← ImageLoRA · All LoRA guides
SDXL LoRA training with kohya sd-scripts: setup guide
Setting up kohya sd-scripts to train a SDXL LoRA: support status, the settings known to work, and links to the docs. Status in kohya sd-scripts: supported.
About kohya sd-scripts
The long-standing kohya training scripts (also used by the kohya_ss GUI); strongest for SD1.5/SDXL-era models. Sources: github.com/kohya-ss/sd-scripts
VRAM
low-VRAM friendly with gradient checkpointing, 8-bit optimizer, cached latents (exact GB unknown)
Settings that work for SDXL (any trainer)
- Gradient checkpointing
- 8-bit optimizer
- Cache latents
- OneTrainer: 'compile' + int8 quant on RTX 20xx+
General tips
- Gated Hugging Face models: click 'Agree and access' on each model page, or you'll get 403 errors
- Steps scale with dataset size (steps = epochs x images at batch 1)
- A safe start: LoKr with auto rank (~64), batch/grad accumulation 4
- Leave out of your captions whatever you want burned into the LoRA; caption what should stay changeable
- ai-toolkit dataset folder weights let you train the same images with long, medium and short captions in separate folders
- ai-toolkit supports validation loss, which shows when a run stops improving
- In ai-toolkit, Cache text embeddings works for basically every model and frees the text encoder from VRAM and RAM
- Prefer int8 convrot over qfloat8 quantization in ai-toolkit: better quality at the same memory
- Gradient accumulation and batch size don't scale the learning rate in ai-toolkit; a bigger batch needs about 1.5-2.5x the steps
Compare with other trainers
Where to rent a 16 GB+ GPU
Cheapest 16 GB+ cards (2026-10-20 08:06 UTC) from RunPod and Vast.ai. Prices move constantly; see the live price table.
| GPU | VRAM | RunPod community $/hr | Vast.ai cheapest $/hr |
|---|
| Tesla V100 | 16 GB | $0.19 | $0.08 |
| RTX 3090 | 24 GB | $0.22 | $0.12 |
| RTX 5060 Ti | 16 GB | — | $0.14 |
| Tesla V100S | 32 GB | — | $0.14 |
| RTX A5000 | 24 GB | $0.16 | — |
| RTX A4000 | 16 GB | $0.17 | — |
Sources
FAQ
Does kohya sd-scripts support SDXL?
Yes: supported (see sources below).
How much VRAM do I need for a SDXL LoRA?
low-VRAM friendly with gradient checkpointing, 8-bit optimizer, cached latents (exact GB unknown)
Weekly newsletter: new models, trainer releases, cheapest GPUs.
Subscribe
Data updated 2026-10-20. Disclosure: RunPod and Vast.ai links are referral links; ImageLoRA may earn a commission at no cost to you. unknown = no data yet. · Raw data (JSON) · llms.txt