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basic

Basic inference pipelines for fastvideo.

This package contains basic pipelines for video and image generation.

Modules

fastvideo.pipelines.basic.cosmos

Modules

fastvideo.pipelines.basic.cosmos.cosmos2_5_pipeline

Cosmos 2.5 pipeline entry (staged pipeline).

Classes
fastvideo.pipelines.basic.cosmos.cosmos2_5_pipeline.Cosmos2_5Pipeline 
Cosmos2_5Pipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Cosmos 2.5 video generation pipeline.

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.cosmos.cosmos_pipeline

Cosmos video diffusion pipeline implementation.

This module contains an implementation of the Cosmos video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.cosmos.cosmos_pipeline.Cosmos2VideoToWorldPipeline 
Cosmos2VideoToWorldPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.cosmos.cosmos_pipeline.Cosmos2VideoToWorldPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/cosmos/cosmos_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="latent_preparation_stage",
                   stage=CosmosLatentPreparationStage(scheduler=self.get_module("scheduler"),
                                                      transformer=self.get_module("transformer"),
                                                      vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=CosmosDenoisingStage(transformer=self.get_module("transformer"),
                                              scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions

fastvideo.pipelines.basic.gamecraft

HunyuanGameCraft pipeline implementations.

Modules

fastvideo.pipelines.basic.gamecraft.gamecraft_pipeline

HunyuanGameCraft video diffusion pipeline implementation.

This module implements the HunyuanGameCraft pipeline for camera/action-conditioned video generation with the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.gamecraft.gamecraft_pipeline.HunyuanGameCraftPipeline 
HunyuanGameCraftPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Pipeline for HunyuanGameCraft video generation.

This pipeline supports: - Text-to-video generation with camera/action conditioning - Autoregressive generation with history frames - 33-channel input (16 latent + 16 gt_latent + 1 mask) - CameraNet for encoding Plücker coordinates

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.gamecraft.gamecraft_pipeline.HunyuanGameCraftPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
        stage=TextEncodingStage(
            text_encoders=[
                self.get_module("text_encoder"),
                self.get_module("text_encoder_2"),
            ],
            tokenizers=[
                self.get_module("tokenizer"),
                self.get_module("tokenizer_2"),
            ],
        ),
    )

    self.add_stage(
        stage_name="conditioning_stage",
        stage=ConditioningStage(),
    )

    self.add_stage(
        stage_name="timestep_preparation_stage",
        stage=TimestepPreparationStage(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"),
        ),
    )

    self.add_stage(
        stage_name="denoising_stage",
        stage=GameCraftDenoisingStage(
            transformer=self.get_module("transformer"),
            scheduler=self.get_module("scheduler"),
        ),
    )

    self.add_stage(
        stage_name="decoding_stage",
        stage=DecodingStage(vae=self.get_module("vae")),
    )
Functions

fastvideo.pipelines.basic.hunyuan

Modules

fastvideo.pipelines.basic.hunyuan.hunyuan_pipeline

Hunyuan video diffusion pipeline implementation.

This module contains an implementation of the Hunyuan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.hunyuan.hunyuan_pipeline.HunyuanVideoPipeline 
HunyuanVideoPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hunyuan.hunyuan_pipeline.HunyuanVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/hunyuan/hunyuan_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions

fastvideo.pipelines.basic.hunyuan15

Modules

fastvideo.pipelines.basic.hunyuan15.hunyuan15_2sr_pipeline

Hunyuan video diffusion pipeline implementation.

