37 lines
1.2 KiB
Bash
37 lines
1.2 KiB
Bash
export MODEL_NAME="models/Diffusion_Transformer/Wan2.1-T2V-1.3B"
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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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# Set 1 for Wan2.1-T2V-14B
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export BACKPROP_NUM_STEPS=5
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accelerate launch --mixed_precision="bf16" scripts/wan2.1/train_reward_lora.py \
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--config_path="config/wan2.1/wan_civitai.yaml" \
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--pretrained_model_name_or_path=$MODEL_NAME \
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--rank=32 \
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--network_alpha=16 \
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--train_batch_size=1 \
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--gradient_accumulation_steps=1 \
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--max_train_steps=10000 \
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--checkpointing_steps=100 \
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--learning_rate=1e-05 \
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--seed=42 \
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--output_dir="output_dir" \
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--gradient_checkpointing \
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--vae_gradient_checkpointing \
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--mixed_precision="bf16" \
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--adam_weight_decay=3e-2 \
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--adam_epsilon=1e-10 \
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--max_grad_norm=0.3 \
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--low_vram \
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--prompt_path=$TRAIN_PROMPT_PATH \
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--train_sample_height=256 \
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--train_sample_width=256 \
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--num_inference_steps=30 \
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--video_length=81 \
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--num_decoded_latents=1 \
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--reward_fn="HPSReward" \
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--reward_fn_kwargs='{"version": "v2.1"}' \
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--backprop_strategy "tail" \
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--backprop_num_steps=$BACKPROP_NUM_STEPS \
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--backprop |