57 lines
2.2 KiB
Bash
57 lines
2.2 KiB
Bash
export WANDB_BASE_URL="https://api.wandb.ai"
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export WANDB_MODE=online
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export WANDB_API_KEY='2f25ad37933894dbf0966c838c0b8494987f9f2f'
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# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
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export TRITON_CACHE_DIR=/tmp/triton_cache
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# DATA_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/
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DATA_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol_processed_t2v/combined_parquet_dataset
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# VALIDATION_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/mixkit/validation_8.json
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VALIDATION_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol-single_processed_t2v/validation.json
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NUM_GPUS=8
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export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
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# export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
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# export CUDA_VISIBLE_DEVICES=4,5
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# IP=[MASTER NODE IP]
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CHECKPOINT_PATH="outputs_train_test/wan_finetune/checkpoint-10"
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# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
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torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
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fastvideo/training/wan_training_pipeline.py \
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--model_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
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--inference_mode False\
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--pretrained_model_name_or_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
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--cache_dir "/home/ray/.cache" \
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--data_path "$DATA_DIR" \
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--validation_dataset_file "$VALIDATION_DIR" \
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--train_batch_size 1 \
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--num_latent_t 16\
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--sp_size 4 \
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--tp_size 1 \
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--num_gpus $NUM_GPUS \
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--hsdp_replicate_dim 1 \
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--hsdp-shard-dim 8 \
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--train_sp_batch_size 1 \
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--dataloader_num_workers 4 \
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--gradient_accumulation_steps 1 \
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--max_train_steps 30000 \
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--learning_rate 1e-5 \
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--mixed_precision "bf16" \
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--checkpointing_steps 1000 \
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--validation_steps 30 \
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--validation_sampling_steps "40" \
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--log_validation True \
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--checkpoints_total_limit 3 \
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--ema_start_step 0 \
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--training_cfg_rate 0.1 \
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--seed 1024 \
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--output_dir "outputs_train_test/wan_finetune_v1" \
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--tracker_project_name VSA_finetune \
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--num_height 448 \
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--num_width 832 \
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--num_frames 61 \
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--flow_shift 5 \
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--validation_guidance_scale "5.0" \
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--num_euler_timesteps 50 \
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--master_weight_type "fp32" \
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--dit_precision "fp32" \
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--weight_decay 0.01 \
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--max_grad_norm 1.0 |