63 lines
2.2 KiB
Bash
63 lines
2.2 KiB
Bash
export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-InP"
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export TRAIN_PROMPT_PATH="MovieGenVideoBench_train.txt"
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# Performing validation simultaneously with training will increase time and GPU memory usage.
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export VALIDATION_PROMPT_PATH="MovieGenVideoBench_val.txt"
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# Use 49 for V1 and V1.1; Use 85 for V1.5.
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export VIDEO_LENGTH=49
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accelerate launch --num_processes=1 --mixed_precision="bf16" scripts/cogvideox_fun/train_reward_lora.py \
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--pretrained_model_name_or_path=$MODEL_NAME \
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--rank=32 \
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--network_alpha=16 \
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--train_batch_size=1 \
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--gradient_accumulation_steps=1 \
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--max_train_steps=10000 \
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--checkpointing_steps=100 \
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--learning_rate=1e-05 \
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--seed=42 \
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--output_dir="output_dir" \
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--gradient_checkpointing \
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--vae_gradient_checkpointing \
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--mixed_precision="bf16" \
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--adam_weight_decay=3e-2 \
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--adam_epsilon=1e-10 \
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--max_grad_norm=0.3 \
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--prompt_path=$TRAIN_PROMPT_PATH \
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--train_sample_height=224 \
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--train_sample_width=224 \
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--video_length=$VIDEO_LENGTH \
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--num_decoded_latents=1 \
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--num_sampled_frames=1 \
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--reward_fn="HPSReward" \
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--reward_fn_kwargs='{"version": "v2.1"}' \
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--backprop
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# Training command for CogVideoX-Fun-V1.1-2b-InP-HPS2.1.safetensors (with 8 A100 GPUs)
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# accelerate launch --num_processes=8 --mixed_precision="bf16" --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json scripts/cogvideox_fun/train_reward_lora.py \
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# --pretrained_model_name_or_path=$MODEL_NAME \
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# --rank=128 \
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# --network_alpha=64 \
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# --train_batch_size=1 \
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# --gradient_accumulation_steps=1 \
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# --max_train_steps=10000 \
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# --checkpointing_steps=100 \
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# --learning_rate=1e-05 \
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# --seed=42 \
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# --output_dir="output_dir" \
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# --gradient_checkpointing \
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# --mixed_precision="bf16" \
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# --adam_weight_decay=3e-2 \
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# --adam_epsilon=1e-10 \
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# --max_grad_norm=0.3 \
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# --prompt_path=$TRAIN_PROMPT_PATH \
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# --train_sample_height=256 \
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# --train_sample_width=256 \
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# --video_length=$VIDEO_LENGTH \
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# --validation_prompt_path=$VALIDATION_PROMPT_PATH \
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# --validation_steps=100 \
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# --validation_batch_size=8 \
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# --num_decoded_latents=1 \
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# --num_sampled_frames=1 \
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# --reward_fn="HPSReward" \
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# --reward_fn_kwargs='{"version": "v2.1"}' \
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# --backprop |