Training Code

The default training commands for the different versions are as follows:

We can choose whether to use fsdp in FantasyTalking, which can save a lot of video memory.

The metadata_control.json is a little different from normal json in FantasyTalking, you need to add a audio_path.

[
    {
      "file_path": "train/00000001.mp4",
      "audio_path": "wav/00000001.wav",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    .....
]

Some parameters in the sh file can be confusing, and they are explained in this document:

enable_bucket is used to enable bucket training. When enabled, the model does not crop the videos at the center, but instead, it trains the videos after grouping them into buckets based on resolution.
random_frame_crop is used for random cropping on video frames to simulate videos with different frame counts.
random_hw_adapt is used to enable automatic height and width scaling for videos. When random_hw_adapt is enabled, for training videos, the height and width will be set to video_sample_size as the maximum and 512 as the minimum.
- For example, when random_hw_adapt is enabled, with video_sample_n_frames=49, video_sample_size=768, the resolution of video inputs for training is 512x512x49, 768x768x49.
training_with_video_token_length specifies training the model according to token length. For training videos, the height and width will be set to video_sample_size as the maximum and 256 as the minimum.
- For example, when training_with_video_token_length is enabled, with video_sample_n_frames=49, token_sample_size=512, video_sample_size=768, the resolution of video inputs for training is 256x256x49, 512x512x49, 768x768x21.
- The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the token_sample_size = 512.
  - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512).
  - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768).
  - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024).
  - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes.
resume_from_checkpoint is used to set the training should be resumed from a previous checkpoint. Use a path or "latest" to automatically select the last available checkpoint.

When train model with multi machines, please set the params as follows:

export MASTER_ADDR="your master address"
export MASTER_PORT=10086
export WORLD_SIZE=1 # The number of machines
export NUM_PROCESS=8 # The number of processes, such as WORLD_SIZE * 8
export RANK=0 # The rank of this machine

accelerate launch --mixed_precision="bf16" --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK scripts/xxx/xxx.py

FantasyTalking without deepspeed:

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --mixed_precision="bf16" scripts/fantasytalking/train.py \
  --config_path="config/wan2.1/wan_civitai.yaml" \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --video_sample_size=512 \
  --token_sample_size=512 \
  --video_sample_stride=1 \
  --video_sample_n_frames=81 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=2e-05 \
  --lr_scheduler="constant_with_warmup" \
  --lr_warmup_steps=100 \
  --seed=42 \
  --output_dir="output_dir" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --enable_bucket \
  --uniform_sampling \
  --low_vram \
  --transformer_path="models/FantasyTalking/fantasytalking_model.ckpt" \
  --trainable_modules "processor." "proj_model."

FantasyTalking with Deepspeed Zero-2:

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/fantasytalking/train.py \
  --config_path="config/wan2.1/wan_civitai.yaml" \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --video_sample_size=512 \
  --token_sample_size=512 \
  --video_sample_stride=1 \
  --video_sample_n_frames=81 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=2e-05 \
  --lr_scheduler="constant_with_warmup" \
  --lr_warmup_steps=100 \
  --seed=42 \
  --output_dir="output_dir" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --enable_bucket \
  --uniform_sampling \
  --low_vram \
  --transformer_path="models/FantasyTalking/fantasytalking_model.ckpt" \
  --trainable_modules "processor." "proj_model."

DeepSpeed Zero-3 is not highly recommended at the moment. In this repository, using FSDP has fewer errors and is more stable.

FantasyTalking with DeepSpeed Zero-3:

python scripts/zero_to_bf16.py output_dir/checkpoint-{our-num-steps} output_dir/checkpoint-{your-num-steps}-outputs --max_shard_size 80GB --safe_serialization

Training shell command is as follows:

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --zero_stage 3 --zero3_save_16bit_model true --zero3_init_flag true --use_deepspeed --deepspeed_config_file config/zero_stage3_config.json --deepspeed_multinode_launcher standard scripts/fantasytalking/train.py \
  --config_path="config/wan2.1/wan_civitai.yaml" \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --video_sample_size=512 \
  --token_sample_size=512 \
  --video_sample_stride=1 \
  --video_sample_n_frames=81 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=2e-05 \
  --lr_scheduler="constant_with_warmup" \
  --lr_warmup_steps=100 \
  --seed=42 \
  --output_dir="output_dir" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --enable_bucket \
  --uniform_sampling \
  --low_vram \
  --transformer_path="models/FantasyTalking/fantasytalking_model.ckpt" \
  --trainable_modules "processor." "proj_model."

FantasyTalking with FSDP:

Wan with FSDP is suitable for 14B Wan at high resolutions. Training shell command is as follows:

export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-I2V-14B-720P"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA. 
# export NCCL_IB_DISABLE=1
# export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

accelerate launch --mixed_precision="bf16" --use_fsdp --fsdp_auto_wrap_policy TRANSFORMER_BASED_WRAP --fsdp_transformer_layer_cls_to_wrap=AudioAttentionBlock --fsdp_sharding_strategy "FULL_SHARD" --fsdp_state_dict_type=SHARDED_STATE_DICT --fsdp_backward_prefetch "BACKWARD_PRE" --fsdp_cpu_ram_efficient_loading False  scripts/fantasytalking/train.py \
  --config_path="config/wan2.1/wan_civitai.yaml" \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --video_sample_size=512 \
  --token_sample_size=512 \
  --video_sample_stride=1 \
  --video_sample_n_frames=81 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=50 \
  --learning_rate=2e-05 \
  --lr_scheduler="constant_with_warmup" \
  --lr_warmup_steps=100 \
  --seed=42 \
  --output_dir="output_dir" \
  --gradient_checkpointing \
  --mixed_precision="bf16" \
  --adam_weight_decay=3e-2 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --enable_bucket \
  --uniform_sampling \
  --low_vram \
  --transformer_path="models/FantasyTalking/fantasytalking_model.ckpt" \
  --trainable_modules "processor." "proj_model."