Sign In

Anima LoRA Trainer for ComfyUI

Updated: Mar 29, 2026

tooltrainingcomfyuiloraanimakohya

Type

Workflows

Stats

51

0

Reviews

Published

Mar 29, 2026

Base Model

Anima

Hash

AutoV2
4C78DE0DB1
default creator card background decoration
St Patrick's Day Badge
7err4's Avatar

7err4

A ComfyUI custom node pack for training LoRA models on the Anima architecture using kohya-ss/sd-scripts. Train Anima LoRAs directly from ComfyUI with zero command-line knowledge required.

Installation

Download and extract the zip into your ComfyUI/custom_nodes/ folder, then restart ComfyUI. The nodes will appear under the "Anima Training" category.

Features

  • One-click model downloading from HuggingFace (always fetches the latest Anima base model)

  • Two workflow modes: Wizard (questionnaire-driven auto-config) and Manual (full control over all parameters)

  • Automatic sd-scripts installation and dependency management

  • TOML config generation with inline annotations explaining every setting

  • Smart VRAM presets (12GB, 16GB, 24GB) with automatic memory optimization

  • Live config preview inside ComfyUI before launching training

  • Cross-platform support (Windows, Linux, macOS)

  • Zero pip dependencies — pure Python stdlib

Included Nodes

  • Model Downloader — Downloads Anima models from HuggingFace

  • SD-Scripts Manager — Installs/updates kohya-ss/sd-scripts

  • Model Selector — Finds and validates model files

  • Training Wizard — Questionnaire-driven auto-configuration

  • Basic/Advanced/Memory Params — Manual parameter control

  • Training Launcher — Generates config and launches training

  • Config Preview — Shows annotated TOML config inline

Requirements

  • ComfyUI (any recent version)

  • Python 3.10+

  • kohya-ss/sd-scripts (auto-installed by the SD-Scripts Manager node)

  • Anima base model files (auto-downloaded by the Model Downloader node)

Dataset Preparation

Your training images should follow the sd-scripts convention: place images in a folder named <repeats>_<concept> inside your train_data_dir, with matching .txt caption files for each image.