The companion episode — Aillex narrates this exact recipe, stunt-double disaster included.
Every image of our presenter — five episodes, a music video, hundreds of shorts — is the same character. Same face, same hair gradient, same person across photoreal, anime, comic, and 3D-game styles. That consistency comes from one thing: a character LoRA, and this guide is the exact recipe.
What a LoRA actually is
Your base image model (~12 GB) knows how to draw people — millions of them. It doesn’t know you (or your character). A LoRA is a small add-on file (~150 MB) trained to teach it exactly one new thing: how to draw this specific subject. You invoke it with a trigger word in your prompt, and the model reaches for the LoRA instead of inventing a stranger.
WHO vs HOW — the two kinds you’ll use
- A character LoRA teaches WHO — the face, hair, build. Identity stays constant while outfits and scenes change.
- A style LoRA teaches HOW — an art style (anime, comic, painterly). It changes rendering, not identity.
- They stack. Our character underneath + a borrowed style on top = our girl, in any art style. This is the single most useful trick in AI character work.
The training set (where quality is decided)
Everything downstream inherits from the dataset. Our working rules:
- 20–40 images. More isn’t better past ~40; curation beats volume.
- Vary everything except identity: angles (front, ¾, profile), lighting, distance, expression, outfit. If every training image is the same pose, the LoRA memorizes the pose along with the face.
- Caption each image in plain language, describing everything except the identity (“a woman in a blazer at a desk, soft light”) — what you don’t caption is what the LoRA absorbs as the subject.
- Pick a weird, unique trigger word. Real words leak the base model’s associations into your character. Ours is invented and appears nowhere in the training captions except as the subject tag.
- Consistent character across the set — if you’re bootstrapping a new character from generations, cull ruthlessly: any image where the face drifts trains the drift in.
Train it: local (free) vs cloud (hands-off)
| AI Toolkit (local) | civitai.com (cloud) | |
|---|---|---|
| Cost | free — your GPU + electricity | Buzz (platform credits) |
| Hardware | a decent card; a character LoRA is small — roughly an evening’s run | none — trains in the browser |
| Privacy | dataset never leaves your machine | uploaded to the platform |
| Output | file lands on disk | finished file handed to you |
Both produce the same artifact. We advocate local (the whole point of this site) but cloud training is a legitimate first LoRA — the browser flow on civitai.com (referral link — supports the channel at no extra cost) walks you through dataset upload, captioning, and the dials below.
There’s also a middle path: rent a big-VRAM card by the hour on RunPod (referral link — new accounts get a signup credit bonus) and run AI Toolkit on it. You keep the local workflow — your recipe, your dials, the file lands where you put it — without owning the hardware; a character LoRA’s evening-length run costs a few dollars.
The dials that matter (both tools expose them):
- Epochs — how many times training studies your set. Too few = it never learns you; too many = it can only draw the training images.
- Rank (network dim) — how much detail capacity it has. Bigger isn’t better: a face doesn’t need a huge rank, and oversized ranks memorize noise.
- Our trick: train two passes and blend — one at half strength — then test both and the blend against a fixed seed grid before committing.
The four rules we paid to learn
These cost us real re-renders. They’re yours free:
- Never drop the stack. We once skipped the LoRA on a wide shot, reasoning she’d be small in frame. The model sent a stunt double — wrong skin, wrong everything. Identity LoRAs earn their keep at every distance; if the framing fights the LoRA, fix it with wording (“close enough to read her face”), never by dropping the stack.
- Describe the whole outfit. Crop the prompt at the waist and the model invents pants. Different pants. Every shot. If any downstream step (video, animation) will reveal more of the body than your still shows, the prompt must describe all of it.
- She has a front. A face LoRA refuses a back-of-head shot — it will helpfully spin a new face around to the camera. Plan shots accordingly or train back/side references into the set.
- Balance style strength. Stacking a style LoRA too strong eats the identity (our comic style at 0.9 erased her; at 0.55 it’s her in comic form). Find the highest style strength that keeps the face, and audit every LoRA in your stack — including ones that only misbehave in combination.
Bonus rule for the modest-of-heart: hairstyle is identity. Changing her flowing gradient to a ponytail weakened recognition as much as changing the face. If you want alternate hairstyles, train them in.
Bonus: instant character sheets
Once the LoRA holds, a character sheet is just a fixed-seed grid: same seed family, one variable per row (outfits, angles, expressions, styles). We generate labeled contact sheets for every wardrobe and style decision — that’s how every look on this channel gets picked. One LoRA in, model sheet out.
The payoff
One trained file holds her identity. Everything else — outfit, scene, style, medium — is a prompt away, and she’s her in all of it. Our image-to-3D pipeline, lip-sync, and every video on the channel sit on top of this single foundation.
Watch the companion episode — How I Stay Me — she narrates her own identity recipe, stunt-double disaster included. Questions or want to show off your own character LoRA? Join us at r/aillex.