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