SDXL LoRA training with SimpleTuner: setup guide

Setting up SimpleTuner to train a SDXL LoRA: support status, the settings known to work, and links to the docs. Status in SimpleTuner: supported.

About SimpleTuner

bghira's trainer; more powerful (e.g. teacher-student regularization), but the data loader is harder to set up. Sources: github.com/bghira/SimpleTuner, www.reddit.com/r/FluxAI/comments/1ogw37j/best_flux_lora_trainer/

Repo	bghira/SimpleTuner
GitHub stars	2942
Last commit	2026-10-20
Latest release	v4.10.1

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 SimpleTuner 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)

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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