This module contains an implementation of the Hunyuan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.hunyuan15.hunyuan15_2sr_pipeline.HunyuanVideo152SRPipeline 
HunyuanVideo152SRPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_2sr_pipeline.HunyuanVideo152SRPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_2sr_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=Hy15ImageEncodingStage(image_encoder=None, image_processor=None))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="sr_720p_latent_preparation_stage",
                   stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
                                                transformer=self.get_module("transformer_2")))

    self.add_stage(stage_name="sr_720p_denoising_stage",
                   stage=SRDenoisingStage(transformer=self.get_module("transformer_2"),
                                          scheduler=self.get_module("scheduler"),
                                          upsampler=self.get_module("upsampler")))

    self.add_stage(stage_name="sr_1080p_latent_preparation_stage",
                   stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
                                                transformer=self.get_module("transformer_3")))

    self.add_stage(stage_name="sr_1080p_denoising_stage",
                   stage=SRDenoisingStage(transformer=self.get_module("transformer_3"),
                                          scheduler=self.get_module("scheduler"),
                                          upsampler=self.get_module("upsampler_2")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
fastvideo.pipelines.basic.hunyuan15.hunyuan15_2sr_pipeline.HunyuanVideo152SRPipeline.forward 
forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Generate a video or image using the pipeline.

Parameters:

Name Type Description Default
batch ForwardBatch

The batch to generate from.

 required
fastvideo_args FastVideoArgs

The inference arguments.

 required

Returns: ForwardBatch: The batch with the generated video or image.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_2sr_pipeline.py 
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@torch.no_grad()
def forward(
    self,
    batch: ForwardBatch,
    fastvideo_args: FastVideoArgs,
) -> ForwardBatch:
    """
    Generate a video or image using the pipeline.

    Args:
        batch: The batch to generate from.
        fastvideo_args: The inference arguments.
    Returns:
        ForwardBatch: The batch with the generated video or image.
    """
    if not self.post_init_called:
        self.post_init()

    self.get_module("transformer").to(get_local_torch_device())
    # Execute each stage
    logger.info("Running pipeline stages: %s", self._stage_name_mapping.keys())
    # logger.info("Batch: %s", batch)
    batch = self.input_validation_stage(batch, fastvideo_args)
    batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
    batch = self.conditioning_stage(batch, fastvideo_args)
    batch = self.timestep_preparation_stage(batch, fastvideo_args)
    batch = self.latent_preparation_stage(batch, fastvideo_args)
    batch = self.image_encoding_stage(batch, fastvideo_args)
    batch = self.denoising_stage(batch, fastvideo_args)
    self.get_module("transformer").to("cpu")

    # 720p SR
    self.get_module("transformer_2").to(get_local_torch_device())
    batch.lq_latents = batch.latents
    batch.latents = None
    batch.height = 720
    batch.width = 1280
    batch.num_inference_steps_sr = 6
    batch = self.sr_720p_latent_preparation_stage(batch, fastvideo_args)
    batch = self.image_encoding_stage(batch, fastvideo_args)
    batch = self.sr_720p_denoising_stage(batch, fastvideo_args)
    self.get_module("transformer_2").to("cpu")

    # 1080p SR
    self.get_module("transformer_3").to(get_local_torch_device())
    batch.lq_latents = batch.latents
    batch.latents = None
    batch.height = 1072
    batch.width = 1920
    batch.num_inference_steps_sr = 8
    batch = self.sr_1080p_latent_preparation_stage(batch, fastvideo_args)
    batch = self.image_encoding_stage(batch, fastvideo_args)
    batch = self.sr_1080p_denoising_stage(batch, fastvideo_args)
    self.get_module("transformer_3").to("cpu")

    start_time = time.time()
    batch = self.decoding_stage(batch, fastvideo_args)
    end_time = time.time()
    logger.info("Decoding time: %s seconds", end_time - start_time)

    # Return the output
    return batch
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_i2v_pipeline

Hunyuan video diffusion pipeline implementation.

This module contains an implementation of the Hunyuan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.hunyuan15.hunyuan15_i2v_pipeline.HunyuanVideo15ImageToVideoPipeline 
HunyuanVideo15ImageToVideoPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_i2v_pipeline.HunyuanVideo15ImageToVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_i2v_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=Hy15ImageEncodingStage(image_encoder=None, image_processor=None))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_pipeline

Hunyuan video diffusion pipeline implementation.

