Source examples/training/finetune/wan_i2v_14B_480p/crush_smol.

Wan2.1-I2V-14B-480P Crush-Smol Example

These are e2e examples scripts for finetuning Wan2.1 I2V 14B 480P on the crush-smol dataset.

Execute the following commands from FastVideo/ to run training:

Download crush-smol dataset:

bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh

Preprocess the videos and captions into latents:

bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh

Edit the following file and run finetuning:

bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh

Example materials

download_dataset.sh

#!/bin/bash

python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"

finetune_i2v.sh

#!/bin/bash

export WANDB_BASE_URL="https://api.wandb.ai"
export WANDB_MODE=online
export TOKENIZERS_PARALLELISM=false
# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
NUM_GPUS=8
# export CUDA_VISIBLE_DEVICES=4,5
# IP=[MASTER NODE IP]

# Training arguments
training_args=(
  --tracker_project_name "wan_i2v_finetune"
  --output_dir "checkpoints/wan_i2v_finetune"
  --max_train_steps 2000
  --train_batch_size 1
  --train_sp_batch_size 1
  --gradient_accumulation_steps 1
  --num_latent_t 8
  --num_height 480
  --num_width 832
  --num_frames 77
  --enable_gradient_checkpointing_type "full"
)

# Parallel arguments
parallel_args=(
  --num_gpus $NUM_GPUS
  --sp_size 8
  --tp_size 1
  --hsdp_replicate_dim 1
  --hsdp_shard_dim 8
)

# Model arguments
model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
)

# Dataset arguments
dataset_args=(
  --data_path "$DATA_DIR"
  --dataloader_num_workers 1
)

# Validation arguments
validation_args=(
  --log_validation
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
  --validation_guidance_scale "1.0"
)

# Optimizer arguments
optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
)

# Miscellaneous arguments
miscellaneous_args=(
  --inference_mode False
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
  --not_apply_cfg_solver
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
  --enable_gradient_checkpointing_type "full"
)

# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
torchrun \
  --nnodes 1 \
  --nproc_per_node $NUM_GPUS \
    fastvideo/training/wan_i2v_training_pipeline.py \
    "${parallel_args[@]}" \
    "${model_args[@]}" \
    "${dataset_args[@]}" \
    "${training_args[@]}" \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}"

finetune_i2v.slurm

#!/bin/bash
#SBATCH --job-name=i2v
#SBATCH --partition=main
#SBATCH --nodes=4
#SBATCH --ntasks=4
#SBATCH --ntasks-per-node=1
#SBATCH --gres=gpu:8
#SBATCH --cpus-per-task=128
#SBATCH --mem=1440G
#SBATCH --output=i2v_output/i2v_%j.out
#SBATCH --error=i2v_output/i2v_%j.err
#SBATCH --exclusive
set -e -x

# Environment Setup
source ~/conda/miniconda/bin/activate
conda activate will-fv

# Basic Info
export WANDB_MODE="online"
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
# different cache dir for different processes
export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
export MASTER_PORT=29500
export NODE_RANK=$SLURM_PROCID
nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
export MASTER_ADDR=${nodes[0]}
export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
export TOKENIZERS_PARALLELISM=false
export WANDB_BASE_URL="https://api.wandb.ai"
export WANDB_MODE=online
# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

echo "MASTER_ADDR: $MASTER_ADDR"
echo "NODE_RANK: $NODE_RANK"

# Configs
NUM_GPUS=8

MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
VALIDATION_DATASET_FILE="examples/training/finetune/wan_i2v_14b_480p/crush_smol/validation.json"
# 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

# Training arguments
training_args=(
  --tracker_project_name wan_i2v_finetune
  --output_dir="checkpoints/wan_i2v_finetune"
  --max_train_steps 2000
  --train_batch_size 2
  --train_sp_batch_size 1
  --gradient_accumulation_steps 1
  --num_latent_t 8
  --num_height 480
  --num_width 832
  --num_frames 77
  --enable_gradient_checkpointing_type "full"
)

# Parallel arguments
parallel_args=(
  --num_gpus $NUM_GPUS
  --sp_size $NUM_GPUS
  --tp_size 1
  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
  --hsdp_shard_dim $NUM_GPUS
)

