Ver Anima V1
Trained base on Anima V0.1 base with LoRA_Easy_Training_Scripts. See about this version for training details. Require lots of patience when using it. Anima does not support this concept generation very well. Recommand using multiple highweighted size words together, espcially "gigantic xx". Anima has a low level of learning for this Lora and these tags require high weights to be effective.
Added five triggerwords for new scenes. Check the examples first.
Ver NOOB V-PRED V3 EQ
Trained with improved parameters based on NOOB V-PRED EQ VAE exprimental version: https://civarchive.com/models/1858821/noobai-v-pred-10-with-eq-vae
In this version, I subdivided the trigger words for more accurate generate. 10 triggerwords set. See the examples first.
NOOB V-PRED V2
Trained base on NOOB V-PRED 1.0 with Kohya SS. See about this version for training details. Dataset updated and triggerwords changed.
The main upgrade of V2 is the understanding of multi futa scenes after changing training parameters and dataset, as well as the compatibility of finetuned checkpoints and multi loras. But it still need some trials to get the desired generation.
NOOB V-PRED V1
Trained base on NOOB V-PRED 1.0 with OneTrainer. See about this version for training details. Dataset updated and triggerwords changed.
The reason for this update is when I applied multiple Loras at the same time, increasing the weight of gigantic penis to 2 still couldn't achieve the result I needed. I think this Lora will solve the problem of giant pp generation when multiple Loras work together. After testing, the effect is good.
About PonyXL V2.0
Used regularization techniques and updated dataset to get less influence on artstyle and better penis shape. Also improved the effect when using other loras together. I chose Lora with less bake to achieve better interaction, but the disadvantage is that in some scenarios, the weight of penis needs to be increased.
Added "horse penis" for penis type.
About PonyXL
Used PonyXL to train this one. Updated dataset to achieve better performance. It enhanced this concept on PonyXL and related models.
Adding a concept: gigantic_dark
About V3.0
Updated dataset and used multires technique in this version. It would bring more light and shadow with quality tag, such as masterpiece, highly_detailed, best_quality ,etc.
Bewared that may affect the result of generation. You may get chaos result if you use these tags and overweight them.
Multires technique parameter:
multires_noise_iterations="6"
multires_noise_discount=0.3
IMPORTANT: Now I set the trigger words as gigantic_penis and gigantic_penis_ejaculation.
About V2.0
In v2.0, I removed all the images with black bar censor and retrain this locon. That will do help to get better result. The annoying censor bar won't be there.
I set batch size = 2 to acclerate training. The final results are good.
Introduction
This LyCORIS is used to generate futanari with gigantic penis. If you put it in a long picuture like (1024X768), you can get a massive long cock. You may find the faces are blurry but you can fix it with inpaint or img to img. Adding hyper futanari LyCORIS can improve the texture of penis.
How to use
I recommand using it in weight 0.75-0.85. When working with other loras, it is not easy to control. But you can get what you want after some tries.
You may get good result in lower weight when use it alone. Stiil, it needs more tests.
Training details:
Traingset is of about 145 images. They are mirrored before training. Half of them are chosen as regularization images. Total steps are about 8800.
Regularization = true
resolution=512
batch_size=2
epoch=10
network_dim=32
network_alpha=32
clip_skip=2
Using AdamW8bits as optimizer:
lr="1e-4"
unet_lr="1e-4"
text_encoder_lr="1e-5"
Locon parameters:
conv_dim=4
conv_alpha=4
Description
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/12_enormoustucked"
keep_tokens = 1
name = "12"
num_repeats = 12
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_enormouserect"
keep_tokens = 1
name = "5"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_enormousflaccid"
keep_tokens = 1
name = "5_1790613253772329800"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_gianterect"
keep_tokens = 1
name = "5_1790613253787849500"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_giantflaccid"
keep_tokens = 1
name = "5_1790613253802920100"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hebulge"
keep_tokens = 1
name = "5_1790613253818427700"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hedress"
keep_tokens = 1
name = "5_1790613253832938100"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hepanties"
keep_tokens = 1
name = "5_1790613253848948200"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hfunderdress"
keep_tokens = 1
name = "5_1790613253864150700"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hypererect"
keep_tokens = 1
name = "5_1790613253880661100"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hyperflaccid"
keep_tokens = 1
name = "5_1790613253897378000"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_hypertucked"
keep_tokens = 1
name = "5_1790613253912402600"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[[subsets]]
caption_extension = ".txt"
caption_tag_dropout_rate = 0.05
image_dir = "G:/dataset/concept/hyper_futanari_V1/5_longerect"
keep_tokens = 1
name = "5_1790613253927511400"
num_repeats = 5
random_crop_padding_percent = 0.05
shuffle_caption = true
[train_mode]
train_mode = "lora"
[general_args.args]
persistent_data_loader_workers = true
vae_batch_size = 5
pretrained_model_name_or_path = ""
mixed_precision = "bf16"
gradient_checkpointing = true
gradient_accumulation_steps = 1
seed = 42
max_data_loader_n_workers = 1
max_token_length = 225
prior_loss_weight = 1.0
sdpa = true
max_train_epochs = 20
cache_latents = true
cache_latents_to_disk = true
[general_args.dataset_args]
resolution = 1024
batch_size = 5
[network_args.args]
network_dim = 32
network_alpha = 16.0
min_timestep = 0
max_timestep = 1000
network_train_unet_only = true
[optimizer_args.args]
optimizer_type = "ProdigyPlusScheduleFree"
lr_scheduler = "constant"
loss_type = "l2"
learning_rate = 1.0
unet_lr = 1.0
max_grad_norm = 1.0
[saving_args.args]
output_dir = "G:/LoRA_Easy_Training_Scripts/output"
output_name = "hyper_futanari-anima1.0-V1-loha-dim32conv16AR0.5-NewTag"
save_precision = "bf16"
save_model_as = "safetensors"
save_every_n_epochs = 1
save_toml = true
save_toml_location = "G:/LoRA_Easy_Training_Scripts/output"
[logging_args.args]
log_prefix_mode = "disabled"
run_name_mode = "default"
[anima_args.args]
pretrained_model_name_or_path = "G:/sd-webui-forge-neo/models/Stable-diffusion/ANIMA/anima_baseV10.safetensors"
qwen3 = "G:/sd-webui-forge-neo/models/text_encoder/qwen_3_06b_base.safetensors"
vae = "G:/sd-webui-forge-neo/models/VAE/qwen_image_vae.safetensors"
qwen3_max_token_length = 512
t5_max_token_length = 512
timestep_sampling = "sigmoid"
sigmoid_scale = 1.0
[edm_loss_args.args]
edm2_loss_weighting = false
[bucket_args.dataset_args]
enable_bucket = true
min_bucket_reso = 256
max_bucket_reso = 2048
bucket_reso_steps = 64
[network_args.args.network_args]
conv_dim = 16
conv_alpha = 8.0
algo = "loha"
train_llm_adapter = "false"
[optimizer_args.args.optimizer_args]
betas = "0.95,0.99"
beta3 = "None"
weight_decay = "0"
weight_decay_by_lr = "True"
d0 = "2e-5"
d_coef = "1"
d_limiter = "True"
schedulefree_c = "0"
eps = "None"
split_groups = "True"
split_groups_mean = "False"
factored = "True"
factored_fp32 = "True"
use_bias_correction = "True"
use_stableadamw = "True"
use_schedulefree = "True"
use_speed = "False"
stochastic_rounding = "True"
fused_back_pass = "False"
use_cautious = "False"
use_grams = "False"
use_adopt = "False"
use_orthograd = "False"
use_focus = "False"