<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. See the examples first.Back with taur frontal genital. No need for a seperate lora to generate it.To control the size of penis I set XX taur penis/ XX taur testicles (XX here is large/huge/gigantic/hyper) as a second triggerwords. Works not well but you can add more weight to make it effective. <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>In this version, I removed some trigger words. They are now serparte loras. Only 9 triggerwords left. See the examples first.I changed the name of this lora to accurately describe its effect. Now it supports the generation of many types of taur.Most trigger words mean same to the CF-prefix ones. Taurrearing means the taur standing on hind legs.<h3 id="trigger-words-explaination-for-previous-versions:">Trigger words explaination for previous versions:</h3>CFback: back view of futanari centaurCFonelegup: lifting one foreleg pose of futanari centaurCFstanding: standing pose with all four legs on the groundCFtwolegsup: lifting two forelegs pose of futanari centaurCFlying: lying pose of futanari centaurCFkneeling: kneeling pose of futanari centaurCFdiphallia: standing pose of a futanari centaur with horse penis and humanoid penisCFhuman: human taur conceptCFUCup: carrying girl with legs up under the belly of futanari centaurCFUCdown: carrying girl with legs down under the belly of futanari centaurCFUCfellatio: carrying girl under the belly of futanari centaur while giving oralCFdoggy: a futanari centaur has sex with another in doggystyle positionCFmissionary: a futanari centaur has sex with another in missionary positionCFfellatio: a girl/boy is performing fellatio on a futanari centaurCFreversedoggy: a futanari centaur has sex with another in reversedoggy position<h2 id="ver-noob-v-pred-v2.0">Ver NOOB V-PRED V2.0</h2>Trained based on NOOB V-PRED V1.0 with OneTrainer. See about this version for training details. Changed keywords and added serval concepts.<h2 id="ver-noob-v-pred-v1.0">Ver NOOB V-PRED V1.0</h2>Trained based on NOOB V-PRED V1.0. Change keyword CFundercarriage to CFUC to prevent the appearance of carriages.<h2 id="ver-noob-e-pred-v1.0">Ver NOOB E-PRED V1.0</h2>Trained based on NOOB E-PRED V1.0. Notice that some examples are generated with NOOB V-PRED 0.5.<h2 id="updated">Updated</h2>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.Typo issue solved. Now undercarriage is correctly spelled.Fur color issue solved. See the examples.Please be sure to use trigger words and hires.fix in generation. Some of triggerwords seems to overbake the images or bring bad styles when upweighting them. Reason may be I used 3d images when training. Use highres.fix to prevent this from happening.Adding concepts: doggy, diphallia, undercarriage<h3 id="about-v2.0">About V2.0</h3>In this version, finally I have different poses with weighted cation. Also with multires technque.Updated keywords will prevent the models from misreading the tokens like kneeling, standing, etc.Currently now have five keywords for different poses:CFFB: back to the viewer;CFKN: kneeling;CFLU: two legs up;CFLY: lyingCFST: standing or with single leg up.The bad thing is, still, prompt is not sensitive, and many times the penis will mistaken for hooves or the centaur having multiple legs. You can use some horse penis lora to improve it. But it still needs dozens of trials.Recommend using highres fix with good seeds to generate good images.WORKS BADLY IN 512X512Multires technique parameter:multires_noise_iterations="6"multires_noise_discount=0.3<h3 id="introduction">Introduction</h3>This LyCORIS is used to generate futanari centaur.<h3 id="how-to-use-it">How to use it</h3>I recommand using it in weight 0.7-0.8. It works well with other loras. But sometimes the cock will be mistaken for hooves.<h3 id="training-details">Training details</h3>Traingset is of about 220 images. They are mirrored before training. Half of them are chosen as regularization images. Total steps are about 15000.I divided the training set into 5 subsets and tag the gesture. It is to control the results. But it didn't work, sad. If you want to give it a try, the trigger tags is the folder name below.Regularization = trueresolution=512batch_size=2epoch=10network_dim=32network_alpha=32clip_skip=2Using AdamW8bits as optimizer:<ul><li>lr="1e-4"</li><li>unet_lr="1e-4"</li><li>text_encoder_lr="1e-5"</li></ul>Locon parameters:<ul><li>conv_dim=4</li><li>conv_alpha=4</li></ul>

Taur futanari

[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_humantaur"keep_tokens = 1name = "5"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurfrontalpenis"keep_tokens = 1name = "5_1790613374084299600"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurfrontalpussy"keep_tokens = 1name = "5_1790613374099814600"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurhyper"keep_tokens = 1name = "5_1790613374114867000"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurkneeling"keep_tokens = 1name = "5_1790613374129750000"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_tauronback"keep_tokens = 1name = "5_1790613374144775100"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_tauronelegup"keep_tokens = 1name = "5_1790613374160265600"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_tauronside"keep_tokens = 1name = "5_1790613374175776200"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurrearing"keep_tokens = 1name = "5_1790613374190284200"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taursitting"keep_tokens = 1name = "5_1790613374206290200"num_repeats = 5random_crop_padding_percent = 0.05shuffle_caption = true[[subsets]]caption_extension = ".txt"caption_tag_dropout_rate = 0.05image_dir = "G:/dataset/concept/taur_futanari/5_taurstanding"keep_tokens = 1name = "5_1790613374221799300"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 = "taur_futanari-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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