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The model is trained on the style of Kaisen Chuui's later works, focusing primarily on a specific uniform. The training dataset also includes some NSFW and synthetically generated images for variety.Does not require a specific trigger word.The recommended weight is 1, but it can be varied from 0.1 to 1.5.The base model: Anima base-v1.0.Automatic tag captions for images: SmilingWolf/wd-vit-large-tagger-v3 (pony/Illustrious/Danbooru-style tags)The model was trained with random tag dropout: 0.1.Mixed res training on [512, 1024]The kohya-ss/sd-scripts repository was used for training. The first pass consists of 40 epochs with a high learning rate of 6e-4, and the second of 40 epochs with a lower rate of 2e-5 for stabilization.Params:accelerate launch --num_cpu_threads_per_process 1 anima_train_network.py --pretrained_model_name_or_path="..\models\diffusion_models\anima_baseV10.safetensors" --qwen3="..\models\text_encoders\qwen_3_06b_base.safetensors" --vae="..\models\vae\qwen_image_vae.safetensors" --dataset_config="..index.toml" --output_dir="output" --output_name="kaisen_chuui" --save_model_as=safetensors --network_module=networks.lora_anima --network_dim=64 --learning_rate=2e-5 --optimizer_type="AdamW8bit" --lr_scheduler="constant" --timestep_sampling="sigmoid" --discrete_flow_shift=1.0 --max_train_epochs=45 --save_every_n_epochs=1 --mixed_precision="bf16" --gradient_checkpointing --cache_latents --vae_chunk_size=64 --network_train_unet_only --vae_disable_cache --network_dropout 0.1 --sample_at_first --sample_every_n_epochs 1 --sample_prompts="..\promt.txt"