Style Reference

Summary

FLORA’s Styles feature allows you to apply specific visual styles to your image generations. Styles Feature enhances t2i (text-to-image) and i2i (image-to-image) generation by allowing users to apply predefined aesthetic filters or train their own custom styles. This feature enables greater creative control, ensuring that outputs align with specific artistic or brand identities.

1. Predefined Styles

FLORA provides a curated selection of predefined styles, optimized for high-quality and consistent results across different image generations. These styles modify the color grading, texture, composition, and rendering techniques of generated images.

1.1 Style Categories

Users can choose from a growing library of styles, categorized as follows:

Photorealistic – Enhances realism, fine detail, and natural lighting.
Illustrative – Generates images with hand-drawn aesthetics, including watercolor, ink, and vector styles.
Cinematic – Inspired by film lighting, color grading, and composition techniques.
Surreal & Abstract – Non-traditional, dreamlike visuals with unique distortions.
Historical & Vintage – Emulates artistic styles from different time periods, such as Renaissance, Art Deco, or 90s cyber aesthetics.
AI-Generated Hybrid Styles – Experimental neural-network-based textures and forms.

Each style adjusts image attributes such as contrast, brushstroke weight, detail density, and color schemes.

2. Custom Style Training

For users seeking full creative ownership, FLORA enables custom style training, allowing users to upload reference images and train a model that adapts to their unique aesthetic.

2.1 How Custom Style Training Works

Upload Reference Images – Provide a set of images that represent the desired visual style.
FLORA Analyzes Features – The system extracts patterns in color, texture, contrast, and structure.
Training & Optimization – The user can refine parameters such as style strength, interpolation settings, and fidelity adjustments.
Preview & Iterate – Generated outputs can be fine-tuned through additional feedback loops.

2.2 Style Adjustments & Controls

Users can modify:

Style Strength – Adjusts how aggressively the selected style is applied.
Blend Mode – Mixes multiple styles to create hybrid aesthetics.
Detail Refinement – Controls the level of abstraction or sharpness in the output.

PreviousCollaboration & Share NextCommunity

Last updated 5 days ago

Summary

1. Predefined Styles

1.1 Style Categories

Users can choose from a growing library of styles, categorized as follows:

Photorealistic – Enhances realism, fine detail, and natural lighting.

Illustrative – Generates images with hand-drawn aesthetics, including watercolor, ink, and vector styles.

Cinematic – Inspired by film lighting, color grading, and composition techniques.

Surreal & Abstract – Non-traditional, dreamlike visuals with unique distortions.

Historical & Vintage – Emulates artistic styles from different time periods, such as Renaissance, Art Deco, or 90s cyber aesthetics.

AI-Generated Hybrid Styles – Experimental neural-network-based textures and forms.

Each style adjusts image attributes such as contrast, brushstroke weight, detail density, and color schemes.

2. Custom Style Training

For users seeking full creative ownership, FLORA enables custom style training, allowing users to upload reference images and train a model that adapts to their unique aesthetic.

2.1 How Custom Style Training Works

Upload Reference Images – Provide a set of images that represent the desired visual style.

FLORA Analyzes Features – The system extracts patterns in color, texture, contrast, and structure.

Training & Optimization – The user can refine parameters such as style strength, interpolation settings, and fidelity adjustments.

Preview & Iterate – Generated outputs can be fine-tuned through additional feedback loops.

2.2 Style Adjustments & Controls

Users can modify:

Style Strength – Adjusts how aggressively the selected style is applied.

Blend Mode – Mixes multiple styles to create hybrid aesthetics.

Detail Refinement – Controls the level of abstraction or sharpness in the output.