Anima TrainFlow collapses LoRA training into a single-page interface for 6GB GPUs

Anima TrainFlow is a single-page LoRA trainer for Anima 2B that runs on 6GB VRAM and replaces the usual multi-tab interface with a zero-tab design. Released this week on GitHub, the tool bundles a pre-configured environment and a Gradio UI that surfaces only the parameters most users actually change between runs.

The project grew out of frustration with existing trainers, where critical settings are scattered across sub-menus and a single missed checkbox can waste hours of GPU time. Developer ThetaCursed spent 20+ training runs testing parameter combinations specific to Anima 2B's architecture, then baked the stable defaults into the UI so users only adjust dataset path, output name, and a handful of hyperparameters on one screen.

What stands out

1. 01Step-based training replaces epochs. The tool defaults to a fixed step count rather than epoch math. Testing showed Anima 2B LoRAs typically converge around 1,800 steps and begin overfitting past 2,400–3,000 steps, regardless of dataset size. The UI exposes total steps directly, eliminating repeat-multiplier calculations.
2. 02Live preview gallery. A built-in gallery updates in real time as the trainer generates sample images during checkpoints, so you can monitor quality without switching windows.
3. 03Auto-resolution bucketing. A dataset analyzer scans input images and calculates optimal resolution buckets before training starts, removing manual aspect-ratio guesswork.
4. 04Prodigy optimizer by default. The config ships with Prodigy, an adaptive optimizer that adjusts learning rate on the fly. The developer reports this combination produced the most stable results across the 20+ test LoRAs.
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