This module contains an implementation of the Hunyuan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.hunyuan15.hunyuan15_pipeline.HunyuanVideo15Pipeline 
HunyuanVideo15Pipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_pipeline.HunyuanVideo15Pipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=Hy15ImageEncodingStage(image_encoder=None, image_processor=None))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_sr_pipeline

Hunyuan video diffusion pipeline implementation.

This module contains an implementation of the Hunyuan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.hunyuan15.hunyuan15_sr_pipeline.HunyuanVideo15SRPipeline 
HunyuanVideo15SRPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hunyuan15.hunyuan15_sr_pipeline.HunyuanVideo15SRPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_sr_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=Hy15ImageEncodingStage(image_encoder=None, image_processor=None))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="sr_latent_preparation_stage",
                   stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
                                                transformer=self.get_module("transformer_2")))

    self.add_stage(stage_name="sr_denoising_stage",
                   stage=SRDenoisingStage(transformer=self.get_module("transformer_2"),
                                          scheduler=self.get_module("scheduler"),
                                          upsampler=self.get_module("upsampler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
fastvideo.pipelines.basic.hunyuan15.hunyuan15_sr_pipeline.HunyuanVideo15SRPipeline.forward 
forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Generate a video or image using the pipeline.

Parameters:

Name Type Description Default
batch ForwardBatch

The batch to generate from.

 required
fastvideo_args FastVideoArgs

The inference arguments.

 required

Returns: ForwardBatch: The batch with the generated video or image.

Source code in fastvideo/pipelines/basic/hunyuan15/hunyuan15_sr_pipeline.py 
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@torch.no_grad()
def forward(
    self,
    batch: ForwardBatch,
    fastvideo_args: FastVideoArgs,
) -> ForwardBatch:
    """
    Generate a video or image using the pipeline.

    Args:
        batch: The batch to generate from.
        fastvideo_args: The inference arguments.
    Returns:
        ForwardBatch: The batch with the generated video or image.
    """
    if not self.post_init_called:
        self.post_init()

    # Execute each stage
    logger.info("Running pipeline stages: %s", self._stage_name_mapping.keys())
    # logger.info("Batch: %s", batch)
    self.get_module("transformer").to(get_local_torch_device())
    batch = self.input_validation_stage(batch, fastvideo_args)
    batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
    batch = self.conditioning_stage(batch, fastvideo_args)
    batch = self.timestep_preparation_stage(batch, fastvideo_args)
    batch = self.latent_preparation_stage(batch, fastvideo_args)
    batch = self.image_encoding_stage(batch, fastvideo_args)
    batch = self.denoising_stage(batch, fastvideo_args)
    self.get_module("transformer").to("cpu")

    self.get_module("transformer_2").to(get_local_torch_device())
    batch.lq_latents = batch.latents
    batch.latents = None
    batch.height = batch.height_sr
    batch.width = batch.width_sr
    batch = self.sr_latent_preparation_stage(batch, fastvideo_args)
    batch = self.image_encoding_stage(batch, fastvideo_args)
    batch = self.sr_denoising_stage(batch, fastvideo_args)
    self.get_module("transformer_2").to("cpu")

    start_time = time.time()
    batch = self.decoding_stage(batch, fastvideo_args)
    end_time = time.time()
    logger.info("Decoding time: %s seconds", end_time - start_time)

    # Return the output
    return batch
Functions

fastvideo.pipelines.basic.hyworld

Modules

fastvideo.pipelines.basic.hyworld.hyworld_pipeline

HYWorld video diffusion pipeline implementation.

This module contains an implementation of the HYWorld video diffusion pipeline using the modular pipeline architecture with HYWorld-specific denoising stage for chunk-based video generation with context frame selection.

Classes
fastvideo.pipelines.basic.hyworld.hyworld_pipeline.HYWorldPipeline 
HYWorldPipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

HYWorld video diffusion pipeline.

This pipeline implements chunk-based video generation with context frame selection for 3D-aware generation using HYWorldDenoisingStage.

