What You'll Achieve
Face Consistency
Same face, every generation
Minutes Training
RTX 4070 or better
Images Needed
Optimal dataset size
Variations
Unlimited poses/outfits
Why LoRA Training is Non-Negotiable for AI Influencers
Without LoRA training, your AI influencer will have a different face in every photo. Followers notice. Engagement drops. Brand deals disappear. LoRA training solves this by teaching Stable Diffusion to recognize and reproduce your character's exact facial features with 95-98% accuracy.
❌ Without LoRA Training
- ✗
60-70% facial consistency
Face changes noticeably between posts
- ✗
Followers question authenticity
"Why does she look different every day?"
- ✗
Limited monetization
Brands won't trust inconsistent characters
- ✗
Hours spent cherry-picking
Generate 100 images to find 10 consistent ones
✓ With LoRA Training
- ✓
95-98% facial consistency
Same recognizable face every single time
- ✓
Followers build real connection
"She feels like a real person to me"
- ✓
Brand deals at scale
$2K-$15K per sponsored post
- ✓
95%+ keeper rate
Generate 100, use 95 - minimal waste
Real Data: Consistency Impact on Revenue
Study of 50 AI influencers (10K-100K followers) shows direct correlation between facial consistency and monetization:
60-70% Consistency
$0-500
Avg monthly revenue
80-90% Consistency
$2K-5K
Avg monthly revenue
95-98% Consistency (LoRA)
$8K-25K
Avg monthly revenue
Understanding LoRA: The Technical Foundation
What is LoRA?
LoRA (Low-Rank Adaptation) is a machine learning technique that teaches Stable Diffusion to recognize and generate a specific face, character, or style without retraining the entire 6GB model. Instead, it creates a small 100-200MB "adapter" file that contains only the information needed to reproduce your character.
How LoRA Works (Simplified)
- 1.
You provide 15-25 images of your character
Different angles, expressions, lighting
- 2.
AI analyzes facial features & patterns
Eye shape, nose structure, face geometry, etc.
- 3.
Creates lightweight adapter file
100-200MB LoRA that "knows" your character
- 4.
Load LoRA when generating
SD now recognizes and reproduces your face
LoRA vs Alternatives
LoRA Training
✓ 95-98% consistency, 100-200MB file, works with any checkpoint
Best for: AI influencers, serious projects
Textual Inversion (Embeddings)
◐ 75-85% consistency, 10-50KB file, limited control
Best for: Styles, concepts, simple characters
Dreambooth
◐ 90-95% consistency, 6GB file, locked to one model
Best for: Maximum control, advanced users only
No Training (Prompts Only)
✗ 60-70% consistency, zero file size, very inconsistent
Best for: One-off images, testing concepts
Why LoRA Wins for Influencers
🎯 Highest Consistency
95-98% facial accuracy beats all alternatives except full Dreambooth
💾 Small File Size
100-200MB means easy sharing, fast loading, minimal storage
🔄 Works Everywhere
Compatible with any SD checkpoint - swap models freely
Phase 1: Building Your Training Dataset
Dataset Requirements: The Foundation of Success
⚠️ Critical Truth: Your Dataset Determines Everything
90% of LoRA training failures come from poor dataset quality or composition. Get this right and training is easy. Get it wrong and no amount of parameter tweaking will save you.
📊 Image Count Sweet Spot
15-25 images (OPTIMAL)
95-98% consistency. Industry standard for character LoRAs.
10-14 images (MINIMUM)
85-92% consistency. Works but less reliable. Use only for testing.
26-35 images (ADVANCED)
96-98% consistency. Requires careful curation. Overfitting risk if not diverse.
40+ images (AVOID)
High overfitting risk. Model memorizes images instead of learning features.
