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@@ -42,8 +42,8 @@ export WANDB_BASE_URL="https://api.wandb.ai"
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export WANDB_MODE=online
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# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
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DATA_DIR=data/crush-smol_preprocessed/combined_parquet_dataset
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VALIDATION_DIR=data/crush-smol_preprocessed/validation_parquet_dataset
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DATA_DIR=data/crush-smol_processed/combined_parquet_dataset
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VALIDATION_DIR=data/crush-smol_processed/validation_parquet_dataset
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NUM_GPUS=8
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# export CUDA_VISIBLE_DEVICES=4,5
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# IP=[MASTER NODE IP]
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@@ -67,8 +67,8 @@ srun torchrun \
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--num_gpus $NUM_GPUS \
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--sp_size $NUM_GPUS \
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--tp_size $NUM_GPUS \
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--dp_size $SLURM_JOB_NUM_NODES \
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--dp_shards $NUM_GPUS \
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--hsdp_replicate_dim $SLURM_JOB_NUM_NODES \
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--hsdp_shard_dim $NUM_GPUS \
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--train_sp_batch_size 1\
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--dataloader_num_workers 10\
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--gradient_accumulation_steps=2\
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@@ -87,12 +87,11 @@ srun torchrun \
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--tracker_project_name wan_i2v_finetune \
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--num_height 480 \
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--num_width 832 \
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--num_frames 81 \
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--shift 3 \
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--num_frames 77 \
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--validation_guidance_scale "1.0" \
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--num_euler_timesteps 50 \
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--multi_phased_distill_schedule "4000-1" \
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--weight_decay 1e-4 \
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--not_apply_cfg_solver \
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--master_weight_type "fp32" \
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--dit_precision "fp32" \
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--max_grad_norm 1.0
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@@ -10,7 +10,7 @@ NUM_GPUS=8
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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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fastvideo/v1/training/wan_i2v_training_pipeline.py\
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--model_path Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
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--inference_mode False\
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--pretrained_model_name_or_path Wan-AI/Wan2.1-I2V-14B-480P-Diffusers \
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@@ -48,4 +48,4 @@ torchrun --nnodes 1 --nproc_per_node $NUM_GPUS\
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--weight_decay 1e-4 \
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--not_apply_cfg_solver \
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--dit_precision "fp32" \
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--max_grad_norm 1.0
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--max_grad_norm 1.0
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