aigc-apps-EasyAnimate/scripts/README_TRAIN_CONTROL.md

Training Code

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

We can choose whether to use deep speed in EasyAnimate, which can save a lot of video memory.

The metadata_control.json is a little different from normal json in EasyAnimate, you need to add a control_file_path, and DWPose is suggested as tool to generate control file.

[
    {
      "file_path": "train/00000001.mp4",
      "control_file_path": "control/00000001.mp4",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "video"
    },
    {
      "file_path": "train/00000002.jpg",
      "control_file_path": "control/00000002.jpg",
      "text": "A group of young men in suits and sunglasses are walking down a city street.",
      "type": "image"
    },
    .....
]

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 images and videos at the center, but instead, it trains the entire images and 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 images and videos. When random_hw_adapt is enabled, the training images will have their height and width set to image_sample_size as the maximum and min(video_sample_size, 512) as the minimum. For training videos, the height and width will be set to image_sample_size as the maximum and min(video_sample_size, 512) as the minimum.
  • For example, when random_hw_adapt is enabled, with video_sample_n_frames=49, video_sample_size=1024, and image_sample_size=1024, the resolution of image inputs for training is 512x512 to 1024x1024, and the resolution of video inputs for training is 512x512x49 to 1024x1024x49.
  • For example, when random_hw_adapt is enabled, with video_sample_n_frames=49, video_sample_size=1024, and image_sample_size=256, the resolution of image inputs for training is 256x256 to 1024x1024, and the resolution of video inputs for training is 256x256x49.
• training_with_video_token_length specifies training the model according to token length. For training images and videos, the height and width will be set to image_sample_size as the maximum and video_sample_size as the minimum.
  • For example, when training_with_video_token_length is enabled, with video_sample_n_frames=49, token_sample_size=1024, video_sample_size=1024, and image_sample_size=256, the resolution of image inputs for training is 256x256 to 1024x1024, and the resolution of video inputs for training is 256x256x49 to 1024x1024x49.
  • For example, when training_with_video_token_length is enabled, with video_sample_n_frames=49, token_sample_size=512, video_sample_size=1024, and image_sample_size=256, the resolution of image inputs for training is 256x256 to 1024x1024, and the resolution of video inputs for training is 256x256x49 to 1024x1024x9.
  • 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.
• loss_type: The loss type for training. Currently, flow is used in v5.1, ddpm is used in v5 and v4, sigma is used in v3, v2 and v1.

EasyAnimateV5.1 without deepspeed:

export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5.1-12b-zh-Control"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
accelerate launch --mixed_precision="bf16" scripts/train_control.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --config_path "config/easyanimate_video_v5.1_magvit_qwen.yaml" \
  --image_sample_size=1024 \
  --video_sample_size=256 \
  --token_sample_size=512 \
  --video_sample_stride=3 \
  --video_sample_n_frames=49 \
  --train_batch_size=1 \
  --video_repeat=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=100 \
  --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=5e-3 \
  --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 \
  --loss_type="flow" \
  --train_mode="control_ref" \
  --control_ref_image="first_frame" \
  --trainable_modules "."

EasyAnimateV5.1 with deepspeed:

export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5.1-12b-zh-Control"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/train_control.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --config_path "config/easyanimate_video_v5.1_magvit_qwen.yaml" \
  --image_sample_size=1024 \
  --video_sample_size=256 \
  --token_sample_size=512 \
  --video_sample_stride=3 \
  --video_sample_n_frames=49 \
  --train_batch_size=1 \
  --video_repeat=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=100 \
  --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=5e-3 \
  --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 \
  --loss_type="flow" \
  --train_mode="control_ref" \
  --control_ref_image="first_frame" \
  --use_deepspeed \
  --trainable_modules "."

EasyAnimateV5 without deepspeed:

export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5-12b-zh-Control"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
accelerate launch --mixed_precision="bf16" scripts/train_control.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --config_path "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml" \
  --image_sample_size=1024 \
  --video_sample_size=256 \
  --token_sample_size=512 \
  --video_sample_stride=3 \
  --video_sample_n_frames=49 \
  --train_batch_size=1 \
  --video_repeat=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=100 \
  --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=5e-3 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --loss_type="ddpm" \
  --enable_bucket \
  --uniform_sampling \
  --trainable_modules "."

EasyAnimateV5 with deepspeed:

export MODEL_NAME="models/Diffusion_Transformer/EasyAnimateV5-12b-zh-Control"
export DATASET_NAME="datasets/internal_datasets/"
export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
NCCL_DEBUG=INFO

# When train model with multi machines, use "--config_file accelerate.yaml" instead of "--mixed_precision='bf16'".
accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/train_control.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATASET_NAME \
  --train_data_meta=$DATASET_META_NAME \
  --config_path "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml" \
  --image_sample_size=1024 \
  --video_sample_size=256 \
  --token_sample_size=512 \
  --video_sample_stride=3 \
  --video_sample_n_frames=49 \
  --train_batch_size=1 \
  --video_repeat=1 \
  --gradient_accumulation_steps=1 \
  --dataloader_num_workers=8 \
  --num_train_epochs=100 \
  --checkpointing_steps=100 \
  --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=5e-3 \
  --adam_epsilon=1e-10 \
  --vae_mini_batch=1 \
  --max_grad_norm=0.05 \
  --random_hw_adapt \
  --training_with_video_token_length \
  --loss_type="ddpm" \
  --enable_bucket \
  --uniform_sampling \
  --use_deepspeed \
  --trainable_modules "."