Introduction

Creating consistently high-quality AI-generated images is challenging, even with powerful tools like Stable Diffusion or Pony Diffusion. One helpful innovation is using score tags, special keywords such as score_9, score_8_up, and score_7_up. These tags guide the AI model to produce better images based on human preferences.

In this post, we'll clearly explain what score tags are, how they work, and why they're important for improving AI-generated images.

What Are Score Tags?

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score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up

Score tags are labels added to images during training to indicate their visual quality based on human ratings. Here's a simple breakdown:

• score_9 → Top 10% quality images
• score_8_up → Top 20% (80–90% range)
• score_7_up → Top 30% (70–80% range)
• score_6_up → Top 40% (60–70% range)
• score_5_up → Top 50% (50–60% range)
• score_4_up → Top 60% (40–50% range)

These tags help the AI model understand what humans consider visually appealing.

Why Are Score Tags Important?

Score tags are crucial because they clearly show the AI model the difference between average and exceptional images. By regularly seeing high-quality examples, the model learns which visual features contribute to better aesthetics. This learning process enhances its ability to generate consistently attractive images.

Additionally, score tags provide users with precise control over the quality of AI-generated images. For example, by using the tag score_9, users instruct the model to aim for the highest possible quality. Alternatively, using tags like score_6_up ensures the resulting images will at least be above average. This flexibility allows users to fine-tune image generation to meet their specific needs.

Score tags also help improve the quality of the training data itself. Not every image in a dataset is of equal quality. Tags help filter out lower-quality images, allowing the model to focus its learning on the best examples available. This results in a more robust and reliable AI model overall.

How Are Score Tags Used?

During Training

Here's how the training process usually works:

1. Image Collection → Collect many images from various sources.
2. Quality Assessment → Human reviewers (or automated systems) rate the visual quality.
3. Tagging → Assign appropriate score tags (score_9, score_8_up, etc.) based on ratings.

Once the images are tagged, the AI model learns the visual features associated with each quality level.

During Image Generation

When generating new images, users can add score tags to prompts. For example:

• score_9 → Model tries to produce the best possible image.
• score_6_up → Model produces at least above-average quality.

This method lets users precisely control image quality.

Final Thoughts

Score tags are an effective way to enhance AI-generated images in Stable Diffusion and Pony Diffusion models. They teach the model what makes images appealing, offer users greater control, and help maintain high-quality training data. As technology evolves, expect even better results and more advanced tagging systems.

If you use Stable Diffusion or Pony Diffusion, try adding score tags to your prompts—you'll likely notice a significant improvement!