Tuning & Troubleshooting

VRAM-smart defaults, practical knobs, and quick fixes for LoRA & DreamBooth.

Quick checklist

  • Clean data: one subject/style per folder; remove blurs/near-dupes.
  • Family match: SD 1.5 vs SDXL must match your base checkpoint and your generation plan.
  • System headroom: check Launcher → System for VRAM & disk before training.
  • A1111 only for auto-caption: run it for captioning, then stop to free VRAM.
System panel showing VRAM and disk usage
System panel — confirm VRAM/disk headroom before you hit Start.
Family mismatch warning example
Family mismatch symptoms — muddy results or model not appearing.

VRAM strategy ladder

  1. Lower resolution (long side): first & biggest lever.
  2. Batch = 1; increase grad-accum to simulate larger batch.
  3. Keep precision FP16 from presets; avoid exotic half-modes unless you know them well.
  4. Close other GPU apps; only keep A1111 on for captioning.
  5. If still OOM: shorten steps for a test pass, then resume longer.
VRAM ladder diagram
Lower resolution → batch 1 → grad-accum — safest path.
Batch vs gradient accumulation panel
Batch stays small; use accumulation to approximate larger effective batch.

Resolution & aspect

  • Portrait LoRA: start long side ~640–768 px.
  • Full-body DB: start with a taller canvas (e.g., 832×1216 class) but keep moderate first.
  • Normalize sizes to reduce gradient noise from extreme aspect shifts.

Optimizer & learning rate

Presets ship stable combos. If you must tweak:

  • If loss explodes/NaN: lower LR slightly; keep optimizer as preset.
  • If underfitting: extend steps after verifying data quality.
  • Avoid stacking many “advanced” toggles at once — change one thing and re-test.

Steps, epochs & resume

  • Start modest; evaluate mid-run samples if enabled.
  • Use Resume from last good step instead of restarting from scratch.
  • Overtraining shows as oversaturated, waxy faces, or collapsed diversity — stop earlier next time.
Resume training dialog
Resume picks up from the last saved step.

Dataset pitfalls

  • Mixed subjects in one folder → identity drift.
  • Heavy compression → artifacts the model will learn.
  • Caption noise → conflicting tokens; keep short & consistent.
  • Missing limbs in full-body sets → add clear, upright poses.

Reproducibility & versioning

Take screenshots for your job config. Keep it with your outputs.

  • Use semantic names: mascot_sdxl_fb_v1.safetensors, ..._v2 for later runs.
  • Keep a tiny “sanity” dataset to quickly validate new settings before full runs.
params.json next to outputs
Keep config screenshots with the artifact for exact repro.
Versioned model naming examples
Version your models — it saves you from “which one was good?”.

Common issues & fixes

OOM (out of memory)

  • Lower resolution → set batch = 1 → use grad-accum.
  • Close A1111 and other GPU apps; keep FP16.

Exploding loss / NaN

  • Lower learning rate; revert any recent “advanced” toggles.
  • Audit data for corrupted images or extreme outliers.

No improvement / muddy results

  • Confirm family and checkpoint match (1.5 vs XL).
  • Improve dataset: sharper images, consistent framing, better captions.
  • Train a bit longer, but watch for overbake symptoms.

Overbake / “waxy” look

  • Stop earlier; reduce steps or add varied but on-style samples.
  • For LoRA inference, try weight 0.6–0.8 first.

Throughput too slow

  • Lower resolution slightly; keep batch at 1 with accumulation.
  • Ensure the dataset is on a fast local path with enough free disk.

After training — A1111 usage

  • LoRA: refresh the LoRA list; insert <lora:NAME:0.6–0.8> and iterate.
  • DreamBooth: refresh the Stable Diffusion checkpoint list; keep family consistent.
  • ControlNet: layer it after selecting your checkpoint if needed.
A1111 refresh model lists
Refresh model lists in A1111 to pick up new outputs.
Side-by-side comparison of weights/steps
Compare weights/steps side-by-side to find the sweet spot.

What’s next?

Change one knob. Test. Repeat.
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