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Train a custom anime LoRA on the ANIMA base model — Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup, dataset prep, training params, and using the result in the anima-base workflow
Passed 22 of 22 test cases with the skill, and 5 of 22 without it.
and 24 more in the full report
17 fixed5 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
When Citron's Anima LoRA Trainer generates its training TOML config, several core network parameters are locked internally and not exposed in the Gradio UI. What network module name is hardcoded in the generated TOML, what is the hardcoded value for `qwen3_max_token_length`, and is UNet-only training enabled by default in this configuration? Please list these specific settings.
| Checked | Without skill | With skill |
|---|---|---|
| Identifies networks.lora_anima as the hardcoded network_module value. | Missed | Passed |
| Identifies 512 as the hardcoded qwen3_max_token_length value. | Missed | Passed |
| Confirms that network_train_unet_only is set to true in the generated config. | Passed | Passed |
The instructions your model reads when the skill loads.
Citron's Anima LoRA Trainer (app.py, titled "Citron's Anima LoRA Trainer" in the UI) is a local Gradio UI for training LoRA adapters on the Anima diffusion model using kohya-ss/sd-scripts. It trains on ~6GB VRAM with the default settings, the same low-VRAM profile as Anima generation.
https://github.com/citronlegacy/citron-anima-lora-trainer-ui. The Aitrepreneur adaptive installers clone the fork https://github.com/aitrepreneur/citron-anima-lora-trainer-ui.kohya-ss/sd-scripts (https://github.com/kohya-ss/sd-scripts), launched via accelerate launch..safetensors LoRA usable directly in the anima-base ComfyUI workflow.The network module is
networks.lora_animaand the training script issd-scripts/anima_train_network.py(an Anima-specific kohya script the installer expects). Confirm these exist after the installer'sgit cloneof sd-scripts.app.pyreferences them, but they are pulled from the upstream repo at install time.
Run CITRON_ANIMA_LORA_TRAINER-V2.bat. It:
app.py defaults (base_model → anima-preview3-base, mixed_precision → detected value), writes app_configs/accelerate_gpu.yaml..venv, installs PyTorch, clones and installs sd-scripts, installs app requirements.txt.models/anima/{dit,text_encoder,vae}/ from https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/...:
dit/anima-base-v1.0.safetensors (~4GB)text_encoder/qwen_3_06b_base.safetensors (~1.19GB)vae/qwen_image_vae.safetensors (~254MB)run_anima_base_windows.bat.Run CITRON_ANIMA_LORA_TRAINER-RUNPOD-V2.sh. Same flow into /workspace/citron-anima-lora-trainer-ui; it patches server_name to 0.0.0.0. Expose HTTP port 7860 and open Connect → HTTP Service 7860 (or https://${RUNPOD_POD_ID}-7860.proxy.runpod.net).
app.py runs Gradio on 0.0.0.0:7860, so open http://127.0.0.1:7860. Re-launch later with run_anima_base_windows.bat (Win) or ./run_anima_base_runpod.sh (RunPod). The DiT base model auto-downloads on the first "Start Training" if not already present (uses wget).
A flat folder of images, each with a matching .txt caption of the same basename (image-side captioning, kohya style):
test cases passed, vs 5 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.