train_network.py: error (resolution based)

[WingedWalrusLandingOnWater]

I keep getting train_network.py: error: unrecognized arguments: #

where # = the height value in maximum resolution. It doesn't matter if i set it to 1 or 9999

To create a public link, set share=True in launch().
Folder 100_MagellanicClouds: 72 images found
Folder 100_MagellanicClouds: 7200 steps
max_train_steps = 3600
stop_text_encoder_training = 0
lr_warmup_steps = 0
accelerate launch --num_cpu_threads_per_process=2 "train_network.py" --enable_bucket --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" --train_data_dir="C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/image" --resolution=768, 768 --output_dir="C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/model" --logging_dir="C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/log" --network_alpha="128" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-5 --unet_lr=0.0001 --network_dim=128 --output_name="MagellanicClouds" --lr_scheduler_num_cycles="1" --learning_rate="0.0001" --lr_scheduler="constant" --train_batch_size="2" --max_train_steps="3600" --save_every_n_epochs="1" --mixed_precision="bf16" --save_precision="bf16" --seed="1234" --caption_extension=".txt" --cache_latents --optimizer_type="AdamW8bit" --max_data_loader_n_workers="1" --clip_skip=2 --bucket_reso_steps=64 --xformers --bucket_no_upscale
usage: train_network.py [-h] [--v2] [--v_parameterization] [--pretrained_model_name_or_path PRETRAINED_MODEL_NAME_OR_PATH] [--tokenizer_cache_dir TOKENIZER_CACHE_DIR] [--train_data_dir TRAIN_DATA_DIR] [--shuffle_caption]
[--caption_extension CAPTION_EXTENSION] [--caption_extention CAPTION_EXTENTION] [--keep_tokens KEEP_TOKENS] [--color_aug] [--flip_aug] [--face_crop_aug_range FACE_CROP_AUG_RANGE] [--random_crop]
[--debug_dataset] [--resolution RESOLUTION] [--cache_latents] [--vae_batch_size VAE_BATCH_SIZE] [--cache_latents_to_disk] [--enable_bucket] [--min_bucket_reso MIN_BUCKET_RESO] [--max_bucket_reso MAX_BUCKET_RESO]
[--bucket_reso_steps BUCKET_RESO_STEPS] [--bucket_no_upscale] [--token_warmup_min TOKEN_WARMUP_MIN] [--token_warmup_step TOKEN_WARMUP_STEP] [--caption_dropout_rate CAPTION_DROPOUT_RATE]
[--caption_dropout_every_n_epochs CAPTION_DROPOUT_EVERY_N_EPOCHS] [--caption_tag_dropout_rate CAPTION_TAG_DROPOUT_RATE] [--reg_data_dir REG_DATA_DIR] [--in_json IN_JSON] [--dataset_repeats DATASET_REPEATS]
[--output_dir OUTPUT_DIR] [--output_name OUTPUT_NAME] [--huggingface_repo_id HUGGINGFACE_REPO_ID] [--huggingface_repo_type HUGGINGFACE_REPO_TYPE] [--huggingface_path_in_repo HUGGINGFACE_PATH_IN_REPO]
[--huggingface_token HUGGINGFACE_TOKEN] [--huggingface_repo_visibility HUGGINGFACE_REPO_VISIBILITY] [--save_state_to_huggingface] [--resume_from_huggingface] [--async_upload]
[--save_precision {None,float,fp16,bf16}] [--save_every_n_epochs SAVE_EVERY_N_EPOCHS] [--save_n_epoch_ratio SAVE_N_EPOCH_RATIO] [--save_last_n_epochs SAVE_LAST_N_EPOCHS]
[--save_last_n_epochs_state SAVE_LAST_N_EPOCHS_STATE] [--save_state] [--resume RESUME] [--train_batch_size TRAIN_BATCH_SIZE] [--max_token_length {None,150,225}] [--mem_eff_attn] [--xformers] [--vae VAE]
[--max_train_steps MAX_TRAIN_STEPS] [--max_train_epochs MAX_TRAIN_EPOCHS] [--max_data_loader_n_workers MAX_DATA_LOADER_N_WORKERS] [--persistent_data_loader_workers] [--seed SEED] [--gradient_checkpointing]
[--gradient_accumulation_steps GRADIENT_ACCUMULATION_STEPS] [--mixed_precision {no,fp16,bf16}] [--full_fp16] [--clip_skip CLIP_SKIP] [--logging_dir LOGGING_DIR] [--log_prefix LOG_PREFIX]
[--noise_offset NOISE_OFFSET] [--lowram] [--sample_every_n_steps SAMPLE_EVERY_N_STEPS] [--sample_every_n_epochs SAMPLE_EVERY_N_EPOCHS] [--sample_prompts SAMPLE_PROMPTS]