Note: HYWorld only uses a single LLM-based text encoder, unlike SDXL-style dual encoder setups. The text_encoder_2/tokenizer_2 are not used.

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions
fastvideo.pipelines.basic.hyworld.hyworld_pipeline.HYWorldPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with HYWorld-specific denoising stage.

Source code in fastvideo/pipelines/basic/hyworld/hyworld_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """Set up pipeline stages with HYWorld-specific denoising stage."""

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

    self.add_stage(stage_name="prompt_encoding_stage_primary",
                   stage=TextEncodingStage(
                       text_encoders=[self.get_module("text_encoder"),
                                      self.get_module("text_encoder_2")],
                       tokenizers=[self.get_module("tokenizer"),
                                   self.get_module("tokenizer_2")]))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=HYWorldImageEncodingStage(image_encoder=self.get_module("image_encoder"),
                                                   image_processor=self.get_module("feature_extractor"),
                                                   vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=HYWorldDenoisingStage(transformer=self.get_module("transformer"),
                                               scheduler=self.get_module("scheduler"),
                                               pipeline=self))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions

fastvideo.pipelines.basic.lingbotworld

Modules

fastvideo.pipelines.basic.lingbotworld.lingbotworld_pipeline

Wan video diffusion pipeline implementation.

This module contains an implementation of the Wan video diffusion pipeline using the modular pipeline architecture.

Classes

fastvideo.pipelines.basic.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

fastvideo.pipelines.basic.ltx2

Modules

fastvideo.pipelines.basic.ltx2.ltx2_pipeline

LTX-2 text-to-video pipeline.

Classes
Functions

fastvideo.pipelines.basic.matrixgame

Modules

fastvideo.pipelines.basic.matrixgame.matrixgame_causal_dmd_pipeline

Matrix-Game causal DMD pipeline implementation.

Classes
Functions
fastvideo.pipelines.basic.matrixgame.matrixgame_i2v_pipeline

Matrix-Game I2V pipeline implementation.

Classes
Functions

fastvideo.pipelines.basic.sd35

Modules

fastvideo.pipelines.basic.sd35.sd35_pipeline
Classes
fastvideo.pipelines.basic.sd35.sd35_pipeline.SD35Pipeline 
SD35Pipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: ComposedPipelineBase

Minimal SD3.5 Medium text-to-image pipeline (treat as num_frames=1).

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
fastvideo.pipelines.basic.sd35.sd35_pipeline.StableDiffusion3Pipeline 
StableDiffusion3Pipeline(model_path: str, fastvideo_args: FastVideoArgs | TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, Module] | None = None)

Bases: SD35Pipeline

Alias name to match SD3.5 diffusers model_index.json _class_name.

Source code in fastvideo/pipelines/composed_pipeline_base.py 
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def __init__(self,
             model_path: str,
             fastvideo_args: FastVideoArgs | TrainingArgs,
             required_config_modules: list[str] | None = None,
             loaded_modules: dict[str, torch.nn.Module] | None = None):
    """
    Initialize the pipeline. After __init__, the pipeline should be ready to
    use. The pipeline should be stateless and not hold any batch state.
    """
    self.fastvideo_args = fastvideo_args

    self.model_path: str = model_path
    self._stages: list[PipelineStage] = []
    self._stage_name_mapping: dict[str, PipelineStage] = {}

    if required_config_modules is not None:
        self._required_config_modules = required_config_modules

    if self._required_config_modules is None:
        raise NotImplementedError("Subclass must set _required_config_modules")

    maybe_init_distributed_environment_and_model_parallel(fastvideo_args.tp_size, fastvideo_args.sp_size)

