Train a brand style LoRA
Fine-tune a LoRA on a brand's existing visual assets (marketing images, product renders) so future generations match a consistent brand look.
Estimated $3–$10 per training run · 20–50 min
What inputs you need
- 20–50 brand reference images
- Style captions describing the brand look
- Training config
What Badgr returns
- Trained brand-style LoRA
- Training loss curve
- Side-by-side sample generations vs. brand references
Recommended GPU routes
Estimated cost: $3–$10 per training run · Estimated runtime: 20–50 min
Example command
badgr run "python train_lora.py --config brand_style.yaml" \
--gpu A100_40GB \
--max-cost 10 \
--max-runtime 60Common setup failures Badgr avoids
Style LoRA that drifts from the brand's actual palette — sample generations are logged at each checkpoint against reference images for comparison.
Training job exceeds max-cost before convergence — lower rank or split into checkpointed resumable runs.
Ready to run this?
Launch from the dashboard, CLI, or Compute API. Max-cost protection included.