59 lines
2.0 KiB
Bash
59 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=
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export TRITON_CACHE_DIR=/tmp/triton_cache
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DATA_DIR=mini_i2v_dataset/crush-smol_preprocessed/combined_parquet_dataset/
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VALIDATION_DIR=mini_i2v_dataset/crush-smol_raw/validation.json
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NUM_GPUS=8
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export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
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export TOKENIZERS_PARALLELISM=false
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# make sure that num_latent_t is a multiple of sp_size
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torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
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fastvideo/training/wan_distillation_pipeline.py \
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--model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--real_score_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--fake_score_model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--inference_mode False\
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--pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
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--cache_dir "/home/ray/.cache" \
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--data_path "$DATA_DIR" \
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--validation_dataset_file "$VALIDATION_DIR" \
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--train_batch_size 1 \
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--num_latent_t 20 \
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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 0 \
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--gradient_accumulation_steps 8 \
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--max_train_steps 30000 \
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--learning_rate 2e-6 \
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--mixed_precision "bf16" \
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--training_state_checkpointing_steps 400 \
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--validation_steps 100 \
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--validation_sampling_steps "3" \
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--log_validation \
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--checkpoints_total_limit 3 \
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--ema_start_step 0 \
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--training_cfg_rate 0.0 \
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--output_dir "outputs_dmd/wan_finetune" \
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--tracker_project_name Wan_distillation \
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--num_height 448 \
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--num_width 832 \
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--num_frames 77 \
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--flow_shift 8 \
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--validation_guidance_scale "6.0" \
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--master_weight_type "fp32" \
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--dit_precision "fp32" \
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--vae_precision "bf16" \
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--weight_decay 0.01 \
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--max_grad_norm 1.0 \
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--generator_update_interval 5 \
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--dmd_denoising_steps '1000,757,522' \
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--min_timestep_ratio 0.02 \
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--max_timestep_ratio 0.98 \
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--real_score_guidance_scale 3.5 \
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--seed 1024 |