The Ultimate Pony Diffusion Prompt Guide: Mastering Score Tags and Improving Your AI Art

Pony Diffusion has emerged as a powerful tool for generating unique and imaginative images, particularly within the realm of stylized characters and art. To truly harness the potential of Pony Diffusion V6 XL and similar models, understanding its prompting system is crucial. This guide will dive deep into the essential “score tags” and other valuable prompting techniques that can significantly elevate the quality and style of your AI-generated artwork.

This information is compiled to provide a comprehensive resource, drawing from community knowledge and expert insights, including an explanation of the often-seen “score_9” tag, originally clarified by AstraliteHeart from Purplesmart’s Discord server.

Understanding Score Tags in Pony Diffusion

You might have encountered prompts using tags like score_9, score_8_up, and others. These aren’t arbitrary additions; they are integral to how Pony Diffusion models, especially V6 XL, understand and generate images. Let’s break down what they are and why they are so important.

What are Score Tags?

Score tags such as score_9, score_8_up, score_7_up, score_6_up, score_5_up, and score_4_up represent aesthetic quality ratings embedded within the Pony Diffusion model. These tags are not simply arbitrary labels; they are directly linked to a curated dataset of images that were human-rated for aesthetic appeal. Think of them as quality filters, guiding the AI to draw inspiration from images within specific quality tiers.

The “Why” Behind Score Tags: Training for Aesthetic Excellence

To understand the necessity of score tags, it’s helpful to grasp the basic lifecycle of an AI model: Training and Inference.

During Training, the AI model learns to associate images with corresponding text descriptions. For Pony Diffusion V6, this involved months of processing vast amounts of data on powerful computers to teach the model about ponies and related artistic styles. Inference is when we, as users, utilize the trained model to generate new images through prompts.

A significant challenge in AI image generation is ensuring the output is “good” or aesthetically pleasing. Computers lack inherent understanding of human aesthetics. A naive approach might be to train models only on “good” data. However, defining “good” data is subjective, and limiting the dataset too much can restrict the model’s diversity and ability to learn nuanced concepts. To create a versatile model capable of generating diverse content, including less conventional styles, a large and varied dataset is needed, even if it includes images of varying quality.

This is where the concept of teaching machines to recognize “good” aesthetics becomes vital.

Teaching Machines to Know What is “Good”

Pony Diffusion utilizes a technique called “CLIP based aesthetic ranking” to address this. CLIP (Contrastive Language-Image Pre-training) is another AI model trained to understand the relationship between images and text. It learns to associate images with descriptive captions, including aesthetic descriptors like “masterpiece,” “best quality,” or “HD.” These terms frequently appear in captions created by humans describing high-quality images.

While CLIP itself isn’t perfect for all types of content (it performs better with photorealistic and mainstream content compared to stylized art like ponies or furry art), it provides valuable internal signals. These signals, when properly leveraged, can help differentiate between images of varying aesthetic quality, even within niche art styles.

Data Labeling: The Human Element

To effectively use CLIP’s signals, a crucial step was data labeling. This involved curating a large dataset of images and ranking them based on human aesthetic judgment. While initial models might have used existing booru scores, these scores can be biased by content popularity (e.g., NSFW content) or character preference, rather than pure aesthetic quality.

For Pony Diffusion V6, a dedicated effort was made to rank approximately 20,000 images on a scale, for example, from 1 to 5, based purely on visual appeal, encompassing various art styles (3D, sketches, semi-realistic, etc.). This meticulous human-led ranking process created the aesthetic dataset that underpins the score tags.

This ranked dataset was then used to train a separate model to predict aesthetic scores based on CLIP embeddings. This allowed for automatically assigning a 0 to 1 score (later scaled and simplified to score_4 to score_9 tags) to a vast number of images, effectively categorizing them by perceived aesthetic quality. This process allows the model to be trained on data labeled with these score tags, connecting specific quality levels to the generated output when these tags are used in prompts.