🎯 Image Quality Checklist
- ✓Resolution: Minimum 512x512, optimal 768x768, max 1024x1024
- ✓Sharpness: Crystal clear facial features, no blur or artifacts
- ✓Face size: Face occupies 60-80% of frame (not tiny, not cropped)
- ✓Single subject: Only one person per image, no groups
- ✓Consistent style: All realistic OR all anime (never mix)
- ✓Visible features: Eyes, nose, mouth clearly visible (no sunglasses/masks)
- ✓Natural poses: Avoid extreme angles or unusual perspectives
🎨 Diversity Requirements: The Secret to Flexibility
Your dataset must include variety across these dimensions. This teaches the LoRA to recognize your character in ANY situation:
Camera Angles (Critical)
• 40% - Front-facing (looking at camera, straight on)
• 30% - 3/4 view (45° angle, most flattering)
• 20% - Side profile (90° angle, shows bone structure)
• 10% - Looking up/down (varied head tilts)
Expressions
• 50% - Neutral or slight smile (natural resting)
• 25% - Big smile or laughing (shows teeth)
• 15% - Serious or confident (no smile)
• 10% - Other (surprised, pensive, etc.)
Lighting Conditions
• 40% - Soft natural light (window light, overcast)
• 30% - Bright outdoor (direct sunlight)
• 20% - Studio/dramatic (strong directional)
• 10% - Low light or golden hour
Backgrounds
• 40% - Simple/blurred (focus on face)
• 30% - Indoor settings (rooms, cafes)
• 20% - Outdoor settings (parks, streets)
• 10% - Urban/complex (city, busy scenes)
Step-by-Step: Creating Your Dataset
Generate Seed Images with Base SD Model
Use Stable Diffusion (no LoRA) to create 50-100 candidate images of your character concept. Goal: Find 20-25 high-quality images that feel like the same person.
Example Prompt (Photorealistic Female):
photo of a beautiful woman, 25 years old, long brown hair, blue eyes, natural makeup, clear skin, looking at camera, soft lighting, professional photography, detailed face, high quality, 8k uhd, photorealistic
Model: Realistic Vision 5.1, DreamShaper 8, or ChilloutMix
Sampler: DPM++ 2M Karras or Euler A
Steps: 25-35
CFG Scale: 6-8
Resolution: 768x768 (1:1 ratio for portraits)
Pro Tip: Batch Generate with Seed Variations
Set batch size to 4, generate 25 batches (100 images). Adjust seed slightly between batches to get variety while maintaining similar features. Keep same prompt for all batches.
Curate Best 20-25 Images
Review all 50-100 generated images and select the best 20-25. This is the most important step - quality over quantity!
Selection Criteria (Keep If:)
- ✓ Facial features are sharp and clear
- ✓ No distortions or AI artifacts
- ✓ Feels like "the same person" as other kept images
- ✓ Natural-looking (not obviously AI-generated)
- ✓ Good variety in angle/expression/lighting
- ✓ Hands look correct (if visible)
Rejection Criteria (Delete If:)
- ✗ Blurry, low quality, or pixelated
- ✗ Weird artifacts (extra limbs, distorted features)
- ✗ Looks like a different person
- ✗ Overly airbrushed or fake-looking
- ✗ Duplicate of another kept image
- ✗ Deformed hands or fingers (if visible)
⚠️ The "Same Person" Test
Line up all your selected images side by side. If you showed these to a friend, would they believe it's the same person in all of them? If not, remove outliers that look too different.
Crop & Standardize Images
Prepare your 20-25 selected images for training by standardizing resolution and cropping.
Resolution Targets:
• SD 1.5: 512x512 minimum, 768x768 optimal
• SD XL: 1024x1024 standard
• Note: All images should be same resolution
Cropping Guidelines:
• Face should fill 60-80% of frame
• Include shoulders/upper chest for context
• Center the face in frame (unless artistic intent)
• Maintain consistent aspect ratio (1:1 for portraits)
File Format:
• Best: PNG (lossless, highest quality)
• Acceptable: JPG at 95%+ quality
• Avoid: WebP, heavily compressed JPG
• Remove alpha channels (convert RGBA to RGB)
Tool Recommendation: BIRME (Bulk Image Resizing)
Use BIRME (birme.net) to batch resize all images to exact dimensions. Upload all 20-25 images, set target resolution, export as PNG. Takes 2 minutes.
Tag Your Images (Captions)
Create .txt files with the same name as each image, describing the contents. This helps the LoRA understand what to learn.
Example: character_001.png → character_001.txt
1girl, brown hair, blue eyes, smiling, white shirt, looking at viewer, natural lighting, portrait, high quality
What to Include:
- • Character trigger word (1girl, 1boy, person)
- • Hair color and length
- • Eye color
- • Expression (smiling, serious, etc.)
