File size: 4,291 Bytes
d6162c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | #!/bin/bash
echo "正在启动 8 个并行的独立任务，并为每个任务分配不同参数..."
# echo "等待 1 小时后启动任务..."
# sleep 3600 # 等待 3600 秒，即 1 小时
source activate wan
export LD_LIBRARY_PATH=/usr/local/cuda/lib64
# 1. 在这里预先定义你的参数数组 (a_i)
# 数组元素的数量应该与你的任务数量(8)相匹配。
# 参数可以是任何字符串，比如文件名、配置名、数值等。
save_folder='validation_samples/samples_vbench2_all_wan_aug'
sampler_name='Flow'
num_generated_videos=-1
prompt_list_path1='/nfs/ywang29/Reward_finetuning/VideoX-Fun/VBench/VBench-2.0/prompts/prompt_aug/Wanx_full_text_aug_part1.txt'
prompt_list_path2='/nfs/ywang29/Reward_finetuning/VideoX-Fun/VBench/VBench-2.0/prompts/prompt_aug/Wanx_full_text_aug_part2.txt'
save_folder_videogen='validation_samples/samples_videogen_eval'
prompt_list_path3='/nfs/ywang29/Reward_finetuning/VideoX-Fun/VideoGen-Eval.txt'
# enable_teacache=False
# num_inference_steps=50
save_folder_div='validation_samples/samples_vbench2_all_wan_aug/Diversity'
prompt_list_path_div='/nfs/ywang29/Reward_finetuning/VideoX-Fun/VBench/VBench-2.0/prompts/prompt_aug/wanx_aug_prompt/Diversity_dup.txt'
model_name='sd_7b_3'
steps=1000
params=(
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r1_p1 --prompt_list_path ${prompt_list_path1}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r2_p1 --prompt_list_path ${prompt_list_path1}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r3_p1 --prompt_list_path ${prompt_list_path1}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r1_p2 --prompt_list_path ${prompt_list_path2}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r2_p2 --prompt_list_path ${prompt_list_path2}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder}/r3_p2 --prompt_list_path ${prompt_list_path2}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed 42 --save_folder ${save_folder_videogen} --prompt_list_path ${prompt_list_path3}"
"--transformer_path /nfs/ywang29/Reward_finetuning/VideoX-Fun/output_${model_name}/checkpoint-${steps}/transformer/diffusion_pytorch_model.safetensors --seed -1 --save_folder ${save_folder_div}/r1 --prompt_list_path ${prompt_list_path_div}"
)
# 2. 修改循环以遍历数组的索引
# ${!params[@]} 会获取数组 params 的所有索引 (0 1 2 3 4 5 6 7)
for i in "${!params[@]}"
do
# 从数组中获取当前索引对应的参数值
current_param="${params[$i]}"
# CUDA_VISIBLE_DEVICES=$i 告诉程序只能“看见”并使用第 i 张 GPU
# python X.py --p "$current_param" 将当前参数传递给脚本
# & 让命令在后台运行
echo "启动任务 $i (GPU $i)，参数为: $current_param"
CUDA_VISIBLE_DEVICES=$i python ./examples/wan2.1/predict_t2v_val_sd.py --num_generated_videos $num_generated_videos --sampler_name $sampler_name $current_param &
done
# 'wait' 命令会等待所有后台任务都执行完毕
echo "所有任务已启动。等待它们全部完成..."
wait
echo "所有任务已完成。"
python /nfs/ywang29/Reward_finetuning/VideoX-Fun/validation_samples/rename_v1.py --model_name ${model_name} --steps ${steps}
source activate vbench2
cd /nfs/ywang29/Reward_finetuning/VideoX-Fun/VBench/VBench-2.0
bash ./run_evaluate.sh "wan1.3b_rwft_${model_name}_${steps}_wan_aug1"
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