Recommended strength for this version: 1.0 ~ 1.2

Only took 700 Steps this time. I got lucky I guess. I think you can specify artist names and it'll cater towards their style more. Haven't tried it though. The penis size is kind of wack in this version as it is very sensitive and either makes it way too large or too small. Might be due to the cropped images in the training data?

I had good results around 1900~2000 steps, but it did impact the overall image style quite a bit. Updated training images and tags a bunch and finally got something I liked.

This time around, I used LoRA_Easy_Training_Scripts which is way easier than using KohyaSS directly. DatasetProcessorDesktop to crop and sort images and BooruDatasetTagManager to manage tags.

Here is the training TOML:

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "aya_shobon"
num_repeats = 1

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "canvassolaris"
num_repeats = 2

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "elfk"
num_repeats = 1

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "manzai_sugar"
num_repeats = 2

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "nonorigo"
num_repeats = 1

[[subsets]]
caption_extension = ".txt"
image_dir = "redacted"
name = "stickyspoodge"
num_repeats = 1

[train_mode]
train_mode = "lora"

[general_args.args]
max_data_loader_n_workers = 1
persistent_data_loader_workers = true
pretrained_model_name_or_path = "illustriousXL_v01.safetensors"
vae = "sdxlVAE_sdxlVAE.safetensors"
mixed_precision = "bf16"
gradient_checkpointing = true
gradient_accumulation_steps = 1
seed = 1337
max_token_length = 225
prior_loss_weight = 1.0
xformers = true
cache_latents = true
cache_latents_to_disk = true
sdxl = true
max_train_steps = 2500

[general_args.dataset_args]
resolution = 1024
batch_size = 2

[network_args.args]
network_dim = 32
network_alpha = 32.0
min_timestep = 0
max_timestep = 1000

[optimizer_args.args]
optimizer_type = "Prodigy"
lr_scheduler = "cosine"
loss_type = "l2"
learning_rate = 1.0
max_grad_norm = 1.0
min_snr_gamma = 5

[saving_args.args]
output_dir = "redacted"
save_precision = "fp16"
save_model_as = "safetensors"
save_last_n_epochs = 1
save_every_n_steps = 100
output_name = "detached_foreskin-v4.6"

[noise_args.args]
multires_noise_iterations = 8
multires_noise_discount = 0.4

[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 = 32
conv_alpha = 32.0
algo = "locon"

[optimizer_args.args.optimizer_args]
weight_decay = "0.1"
betas = "0.9, 0.99"
decouple = "True"
d_coef = "1.0"