Training with Score Tags: Guiding the Model

With the data labeled using these aesthetic scores, the actual Pony Diffusion model training could incorporate these tags. By training the model on images paired with score tags in their captions, the model learns to associate specific score tags with corresponding levels of aesthetic quality. This enables users to directly request “good” images by using these score tags in their prompts.

Interestingly, during the development of V6, a slight anomaly occurred. Instead of using simple tags like score_9, more verbose tags like score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up were used. This was initially intended to test different score ranges but inadvertently led the model to associate the entire long string with “good looking” images, rather than individual score levels. Despite this, the training continued, and the model effectively learned to respond to these longer score tag strings.

Effectively Using Score Tags in Your Prompts

So, how do you leverage these score tags to enhance your Pony Diffusion generations?

Using Score Tags in Discord Bots and Local Clients

Many Discord bots that utilize Pony Diffusion automatically append the score_9 tag to prompts. This is designed for user convenience, ensuring that by default, users receive aesthetically pleasing images without needing to delve into complex prompting. However, for more control, or when using local installations like Automatic1111 or ComfyUI, you need to understand how to use these tags directly.

When using prompts from websites or Discord bots in local applications, remember to translate the simplified score_9 tag. Replace score_9 with the full string: score_9, score_8_up, score_7_up, score_6_up, score_5_up, score_4_up. This ensures consistency between different platforms and achieves the intended quality filtering in your local generations.

Prompting Tips and Tools: Beyond Score Tags

Beyond score tags, Pony Diffusion offers other powerful prompting tools to refine your image generation:

Source Tags: source_pony, source_furry, source_anime, source_cartoon. These tags act as dataset filters, biasing the generation towards images originating from those specific sources.
- Use them in the positive prompt to emphasize a particular style (e.g., source_furry to generate furry characters).
- Use them in the negative prompt to avoid certain styles (e.g., -source_pony to prevent pony-like results when prompting for something else).
Rating Tags: rating_safe, rating_questionable, rating_explicit. These tags constrain the generated images to specific content ratings, allowing control over the generated content’s maturity level.
Score Tag Variations: Experiment with using a subset of score tags. For example, score_9, score_8_up, score_7_up, score_6_up or even just score_9. Using fewer score tags can tighten the dataset focus, potentially leading to more consistent or stylistically focused results, although it might also limit diversity.
Negative Score Tags: You can even use score tags in your negative prompt. For instance, score_6, score_5, score_4, chromatic aberration, artifacts, ugly, bad image can be used to actively discourage lower-quality outputs and common image generation artifacts.
Spacing Trick for Numbered Subjects: If prompting for multiple subjects like “2 girls” or “3 boys” isn’t working as expected, try adding a space: “4 girls”. This can sometimes improve subject recognition.
Specific Character/Style Tags: Tags like team_rocket_uniform can trigger highly specific stylistic or character-related outputs, demonstrating the model’s detailed understanding of certain visual cues.

Tricks for Achieving Anime Style

Pony Diffusion, while versatile, may exhibit a slight bias towards Western art styles, especially when score tags are heavily used. This is partly because the model was developed within a furry art context. To nudge generations towards a more anime aesthetic, consider these techniques:

Anime Source Tagging for LoRAs: When creating LoRAs (adapters for specific styles or characters), tagging your training images with score_9, source_anime can encourage a stronger anime influence in the LoRA’s output.
Negative Prompts for Anime Style: In your negative prompt, include terms like source_cartoon, source_furry, source_pony, sketch, painting, monochrome. This can help suppress Western art style biases and promote a more anime-like rendering.
Experiment with Lower Score Tags for LoRAs: If a LoRA trained with score_9 still leans towards Western styles, try prompting with source_anime, score_9, score_6_up, score_5_up, score_4_up. The lower score ranges might exhibit less of the Western art bias.

Conclusion

Mastering Pony Diffusion prompting involves understanding and strategically utilizing score tags, source tags, rating tags, and negative prompts. These tools provide granular control over the aesthetic quality, style, and content of your AI-generated images. By experimenting with these techniques and continuously refining your prompts, you can unlock the full creative potential of Pony Diffusion and bring your imaginative visions to life with stunning clarity and artistic flair.