[--sample_sampler {ddim,pndm,lms,euler,euler_a,heun,dpm_2,dpm_2_a,dpmsolver,dpmsolver++,dpmsingle,k_lms,k_euler,k_euler_a,k_dpm_2,k_dpm_2_a}] [--config_file CONFIG_FILE] [--output_config]
[--prior_loss_weight PRIOR_LOSS_WEIGHT] [--optimizer_type OPTIMIZER_TYPE] [--use_8bit_adam] [--use_lion_optimizer] [--learning_rate LEARNING_RATE] [--max_grad_norm MAX_GRAD_NORM]
[--optimizer_args [OPTIMIZER_ARGS ...]] [--lr_scheduler_type LR_SCHEDULER_TYPE] [--lr_scheduler_args [LR_SCHEDULER_ARGS ...]] [--lr_scheduler LR_SCHEDULER] [--lr_warmup_steps LR_WARMUP_STEPS]
[--lr_scheduler_num_cycles LR_SCHEDULER_NUM_CYCLES] [--lr_scheduler_power LR_SCHEDULER_POWER] [--dataset_config DATASET_CONFIG] [--min_snr_gamma MIN_SNR_GAMMA] [--weighted_captions] [--no_metadata]
[--save_model_as {None,ckpt,pt,safetensors}] [--unet_lr UNET_LR] [--text_encoder_lr TEXT_ENCODER_LR] [--network_weights NETWORK_WEIGHTS] [--network_module NETWORK_MODULE] [--network_dim NETWORK_DIM]
[--network_alpha NETWORK_ALPHA] [--network_args [NETWORK_ARGS ...]] [--network_train_unet_only] [--network_train_text_encoder_only] [--training_comment TRAINING_COMMENT]
train_network.py: error: unrecognized arguments: 768
Traceback (most recent call last):
File "C:\Users\SysOp\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\SysOp\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in run_code
exec(code, run_globals)
File "C:\Users\SysOp\Anuciv\kohya_ss\venv\Scripts\accelerate.exe_main.py", line 7, in
File "C:\Users\SysOp\Anuciv\kohya_ss\venv\lib\site-packages\accelerate\commands\accelerate_cli.py", line 45, in main
args.func(args)
File "C:\Users\SysOp\Anuciv\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 1104, in launch_command
simple_launcher(args)
File "C:\Users\SysOp\Anuciv\kohya_ss\venv\lib\site-packages\accelerate\commands\launch.py", line 567, in simple_launcher
raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['C:\Users\SysOp\Anuciv\kohya_ss\venv\Scripts\python.exe', 'train_network.py', '--enable_bucket', '--pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5', '--train_data_dir=C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/image', '--resolution=768,', '768', '--output_dir=C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/model', '--logging_dir=C:/Users/SysOp/Desktop/Hair/MagellanicCloudsLora/log', '--network_alpha=128', '--save_model_as=safetensors', '--network_module=networks.lora', '--text_encoder_lr=5e-5', '--unet_lr=0.0001', '--network_dim=128', '--output_name=MagellanicClouds', '--lr_scheduler_num_cycles=1', '--learning_rate=0.0001', '--lr_scheduler=constant', '--train_batch_size=2', '--max_train_steps=3600', '--save_every_n_epochs=1', '--mixed_precision=bf16', '--save_precision=bf16', '--seed=1234', '--caption_extension=.txt', '--cache_latents', '--optimizer_type=AdamW8bit', '--max_data_loader_n_workers=1', '--clip_skip=2', '--bucket_reso_steps=64', '--xformers', '--bucket_no_upscale']' returned non-zero exit status 2.

I've done a search for similar discussions: i tried fixing it by changing all of the optimizers: AdamW, AdamW8bit, Adafactor, Dadaptation, Lion, SGDNesterov, SGDNesterov8bit. I've turned buckets on and off. I've changed the source models to all the options in the quick pick. And i've used both bf16 and fp16. I also tried replacing train_network.py

[laksjdjf]

convert --resolution=768, 768 to --resolution='768,768'

[WingedWalrusLandingOnWater]

I appologize for posting this in the wrong forum in error, I was tired. @rockerBOO was correct. Thanks for not calling me out on this although I am still shamefaced.