wehub-resource-sync
103 lines
2.7 KiB
YAML
103 lines
2.7 KiB
YAML
# Distributed distillation runtime.
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infra:
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sharding_strategy: hybrid_full
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mixed_precision: true
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gradient_checkpointing: true
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generator_fsdp_wrap_strategy: size
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real_score_fsdp_wrap_strategy: size
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fake_score_fsdp_wrap_strategy: size
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text_encoder_fsdp_wrap_strategy: size
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# Shared Wan backbone settings for the student, real-score teacher, and fake-score critic.
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# Role-specific model kwargs can still be provided as real_model_kwargs/fake_model_kwargs
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# if a future run needs different model construction.
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model_kwargs:
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model_name: Wan2.2-TI2V-5B
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timestep_shift: 5.0
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num_frame_per_block: 8
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local_attn_size: 32
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# Optional initialization checkpoints. These are flattened by normalize_config
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# so the trainer still reads generator_ckpt/real_score_ckpt/fake_score_ckpt.
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# With adapter enabled, generator_ckpt initializes the base generator before LoRA wrapping.
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checkpoints:
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generator_ckpt: null
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real_score_ckpt: null
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fake_score_ckpt: null
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# DMD distillation objective.
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algorithm:
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trainer: score_distillation
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distribution_loss: dmd
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all_causal: true
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ts_schedule: false
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real_guidance_scale: 3.0
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fake_guidance_scale: 0.0
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training:
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lr: 1.0e-05
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lr_critic: 2.0e-06
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weight_decay: 0.0
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beta1: 0.0
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beta2: 0.999
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beta1_critic: 0.0
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beta2_critic: 0.999
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batch_size: 1
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gradient_accumulation_steps: 1
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ema_weight: 0.99
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ema_start_step: 200
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log_iters: 50
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max_checkpoints: 50
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max_iters: 5000
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gc_interval: 100
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dfake_gen_update_ratio: 5
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# Backward simulation samples a rollout length in [min_num_training_frames, num_training_frames].
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# Setting both to 32 gives a fixed 32-latent DMD rollout.
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min_num_training_frames: 32
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num_training_frames: 32
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# Keep this many tail latents for the DMD/critic losses after backward simulation.
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# With a 32-latent rollout, 32 keeps the full generated window.
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slice_last_frames: 32
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data:
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data_path: /path/to/longlive2/train_dataset
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eval_data_path: /path/to/longlive2/eval_prompts
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image_or_video_shape:
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- 1
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- 32
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- 48
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- 44
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- 80
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# The distillation loop generates video trajectories, then computes DMD losses.
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inference:
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sampling_steps: 4
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sink_size: 0
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multi_shot_rope_offset: 8
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# Optional validation generation from the distilled model. Uses inference sampling/cache settings by default.
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evaluation:
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interval: 1
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num_frames: 32
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use_ema: false
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# Can be raised when validation memory allows; 1 is the safe default.
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val_batch_size: 1
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save_latents_only: true
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# Presence of this section enables LoRA distillation; remove it for full fine-tuning.
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adapter:
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type: lora
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rank: 128
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alpha: 128
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dropout: 0.0
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apply_to_critic: true
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verbose: true
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logging:
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seed: 0
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wandb_key: null
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wandb_entity: null
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wandb_project: LongLive2-DMD
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