hello, my training is diffrent from most. first i use

https://github.com/derrian-distro/LoRA_Easy_Training_Scripts/tree/dev

this for the trainer. it is the dev version since that has features i use. second i try to train on 100 images but if not 40-150 images. here is the config file for the current model

[[subsets]]

caption_extension = ".txt"

image_dir = "C:\\Users\\user\\Videos\\ai\\image\\image_gen\\dreambooth\\datasets\\need_to_do\\character\\Shangri_La_Frontier\\Vysache"

keep_tokens = 8

name = "data"

num_repeats = 3

shuffle_caption = true

[general_args.args]

max_data_loader_n_workers = 1

persistent_data_loader_workers = true

pretrained_model_name_or_path = "C:/Users/user/Videos/ai/image/image_gen/dreambooth/LoRA_Easy_Training_Scripts/ponyDiffusionV6XL_v6StartWithThisOne.safetensors"

sdxl = true

mixed_precision = "bf16"

gradient_checkpointing = true

gradient_accumulation_steps = 3

seed = 23

max_token_length = 225

prior_loss_weight = 1.0

xformers = true

max_train_epochs = 6

cache_latents = true

[general_args.dataset_args]

resolution = 1024

batch_size = 1

[network_args.args]

network_dim = 8

network_alpha = 4.0

min_timestep = 0

max_timestep = 1000

network_train_unet_only = true

[optimizer_args.args]

lr_scheduler = "cosine"

optimizer_type = "pytorch_optimizer.optimizer.came.CAME"

lr_scheduler_type = "LoraEasyCustomOptimizer.CustomOptimizers.Rex"

learning_rate = 0.0001

warmup_ratio = 0.15

scale_weight_norms = 5.0

max_grad_norm = 1.0

[saving_args.args]

save_precision = "fp16"

save_model_as = "safetensors"

save_every_n_epochs = 1

output_name = "Vysache"

output_dir = "C:/Users/user/Videos/ai/image/image_gen/dreambooth/datasets/training/Shangri_La_Frontier/Vysache"

[bucket_args.dataset_args]

enable_bucket = true

min_bucket_reso = 512

max_bucket_reso = 2048

bucket_reso_steps = 64

[network_args.args.network_args]

conv_dim = 4

conv_alpha = 2.0

algo = "locon"

[optimizer_args.args.lr_scheduler_args]

min_lr = 1e-6

[optimizer_args.args.optimizer_args]

weight_decay = "0.1"