DiffSynth-Studio/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh at main · modelscope/DiffSynth-Studio · GitHub

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modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --include "wanvideo/Wan2.2-VACE-Fun-A14B/*" --local_dir ./data/diffsynth_example_dataset

accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \
  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \
  --data_file_keys "video,vace_video,vace_reference_image" \
  --height 480 \
  --width 832 \
  --num_frames 17 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
  --learning_rate 5e-5 \
  --num_epochs 2 \
  --remove_prefix_in_ckpt "pipe.vace." \
  --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \
  --trainable_models "vace" \
  --extra_inputs "vace_video,vace_reference_image" \
  --use_gradient_checkpointing_offload \
  --max_timestep_boundary 0.358 \
  --min_timestep_boundary 0 \
  --initialize_model_on_cpu
# boundary corresponds to timesteps [900, 1000]
# The learning rate is kept consistent with the settings in the original paper


accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \
  --dataset_base_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B \
  --dataset_metadata_path data/diffsynth_example_dataset/wanvideo/Wan2.2-VACE-Fun-A14B/metadata.csv \
  --data_file_keys "video,vace_video,vace_reference_image" \
  --height 480 \
  --width 832 \
  --num_frames 17 \
  --dataset_repeat 100 \
  --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \
  --learning_rate 5e-5 \
  --num_epochs 2 \
  --remove_prefix_in_ckpt "pipe.vace." \
  --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \
  --trainable_models "vace" \
  --extra_inputs "vace_video,vace_reference_image" \
  --use_gradient_checkpointing_offload \
  --max_timestep_boundary 1 \
  --min_timestep_boundary 0.358 \
  --initialize_model_on_cpu
# boundary corresponds to timesteps [0, 900]
# The learning rate is kept consistent with the settings in the original paper