ok submitting this version.<pre><code>[[datasets]]

[[datasets.subsets]]
num_repeats = 4

[general]
resolution = 1024
shuffle_caption = true
keep_tokens = 2
flip_aug = true
caption_extension = ".txt"
enable_bucket = true
bucket_reso_steps = 64
bucket_no_upscale = true
min_bucket_reso = 256
max_bucket_reso = 2048</code></pre>

big beefy man - V2

You may want to refer to the sample images and the prompts used below<pre><code>[
{"prompt": "manly old mature"}, 
{"prompt": "malay beefy farmer"}, 
{"prompt": "grandfather working in the construction yard"}
]</code></pre><pre><code>[[datasets]]

[[datasets.subsets]]
num_repeats = 7

[general]
resolution = 1024
shuffle_caption = true
keep_tokens = 2
flip_aug = true
caption_extension = ".txt"
enable_bucket = true
bucket_reso_steps = 64
bucket_no_upscale = true
min_bucket_reso = 256
max_bucket_reso = 2048</code></pre><pre><code>[additional_network_arguments]
unet_lr = 0.0005
text_encoder_lr = 5e-5
network_dim = 128
network_alpha = 16
network_module = "networks.lora"

[optimizer_arguments]
learning_rate = 0.0005
lr_scheduler = "cosine_with_restarts"
lr_scheduler_num_cycles = 3
lr_warmup_steps = 0
optimizer_type = "AdamW8Bit"
optimizer_args = [ "weight_decay=0.1",]

[training_arguments]
max_train_steps = 0
max_train_epochs = 68
sample_sampler = "euler_a"
train_batch_size = 9
noise_offset = 0.1
clip_skip = 1
weighted_captions = false
max_token_length = 225
lowram = false
max_data_loader_n_workers = 8
persistent_data_loader_workers = true
save_precision = "bf16"
mixed_precision = "bf16"
save_state = false
xformers = true
sdpa = true
no_half_vae = true
gradient_checkpointing = true
gradient_accumulation_steps = 1

[advanced_training_config]
multires_noise_iterations = 6
multires_noise_discount = 0.3
min_snr_gamma = 5.0

[model_arguments]
v2 = false

[saving_arguments]
save_model_as = "safetensors"

[dreambooth_arguments]
prior_loss_weight = 1.0

[dataset_arguments]
cache_latents = true</code></pre>

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