Kohya_ss
The standard SDXL/Flux LoRA training UI.
Open Source 12–24 GB VRAM
- Min VRAM
- 12 GB
- GPU class
- High-end GPU
- Quant
- FP16
Actually FreeNo SignupOpen SourceWatermark-Free
AI-Toolkit (Ostris)
Modern training framework — Flux, SDXL, SD3 LoRAs in YAML.
Open Source 16–24 GB VRAMRuns locally
Actually FreeNo SignupOpen SourceWatermark-FreeHobbyist-Friendly
Visit AI-Toolkit (Ostris)Updated 2026-05-11 · Direct link
Hardware requirements
Runs locally · High-end GPU (16–24 GB)
Min VRAM
16 GB
Rec. VRAM
24 GB
Min RAM
32 GB
Rec. RAM
64 GB
Disk
80 GB
GPU class
High-end GPU
12.1+No Apple SiliconGPU RequiredQuant: BF16, FP8
Flux LoRA needs 24 GB; SDXL LoRA fits in 16 GB with care.
Screenshot placeholder · AI-Toolkit (Ostris)
What is AI-Toolkit (Ostris)?
AI-Toolkit by Ostris is the current go-to for training Flux LoRAs and is rapidly becoming the modern equivalent of Kohya for SDXL/SD3. YAML-driven configs, tight memory optimisations (8-bit Adam, gradient checkpointing), and reliable training on 16-24 GB GPUs.
Pros & cons
Pros
- Reliable Flux LoRA training on 24 GB
- YAML configs are version-controllable
- Tracks experiments via wandb / TensorBoard out of the box
Cons
- Less documented than Kohya for older models
- Requires Python/CLI fluency
What's actually free?
MIT.
✓ Actually FreeNo SignupOpen SourceWatermark-Free
Alternatives
FluxGym
Dead-simple Flux LoRA training in a Gradio UI.
Open Source 12–20 GB VRAM
- Min VRAM
- 12 GB
- GPU class
- Mid GPU
- Quant
- FP8
Actually FreeNo SignupOpen SourceWatermark-Free
OneTrainer
Modern alternative trainer for SD/SDXL/Flux.
Open Source 12–24 GB VRAM
- Min VRAM
- 12 GB
- GPU class
- High-end GPU
- Quant
- FP16
Actually FreeNo SignupOpen SourceWatermark-Free