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#!/usr/bin/env bash
# Prevent tokenizer parallelism issues
export TOKENIZERS_PARALLELISM=false
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
# Model Configuration
MODEL_ARGS=(
--model_path "THUDM/CogVideoX-5b-I2V"
--model_name "cogvideox-flovd" # ["cogvideox-i2v" or "cogvideox-flovd"]
--model_type "i2vFlow" # ["t2v", "i2v", "i2vFlow"]
--training_type "controlnet"
# --additional_save_blocks "patch_embed" # additional blocks to update when using lora. e.g., "patch_embed,text_proj"
)
# Output Configuration
OUTPUT_ARGS=(
--output_dir "absolute/path/to/output"
--report_to "wandb"
--run_name "FloVD_CogVideoX_controlnet"
)
# Data Configuration
DATA_ARGS=(
--data_root "absolute/path/to/whole_data"
--caption_column "prompt.txt" # Do not need
--video_column "videos.txt" # Do not need
# --image_column "images.txt" # comment this line will use first frame of video as image conditioning
--train_resolution "49x480x720" # (frames x height x width), frames should be 8N+1
)
# Training Configuration
TRAIN_ARGS=(
--train_epochs 10 # number of training epochs
--seed 42 # random seed
--batch_size 1
--gradient_accumulation_steps 2
--mixed_precision "bf16" # ["no", "fp16"] # Only CogVideoX-2B supports fp16 training
--learning_rate 1e-5
)
# System Configuration
SYSTEM_ARGS=(
--num_workers 8
--pin_memory True
--nccl_timeout 1800
)
# Checkpointing Configuration
CHECKPOINT_ARGS=(
--checkpointing_steps 2000 # save checkpoint every x steps
--checkpointing_limit 2 # maximum number of checkpoints to keep, after which the oldest one is deleted
# --resume_from_checkpoint /path/to/ckpt # if you want to resume from a checkpoint, otherwise, comment this line
)
# Validation Configuration
VALIDATION_ARGS=(
--do_validation true # ["true", "false"]
--validation_dir "absolute/path/to/whole_data"
--validation_steps 2000 # should be multiple of checkpointing_steps
--validation_prompts "prompts.txt" # Do not need
--validation_images "images.txt" # Do not need
--gen_fps 16
--max_scene 4
)
# Controlnet Configuration
CONTROLNET_ARGS=(
--controlnet_transformer_num_layers 6
--controlnet_input_channels 16
--controlnet_weights 1.0
--controlnet_guidance_start 0.0
--controlnet_guidance_end 0.4
--controlnet_out_proj_dim_factor 64
--enable_time_sampling false
--time_sampling_type "truncated_normal"
--time_sampling_mean 0.95
--time_sampling_std 0.1
--notextinflow true
)
# Combine all arguments and launch training
accelerate launch --config_file accelerate_config.yaml train.py \
"${MODEL_ARGS[@]}" \
"${OUTPUT_ARGS[@]}" \
"${DATA_ARGS[@]}" \
"${TRAIN_ARGS[@]}" \
"${SYSTEM_ARGS[@]}" \
"${CHECKPOINT_ARGS[@]}" \
"${VALIDATION_ARGS[@]}" \
"${CONTROLNET_ARGS[@]}"
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