# Kohya_ss vs Ostris AI-Toolkit: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Kohya_ss wins on comprehensive multi-architecture fine-tuning (Flux.1, SDXL, SD3, SD1.5) across LoRA, LoHa, LoKr, LyCORIS, and full DreamBooth with 12GB VRAM FP8 optimizations across 12.6k+ GitHub stars (94.6), while Ostris AI-Toolkit wins on permissive MIT licensing and rapid YAML/Gradio training for newest Diffusion Transformers (Flux.1-dev/schnell and Wan2.1 14B video LoRAs) across 12.3k+ GitHub stars (94.3).

- **Category**: Comprehensive LoRA/DreamBooth GUI vs DiT Flux.1 & Wan2.1 LoRA Trainer Shootout
- **Canonical URL**: https://exactbaseline.com/vs/kohya-ss-vs-ai-toolkit
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## Kohya_ss Key Advantages
- 12,632+ GitHub stars under Apache-2.0 with full Gradio GUI and CLI for LoRA, LyCORIS (LoHa/LoKr), and DreamBooth
- Trains Flux.1 and SDXL LoRAs in as little as 12GB VRAM using FP8 base weights, gradient checkpointing, and Prodigy/AdamW8bit
- Built-in dataset captioning utilities (BLIP/WD14 tagger), bucket resolution scaling, and TensorBoard loss tracking

## Ostris AI-Toolkit Key Advantages
- 12,329+ GitHub stars under a 100% permissive MIT license purpose-built for 24GB consumer GPUs (RTX 3090/4090)
- Fastest day-zero support for new Diffusion Transformer (DiT) architectures including Flux.1-dev, Flux.1-schnell, and Wan2.1 video LoRAs
- Clean single-file YAML configs plus built-in Gradio training UI with automatic sample image generation at every N steps