NIN_Captioer — Local AI Captioning, Krea 2 LoRA Train & Test
NIN_Captioer is a Windows desktop tool for the full Caption → Train → Test loop. Start by preparing LoRA datasets with local Auto Caption and bilingual review, then train a native Krea 2 LoRA and verify results image.
Scope: Training is Krea 2 only (train on Krea-2-Raw).
Other bases (SD / SDXL / Flux, etc.) are not supported for training.
Tested on: Windows x64 + NVIDIA RTX 4090 only. Other GPUs / OS setups are untested.
Core Features
Auto Natural Language Captioning
Seamlessly connects to LM Studio or Ollama via local APIs
Uses local Vision-LLM models to generate detailed natural language captions suited for Flux / Krea2-style datasets
Optional WD14 Danbooru tags (ONNX) for tag-style captioning
Batch Auto Caption for images missing
.txt, plus reCaption for the current imageCaption Analysis: coverage, LoRA Health Score, category distributions
Real-Time Translation & Side-by-Side Comparison
Instant side-by-side bilingual translation next to English captions
Review, verify, and fine-tune AI captions without language barriers
Edits on either side stay in sync (default target: Traditional Chinese)
Lora Train (Krea 2 only)
Native Krea 2 LoRA trainer in-app (Raw train → Turbo apply / sample)
Multi-job Start / Stop, progress log, loss chart, sample previews
Loss spike markers and OOM detection surfaced in the UI and log
Resource monitor (CPU / RAM / GPU)
Low VRAM helpers: layer offload, quantization, gradient checkpointing
Hugging Face model Download / Update, Python path probe and install helper
LoRA export compatible with ComfyUI key format
Lora Test (ComfyUI)
Install / start / stop ComfyUI from the app
Generate with trained Krea 2 checkpoints, multi-prompt runs, and a result gallery
Smoke-test LoRAs right after training without switching tools
Suggested workflow
Add your image folder → connect LM Studio / Ollama
Auto Caption → review with translation → Analyze Health Score
Lora Train (Krea 2 Raw / Turbo) → Start Train
Lora Test → start ComfyUI → pick checkpoint → generate & review gallery
Links
GitHub Repository: BulbulLeung/NIN-Captioer