    # Torch profiler. Enabled and configured through env vars:
    # FASTVIDEO_TORCH_PROFILER_DIR=/path/to/save/trace
    trace_dir = envs.FASTVIDEO_TORCH_PROFILER_DIR
    self.profiler_controller = get_or_create_profiler(trace_dir)
    self.profiler = self.profiler_controller.profiler

    self.local_rank = get_world_group().local_rank

    # Load modules directly in initialization
    logger.info("Loading pipeline modules...")
    with self.profiler_controller.region("profiler_region_model_loading"):
        self.modules = self.load_modules(fastvideo_args, loaded_modules)
Functions

fastvideo.pipelines.basic.turbodiffusion

Classes

fastvideo.pipelines.basic.turbodiffusion.TurboDiffusionI2VPipeline 
TurboDiffusionI2VPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

TurboDiffusion I2V pipeline for 1-4 step image-to-video generation.

Uses RCM scheduler, SLA attention, and dual model switching for high-quality I2V generation.

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.turbodiffusion.TurboDiffusionI2VPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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)))

    # I2V: Encode initial image to latent space
    self.add_stage(stage_name="image_latent_preparation_stage",
                   stage=ImageVAEEncodingStage(vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(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.turbodiffusion.TurboDiffusionPipeline 
TurboDiffusionPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

TurboDiffusion video pipeline for 1-4 step generation.

Uses RCM scheduler and SLA attention for fast, high-quality video generation.

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.turbodiffusion.TurboDiffusionPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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=DenoisingStage(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))

Modules

fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_i2v_pipeline

TurboDiffusion I2V (Image-to-Video) Pipeline Implementation.

This module contains an implementation of the TurboDiffusion I2V pipeline for 1-4 step image-to-video generation using rCM (recurrent Consistency Model) sampling with SLA (Sparse-Linear Attention).

Key differences from T2V: - Uses dual models (high/low noise) with boundary switching - sigma_max=200 (vs 80 for T2V) - Mask conditioning with encoded first frame

Classes
fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_i2v_pipeline.TurboDiffusionI2VPipeline 
TurboDiffusionI2VPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

TurboDiffusion I2V pipeline for 1-4 step image-to-video generation.

Uses RCM scheduler, SLA attention, and dual model switching for high-quality I2V generation.

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.turbodiffusion.turbodiffusion_i2v_pipeline.TurboDiffusionI2VPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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)))

    # I2V: Encode initial image to latent space
    self.add_stage(stage_name="image_latent_preparation_stage",
                   stage=ImageVAEEncodingStage(vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(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))
Functions
fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_pipeline

TurboDiffusion Video Pipeline Implementation.

This module contains an implementation of the TurboDiffusion video diffusion pipeline for 1-4 step video generation using rCM (recurrent Consistency Model) sampling with SLA (Sparse-Linear Attention).

Classes
fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_pipeline.TurboDiffusionPipeline 
TurboDiffusionPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

TurboDiffusion video pipeline for 1-4 step generation.

Uses RCM scheduler and SLA attention for fast, high-quality video generation.

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.turbodiffusion.turbodiffusion_pipeline.TurboDiffusionPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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=DenoisingStage(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))
Functions

fastvideo.pipelines.basic.wan

Modules

fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline

Wan causal DMD pipeline implementation.

This module wires the causal DMD denoising stage into the modular pipeline.

Classes
fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline 
WanCausalDMDPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

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.wan.wan_causal_dmd_pipeline.WanCausalDMDPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_causal_dmd_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    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=CausalDMDDenosingStage(transformer=self.get_module("transformer"),
                                                transformer_2=self.get_module("transformer_2", None),
                                                scheduler=self.get_module("scheduler"),
                                                vae=self.get_module("vae")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.wan.wan_causal_pipeline

Wan causal pipeline with standard multi-step denoising.

Block-by-block causal inference with KV caching, using the full scheduler timestep schedule (40-50 steps) rather than DMD few-step.

Classes
fastvideo.pipelines.basic.wan.wan_causal_pipeline.WanCausalPipeline 
WanCausalPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

Wan causal pipeline with standard multi-step 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.wan.wan_dmd_pipeline

Wan video diffusion pipeline implementation.

