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Corrects path to train_network.py when using advanced training node #89

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Corrects path to train_network.py when using advanced training node #89

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4 changes: 2 additions & 2 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,7 +419,7 @@ def loratraining(self, ckpt_name, v2, networkmodule, networkdimension, networkal

nodespath = nodespath.replace( "\\", "/")

command = "python -m accelerate.commands.launch " + launchargs + f'--num_cpu_threads_per_process=8 "custom_nodes/Lora-Training-in-Comfy/sd-scripts/train_network.py" --enable_bucket --pretrained_model_name_or_path={pretrained_model} --train_data_dir="{train_data_dir}" --output_dir="{output_dir}" --logging_dir="./logs" --log_prefix={output_name} --resolution={resolution} --network_module={network_module} --max_train_epochs={max_train_epoches} --learning_rate={lr} --unet_lr={unet_lr} --text_encoder_lr={text_encoder_lr} --lr_scheduler={lr_scheduler} --lr_warmup_steps={lr_warmup_steps} --lr_scheduler_num_cycles={lr_restart_cycles} --network_dim={network_dim} --network_alpha={network_alpha} --output_name={output_name} --train_batch_size={batch_size} --save_every_n_epochs={save_every_n_epochs} --mixed_precision="fp16" --save_precision="fp16" --seed={theseed} --cache_latents --prior_loss_weight=1 --max_token_length=225 --caption_extension=".txt" --save_model_as={save_model_as} --min_bucket_reso={min_bucket_reso} --max_bucket_reso={max_bucket_reso} --keep_tokens={keep_tokens} --xformers --shuffle_caption ' + extargs
command = "python -m accelerate.commands.launch " + launchargs + f'--num_cpu_threads_per_process=8 "{nodespath}" --enable_bucket --pretrained_model_name_or_path={pretrained_model} --train_data_dir="{train_data_dir}" --output_dir="{output_dir}" --logging_dir="./logs" --log_prefix={output_name} --resolution={resolution} --network_module={network_module} --max_train_epochs={max_train_epoches} --learning_rate={lr} --unet_lr={unet_lr} --text_encoder_lr={text_encoder_lr} --lr_scheduler={lr_scheduler} --lr_warmup_steps={lr_warmup_steps} --lr_scheduler_num_cycles={lr_restart_cycles} --network_dim={network_dim} --network_alpha={network_alpha} --output_name={output_name} --train_batch_size={batch_size} --save_every_n_epochs={save_every_n_epochs} --mixed_precision="fp16" --save_precision="fp16" --seed={theseed} --cache_latents --prior_loss_weight=1 --max_token_length=225 --caption_extension=".txt" --save_model_as={save_model_as} --min_bucket_reso={min_bucket_reso} --max_bucket_reso={max_bucket_reso} --keep_tokens={keep_tokens} --xformers --shuffle_caption ' + extargs
#print(command)
subprocess.run(command, shell=True)
print("Train finished")
Expand Down Expand Up @@ -451,4 +451,4 @@ def INPUT_TYPES(s):
def opentensorboard(self):
command = 'tensorboard --logdir="logs"'
subprocess.Popen(command, shell=True)
return()
return()