95%
Fewer parameters than full fine-tuning
LoRA adds lightweight adapter layers instead of rewriting an entire model.
LoRA Training Tool
LoRA Training Online for Consistent AI Characters
Turn a small reference set into a reusable character LoRA. Generate unlimited variations--new poses, outfits, scenes--while keeping the same face and personality.
10–30 images → 15–30 minutes → production-ready consistency.
What’s your story?
Paste your story, then choose the page count and layout.
Edit panels, dialogue, and layouts in the studio.
Training plan
The fastest path to character consistency
1-10min
Typical training time
Fast enough to iterate, stable enough for production workflows.
1-10MB
Typical LoRA file size
Small, portable models you can reuse and stack.
10-30
Reference images needed
A curated dataset beats a huge messy folder.
Built for the work
Key features that feel unfair
LoRA Training: custom character models for consistency that actually holds up in production.
Fast training times
Train in minutes, not days. Iterate like a creator, not a research lab.
Perfect character consistency
Same eyes, nose, mouth, and proportions across generations—no identity drift.
Minimal training data
Great results with just 10–30 carefully selected reference images.
Small model sizes
2–10MB adapters are easy to store, share, and reuse across projects.
Style preservation
Keep your artistic signature—line quality, palette, composition—while changing scenes.
Flexible fine-tuning
From strict identity lock to softer stylistic guidance, tune for the job.
Multi-model stacking
Combine character LoRAs with style LoRAs for deeper control.
Production ready
Generate assets for manga, games, storyboards, marketing, and series pipelines.
Foundation
What is LoRA Training?
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that teaches an existing model new, specific knowledge without modifying millions of core weights. Instead, it adds small adapter layers—requiring dramatically less compute while achieving high fidelity. Think of it as training an AI artist to recognise and consistently recreate your character’s DNA: face proportions, expressions, accessories, and style cues. With just 10–30 curated images, you unlock unlimited consistent generations for manga panels, game assets, storyboards, and brand characters.
Continuity
Why character consistency matters
In visual storytelling, consistency is the foundation of trust. When a character’s face shifts between scenes—hair length, eye shape, proportions—viewers feel the break. Standard text-to-image is non-deterministic: the same prompt produces a different identity every time. LoRA training solves this by encoding identity into a reusable model so your character stays recognisable across variations.
Perfect facial consistency
Same eyes, nose, mouth, and proportions in every generation.
Style preservation
Maintain your art direction across scenes and compositions.
Expression flexibility
New emotions without losing identity.
Pose & camera variation
Unlimited angles and framing while keeping the same character.
Outfits & accessories
Change clothing, props, and details without face drift.
Scene adaptability
Recognisable under different lighting, backgrounds, and moods.
How the method works
The science behind LoRA
Full fine-tuning rewrites a model’s core weights—expensive, brittle, and easy to overfit. LoRA keeps the base model intact and learns a low-rank update via lightweight adapters. You get targeted learning (identity, style, concept) with far fewer parameters, smaller files, and faster training—perfect for creator iteration loops.
01 / Proof in the output
Small Dataset, Strong Identity
Teach the model who your character is. A curated reference set locks in the face and key design cues so every new generation still feels like the same person.
02 / Proof in the output
Iterate like a creator (not a lab)
Train, test, adjust, repeat. Dial identity strength, style adherence, and flexibility until the results match your production needs—manga pages, games, marketing, and series work.
Detailed process
How it works
Get started in minutes. No ML background required.
Step 01
Prepare reference images
Collect 10–30 high-quality images. Include variety in pose, angle, and expression while keeping identity consistent. Crop clearly; avoid heavy filters and mixed subjects.
Step 02
Configure training parameters
Pick a base model (anime/realistic/art), then set epochs and learning rate. Our defaults work for most cases; advanced controls help when you need extra precision.
Step 03
Train your custom model
Start training and monitor progress. Most runs finish in 15–30 minutes depending on dataset and complexity.
Step 04
Generate with your LoRA
Use your trained LoRA to generate consistent variations: outfits, poses, scenes, lighting, and expressions—without identity drift.
Explore the full workflow, evaluation criteria, and creator evidence
Where it fits
Use cases
How creators use LoRA training to scale output without sacrificing identity.
Manga and comic creation
Generate consistent characters across hundreds of panels for manga series, webtoons, and graphic novels—chapter one through finale.
Game character assets
Create sprites, portraits, key art, and emotional state variations for protagonists, NPCs, and skins.
Visual novel production
Build complete expression and outfit sets while maintaining continuity across the entire story.
Character sheet development
Generate design sheets with angles, expressions, and outfit packs for animation and game teams.
Brand mascot creation
Maintain brand identity across marketing, social, merch, and ads with a consistent mascot model.
Animation pre-production
Create storyboards, concept scenes, and character studies before committing to final production.
Social media content
Build audience recognition by generating consistent character-based posts and campaigns.
Merchandise design
Create print-ready character art for collectibles and product lines with consistent representation.
Output gallery
Gallery
Examples of what you can create with LoRA training + Character Studio.
Workflow
How LoRA Training Works
A practical workflow for consistent character generation.
Prepare a dataset
Collect images that represent the same identity across angles, expressions, and lighting. Clean inputs = better consistency.
Train the LoRA
Run a lightweight training job to learn the identity/style signal without overfitting.
Generate consistently
Use the trained LoRA in Character Studio to generate new scenes, outfits, and poses while keeping the face stable.
Where it earns its place
Why Train a LoRA
Consistency is what turns pretty images into a usable story pipeline.
Stable identity across scenes
Keep the same character face while changing backgrounds, camera angles, or outfits.
Faster iteration
Once the identity is captured, you spend less time re-rolling generations just to match the character.
Better series continuity
Useful for comics, manga, webtoons, thumbnails, and any multi-image storytelling workflow.
Creator-grade control
Train, test, adjust — aiming for repeatable production output rather than one-off demos.
Reusable assets
Treat a trained LoRA like a reusable character asset you can bring into new projects.
Works with Character Studio
Go straight into /new/characters to generate, refine, and keep building your roster.
FAQ
Frequently Asked Questions
How many images do I need for LoRA training?
Typically 10–30 curated images. Variety helps (angles, expressions, lighting), but identity must stay consistent. Clean data beats more data.
What makes a good dataset?
Clear subject, consistent identity, varied viewpoints, good lighting, and tight crops. Avoid mixed subjects, extreme filters, heavy occlusion, or wildly different styles in the same set.
Will the character stay consistent across outfits and scenes?
Yes—when trained well, LoRA helps preserve facial identity while you change prompts, outfits, environments, and lighting.
How long does LoRA training take?
Most runs complete in 15–30 minutes depending on dataset size and complexity.
What’s the difference between LoRA and full fine-tuning?
Full fine-tuning updates a large portion of the model’s weights (heavier compute, higher risk of overfitting). LoRA learns lightweight adapter layers (faster, smaller, easier to reuse).
Can I combine multiple LoRA models?
Often yes. Stacking a character LoRA with a style LoRA can unlock deeper control—keep identity stable while changing art direction.
Can I use LoRA models commercially?
You own what you create with LlamaGen. Please ensure your dataset and prompts respect third‑party rights and IP.
Where do I use the trained LoRA?
Open Character Studio at /new/characters and generate new images using the trained character identity.
Ready to Build Your Character Roster?
Train once, then generate forever—consistent faces across every scene.