Three Approaches to AI Photo Generation - 这是ChatGPT 5写的

 

Three Approaches to AI Photo Generation

 

 

As AI image tools become more powerful, creators face a common challenge: how to generate realistic photos while maintaining character consistency across multiple scenes. Broadly speaking, three approaches have emerged.

 

1. Plain Text-to-Image Prompting

The simplest method is to describe in words how the characters and background should look. A few lines of text can summon entire worlds: hair color, clothing, setting, mood. This works well for one-off illustrations or when variety is more important than continuity. The weakness, however, is consistency. Characters rarely look exactly the same from one image to the next, unless they are stylized into a cartoon-like form. For projects that demand realism, this approach can be frustrating.

 

2. Reference-Based Editing

A stronger method begins with an actual photo—real or AI-generated—and then instructs the AI to alter facial features, body shape, or other details until the desired look is achieved. This altered image becomes the anchor. From there, the AI can place the same face and body into new scenarios, giving a much more stable sense of character continuity. This method is especially useful for storytelling projects, though it requires more setup and careful anchoring to avoid drift.

 

3. Custom Character Training

For professional use or large-scale projects, creators often go further by training the AI on a curated set of reference images. This produces a custom “character token” that can be invoked in any prompt, reliably reproducing the same individual across countless settings. It combines the flexibility of text prompts with the stability of an anchor image, though it requires technical setup and more data than casual users typically provide.

 

Taken together, these approaches form a spectrum: from quick creativity with text, to careful editing for realism, to custom models for long-term consistency. Which method works best depends on the goal—but all three show how AI image generation is evolving from novelty into a serious creative tool.