fastvideo.fastvideo_args

`fastvideo.fastvideo_args`#

The arguments of FastVideo Inference.

Module Contents#

Classes#

`ExecutionMode`	Enumeration for different pipeline modes.
`FastVideoArgs`
`TrainingArgs`	Training arguments. Inherits from FastVideoArgs and adds training-specific arguments. If there are any conflicts, the training arguments will take precedence.
`WorkloadType`	Enumeration for different workload types.

Functions#

`get_current_fastvideo_args`
`parse_int_list`
`prepare_fastvideo_args`	Prepare the inference arguments from the command line arguments.
`set_current_fastvideo_args`	Temporarily set the current fastvideo config. Used during model initialization. We save the current fastvideo config in a global variable, so that all modules can access it, e.g. custom ops can access the fastvideo config to determine how to dispatch.

Data#

logger

API#

class fastvideo.fastvideo_args.ExecutionMode[source]#

Bases: str, enum.Enum

Enumeration for different pipeline modes.

Inherits from str to allow string comparison for backward compatibility.

Initialization

Initialize self. See help(type(self)) for accurate signature.

DISTILLATION[source]#: ‘distillation’

FINETUNING[source]#: ‘finetuning’

INFERENCE[source]#: ‘inference’

PREPROCESS[source]#: ‘preprocess’

classmethod choices() → list[str][source]#: Get all available choices as strings for argparse.

classmethod from_string(value: str) → fastvideo.fastvideo_args.ExecutionMode[source]#: Convert string to ExecutionMode enum.

class fastvideo.fastvideo_args.FastVideoArgs[source]#

STA_mode: fastvideo.configs.pipelines.base.STA_Mode[source]#: None

VSA_sparsity: float[source]#: 0.0

static add_cli_args(parser: fastvideo.utils.FlexibleArgumentParser) → fastvideo.utils.FlexibleArgumentParser[source]#

cache_strategy: str[source]#: ‘none’

check_fastvideo_args() → None[source]#: Validate inference arguments for consistency

disable_autocast: bool[source]#: False

dist_timeout: int | None[source]#: None

distributed_executor_backend: str[source]#: ‘mp’

dit_cpu_offload: bool[source]#: True

enable_stage_verification: bool[source]#: True

enable_torch_compile: bool[source]#: False

classmethod from_cli_args(args: argparse.Namespace) → fastvideo.fastvideo_args.FastVideoArgs[source]#

classmethod from_kwargs(**kwargs: Any) → fastvideo.fastvideo_args.FastVideoArgs[source]#

hsdp_replicate_dim: int[source]#: 1

hsdp_shard_dim: int[source]#: None

image_encoder_cpu_offload: bool[source]#: True

inference_mode: bool[source]#: True

lora_nickname: str[source]#: ‘default’

lora_path: str | None[source]#: None

lora_target_modules: list[str] | None[source]#: None

mask_strategy_file_path: str | None[source]#: None

master_port: int | None[source]#: None

mode: fastvideo.fastvideo_args.ExecutionMode[source]#: None

model_loaded: dict[str, bool][source]#: ‘field(…)’

model_path: str[source]#: None

model_paths: dict[str, str][source]#: ‘field(…)’

num_gpus: int[source]#: 1

output_type: str[source]#: ‘pil’

pin_cpu_memory: bool[source]#: True

pipeline_config: fastvideo.configs.pipelines.base.PipelineConfig[source]#: ‘field(…)’

preprocess_config: fastvideo.configs.configs.PreprocessConfig | None[source]#: None

prompt_txt: str | None[source]#: None

revision: str | None[source]#: None

skip_time_steps: int[source]#: 15

sp_size: int[source]#: None

text_encoder_cpu_offload: bool[source]#: True

tp_size: int[source]#: None

property training_mode: bool[source]#

trust_remote_code: bool[source]#: False

use_fsdp_inference: bool[source]#: True

vae_cpu_offload: bool[source]#: True

workload_type: fastvideo.fastvideo_args.WorkloadType[source]#: None

class fastvideo.fastvideo_args.TrainingArgs[source]#

Bases: fastvideo.fastvideo_args.FastVideoArgs

Training arguments. Inherits from FastVideoArgs and adds training-specific arguments. If there are any conflicts, the training arguments will take precedence.

