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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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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")))

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

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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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)))

    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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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")))

    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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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()
    self.exclude_lora_layers = self.modules[
        "transformer"].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"
            ]
        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