Mastering AI‑Generated Brand Symbol Posters with Stable Diffusion

Preface

Brand symbol posters are a crucial exposure tool for brand operations. With the continuous development of AI, they gradually replace traditional design methods, improving quality and efficiency.

SD Brand Operation Example – Douyin + Tmall + Text

AI‑generated symbol posters displayed on major design sites feature rich styles, high visual fidelity, and rapid production.

SD Symbol Interpretation Analysis

2.1 Basic Tutorial Analysis

In text‑to‑image mode, brand symbol posters are designed by combining a large model, prompts, and ControlNet (lineart for outer contours). LoRA and designer creativity can further assist.

Basic formula: logo + lineart/canny

Example of a basic‑formula poster for Baijiahao.

3.1 Introductory SD Symbol Generation Mode

To enhance depth control, a deeper ControlNet is added to manage foreground‑background relationships.

Advanced introductory formula: logo + canny/lineart + depth

3.2 Depth Map (Depth Map) Basics

A depth map is a 2‑D image storing depth values for each pixel, expressed in spatial units such as millimetres.

Key points:

Same size as the original image.

Each pixel stores its depth value.

The depth value corresponds to the Z‑coordinate in the camera coordinate system.

Depth values near 1 represent far regions (lighter), while values near 0 represent near regions (darker).

ControlNet offers four processors (midas, zoe, leres, leres++); for symbols, midas and zoe are suitable.

3.3 Primary SD Symbol Generation (Model Assistance)

When AI‑generated symbols lack sufficient three‑dimensionality, auxiliary modeling is required.

Primary formula: 3D logo + depth + canny

Further variations can produce two‑point perspective views, considering occlusion and recognizability.

3.4 Intermediate SD Symbol Generation Mode

By creating a black‑white‑gray depth map, only composition and lighting are defined.

Adding colour segmentation based on semantic segmentation maps assigns specific meanings to each region, enabling finer control.

3.5 Semantic Segmentation (Segmentation)

Semantic segmentation classifies each pixel of an image, dividing it into multiple semantic regions, unlike object detection which only provides bounding boxes.

Comparison of images with and without semantic segmentation.

3.6 Advanced SD Symbol Generation (Style Control)

When a desired style lacks a suitable LoRA, custom style extraction can be performed using ControlNet plugins such as Shuffle (style‑only randomisation) and Reference (style guided by a reference image).

Generated results using Shuffle and Reference.

3.7 Post‑Processing & Image‑to‑Image

Post‑processing mainly uses image‑to‑image techniques such as upscaling, tile‑based detail enhancement, compositing, and local re‑painting.

The Tile model re‑paints while preserving composition and style, adding fine details and improving image quality.

Application in SD Symbol Generation Projects

Examples of brand logo reinterpretations for DuJia, DuoYi, and XinYuan using the described methods.

Conclusion

Purely AI‑driven design enables designers to keep pace with visual trends. The MEUX content ecosystem team continues to explore ways to control AI creativity, style, quality, and efficiency, turning AI into a powerful design tool.