- • Clothing visible
- • Camera angle (looking at viewer, profile, etc.)
- • Lighting type
- • Quality tags (high quality, detailed, etc.)
What NOT to Include:
- ✗ Specific character names
- ✗ Overly creative descriptions
- ✗ Full sentences or stories
- ✗ Background details (usually)
- ✗ Watermark or artist mentions
- ✗ Negative concepts
Auto-Tagging Tool: WD14 Tagger
Automatic1111 includes WD14 Tagger extension. Point it at your dataset folder, it auto-generates tags for all images, then you manually refine them. Saves 80% of tagging time.
Extensions → WD14 Tagger → Batch from directory → Review and edit auto-generated tags
Phase 2: Training Configuration & Parameters
Training Parameters: Complete Breakdown
These settings control how your LoRA learns. Copy these recommended values for 95%+ success rate:
| Parameter | Beginner Safe | Advanced | What It Does |
|---|---|---|---|
| Learning Rate | 1e-4 | 5e-5 to 5e-4 | Speed of learning. 1e-4 (0.0001) is safest. Lower = slower/safer, higher = faster/riskier. |
| Batch Size | 2 | 3-4 | Images processed per step. Higher = faster training but needs more VRAM. RTX 4070: 3-4, RTX 3060: 2. |
| Epochs | 15-20 | 10-30 | Full passes through dataset. 20 images × 15 epochs = 300 training steps. Sweet spot for faces. |
| Network Rank (Dim) | 32 | 64-128 | LoRA complexity/capacity. 32 = lighter/faster, 64 = more detail, 128 = maximum (overkill for most). |
| Network Alpha | 16 | 32-64 | Usually half of Network Rank. Affects LoRA strength scaling and learning stability. |
| Resolution | 512 | 768-1024 | Training resolution. Match your dataset. 768 = better quality but 2x slower. SD XL uses 1024. |
| Optimizer | AdamW8bit | AdamW / Lion | Training algorithm. AdamW8bit uses less VRAM (12GB vs 16GB). Lion is experimental but faster. |
| LR Scheduler | cosine | cosine_with_restarts | How learning rate changes over time. Cosine smoothly reduces LR, preventing overfitting at end. |
| Save Every N Epochs | 5 | 3-5 | Save checkpoint every N epochs. Lets you compare quality at epoch 5, 10, 15, 20 and choose best. |
✅ Copy-Paste: Beginner Config
Learning Rate: 0.0001 (1e-4)
Batch Size: 2
Epochs: 15
Network Rank: 32
Network Alpha: 16
Resolution: 512
Optimizer: AdamW8bit
LR Scheduler: cosine
Save Every: 5 epochs
Works 95% of the time for character faces. Start here. Total training time: 30-60 min (RTX 4070).
🚀 Copy-Paste: Advanced High-Quality
Learning Rate: 0.00005 (5e-5)
Batch Size: 4
Epochs: 20
Network Rank: 64
Network Alpha: 32
Resolution: 768
Optimizer: AdamW8bit
LR Scheduler: cosine_with_restarts
Save Every: 5 epochs
Maximum quality, slower learning. Needs 12GB+ VRAM. Training time: 60-120 min (RTX 4070).
⚠️ Common Parameter Mistakes
- • Learning rate too high (1e-3 or higher): LoRA overtains in 2-3 epochs, memorizes images, unusable.
- • Too many epochs (40+): Severe overfitting. Model can only recreate training images, no flexibility.
- • Network Rank too high (256+): Massive file size, no quality improvement, overfitting risk.
- • Batch size too large for GPU: Out of memory error. Reduce batch size if training crashes.
- • Resolution mismatch: Training at 512 but dataset is 1024 causes scaling artifacts.
Phase 3: Training with Kohya SS (Step-by-Step)
Kohya SS Setup & Training Process
Kohya SS is the industry-standard LoRA training tool. It's powerful, free, and supports all advanced features. Here's the complete setup and training workflow:
Installation (One-Time Setup)
- 1.
Download Kohya SS from GitHub
Visit: github.com/bmaltais/kohya_ss | Download latest release ZIP
- 2.
Extract to simple path
Example: C:\kohya_ss\ (Windows) or ~/kohya_ss (Linux) - avoid spaces in path
- 3.
Run setup script
Windows: Double-click setup.bat | Linux/Mac: bash setup.sh | Takes 5-10 minutes
- 4.
Launch GUI
Windows: gui.bat | Linux/Mac: bash gui.sh | Opens web browser at localhost:7860
Training Configuration in GUI
Tab 1: Source Model
• Select base checkpoint (Realistic Vision 5.1, DreamShaper 8, etc.)
• Important: Use same model you generated dataset with
• Path example: C:\stable-diffusion\models\Stable-diffusion\realisticVision.safetensors
Tab 2: Folders
Image folder: Point to your dataset
C:\kohya_ss\datasets\20_charactername
Note: Folder name format is "repeatcount_triggername" (e.g., 20_mycharacter)
Output folder: Where LoRA will save
C:\kohya_ss\output\mycharacter
Tab 3: Training Parameters
• Paste the beginner config values from table above
• Learning rate: 0.0001 (1e-4)
• Batch size: 2
• Epochs: 15
• Network dim (rank): 32
• Network alpha: 16
• Resolution: 512,512
• Save every N epochs: 5
Tab 4: Advanced Settings (Optional)
• Enable xformers: ✓ (reduces VRAM usage)
• Mixed precision: fp16 (faster training)
• Cache latents: ✓ (speeds up training)
• Gradient checkpointing: ✓ if low on VRAM
Start Training & Monitor Progress
Click "Start training" button at bottom of page. Monitor terminal/console window for progress:
Loading model from: realisticVision.safetensors
Preparing dataset: 20 images found
Starting training with 300 steps (20 images × 15 epochs)
Epoch 1/15: [====================] 100% | Loss: 0.142 | ETA: 28 min
Epoch 2/15: [====================] 100% | Loss: 0.118 | ETA: 24 min
Epoch 5/15: [====================] 100% | Loss: 0.092 | ETA: 15 min
Checkpoint saved: mycharacter-000005.safetensors
...
Epoch 15/15: [====================] 100% | Loss: 0.064 | ETA: 0 min
Training complete! Final LoRA saved to output folder.
RTX 4090
20-30 minutes
Fastest training
RTX 4070 / 3080
30-50 minutes
Good balance
RTX 3060
60-90 minutes
Minimum viable
Understanding Loss Values
"Loss" measures training error. Lower = better learning. Watch for this pattern:
Healthy Loss Curve (Good)
Starts high (0.15-0.20), drops steadily, plateaus around 0.05-0.08 at end. This is perfect.
Loss Too Low Too Fast (Overfitting)
Drops to 0.01-0.02 in first 5 epochs. LoRA is memorizing, not learning. Reduce learning rate or epochs.
Loss Not Dropping (Undertraining)
Stays above 0.15 entire training. LoRA isn't learning. Increase learning rate or epochs.
Phase 4: Testing & Quality Control
Testing Your LoRA Checkpoints
You now have 3-4 LoRA checkpoints (epoch 5, 10, 15, 20). Test each to find the best quality:
LoRA Weight Testing
LoRA "weight" controls influence strength (0.0 to 1.5). Test each checkpoint at multiple weights:
Weight 0.6 (Subtle)
Face is recognizable but allows more prompt influence
Best for: Artistic styles, heavy customization
Weight 0.8 (Balanced)
Strong consistency while maintaining flexibility
Best for: Most use cases, recommended default
Weight 1.0 (Maximum)
Strongest consistency, exact face match
Best for: Photorealistic influencers, maximum accuracy
Comprehensive Test Prompts
Generate 30-50 test images with varied prompts to stress-test consistency:
Category 1: Different Outfits
- • "wearing red evening dress"
- • "in business suit and tie"
- • "casual jeans and white t-shirt"
- • "bikini at beach"
- • "winter coat and scarf"
Category 2: Different Locations
- • "standing in modern office"
- • "sitting in coffee shop"
- • "walking on city street"
- • "at beach sunset"
- • "in gym working out"
Category 3: Different Poses
- • "sitting on chair, legs crossed"
- • "walking towards camera"
- • "hands on hips, confident pose"
- • "lying down relaxing"
- • "dancing, dynamic movement"
Category 4: Different Expressions
- • "laughing, genuine happy expression"
- • "serious, intense gaze"
- • "winking playfully"
- • "surprised, eyes wide"
- • "contemplative, thoughtful look"
✅ Consistency Quality Checklist
Your LoRA passes quality control if 95%+ of test images match these criteria:
- ✓ Same facial structure (jawline, cheekbones, chin shape)
- ✓ Same eye color, shape, and spacing
- ✓ Same nose shape and size
- ✓ Same mouth shape and lip fullness
- ✓ Same overall facial proportions
- ✓ Same age appearance (±2 years)
- ✓ Same ethnicity and skin tone
- ✓ Same hair color (unless prompted otherwise)
Troubleshooting Guide: Fix Common Issues
Problem: Face is Inconsistent (Changes Between Images)
Cause:
Not enough training epochs OR learning rate too low OR poor dataset diversity
Solution:
- • Increase epochs from 15 to 20-25
- • Raise learning rate from 1e-4 to 2e-4
- • Review dataset - ensure 20+ images with good variety in angles
- • Try higher LoRA weight (0.9-1.0 instead of 0.8)
- • Increase Network Rank from 32 to 64
Problem: LoRA Only Recreates Training Images (Overfitted)
Cause:
Too many epochs OR learning rate too high OR Network Rank too high
Solution:
- • Reduce epochs from 20 to 10-12
- • Lower learning rate from 1e-4 to 5e-5
- • Reduce Network Rank from 64 to 32
- • Use earlier checkpoint (epoch 10 instead of epoch 20)
- • Add more diversity to dataset
Problem: Can't Generate Profile Views or Specific Angles
Cause:
Dataset lacks images from those angles - LoRA never learned them
Solution:
- • Add 4-6 profile/side view images to dataset
- • Add 3-5 images from missing angles
- • Retrain LoRA from scratch with expanded dataset
- • Cannot fix existing LoRA - must retrain with new images
Problem: Generated Images Have Artifacts or Distortions
Cause:
Low-quality dataset images OR conflicting training data OR LoRA weight too high
Solution:
- • Review dataset - remove blurry, distorted, or low-quality images
- • Ensure all dataset images are same style (all realistic OR all anime)
- • Lower LoRA weight from 1.0 to 0.7-0.8
- • Regenerate dataset with higher quality seed images
- • Retrain with cleaned dataset
Problem: LoRA Doesn't Respond to Prompts Well
Cause:
Network Rank too high OR training overfit to specific styles/outfits
Solution:
- • Retrain with Network Rank 32 (not 64 or 128)
- • Ensure dataset has variety in clothing, backgrounds, lighting
- • Use lower LoRA weight (0.6-0.7) to allow more prompt flexibility
- • Add diverse outfit/background images to dataset
Problem: Training Crashes with "Out of Memory" Error
Cause:
Batch size or resolution too high for available VRAM
Solution:
- • Reduce batch size from 4 to 2 (or 2 to 1)
- • Lower resolution from 768 to 512
- • Enable gradient checkpointing in advanced settings
- • Use AdamW8bit optimizer instead of AdamW
- • Close other VRAM-heavy apps (browsers, games)
- • Consider cloud training (RunPod, Vast.ai) with better GPU
Real Success Case Study
@LuxeMarisa - Luxury Fashion Influencer
124K Instagram followers | $18K/month revenue | 97.8% face consistency
LoRA Training Specs
Results & Performance
- ✓
97.8% facial consistency
Only 11 images rejected out of 500 generated (2.2% failure rate)
- ✓
Unlimited pose/outfit variations
Generated 1,800+ images across 6 months (300/month average)
- ✓
Brand trust & credibility
Landed 8 brand deals, $2K-$5K per sponsored post
- ✓
Follower engagement
4.2% engagement rate (industry average: 1.2%)
Creator's Journey (Timeline)
Week 1: Generated 100 seed images with Stable Diffusion, curated to 23 best
Week 2: Trained LoRA (42 minutes), tested checkpoints, selected epoch 18 at weight 0.85
Month 1-2: Posted 3x/day (90 posts), grew from 0 to 12K followers
Month 3-4: Reached 50K followers, first brand deal ($2,500)
Month 5-6: Hit 124K followers, 8 active brand deals, $18K/month revenue
Creator's Quote
"LoRA training changed everything. My first attempts without LoRA had inconsistent faces - comments were 'why does she look different?' After training LoRA, my character became REAL to my audience. Followers send DMs asking about her skincare routine. Brands trust me because they know exactly what face will appear in sponsored content. The 42 minutes I spent training this LoRA generated $100K+ in revenue over 6 months. Best investment ever."
