[Feature] Add DMD T2V training pipeline (#651)

2025-07-27 03:35:51 -04:00
parent cb320dfa3a
commit 247fe273d8
17 changed files with 1406 additions and 20 deletions
@@ -0,0 +1,58 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=offline
+export WANDB_API_KEY=
+export TRITON_CACHE_DIR=/tmp/triton_cache
+DATA_DIR=mini_i2v_dataset/crush-smol_preprocessed/combined_parquet_dataset/
+VALIDATION_DIR=mini_i2v_dataset/crush-smol_raw/validation.json
+NUM_GPUS=8
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+export TOKENIZERS_PARALLELISM=false
+
+# make sure that num_latent_t is a multiple of sp_size
+torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
+    fastvideo/training/wan_distillation_pipeline.py \
+    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --inference_mode False\
+    --pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --cache_dir "/home/ray/.cache" \
+    --data_path "$DATA_DIR" \
+    --validation_dataset_file  "$VALIDATION_DIR" \
+    --train_batch_size 1 \
+    --num_latent_t 20 \
+    --sp_size 1 \
+    --tp_size 1 \
+    --num_gpus $NUM_GPUS \
+    --hsdp_replicate_dim $NUM_GPUS  \
+    --hsdp-shard-dim 1 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 0 \
+    --gradient_accumulation_steps 8 \
+    --max_train_steps 30000 \
+    --learning_rate 1e-5 \
+    --mixed_precision "bf16" \
+    --checkpointing_steps 500 \
+    --validation_steps 100 \
+    --validation_sampling_steps "3" \
+    --log_validation \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --training_cfg_rate 0.0 \
+    --output_dir "outputs_dmd/wan_finetune" \
+    --tracker_project_name Wan_distillation \
+    --num_height 448 \
+    --num_width 832 \
+    --num_frames 77 \
+    --flow_shift 8 \
+    --validation_guidance_scale "1.0" \
+    --master_weight_type "fp32" \
+    --dit_precision "fp32" \
+    --vae_precision "bf16" \
+    --weight_decay 0.01 \
+    --max_grad_norm 1.0 \
+    --generator_update_interval 5 \
+    --dmd_denoising_steps '1000,757,522' \
+    --min_timestep_ratio 0.02 \
+    --max_timestep_ratio 0.98 \
+    --real_score_guidance_scale 3.5 \
+    --seed 1024
@@ -0,0 +1,60 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=offline
+export WANDB_API_KEY=
+export TRITON_CACHE_DIR=/tmp/triton_cache
+DATA_DIR=mini_i2v_dataset/crush-smol_preprocessed/combined_parquet_dataset/
+VALIDATION_DIR=mini_i2v_dataset/crush-smol_raw/validation.json
+NUM_GPUS=8
+export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
+export TOKENIZERS_PARALLELISM=false
+
+# Train generator with VSA
+# Make sure that num_latent_t is a multiple of sp_size
+torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
+    fastvideo/training/wan_distillation_pipeline.py \
+    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --inference_mode False\
+    --pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --cache_dir "/home/ray/.cache" \
+    --data_path "$DATA_DIR" \
+    --validation_dataset_file  "$VALIDATION_DIR" \
+    --train_batch_size 1 \
+    --num_latent_t 16 \
+    --sp_size 1 \
+    --tp_size 1 \
+    --num_gpus $NUM_GPUS \
+    --hsdp_replicate_dim $NUM_GPUS  \
+    --hsdp-shard-dim 1 \
+    --train_sp_batch_size 1 \
+    --dataloader_num_workers 0 \
+    --gradient_accumulation_steps 8 \
+    --max_train_steps 30000 \
+    --learning_rate 1e-5 \
+    --mixed_precision "bf16" \
+    --checkpointing_steps 500 \
+    --validation_steps 100 \
+    --validation_sampling_steps "3" \
+    --log_validation \
+    --checkpoints_total_limit 3 \
+    --allow_tf32 \
+    --ema_start_step 0 \
+    --training_cfg_rate 0.0 \
+    --output_dir "outputs_dmd/wan_finetune" \
+    --tracker_project_name Wan_distillation \
+    --num_height 448 \
+    --num_width 832 \
+    --num_frames 61 \
+    --flow_shift 8 \
+    --validation_guidance_scale "1.0" \
+    --master_weight_type "fp32" \
+    --dit_precision "fp32" \
+    --vae_precision "bf16" \
+    --weight_decay 0.01 \
+    --max_grad_norm 1.0 \
+    --generator_update_interval 5 \
+    --dmd_denoising_steps '1000,757,522' \
+    --min_timestep_ratio 0.02 \
+    --max_timestep_ratio 0.98 \
+    --real_score_guidance_scale 3.5 \
+    --seed 1024 \
+    --VSA_sparsity 0.8 
@@ -9,7 +9,7 @@ NUM_GPUS=4
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+# Make sure that num_latent_t is a multiple of sp_size
 torchrun --nnodes 1 --nproc_per_node $NUM_GPUS\
    fastvideo/training/wan_training_pipeline.py\
    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
@@ -14,7 +14,7 @@ export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN

 CHECKPOINT_PATH="$DATA_DIR/outputs/wan_finetune/checkpoint-5"

-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+# Make sure that num_latent_t is a multiple of sp_size
 torchrun --nnodes 1 --nproc_per_node $NUM_GPUS \
    fastvideo/training/wan_training_pipeline.py \
    --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \