusion pipeline using FastVideo's modular pipeline architecture.

Classes

fastvideo.pipelines.basic.longcat.longcat_pipeline.LongCatPipeline 
LongCatPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat video diffusion pipeline with LoRA support.

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore
Functions
fastvideo.pipelines.basic.longcat.longcat_pipeline.LongCatPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/longcat/longcat_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
    """Set up pipeline stages with proper dependency injection."""

    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # Add refine initialization stage (will be skipped if not refining)
    self.add_stage(stage_name="longcat_refine_init_stage",
                   stage=LongCatRefineInitStage(vae=self.get_module("vae")))

    # First prepare generic timesteps (for non-refine paths)
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # Then override timesteps for refinement (will be a no-op if not refining),
    # matching LongCat's generate_refine schedule.
    self.add_stage(stage_name="longcat_refine_timestep_stage",
                   stage=LongCatRefineTimestepStage(
                       scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer", None)))

    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.longcat_pipeline.LongCatPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""

    # Enable BSA (Block Sparse Attention) if configured
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        raise RuntimeError(
            "Transformer module not found during initializing LongCat pipeline."
        )
    # If user toggles BSA via CLI/config
    if pipeline_config.enable_bsa:
        # Build effective BSA params:
        # 1) from explicit CLI overrides if provided
        # 2) else from pipeline_config.bsa_params
        # 3) else fall back to reasonable defaults
        bsa_params_cfg = pipeline_config.bsa_params
        sparsity = pipeline_config.bsa_sparsity
        cdf_threshold = pipeline_config.bsa_cdf_threshold
        chunk_q = pipeline_config.bsa_chunk_q
        chunk_k = pipeline_config.bsa_chunk_k

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k
        # Provide defaults if still missing
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info(
                "Enabling Block Sparse Attention (BSA) for LongCat transformer"
            )
            transformer.enable_bsa()
            # Propagate params to all attention modules
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning(
                        "Failed to set BSA params on all blocks: %s", e)
            logger.info("BSA parameters in effect: %s",
                        effective_bsa_params)
        else:
            logger.warning(
                "BSA is enabled in config but transformer does not support it"
            )
    else:
        # Explicitly disable if present
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

Functions

fastvideo.pipelines.basic.longcat.longcat_vc_pipeline

LongCat Video Continuation (VC) pipeline implementation.

This module implements VC (Video Continuation) generation for LongCat with KV cache optimization for 2-3x speedup.

Supports: - Basic VC (50 steps, guidance_scale=4.0) - Distilled VC with LoRA (16 steps, guidance_scale=1.0) - KV cache for conditioning frames

Classes

fastvideo.pipelines.basic.longcat.longcat_vc_pipeline.LongCatVCLatentPreparationStage 
LongCatVCLatentPreparationStage(scheduler, transformer, use_btchw_layout: bool = False)

Bases: LongCatI2VLatentPreparationStage

Prepare latents with video conditioning for first N frames.

Extends I2V latent preparation to handle video_latent (multiple frames) instead of image_latent (single frame).

Source code in fastvideo/pipelines/stages/latent_preparation.py 
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def __init__(self,
             scheduler,
             transformer,
             use_btchw_layout: bool = False) -> None:
    super().__init__()
    self.scheduler = scheduler
    self.transformer = transformer
    self.use_btchw_layout = use_btchw_layout
Functions
fastvideo.pipelines.basic.longcat.longcat_vc_pipeline.LongCatVCLatentPreparationStage.forward 
forward(batch, fastvideo_args)

Prepare latents with VC conditioning.

Source code in fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py 
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def forward(self, batch, fastvideo_args):
    """Prepare latents with VC conditioning."""

    # Check if we have video_latent (from VC encoding stage)
    video_latent = getattr(batch, 'video_latent', None)
    if video_latent is not None:
        # Set image_latent to video_latent for parent class compatibility
        batch.image_latent = video_latent

    # Call parent class forward
    return super().forward(batch, fastvideo_args)
fastvideo.pipelines.basic.longcat.longcat_vc_pipeline.LongCatVideoContinuationPipeline 
LongCatVideoContinuationPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

LongCat Video Continuation pipeline.

Generates video continuation from multiple conditioning frames using optional KV cache for 2-3x speedup.

Key features: - Takes video input (13+ frames typically) - Encodes conditioning frames via VAE - Optionally pre-computes KV cache for conditioning - Uses cached K/V during denoising for speedup - Concatenates conditioning back after denoising

Source code in fastvideo/pipelines/lora_pipeline.py 
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def __init__(self, *args, **kwargs) -> None:
    super().__init__(*args, **kwargs)
    self.device = get_local_torch_device()
    # build list of trainable transformers
    for transformer_name in self.trainable_transformer_names:
        if transformer_name in self.modules and self.modules[
                transformer_name] is not None:
            self.trainable_transformer_modules[
                transformer_name] = self.modules[transformer_name]
        # check for transformer_2 in case of Wan2.2 MoE or fake_score_transformer_2
        if transformer_name.endswith("_2"):
            raise ValueError(
                f"trainable_transformer_name override in pipelines should not include _2 suffix: {transformer_name}"
            )

        secondary_transformer_name = transformer_name + "_2"
        if secondary_transformer_name in self.modules and self.modules[
                secondary_transformer_name] is not None:
            self.trainable_transformer_modules[
                secondary_transformer_name] = self.modules[
                    secondary_transformer_name]

    logger.info("trainable_transformer_modules: %s",
                self.trainable_transformer_modules.keys())