VSA_decay_interval_steps: int[source]#: 1

VSA_decay_rate: float[source]#: 0.01

static add_cli_args(parser: fastvideo.utils.FlexibleArgumentParser) → fastvideo.utils.FlexibleArgumentParser[source]#

checkpointing_steps: int[source]#: 0

checkpoints_total_limit: int[source]#: 0

data_path: str = <Multiline-String>[source]#

dataloader_num_workers: int[source]#: 0

distill_cfg: float[source]#: 0.0

dit_model_name_or_path: str = <Multiline-String>[source]#

ema_decay: float[source]#: 0.0

ema_start_step: int[source]#: 0

enable_gradient_checkpointing_type: str | None[source]#: None

fake_score_learning_rate: float[source]#: 0.0

fake_score_lr_scheduler: str[source]#: ‘constant’

classmethod from_cli_args(args: argparse.Namespace) → fastvideo.fastvideo_args.TrainingArgs[source]#

fsdp_sharding_startegy: str = <Multiline-String>[source]#

generator_update_interval: int[source]#: 5

gradient_accumulation_steps: int[source]#: 0

group_frame: bool[source]#: False

group_resolution: bool[source]#: False

hunyuan_teacher_disable_cfg: bool[source]#: False

learning_rate: float[source]#: 0.0

linear_quadratic_threshold: float[source]#: 0.0

linear_range: float[source]#: 0.0

log_validation: bool[source]#: False

log_visualization: bool[source]#: False

logit_mean: float[source]#: 0.0

logit_std: float[source]#: 1.0

lora_alpha: int | None[source]#: None

lora_rank: int | None[source]#: None

lora_training: bool[source]#: False

lr_num_cycles: int[source]#: 0

lr_power: float[source]#: 0.0

lr_scheduler: str[source]#: ‘constant’

lr_warmup_steps: int[source]#: 0

master_weight_type: str = <Multiline-String>[source]#

max_grad_norm: float[source]#: 0.0

max_timestep_ratio: float[source]#: 0.98

max_train_steps: int[source]#: 0

min_lr_ratio: float[source]#: 0.5

min_timestep_ratio: float[source]#: 0.2

mixed_precision: str = <Multiline-String>[source]#

mode_scale: float[source]#: 0.0

multi_phased_distill_schedule: str = <Multiline-String>[source]#

not_apply_cfg_solver: bool[source]#: False

num_euler_timesteps: int[source]#: 0

num_frames: int[source]#: 0

num_height: int[source]#: 0

num_latent_t: int[source]#: 0

num_train_epochs: int[source]#: 0

num_width: int[source]#: 0

output_dir: str = <Multiline-String>[source]#

precondition_outputs: bool[source]#: False

pred_decay_type: str = <Multiline-String>[source]#

pred_decay_weight: float[source]#: 0.0

pretrained_model_name_or_path: str = <Multiline-String>[source]#

real_score_guidance_scale: float[source]#: 3.5

resume_from_checkpoint: str = <Multiline-String>[source]#

scale_lr: bool[source]#: False

scheduler_type: str = <Multiline-String>[source]#

seed: int | None[source]#: None

selective_checkpointing: float[source]#: 0.0

simulate_generator_forward: bool[source]#: False

tracker_project_name: str = <Multiline-String>[source]#

train_batch_size: int[source]#: 0

train_sp_batch_size: int[source]#: 0

training_cfg_rate: float[source]#: 0.0

training_state_checkpointing_steps: int[source]#: 0

use_ema: bool[source]#: False

validation_dataset_file: str = <Multiline-String>[source]#

validation_guidance_scale: str = <Multiline-String>[source]#

validation_preprocessed_path: str = <Multiline-String>[source]#

validation_sampling_steps: str = <Multiline-String>[source]#

validation_steps: float[source]#: 0.0

wandb_run_name: str = <Multiline-String>[source]#

weight_decay: float[source]#: 0.0

weight_only_checkpointing_steps: int[source]#: 0

weighting_scheme: str = <Multiline-String>[source]#

class fastvideo.fastvideo_args.WorkloadType[source]#

Bases: str, enum.Enum

Enumeration for different workload types.