Overview

This is Part 1 of a comprehensive two-part guide for creating Original Character (OC) LoRA models. This section explains how to leverage https://perchance.org/ai-text-to-image-generators to create consistent training images for your character. Part 2 will cover organizing these images into a proper dataset and importing it into CivitAI for LoRA training.

Creating an OC LoRA requires maintaining strict feature consistency while providing enough variation for effective training. This guide focuses on keeping your character's defining features within acceptable tolerance ranges while generating diverse training data using this powerful web-based tool.

I will use the prompts from building a training dataset for Marisol as an example.

What is Original Character LoRA Training?

Original Character (OC) LoRA training teaches an AI model to generate a specific fictional character you've created, maintaining their unique visual features across different poses, expressions, and scenarios. The key challenge is balancing consistency (keeping the character recognizable) with diversity (providing enough variation for robust training).

Why I Use Perchance for OC Training Data?

Advantages for Character Creation:

Feature Consistency Tools: Generators that maintain character features across variations
Iterative Refinement: Easy to regenerate images that drift too far from your character
Expression Control: Generate multiple emotions while keeping core features intact
Pose Variation: Create diverse angles and positions with consistent character design
Free Experimentation: Test character concepts without cost barriers

Character-Specific Considerations:

Feature Drift: Generated images may gradually lose key character traits
Style Limitations: Character appearance constrained by generator's training data
Consistency Challenges: Maintaining exact features across all generations requires careful prompting

Step-by-Step Process

1. Define Your Original Character's Core Features

Before generating any images, establish your character's immutable features - the elements that must remain consistent across all training images:

Physical Features (Strict Tolerance):

Facial Structure: Face shape, jawline, cheekbone prominence
Eyes: Color, shape, size, spacing, eyelash style
Hair: Color, texture, length, distinctive styling elements
Skin: Tone, any distinctive marks, freckles, scars
Body Type: Build, proportions, height indicators
Unique Identifiers: Birthmarks, tattoos, piercings, heterochromia

Flexible Features (Moderate Tolerance):

Hair Styling: Different arrangements of the same hair
Makeup: Varying levels of makeup application
Expressions: Different emotions and facial expressions
Clothing: Various outfits and accessories
Lighting: How features appear under different lighting

Feature Tolerance Chart:

Create a reference chart defining acceptable variation ranges:

Example Character - "Marisol_GV":

Hair: Always asymmetric cut, long thick wavy dark hair with side part and colorful highlights
Face: Latina/Chicana features, glossy lips, consistent facial structure
Skin: Light tanned skin tone ±10% variation acceptable
Body: Large soft breasts, consistent body proportions
Signature Accessories: Large hoop earrings (always present)
Trigger Word: "Marisol_GV" (must appear in every prompt)

2. Choose Character-Focused Generators on an external generator.

Navigate to https://perchance.org/ai-text-to-image-generators and select generators optimized for character consistency:

Primary Generators for OC Creation:

AI Character Generator: Best for establishing base character design
Portrait Generator: Ideal for facial feature consistency and expressions
Anime Character Generator: For anime/manga style OCs
Fantasy Character Generator: For characters with unique fantasy elements

Secondary Generators for Variations:

Expression Generator: Different emotions while maintaining features
Pose Generator: Various body positions and angles
Outfit Generator: Clothing variations for the same character

3. Create Feature-Locked Prompt Templates

Develop rigid prompt structures that lock in your character's core features:

Master Character Prompt Template:

[Character Name/Trigger Word] + [Immutable Features] + [Variable Elements] + [Quality/Style Tags]

Master Character Prompt Template:

[Character Name/Trigger Word] + [Immutable Features] + [Variable Elements] + [Quality/Style Tags]

Example for "Marisol_GV" Character:

Master Character Prompt:

Base Template: "Marisol_GV, 1girl, asymmetric cut long thick wavy dark hair, side parted hair with colorful highlights, glossy lips, Latina face, Chicana, large soft breasts, light tanned skin, large hoop earrings"

Optimized Variation Templates:

Expression Focus: "Marisol_GV, 1girl, asymmetric cut long thick wavy dark hair, side parted hair with colorful highlights, glossy lips, Latina face, Chicana, large soft breasts, light tanned skin, large hoop earrings, [shy/confident/smiling], looking at viewer, solo"

Outfit Variation: "Marisol_GV, 1girl, asymmetric cut long thick wavy dark hair, side parted hair with colorful highlights, glossy lips, Latina face, Chicana, large soft breasts, light tanned skin, large hoop earrings, [floral print long skirt and white shirt with belt/casual dress/business attire], solo"

Angle/Pose: "Marisol_GV, 1girl, asymmetric cut long thick wavy dark hair, side parted hair with colorful highlights, glossy lips, Latina face, Chicana, large soft breasts, light tanned skin, large hoop earrings, [front view/three-quarter view/side profile], looking at viewer, solo"

Feature Reinforcement Strategies:

Repetition Method: Include key features multiple times in different ways

"Marisol_GV, Latina woman, Chicana face, asymmetric cut dark hair, thick wavy hair with colorful highlights, side parted hairstyle"

Negative Prompts: Specify what features to avoid

Negative: "straight hair, blonde hair, pale skin, small breasts, no earrings, European features"

Weighted Features: Use emphasis markers for critical features

"Marisol_GV, (asymmetric cut long thick wavy dark hair:1.2), (colorful highlights:1.1), (Latina face:1.2), (large hoop earrings:1.3)"

4. Generate Images with Feature Consistency Checks

Systematic Generation Approach:

Phase 1: Core Feature Validation (10-15 images)

Generate basic portraits to establish your character's appearance:

Front-facing portraits with neutral expressions
Profile views (left and right)
Three-quarter angles
Check each image for feature accuracy before proceeding

Phase 2: Expression Variations (15-20 images)

Test emotional range while maintaining features:

Happy, sad, angry, surprised, thoughtful expressions
Verify that core features remain consistent across emotions
Discard any images where features drift significantly

Phase 3: Pose and Angle Diversity (20-30 images)

Expand to full-body and various poses:

Different body angles and positions
Various distances (close-up, medium, full-body shots)
Ensure face remains recognizable at all distances

Phase 4: Environmental and Outfit Variations (20-25 images)

Add context while keeping character consistent:

Different backgrounds and settings
Various outfits and clothing styles
Different lighting conditions

Real-Time Consistency Monitoring:

The 5-Second Rule: For each generated image, can you identify you character within 5 seconds without reading the prompt? If not, discard the image.

Feature Checklist for Each Marisol_GV Image:

☐ Asymmetric cut hair style present

☐ Long thick wavy dark hair visible

☐ Colorful highlights in hair

☐ Latina/Chicana facial features consistent

☐ Glossy lips visible

☐ Light tanned skin tone matches

☐ Large hoop earrings present

☐ Large soft breasts proportionate

☐ Overall character instantly recognizable

5. Implement Strict Quality Control for Character Consistency

Character Recognition Testing:

Blind Test Method:

Generate 20+ images of your character
Mix them with similar-looking characters from other generations
Have someone unfamiliar with your project identify your character
Images that can't be easily identified need refinement or removal

Feature Consistency Scoring:

Rate each image on a 1-10 scale for:

Core Feature Accuracy (8+ required for training data)
Overall Character Recognition (7+ minimum)
Image Quality (6+ acceptable)

Dataset Composition Guidelines:

Recommended Final Dataset (60-80 images):

15-20 images: Front-facing portraits with various expressions
15-20 images: Profile and three-quarter angles
15-20 images: Full-body shots in different poses
10-15 images: Character in various outfits/settings
5-10 images: Close-up detail shots of distinctive features

Red Flags for Removal:

Hair style changes (straight hair, different cuts, no highlights)
Facial feature inconsistencies (non-Latina features, different lip style)
Skin tone variations beyond acceptable range
Missing signature elements (no hoop earrings)
Body proportion changes that alter character recognition

6. Image Processing and Preparation

Pre-processing Steps:

Resolution Optimization:

Resize images to 512x512 or 768x768 pixels
Maintain aspect ratio consistency
Ensure images are square format

Quality Enhancement:

Use upscaling tools if needed
Crop images to focus on the main subject
Remove or edit unwanted elements

Format Standardization:

Convert all images to PNG or JPG format
Use consistent file naming convention
Organize files in a single folder

7. Create Character-Focused Captions

Captions for OC LoRAs should emphasize the character's unique features:

