63 lines
2.0 KiB
Bash
63 lines
2.0 KiB
Bash
export WANDB_BASE_URL="https://api.wandb.ai"
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export WANDB_MODE=offline
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export WANDB_API_KEY='your_wandb_api_key'
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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=~/train/
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VALIDATION_DIR=~latents/test/
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NUM_GPUS=8
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export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_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="$DATA_DIR/outputs/wan_finetune/checkpoint-5"
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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 $NUM_GPUS \
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fastvideo/v1/training/wan_training_pipeline.py \
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--model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--inference_mode False\
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--pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--cache_dir "/home/ray/.cache" \
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--data_path "$DATA_DIR" \
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--validation_preprocessed_path "$VALIDATION_DIR" \
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--train_batch_size 1 \
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--num_latent_t 16 \
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--sp_size 1 \
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--tp_size 1 \
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--num_gpus $NUM_GPUS \
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--hsdp_replicate_dim $NUM_GPUS \
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--hsdp-shard-dim 1 \
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--train_sp_batch_size 1 \
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--dataloader_num_workers 4 \
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--gradient_accumulation_steps 8 \
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--max_train_steps 30000 \
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--learning_rate 1e-5 \
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--mixed_precision "bf16" \
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--checkpointing_steps 6000 \
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--validation_steps 100 \
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--validation_sampling_steps "50" \
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--log_validation \
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--checkpoints_total_limit 3 \
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--allow_tf32 \
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--ema_start_step 0 \
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--cfg 0.0 \
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--output_dir "$DATA_DIR/outputs/wan_finetune" \
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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 3 \
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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 \
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--VSA_decay_sparsity 0.9 \
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--VSA_decay_rate 0.03 \
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--VSA_decay_interval_steps 30 \
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--VSA_val_sparsity 0.9
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# --resume_from_checkpoint "$CHECKPOINT_PATH"
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