LoRA Training

Train a general art style LoRA

Fine-tune a LoRA on a visual art style (illustration, painting technique, render style) independent of any single subject or character.

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Estimated $3–$10 per training run · 20–50 min

What inputs you need

  • 20–50 images representative of the style
  • Style-focused captions
  • Training config

What Badgr returns

  • Trained style LoRA
  • Training loss curve
  • Sample generations across varied subjects in the trained style

Recommended GPU routes

A100 40GBA6000 48GBA100 80GB

Estimated cost: $3–$10 per training run · Estimated runtime: 20–50 min

Example command

badgr run "python train_lora.py --config style_lora.yaml" \
  --gpu A100_40GB \
  --max-cost 10 \
  --max-runtime 60

Common setup failures Badgr avoids

Style LoRA overfits to one subject in the training set instead of generalizing — dataset should cover varied subjects in the same style; config flags low subject diversity.

Rank set too high for the dataset size, causing memorization instead of style transfer — default config caps rank relative to image count.

Ready to run this?

Launch from the dashboard, CLI, or Compute API. Max-cost protection included.

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