A fine-tuned model specializing in flat color, no lineart, slightly chibi character art.

v4.0

• Change trigger words.

• Update dataset to increase the character's head-to-body ratio

v3.0

To help body proportions, I’ve added super deformed, chibi-style illustrations to the training data.

You can decrease the body-to-head ratio using the “chibi, super deformed” tags and you can also use "realistic" tag to increase it.

Trigger word

flat color, no lineart, deformed

Optional tags

chibi, super deformed

realistic

v2.0

To help adjust color tones and body proportions, I’ve added non-deformed flat color no lineart illustrations to the training data.

You can increase the body-to-head ratio using the “realistic” tag. If you don't want to increase it, set it to a negative value.

Trigger word

flat color, no lineart, deformed

Negative word

chibi, realistic

Initial release

Note: This is my first fine-tuned model. While I'm pleased with this results, the quality may vary compared to established style LoRAs. I appreciate your patience and feedback as I continue to refine this approach. Your input helps me improve future versions!

This model excels at creating characters with a distinctive light deformation style - more expressive than realistic, yet more refined than super-deformed (SD/chibi). Perfect for characters ranging from 3.5 to 7 heads tall, capturing that sweet spot between youthful charm and mature appeal.

Key characteristics:

Clean flat colors (no lineart required)

Soft, natural deformation - ideal for stylized character design

Works beautifully with other character LoRAs to apply different character traits while keeping this style intact

Stable on deformed proportions that danbooru tags struggle to capture

Recommended usage: Combine with character LoRAs for maximum flexibility. The model's style strength is intentionally calibrated to enhance, not overwhelm, other adapters.

Training Details

For transparency and to help with troubleshooting, here's the training command I used with sd-scripts:

accelerate launch --num_cpu_threads_per_process 1 anima_train.py --pretrained_model_name_or_path=/path/to/animaOfficial_preview3Base.safetensors \
	--qwen3=/path/to/qwen_3_06b_base.safetensors \
	--vae=/path/to/qwen_image_vae.safetensors \
	--dataset_config=/path/to/dataset_config_anima.toml \
	--output_dir=/path/to/outputu \
	--output_name=fwaunstp_ckpt_anima_preview3_v1 \
	--save_model_as=safetensors \
	--learning_rate=1e-6 \
	--optimizer_type=AdamW8bit \
	--lr_scheduler=constant \
	--timestep_sampling=sigmoid \
	--discrete_flow_shift=1.0 \
	--max_train_epochs=8 \
	--save_every_n_epochs=1 \
	--mixed_precision=bf16 \
	--gradient_checkpointing \
	--cache_latents \
	--vae_chunk_size=64 \
	--vae_disable_cache \
	--save_state \
	--split_attn \
	--attn_mode=torch

This model was trained using f32 precision (not BF16, which some other Anima Preview 3 models use). If you notice any issues or compatibility problems, please let me know—I'd appreciate feedback on the training parameters or the checkpoint format.