Train a Flux LoRA
Fine-tune a LoRA adapter on Flux.1 from a small image set — for a style, subject, or product look you want Flux to reproduce consistently.
Estimated $3–$12 per training run · 20–60 min
What inputs you need
- 10–30 training images
- Captions (auto or manual)
- Training config (rank, steps, learning rate)
What Badgr returns
- Trained LoRA safetensors file
- Training loss curve / checkpoint log
- Sample generations at checkpoints
Example command
badgr run "python train_lora.py --base flux1 --config flux_lora.yaml" \
--gpu A100_80GB \
--max-cost 12 \
--max-runtime 90Common setup failures Badgr avoids
Out-of-memory during gradient accumulation on large Flux weights — default config uses 80GB routes and gradient checkpointing.
Training exceeds budget before convergence — Badgr checkpoints periodically so a capped run still yields a usable LoRA.
Ready to run this?
Launch from the dashboard, CLI, or Compute API. Max-cost protection included.