# Model arguments
model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
)

# Dataset arguments
dataset_args=(
  --data_path "$DATA_DIR"
  --dataloader_num_workers 10
)

# Validation arguments
validation_args=(
  --log_validation
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
  --validation_steps 100
  --validation_sampling_steps "40"
  --validation_guidance_scale "1.0"
)

# Optimizer arguments
optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
)

# Miscellaneous arguments
miscellaneous_args=(
  --inference_mode False
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
  --not_apply_cfg_solver
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
  --enable_gradient_checkpointing_type "full"
)

srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
    fastvideo/training/wan_i2v_training_pipeline.py \
    "${parallel_args[@]}" \
    "${model_args[@]}" \
    "${dataset_args[@]}" \
    "${training_args[@]}" \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}"

finetune_i2v_lora.sh

#!/bin/bash

export WANDB_BASE_URL="https://api.wandb.ai"
export WANDB_MODE=online
# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
DATA_DIR="data/crush-smol_processed_i2v/combined_parquet_dataset/"
VALIDATION_DIR="data/crush-smol_processed_i2v/validation_parquet_dataset/"
NUM_GPUS=4
# export CUDA_VISIBLE_DEVICES=4,5
# IP=[MASTER NODE IP]

# Training arguments
training_args=(
  --tracker_project_name "wan_i2v_finetune"
  --output_dir "checkpoints/wan_i2v_finetune"
  --max_train_steps 2000
  --train_batch_size 1
  --train_sp_batch_size 1
  --gradient_accumulation_steps 1
  --num_latent_t 8
  --num_height 480
  --num_width 832
  --num_frames 77
  --lora_rank 32
  --lora_training True
)

# Parallel arguments
parallel_args=(
  --num_gpus $NUM_GPUS
  --sp_size $NUM_GPUS
  --tp_size $NUM_GPUS
  --hsdp_replicate_dim 1
  --hsdp_shard_dim $NUM_GPUS
)

# Model arguments
model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
)

# Dataset arguments
dataset_args=(
  --data_path "$DATA_DIR"
  --dataloader_num_workers 1
)

# Validation arguments
validation_args=(
  --log_validation
  --validation_preprocessed_path "$VALIDATION_DIR"
  --validation_steps 100
  --validation_sampling_steps "40"
  --validation_guidance_scale "1.0"
)

# Optimizer arguments
optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
)

# Miscellaneous arguments
miscellaneous_args=(
  --inference_mode False
  --checkpoints_total_limit 3
  --training_cfg_rate 0.1
  --multi_phased_distill_schedule "4000-1"
  --not_apply_cfg_solver
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
)

# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
torchrun \
  --nnodes 1 \
  --nproc_per_node $NUM_GPUS \
    fastvideo/training/wan_i2v_training_pipeline.py \
    "${parallel_args[@]}" \
    "${model_args[@]}" \
    "${dataset_args[@]}" \
    "${training_args[@]}" \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}"

preprocess_wan_data_i2v.sh

#!/bin/bash

GPU_NUM=1 # 2,4,8
MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
MODEL_TYPE="wan"
DATA_MERGE_PATH="data/crush-smol/merge.txt"
OUTPUT_DIR="data/crush-smol_processed_i2v/"

torchrun --nproc_per_node=$GPU_NUM \
    fastvideo/pipelines/preprocess/v1_preprocess.py \
    --model_path $MODEL_PATH \
    --data_merge_path $DATA_MERGE_PATH \
    --preprocess_video_batch_size 8 \
    --seed 42 \
    --max_height 480 \
    --max_width 832 \
    --num_frames 77 \
    --dataloader_num_workers 0 \
    --output_dir=$OUTPUT_DIR \
    --train_fps 16 \
    --samples_per_file 8 \
    --flush_frequency 8 \
    --video_length_tolerance_range 5 \
    --preprocess_task "i2v" 

validation.json

{
  "data": [
    {
      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
      "image_path": null,
      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
      "num_inference_steps": 40,
      "height": 480,
      "width": 832,
      "num_frames": 77
    },
    {
      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
      "image_path": null,
      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
      "num_inference_steps": 40,
      "height": 480,
      "width": 832,
      "num_frames": 77
    },
    {
      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
      "image_path": null,
      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
      "num_inference_steps": 40,
      "height": 480,
      "width": 832,
      "num_frames": 77
    }
  ]
}