This module contains an implementation of the Wan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.wan.wan_dmd_pipeline.WanDMDPipeline 
WanDMDPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

Wan 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.wan.wan_dmd_pipeline.WanDMDPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_dmd_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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),
                                                use_btchw_layout=True))

    self.add_stage(stage_name="denoising_stage",
                   stage=DmdDenoisingStage(transformer=self.get_module("transformer"),
                                           scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.wan.wan_i2v_dmd_pipeline

Wan video diffusion pipeline implementation.

This module contains an implementation of the Wan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.wan.wan_i2v_dmd_pipeline.WanImageToVideoDmdPipeline 
WanImageToVideoDmdPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

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.wan.wan_i2v_dmd_pipeline.WanImageToVideoDmdPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_i2v_dmd_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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")],
                   ))

    self.add_stage(stage_name="image_encoding_stage",
                   stage=ImageEncodingStage(
                       image_encoder=self.get_module("image_encoder"),
                       image_processor=self.get_module("image_processor"),
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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"),
                                                use_btchw_layout=True))

    self.add_stage(stage_name="image_latent_preparation_stage",
                   stage=ImageVAEEncodingStage(vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DmdDenoisingStage(transformer=self.get_module("transformer"),
                                           scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.wan.wan_i2v_pipeline

Wan video diffusion pipeline implementation.

This module contains an implementation of the Wan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.wan.wan_i2v_pipeline.WanImageToVideoPipeline 
WanImageToVideoPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

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.wan.wan_i2v_pipeline.WanImageToVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_i2v_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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")],
                   ))

    if (self.get_module("image_encoder") is not None and self.get_module("image_processor") is not None):
        self.add_stage(stage_name="image_encoding_stage",
                       stage=ImageEncodingStage(
                           image_encoder=self.get_module("image_encoder"),
                           image_processor=self.get_module("image_processor"),
                       ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="image_latent_preparation_stage",
                   stage=ImageVAEEncodingStage(vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        transformer_2=self.get_module("transformer_2"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions
fastvideo.pipelines.basic.wan.wan_pipeline

Wan video diffusion pipeline implementation.

This module contains an implementation of the Wan video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline 
WanPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

Wan 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.wan.wan_pipeline.WanPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs) -> None

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_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")],
                   ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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=DenoisingStage(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))
Functions
fastvideo.pipelines.basic.wan.wan_v2v_pipeline

Wan video-to-video diffusion pipeline implementation.

This module contains an implementation of the Wan video-to-video diffusion pipeline using the modular pipeline architecture.

Classes
fastvideo.pipelines.basic.wan.wan_v2v_pipeline.WanVideoToVideoPipeline 
WanVideoToVideoPipeline(*args, **kwargs)

Bases: LoRAPipeline, ComposedPipelineBase

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.wan.wan_v2v_pipeline.WanVideoToVideoPipeline.create_pipeline_stages 
create_pipeline_stages(fastvideo_args: FastVideoArgs)

Set up pipeline stages with proper dependency injection.

Source code in fastvideo/pipelines/basic/wan/wan_v2v_pipeline.py 
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def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
    """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")],
                   ))

    if (self.get_module("image_encoder") is not None and self.get_module("image_processor") is not None):
        self.add_stage(stage_name="ref_image_encoding_stage",
                       stage=RefImageEncodingStage(
                           image_encoder=self.get_module("image_encoder"),
                           image_processor=self.get_module("image_processor"),
                       ))

    self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())

    self.add_stage(stage_name="timestep_preparation_stage",
                   stage=TimestepPreparationStage(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")))

    self.add_stage(stage_name="video_latent_preparation_stage",
                   stage=VideoVAEEncodingStage(vae=self.get_module("vae")))

    self.add_stage(stage_name="denoising_stage",
                   stage=DenoisingStage(transformer=self.get_module("transformer"),
                                        transformer_2=self.get_module("transformer_2"),
                                        scheduler=self.get_module("scheduler")))

    self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae")))
Functions