<h3 id="ver-anima-v1">Ver Anima V1</h3>Trained base on Anima V0.1 base with LoRA_Easy_Training_Scripts. See about this version for training details.<h3 id="ver-noob-v-pred-v3-eq">Ver NOOB V-PRED V3 EQ</h3><span style="background-color:rgb(26, 27, 30);color:rgb(141, 141, 143);font-family:-apple-system, BlinkMacSystemFont, &quot;Segoe UI&quot;, Roboto, Helvetica, Arial, sans-serif, &quot;Apple Color Emoji&quot;, &quot;Segoe UI Emoji&quot;;font-size:16px">Trained with improved parameters based on NOOB V-PRED EQ VAE exprimental version: <a target="_blank" rel="ugc" href="https://civarchive.com/models/1858821/noobai-v-pred-10-with-eq-vae">https://civarchive.com/models/1858821/noobai-v-pred-10-with-eq-vae</a><h3 id="updated-ver-noob-v-pred-v2">Updated Ver NOOB V-PRED V2</h3>Trained based on NOOB V-PRED 1.0 with OneTrainer. See about this version for training details.It can do better wrinkles on pants and glans shape in this version. Sometimes it has text around but easily to remove with negative prompts.The adaptability to art style has weakened in this version. In this case, upweight the style lora or doing a few more trials can be helpful.<h3 id="updated-ver-noob-v-pred-v1">Updated Ver NOOB V-PRED V1</h3>Trained based on NOOB V-PRED 1.0. Updated dataset to achieve better performance. It enhanced this concept on NOOB series models. See the examples first.Only one trigger word in this version.<h3 id="updated">Updated</h3>Used PonyXL to train this one. Updated dataset to achieve better performance. It enhanced this concept on PonyXL and related models. See the examples first.Seperated into two concepts: bulge down leg with pants/bulge down leg with dress.<h3 id="introduction">Introduction</h3>An experimental LyCORIS, used to generate futanari with bulge down leg.Still a lot of room for improvement.<h3 id="pros">Pros</h3><ul><li>Can generate futanari with bulge down leg.</li></ul><h3 id="cons">Cons</h3><ul><li>Using 3D materials for training, the art style may become strange under high weights.</li><li>Penis twisted with legs.</li><li>Confused the glan with knees.</li><li>Bad penis shape.</li><li>A lot of trials needed.</li></ul><h3 id="how-to-use-it">How to use it</h3>I recommand using it in weight 0.7-0.8.<h3 id="training-details">Training details</h3>Traingset is of about 34 images. Half of them are chosen as regularization images. Total steps are about 1500.resolution=768*768batch_size=2epoch=10network_dim=16network_alpha=16conv_dim=4conv_alpha=4clip_skip=2Using Prodigy as optimizer, setting as the author recommend.

Bulge down leg

[[subsets]]caption_dropout_rate = 0.05caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/bulge_down_leg_V2/5_bdl"keep_tokens = 1name = "5"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[train_mode]train_mode = "lora"[general_args.args]persistent_data_loader_workers = truevae_batch_size = 5pretrained_model_name_or_path = ""mixed_precision = "bf16"gradient_checkpointing = truegradient_accumulation_steps = 1seed = 42max_data_loader_n_workers = 1max_token_length = 225prior_loss_weight = 1.0sdpa = truemax_train_epochs = 20cache_latents = truecache_latents_to_disk = true[general_args.dataset_args]resolution = 1024batch_size = 5[network_args.args]network_dim = 32network_alpha = 16.0min_timestep = 0max_timestep = 1000network_train_unet_only = true[optimizer_args.args]optimizer_type = "ProdigyPlusScheduleFree"lr_scheduler = "constant"loss_type = "l2"learning_rate = 1.0unet_lr = 1.0max_grad_norm = 1.0[saving_args.args]output_dir = "G:/LoRA_Easy_Training_Scripts/output"output_name = "bulge_down_leg-anima1.0-V1-loha-dim32conv16AR0.5-NewTag"save_precision = "bf16"save_model_as = "safetensors"save_every_n_epochs = 1save_toml = truesave_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 = 512t5_max_token_length = 512timestep_sampling = "sigmoid"sigmoid_scale = 1.0[edm_loss_args.args]edm2_loss_weighting = false[bucket_args.dataset_args]enable_bucket = truemin_bucket_reso = 256max_bucket_reso = 2048bucket_reso_steps = 64[network_<a target="_blank" rel="ugc" href="http://args.args.network">args.args.network</a>_args]conv_dim = 16conv_alpha = 8.0algo = "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"

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