Character Caption Structure:

[Trigger Word] + [Key Identifying Features] + [Variable Elements] + [Technical Details]

Caption Examples for "Marisol_GV":

Portrait Focus:

Marisol_GV, Latina woman with asymmetric cut long thick wavy dark hair, colorful highlights, glossy lips, large hoop earrings, front view, portrait

Full Body:

Marisol_GV, Chicana woman with asymmetric cut dark hair and colorful highlights, light tanned skin, large soft breasts, large hoop earrings, standing, floral print long skirt and white shirt, full body

Expression Emphasis:

Marisol_GV, Latina woman with thick wavy dark hair, colorful highlights, glossy lips, large hoop earrings, shy expression, looking at viewer, three-quarter view

Caption Best Practices for Characters:

Always start with your trigger word/character name
Include 3-5 key identifying features in every caption
Be specific about distinctive marks or features
Use consistent terminology across all captions
Avoid generic descriptions that could apply to any character

Feature Keywords to Emphasize:

Create a standardized vocabulary for Marisol_GV:

Hair descriptors: "asymmetric cut", "long thick wavy dark hair", "colorful highlights", "side parted"
Facial descriptors: "Latina face", "Chicana", "glossy lips"
Skin descriptors: "light tanned skin", "warm skin tone"
Body descriptors: "large soft breasts", "curvy build"
Accessory descriptors: "large hoop earrings", "statement earrings"

Advanced Character Consistency Techniques

Stage 1: Base Character Lock-In

Generate 50+ images using core feature prompts
Select the 10 most consistent images that capture your character perfectly
Use these as reference for creating refined prompts

Stage 2: Feature Isolation Testing

Generate images focusing on one feature at a time (eyes only, hair only, etc.)
Identify which descriptors produce the most consistent results
Build optimized prompts using the most reliable feature descriptions

Stage 3: Combination Validation

Test combinations of successful feature prompts
Generate test batches to ensure features work well together
Fine-tune prompt weights and structure

Prompt Evolution Strategy

Version Control Your Prompts:

Luna_v1: "woman with purple eyes, silver hair"
Luna_v2: "woman with violet eyes, silver-white wavy hair, beauty mark"
Luna_v3: "Luna, woman with violet eyes, silver-white wavy hair, beauty mark under left eye, petite build"

A/B Testing Features:

Compare different ways to describe the same feature:

"violet eyes" vs "purple eyes" vs "amethyst eyes"
"silver hair" vs "platinum hair" vs "white-silver hair"
Use whichever generates more consistent results

Character Consistency Validation Tools

Reference Sheet Method:

Create a character reference sheet with your best generated images
Show key features from multiple angles
Use this as a visual guide when evaluating new generations

Feature Drift Detection:

Save your first successful character generation as a "baseline"
Regularly compare new generations to the baseline
If features start drifting, return to earlier successful prompt versions

Conclusion of Part 1

You now have the knowledge and techniques to generate consistent, high-quality training images for your Original Character using https://perchance.org/ai-text-to-image-generators. The key takeaways from Part 1 are:

Essential Skills Mastered:

Defining rigid feature tolerances for character consistency
Creating feature-locked prompt templates that maintain character recognition
Implementing systematic generation and quality control processes
Building diverse image variations while preserving core character identity

Next Steps - Part 2:

With your generated images ready, Part 2 will guide you through:

Organizing images into proper dataset structure for CivitAI
Creating consistent caption files and naming conventions
Preparing metadata and configuration files
Uploading and configuring your dataset on CivitAI
Setting optimal training parameters for character LoRAs
Monitoring training progress and troubleshooting issues

Remember that Part 1 success is measured by the instant recognition test - if you can immediately identify Marisol_GV (or your character) in every generated image, you're ready to proceed to dataset creation and CivitAI training in Part 2.

Image Generation Checklist Before Moving to Part 2:

☐ 60-80 high-quality character images generated

☐ All images pass the 5-second recognition test

☐ Character features consistent within defined tolerances

☐ Good variety of poses, expressions, and angles achieved

☐ No contradictory or off-character images included

☐ Images saved in organized folders with clear naming

Part1: Creating Original Character Training Images Using Perchance AI Text-to-Image Generators