[WIP][Feat] distillation

scripts/distill/distill_v1.sh

@@ -0,0 +1,47 @@
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+
+DATA_DIR=data/HD-Mixkit-Finetune-Wan/combined_parquet_dataset
+VALIDATION_DIR=data/HD-Mixkit-Finetune-Wan/validation_parquet_dataset
+num_gpus=1
+# 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
+    # --gradient_checkpointing\
+    # --pretrained_model_name_or_path hunyuanvideo-community/HunyuanVideo \
+    # --pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+torchrun --nnodes 1 --nproc_per_node $num_gpus\
+    fastvideo/v1/pipelines/distillation_pipeline.py\
+    --mode distill\
+    --pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+    --cache_dir "/home/test/.cache"\
+    --data_path "$DATA_DIR"\
+    --validation_prompt_dir "$VALIDATION_DIR"\
+    --train_batch_size=1 \
+    --num_latent_t 1 \
+    --sp_size $num_gpus \
+    --train_sp_batch_size 1\
+    --dataloader_num_workers $num_gpus\
+    --gradient_accumulation_steps=1\
+    --max_train_steps=320\
+    --learning_rate=1e-6\
+    --mixed_precision="bf16"\
+    --checkpointing_steps=64\
+    --validation_steps 20\
+    --validation_sampling_steps "2,4,8" \
+    --checkpoints_total_limit 3\
+    --allow_tf32\
+    --ema_start_step 0\
+    --cfg 0.0\
+    --log_validation\
+    --output_dir="$DATA_DIR/outputs/hy_phase1_shift17_bs_16_HD"\
+    --tracker_project_name Hunyuan_Distill \
+    --num_height 720 \
+    --num_width 1280 \
+    --num_frames  125 \
+    --shift 17 \
+    --validation_guidance_scale "1.0" \
+    --num_euler_timesteps 50 \
+    --multi_phased_distill_schedule "4000-1" \
+    --not_apply_cfg_solver \
+    --master_weight_type "bf16"

scripts/finetune/finetune_v1.sh

@@ -12,7 +12,7 @@ NUM_GPUS=4
 torchrun --nnodes 1 --nproc_per_node $NUM_GPUS\
     fastvideo/v1/training/wan_training_pipeline.py\
     --model_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
-    --inference_mode False\
+    --mode training\
     --pretrained_model_name_or_path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
     --cache_dir "/home/ray/.cache"\
     --data_path "$DATA_DIR"\