1a383f3f66
Co-authored-by: alexzms <3036648523@qq.com> Co-authored-by: Peiyuan Zhang <a1286225768@slurm-h200-204-215.slurm-compute.tenant-slurm.svc.cluster.local>
195 lines
8.9 KiB
YAML
195 lines
8.9 KiB
YAML
# ==============================================================================
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# Full configuration reference for fastvideo.train
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#
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# Legend:
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# [TYPED] — parsed into a typed dataclass; fields are validated with
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# defaults. Unknown keys are silently ignored.
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# [FREE] — free-form dict passed as-is to the target class / method.
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# Keys depend on the _target_ class constructor / method_config.
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# [RESOLVED] — parsed by PipelineConfig.from_kwargs(); auto-populated from
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# the model's config files. Only scalar overrides are useful.
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# ==============================================================================
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# ------------------------------------------------------------------------------
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# models: [FREE]
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#
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# Each role is instantiated via _target_(*, training_config=..., **kwargs).
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# Keys here are constructor kwargs of the _target_ class (e.g. WanModel).
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# You can define any role name (student, teacher, critic, etc.).
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# ------------------------------------------------------------------------------
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models:
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student:
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_target_: fastvideo.train.models.wan.WanModel # required
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init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers # required: HF repo or local path
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trainable: true # default: true
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disable_custom_init_weights: false # default: false
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flow_shift: 3.0 # default: 3.0
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enable_gradient_checkpointing_type: null # default: null (falls back to training.model)
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teacher:
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_target_: fastvideo.train.models.wan.WanModel
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init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
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trainable: false
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disable_custom_init_weights: true
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critic:
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_target_: fastvideo.train.models.wan.WanModel
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init_from: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
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trainable: true
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disable_custom_init_weights: true
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# ------------------------------------------------------------------------------
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# method: [FREE]
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#
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# Instantiated via _target_(*, cfg=RunConfig, role_models=...).
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# All keys besides _target_ are available in self.method_config (a plain dict).
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# Keys depend entirely on the method class.
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# ------------------------------------------------------------------------------
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method:
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_target_: fastvideo.train.methods.distribution_matching.dmd2.DMD2Method # required
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# --- DMD2-specific keys (read from self.method_config) ---
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rollout_mode: simulate # required: "simulate" or "data_latent"
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generator_update_interval: 5 # default: 1
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dmd_denoising_steps: [1000, 750, 500, 250] # SDE timestep schedule
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# Critic optimizer (all required — no fallback)
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fake_score_learning_rate: 8.0e-6
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fake_score_betas: [0.0, 0.999]
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fake_score_lr_scheduler: constant
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# CFG conditioning policy (optional)
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# cfg_uncond:
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# on_missing: error # "error" or "ignore"
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# text: keep # "keep", "zero", "drop", "negative_prompt"
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# image: keep # "keep", "zero", "drop"
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# action: keep # "keep", "zero", "drop"
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# --- FineTuneMethod keys (if using finetune instead) ---
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# _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
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# ------------------------------------------------------------------------------
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# training: [TYPED] -> TrainingConfig
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#
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# Every field below has a typed default. Unknown keys are ignored.
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# ------------------------------------------------------------------------------
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training:
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# --- training.distributed [TYPED] -> DistributedConfig ---
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distributed:
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num_gpus: 8 # default: 1
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tp_size: 1 # default: 1
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sp_size: 1 # default: 1 (defaults to num_gpus in loader)
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hsdp_replicate_dim: 1 # default: 1
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hsdp_shard_dim: 8 # default: -1 (defaults to num_gpus in loader)
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pin_cpu_memory: false # default: false
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# --- training.data [TYPED] -> DataConfig ---
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data:
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data_path: data/my_dataset # default: ""
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train_batch_size: 1 # default: 1
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dataloader_num_workers: 4 # default: 0
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training_cfg_rate: 0.1 # default: 0.0
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seed: 1000 # default: 0
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num_height: 448 # default: 0
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num_width: 832 # default: 0
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num_latent_t: 20 # default: 0
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num_frames: 77 # default: 0
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# --- training.optimizer [TYPED] -> OptimizerConfig ---
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# Note: only for the student optimizer. Critic optimizer is in method config.
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optimizer:
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learning_rate: 2.0e-6 # default: 0.0
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betas: [0.9, 0.999] # default: [0.9, 0.999]
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weight_decay: 0.01 # default: 0.0
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lr_scheduler: constant # default: "constant"
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lr_warmup_steps: 0 # default: 0
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lr_num_cycles: 0 # default: 0
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lr_power: 0.0 # default: 0.0
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min_lr_ratio: 0.5 # default: 0.5
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# --- training.loop [TYPED] -> TrainingLoopConfig ---
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loop:
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max_train_steps: 10000 # default: 0
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gradient_accumulation_steps: 1 # default: 1
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# --- training.checkpoint [TYPED] -> CheckpointConfig ---
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checkpoint:
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output_dir: outputs/my_run # default: ""
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resume_from_checkpoint: "" # default: "" (or use --resume-from-checkpoint CLI)
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training_state_checkpointing_steps: 1000 # default: 0 (disabled)
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checkpoints_total_limit: 3 # default: 0 (keep all)
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# --- training.tracker [TYPED] -> TrackerConfig ---
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tracker:
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trackers: [] # default: [] (auto-adds "wandb" if project_name is set)
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project_name: my_project # default: "fastvideo"
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run_name: my_run # default: ""
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# --- training.vsa [TYPED] -> VSAConfig ---
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vsa:
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sparsity: 0.0 # default: 0.0 (0.0 = disabled)
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# --- training.model [TYPED] -> ModelTrainingConfig ---
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model:
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weighting_scheme: uniform # default: "uniform"
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logit_mean: 0.0 # default: 0.0
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logit_std: 1.0 # default: 1.0
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mode_scale: 1.0 # default: 1.0
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precondition_outputs: false # default: false
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moba_config: {} # default: {}
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enable_gradient_checkpointing_type: full # default: null ("full" or null)
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# --- training top-level [TYPED] ---
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dit_precision: fp32 # default: "fp32" (master weight precision)
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# model_path: ... # default: "" (auto-derived from models.student.init_from)
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# ------------------------------------------------------------------------------
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# callbacks: [FREE]
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#
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# Each callback is instantiated via _target_(*, **kwargs).
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# The callback name (e.g. "grad_clip") is arbitrary — only _target_ matters.
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# training_config is injected automatically (not from YAML).
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# ------------------------------------------------------------------------------
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callbacks:
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# --- GradNormClipCallback ---
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grad_clip:
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_target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback # optional if using default registry
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max_grad_norm: 1.0 # default: 0.0 (0.0 = disabled)
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log_grad_norms: false # default: false
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# --- EMACallback ---
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# ema:
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# _target_: fastvideo.train.callbacks.ema.EMACallback
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# decay: 0.9999 # default: 0.9999
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# start_iter: 0 # default: 0
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# --- ValidationCallback ---
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validation:
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_target_: fastvideo.train.callbacks.validation.ValidationCallback # optional if using default registry
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pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline # required
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dataset_file: path/to/validation.json # required
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every_steps: 100 # default: 100
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sampling_steps: [4] # default: [40]
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guidance_scale: 5.0 # default: null (uses model default)
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num_frames: null # default: null (derived from training.data)
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output_dir: null # default: null (falls back to training.checkpoint.output_dir)
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sampling_timesteps: null # default: null (explicit timestep list for streaming)
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# ------------------------------------------------------------------------------
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# pipeline: [RESOLVED] -> PipelineConfig
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#
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# Parsed by PipelineConfig.from_kwargs(). Most fields are auto-populated from
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# the model's config files (vae_config, dit_config, text_encoder_configs, etc.).
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# Only scalar overrides are typically needed here.
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# ------------------------------------------------------------------------------
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pipeline:
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flow_shift: 3 # default: null (model-specific)
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# flow_shift_sr: null # default: null (super-resolution shift)
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# embedded_cfg_scale: 6.0 # default: 6.0
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# is_causal: false # default: false
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# vae_tiling: true # default: true
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# vae_sp: true # default: true
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# disable_autocast: false # default: false
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