    for transformer_name, transformer_module in self.trainable_transformer_modules.items(
    ):
        self.exclude_lora_layers[
            transformer_name] = transformer_module.config.arch_config.exclude_lora_layers
    self.lora_target_modules = self.fastvideo_args.lora_target_modules
    self.lora_path = self.fastvideo_args.lora_path
    self.lora_nickname = self.fastvideo_args.lora_nickname
    self.training_mode = self.fastvideo_args.training_mode
    if self.training_mode and getattr(self.fastvideo_args, "lora_training",
                                      False):
        assert isinstance(self.fastvideo_args, TrainingArgs)
        if self.fastvideo_args.lora_alpha is None:
            self.fastvideo_args.lora_alpha = self.fastvideo_args.lora_rank
        self.lora_rank = self.fastvideo_args.lora_rank  # type: ignore
        self.lora_alpha = self.fastvideo_args.lora_alpha  # type: ignore
        logger.info("Using LoRA training with rank %d and alpha %d",
                    self.lora_rank, self.lora_alpha)
        if self.lora_target_modules is None:
            self.lora_target_modules = [
                "q_proj", "k_proj", "v_proj", "o_proj", "to_q", "to_k",
                "to_v", "to_out", "to_qkv", "to_gate_compress"
            ]
            logger.info(
                "Using default lora_target_modules for all transformers: %s",
                self.lora_target_modules)
        else:
            logger.warning(
                "Using custom lora_target_modules for all transformers, which may not be intended: %s",
                self.lora_target_modules)

        self.convert_to_lora_layers()
    # Inference
    elif not self.training_mode and self.lora_path is not None:
        self.convert_to_lora_layers()
        self.set_lora_adapter(
            self.lora_nickname,  # type: ignore
            self.lora_path)  # type: ignore
Functions
fastvideo.pipelines.basic.longcat.longcat_vc_pipeline.LongCatVideoContinuationPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up VC-specific pipeline stages.

Source code in fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """Set up VC-specific pipeline stages."""

    # 1. Input validation
    self.add_stage(stage_name="input_validation_stage",
                   stage=InputValidationStage())

    # 2. Text encoding
    self.add_stage(stage_name="prompt_encoding_stage",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder")],
                       tokenizers=[self.get_module("tokenizer")],
                   ))

    # 3. Video VAE encoding (encodes conditioning frames)
    self.add_stage(
        stage_name="video_vae_encoding_stage",
        stage=LongCatVideoVAEEncodingStage(vae=self.get_module("vae")))

    # 4. Timestep preparation
    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(
                       scheduler=self.get_module("scheduler")))

    # 5. Latent preparation (reuse I2V stage - it handles video_latent too)
    self.add_stage(stage_name="latent_preparation_stage",
                   stage=LongCatVCLatentPreparationStage(
                       scheduler=self.get_module("scheduler"),
                       transformer=self.get_module("transformer")))

    # 6. KV cache initialization (optional, based on config)
    # This is always added but will skip if use_kv_cache=False
    self.add_stage(stage_name="kv_cache_init_stage",
                   stage=LongCatKVCacheInitStage(
                       transformer=self.get_module("transformer")))

    # 7. Denoising with VC and KV cache support
    self.add_stage(stage_name="denoising_stage",
                   stage=LongCatVCDenoisingStage(
                       transformer=self.get_module("transformer"),
                       transformer_2=self.get_module("transformer_2", None),
                       scheduler=self.get_module("scheduler"),
                       vae=self.get_module("vae"),
                       pipeline=self))

    # 8. Decoding
    self.add_stage(stage_name="decoding_stage",
                   stage=DecodingStage(vae=self.get_module("vae"),
                                       pipeline=self))
fastvideo.pipelines.basic.longcat.longcat_vc_pipeline.LongCatVideoContinuationPipeline.initialize_pipeline 
initialize_pipeline(fastvideo_args: FastVideoArgs)

Initialize LongCat-specific components.

Source code in fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py 
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def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
    """Initialize LongCat-specific components."""
    pipeline_config = fastvideo_args.pipeline_config
    transformer = self.get_module("transformer", None)
    if transformer is None:
        return

    # Enable BSA if configured (for VC, BSA may not be needed)
    if getattr(pipeline_config, 'enable_bsa', False):
        bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
        sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
        cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
        chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
        chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)

        effective_bsa_params = dict(bsa_params_cfg) if isinstance(
            bsa_params_cfg, dict) else {}
        if sparsity is not None:
            effective_bsa_params['sparsity'] = sparsity
        if cdf_threshold is not None:
            effective_bsa_params['cdf_threshold'] = cdf_threshold
        if chunk_q is not None:
            effective_bsa_params['chunk_3d_shape_q'] = chunk_q
        if chunk_k is not None:
            effective_bsa_params['chunk_3d_shape_k'] = chunk_k

        # Provide defaults
        effective_bsa_params.setdefault('sparsity', 0.9375)
        effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
        effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])

        if hasattr(transformer, 'enable_bsa'):
            logger.info("Enabling BSA for LongCat VC transformer")
            transformer.enable_bsa()
            if hasattr(transformer, 'blocks'):
                try:
                    for blk in transformer.blocks:
                        if hasattr(blk, 'self_attn'):
                            blk.self_attn.bsa_params = effective_bsa_params
                except Exception as e:
                    logger.warning("Failed to set BSA params: %s", e)
            logger.info("BSA parameters: %s", effective_bsa_params)
    else:
        if hasattr(transformer, 'disable_bsa'):
            transformer.disable_bsa()

Functions