ok submitting this version.there are more sample images this time around.do check them out.hope it is sufficient for your imaginations.but seems there is a lot of confusion. so to help those people out:<pre><code>very handsome </code></pre><pre><code>malay </code></pre><pre><code>burly </code></pre><pre><code>beefy </code></pre><pre><code>grandfather </code></pre><pre><code>just1n</code></pre><pre><code>high quality realistic photo</code></pre><pre><code>general </code></pre><pre><code>army </code></pre><pre><code>military</code></pre><pre><code>full body</code></pre><pre><code>walking </code></pre><pre><code>shopping </code></pre><pre><code>mall</code></pre><pre><code>confident </code></pre><pre><code>fierce </code></pre><pre><code>muscular thighs</code></pre><pre><code>middle aged </code></pre><pre><code>equinecock </code></pre><pre><code>realistic photo</code></pre><pre><code>hairy</code></pre>try to use the above in short 2 -3 words sentences and you should be good.<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 - V1

there are more sample images this time around.do check them out.hope it is sufficient for your imaginations.but seems there is a lot of confusion. so to help those people out:<pre><code>very handsome </code></pre><pre><code>malay </code></pre><pre><code>burly </code></pre><pre><code>beefy </code></pre><pre><code>grandfather </code></pre><pre><code>just1n</code></pre><pre><code>high quality realistic photo</code></pre><pre><code>general </code></pre><pre><code>army </code></pre><pre><code>military</code></pre><pre><code>full body</code></pre><pre><code>walking </code></pre><pre><code>shopping </code></pre><pre><code>mall</code></pre><pre><code>confident </code></pre><pre><code>fierce </code></pre><pre><code>muscular thighs</code></pre><pre><code>middle aged </code></pre><pre><code>equinecock </code></pre><pre><code>realistic photo</code></pre><pre><code>hairy</code></pre>try to use the above in short 2 -3 words sentences and you should be good.You can refer to the sample images and the prompt used below.<pre><code>[
{"prompt": "manly old mature"}, 
{"prompt": "malay beefy farmer"}, 
{"prompt": "grandfather working in the construction yard"}
]</code></pre><pre><code>[additional_network_arguments]
unet_lr = 1.0
text_encoder_lr = 1.0
network_dim = 128
network_alpha = 16
network_module = "networks.lora"

[optimizer_arguments]
learning_rate = 1.0
lr_scheduler = "cosine"
lr_warmup_steps = 0
optimizer_type = "Prodigy"
optimizer_args = [ "decouple=True", "weight_decay=0.5", "betas=0.9,0.99", "use_bias_correction=False",]

[training_arguments]
max_train_steps = 0
max_train_epochs = 66
sample_sampler = "euler_a"
train_batch_size = 5
noise_offset = 0.03
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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