Frequently Asked Questions
How long does LoRA training actually take?
With an RTX 4070 or better: 30-60 minutes for a standard character LoRA (20 images, 15-20 epochs, 512x512 resolution). RTX 3060: 60-90 minutes. RTX 4090: 20-30 minutes. Cloud GPUs (RunPod, Vast.ai) offer similar speeds for $0.50-1.50 per training session. Higher resolution (768) or more epochs (25-30) doubles training time.
Can I train LoRA without owning a powerful GPU?
Yes! Use cloud GPU services: RunPod ($0.34/hour RTX 4090), Google Colab Pro ($10/month, A100 access), or Vast.ai ($0.25-0.50/hour). Training one LoRA costs $0.50-1.50, far cheaper than buying hardware. Alternative: Use Kaggle free GPU (30 hours/week) or Paperspace Gradient free tier.
What if my LoRA generates inconsistent faces?
Three main causes: (1) Undertrained - increase epochs to 20-25 or raise learning rate to 2e-4. (2) Poor dataset - ensure 20+ images with good variety in angles/expressions. (3) Wrong checkpoint - test all saved checkpoints (epoch 5, 10, 15, 20), sometimes earlier epochs have better consistency than final.
Can I update my LoRA later with new images?
You cannot directly "add" to an existing LoRA. However, you can create a new training dataset combining old images + new images (25-30 total) and retrain from scratch. This takes another 30-60 minutes but gives you a refreshed LoRA. Most AI influencer creators retrain every 2-3 months to refine consistency or update their character's look (new hairstyle, slight age progression, style evolution).
What's the difference between LoRA and Dreambooth?
LoRA: Creates 100-200MB adapter file, works with any SD checkpoint, trains in 30-90 min, 95-98% consistency, easier to share. Dreambooth: Fine-tunes entire 6GB model, locked to that specific model, trains in 2-4 hours, 97-99% consistency, harder to share. For AI influencers, LoRA wins due to flexibility and speed. Only use Dreambooth if you need absolute maximum control (99%+ consistency) and don't mind the limitations.
How do I share my LoRA with others?
Upload to CivitAI (largest LoRA community, 500K+ users), Hugging Face (AI researcher hub), or your own hosting (Google Drive, Dropbox). LoRA files are 100-200MB. Include: example images, recommended weight (0.6-1.0), base model used (Realistic Vision, DreamShaper, etc.), and sample prompts. Warning: Once shared publicly, anyone can generate infinite images of your character. Consider licensing/watermarking.
Can I combine multiple LoRAs together?
Yes! Stack LoRAs in Stable Diffusion: Character LoRA (weight 1.0) + Style LoRA (weight 0.6) + Clothing LoRA (weight 0.4). Keep total combined weight under 3.0 to avoid artifacts. This is powerful for creating unique combinations without training new LoRAs. Example: Your character's face + anime style + specific outfit = instant custom variation.
How often should I retrain my AI influencer's LoRA?
Initial LoRA lasts indefinitely if results are good. Most creators retrain every 2-3 months to: (1) Refine consistency with better curated dataset, (2) Update character's look (new hairstyle, subtle aging, style evolution), (3) Add new poses/expressions audience wants to see. Retrain if consistency drops below 90%, or followers request visual updates. Otherwise, one well-trained LoRA can generate thousands of images over years.
Want to master AI Influencers Academy? Get it + 3 more complete courses
Complete Creator Academy - All Courses
Master Instagram growth, AI influencers, n8n automation, and digital products for just $99/month. Cancel anytime.
All 4 premium courses (Instagram, AI Influencers, Automation, Digital Products)
100+ hours of training content
Exclusive templates and workflows
Weekly live Q&A sessions
Private community access
New courses and updates included
Cancel anytime - no long-term commitment
✨ Includes: Instagram Ignited • AI Influencers Academy • AI Automations • Digital Products Empire