Training
Train an AI model without a ComfyUI weekend
You do not have to run fal.ai LoRA jobs to get a usable influencer. fanv.ai trains identity in-product (Genome + dataset). This page explains that path versus DIY adapters — not a Coursera “ML model training” course.
Train your characterIn-product training
- 01
Specify identity
Genome layers are the training signal: they constrain what “her” means before you spend credits on scenes.
- 02
Materialize references
Generate or upload a tight set. Drop outliers. Retrain/regenerate if the face splits into two people.
- 03
Validate
Five scenes (gym, café, street, interior, night). If two of five look like a sister, tighten the set.
LoRA vs Genome (honest)
LoRA
Powerful, portable weights, DIY control. Also: curation, trigger words, version rot. Fizzly productizes this.
Genome
Faster for operators who want a wizard, not a rank-decomposition lecture. Tied to fanv.ai’s stack.
You might still export stills
If you later train an external LoRA, a clean fanv.ai dataset is a better start than random Pinterest faces.
Ambiguous keyword, on purpose
“AI model training” also means PyTorch. Copy on this page is explicitly creator-character training so we do not bait ML students.
Questions
How many images do I need?+
From scratch: zero, Genome is enough to start. For a tight lock, treat ~20–50 varied stills as the serious pack — same order of magnitude LoRA guides recommend.
Related
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Train your character