In Layer, “styles” are a powerful way to guide how your 2D assets are generated.
They help control the look, tone, and consistency of the visuals you produce. However, the term “style” also appears in other generation workflows, such as 3D, video, or editing, where it refers to something entirely different.
This guide explains the differences between workspace-created styles, styles made available on the platform, and models used in other workflows - helping you understand how each fits into the creative process.
What Is a Style?
In Layer, a style refers to any selectable option in the style panel that influences the visual output of a generation. While the term “style” is most directly associated with 2D image generation, it appears in different ways across other generation types — each with its own meaning and behavior.
In 2D Generation
Styles are a core part of the 2D image generation workflow. When you’re generating assets like characters, environments, items, or UI, the style you select directly influences how the image looks - including shape language, color use, line work, rendering finish, and overall tone.
In the 2D style selection panel, located just above the prompt box, you can select from a wide range of style types that guide the visual outcome of your generation.
This includes:
Workspace-created styles - Custom LoRA models trained by your team using your own images and prompts
Layer-created gaming styles - Pretrained LoRA models created by Layer for mobile game asset workflows
Exploration styles - Prompt extensions that inject guiding language into your prompt (not trained models and not created by Layer)
Imported LoRA styles - Custom LoRA files you’ve uploaded into your workspace from external sources
Base models - Foundational AI models (like Flux.1 Dev, SDXL, Imagen 4, etc.), which can also be selected from this panel to determine the underlying generation engine
In 2D generation, a style is any selectable configuration - trained, prompt-based, or model-level - that directly influences the visual characteristics of the asset you’re generating.
A LoRA (Low-Rank Adaptation) is a lightweight, fine-tuned AI model that’s trained to apply a specific visual style or behavior on top of a larger base model without needing to retrain the full model.
Only 2D generation in Layer supports the ability to train and apply your own styles using your own assets, giving you full control over how consistent, IP-aligned visuals are created and reused across projects.
In Other Generation Types (3D, Video, Audio, Editing)
In generation types like 3D, video, audio, or Edit with Prompt, the term “style” may still appear in the interface, but it functions differently. In these cases, you’re not selecting a visual style - you’re selecting a base model, which is the AI engine responsible for interpreting your prompt and generating the output.
Examples include:
Video Generation: Kling 2.1 Master, Veo 3
3D Generation: Trellis, Tripo 2.5
Editing: Flux Kontext, Google Gemini, GPT Image 1
For a full list of models check out this article.
These are standalone models with their own behaviors and limitations. They are not influenced by any trained style, and there is no support for style creation or reuse in these workflows.
💡Only 2D generation supports style training and the creation of consistent, reusable visual systems. Other generation types rely entirely on the capabilities of the selected base model.
Style and Model Options in 2D Generation
When using 2D Forge, you have access to three categories of style-driven options:
1. Workspace-Created Styles
These are trained LoRA models created by your team using Layer’s style training tools. You can use your own images and tags to build specific, reusable styles for your projects.
Created by: Your workspace
Type: Trained LoRA model
Editable: Yes
Visibility: Only available in your workspace
Where shown: Style selection panel and Forge panel
Use case: Consistent asset creation for proprietary IP or branded content
2. Styles Made Available in Layer
Layer provides access to two types of styles that are available to all users in the platform. However, Layer does not create exploration styles or train the base models behind these styles.
A. Gaming Styles (Trained LoRA Models)
These are production-ready LoRA-trained styles designed for asset types common in mobile games. While these styles are made available in Layer, they are not proprietary to Layer and may be sourced externally.
Examples: Game Characters, Game Backgrounds, Game Icons, UI Buttons
Type: Trained LoRA models
Editable: No
Use case: Fast, consistent game asset generation
B. Exploration Styles (Prompt Extensions)
Exploration styles are not trained models. They are prompt templates that add guiding language to your input behind the scenes. These styles are not created by Layer but are made available to help guide creative exploration.
Examples: “Anime,” “Comic,” “Cute 3D”
Type: Prompt extension (SDXL model-based)
Editable: No
Use case: Visual exploration, ideation, and early-stage concepting
3. Base Models in 2D Generation
Every 2D generation is powered by a base model — the core AI engine that interprets your prompt and produces an image. Styles are layered on top of these models to refine or guide the output.
Examples:
Flux.1 Dev
Stable Diffusion 3
Imagen 4
DALL·E 3
These base models are not created or trained by Layer. They are integrated from external providers and made accessible through the Layer platform.
Comparison Table
Feature | Workspace-Created Styles | Gaming Styles (Created by Layer) | Exploration Styles | Base Models |
Created by | Your workspace | Layer | External (not Layer) | External providers |
Type | Trained LoRA model | Trained LoRA model | Prompt extension | Foundational model |
Editable | Yes | No | No | No |
Visibility | Workspace-only | All users | All users | All users |
Used in | 2D generation only | 2D generation only | 2D generation only | All generation types |
Best for | IP-specific workflows | Quick game asset generation | Creative exploration | Core generation behavior |
Best Practices
Use workspace-created styles for projects that require consistency, brand alignment, or proprietary asset generation.
Use gaming styles to generate polished assets quickly without needing to train your own model.
Use exploration styles when you’re still exploring art direction or want to test out different creative approaches.
Always choose the right base model for your goal. Some styles perform better on certain models, so experiment and adjust as needed.