69 lines
1.5 KiB
YAML
69 lines
1.5 KiB
YAML
# Wan 2.1 T2V 1.3B finetune.
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models:
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student:
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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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method:
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_target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
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training:
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distributed:
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num_gpus: 8
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sp_size: 1
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tp_size: 1
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hsdp_replicate_dim: 8
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hsdp_shard_dim: 1
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data:
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data_path: data/preprocessed
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dataloader_num_workers: 4
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train_batch_size: 1
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training_cfg_rate: 0.0
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seed: 1000
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num_latent_t: 20
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num_height: 448
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num_width: 832
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num_frames: 77
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optimizer:
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learning_rate: 1.0e-6
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betas: [0.9, 0.999]
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weight_decay: 0.01
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lr_scheduler: constant
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lr_warmup_steps: 0
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loop:
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max_train_steps: 4000
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gradient_accumulation_steps: 1
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checkpoint:
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output_dir: outputs/wan_finetune
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training_state_checkpointing_steps: 100
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checkpoints_total_limit: 3
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resume_from_checkpoint: latest
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tracker:
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project_name: fastvideo
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run_name: wan_finetune
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model:
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enable_gradient_checkpointing_type: full
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callbacks:
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grad_clip:
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_target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
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max_grad_norm: 1.0
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validation:
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_target_: fastvideo.train.callbacks.validation.ValidationCallback
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pipeline_target: fastvideo.pipelines.basic.wan.wan_pipeline.WanPipeline
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dataset_file: data/validation_prompts.json
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every_steps: 100
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sampling_steps: [50]
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guidance_scale: 5.0
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pipeline:
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flow_shift: 3
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