junvin/erotic-image-prompts

Dataset Card for Erotic Image Prompts

Dataset Description

Dataset Summary

Large language models (LLMs) are surprisingly bad at creatively inventing new things, even though they are masters of hallucination. Asking an LLM — even an abliterated one — to produce a list of random erotic prompts therefore yields a rather boring, narrow-minded result. This is easy to overcome: give the model some inspiration and let it do what it does best — transform that inspiration into prompts in the style you need.

This dataset contains more than 80,000 erotic-leaning text-to-image prompts, focused on mainstream tastes and female subjects. The author is convinced that erotic and pornographic are different topics, while acknowledging that they are not strictly orthogonal.

To make it easy to reduce the large collection to a workable subset, each prompt is accompanied by metadata and a suggested aspect ratio.

Languages

All prompts are written in English.

Dataset Structure

Data Fields

Each entry consists of a text prompt plus the following metadata fields:

Note on the score columns: Artistic Quality, Erotic Score, and Pornographic Score were generated by an LLM in a zero-shot fashion and independently of every other prompt in the set. Expect heavy hallucination in these three values and never use them for training purposes. Their reliability is expected to be very poor; they are provided only as a service to very quickly reduce the number of prompts for further processing.

The same data is provided twice: captions.parquet is the data file used by the dataset viewer and by the datasets library (Clothing is a real list of tags); captions.csv is a plain-file twin with identical rows and columns, where Clothing is written as a tag string list, e.g. ['fabric', 'lace', 'top'] ([] when no garment is mentioned). The CSV is meant for tools that read files instead of using a library; its Clothing cell is a Python list literal, so ast.literal_eval() turns it back into a list.

Naming Convention

Every person in a prompt is given a placeholder name, in order of appearance:

• —Women: Alice, Bella, Clara, Diana, Emma
• —Men: Adam, Bob, Charlie, David, Eric

To test a character LoRA, simply replace Alice (or Adam) with the trigger word for your character.

Data Splits

The dataset is provided as a single set, with no predefined train/validation/test split.

Dataset Creation

The prompts were created by having an LLM transform source inspiration material into erotic text-to-image prompts in the required style, rather than generate them from scratch.

Considerations for Using the Data

Discussion of Biases

Erotic is a highly subjective matter, so expect strong bias. To mitigate this, the sample size is large, so with some filtering you should be able to obtain a subset that matches the bias you have in mind more closely. Even then, the underlying content reflects what people generally consider "vanilla", so special interests are barely covered — or not included at all.

Known Limitations

The Artistic Quality, Erotic Score, and Pornographic Score fields are LLM-generated and unreliable (see the note above). Do not treat them as ground truth.

Safety

As erotic is a sensitive topic, take care with regard to safety. Never create images depicting real persons who have not given clear consent, and only use these prompts to depict persons who, in reality and visually, meet the legally required minimum age.

How to Use

You can use any tool that accepts text prompts. For convenience with ComfyUI, you can use the provided workflow, which combines the Hugging Face dataset and the Basic Data Handling custom nodes as the input to the official Krea 2 Turbo workflow example.

Use Cases

These prompts are ideal for testing the capabilities of a text-to-image model. They can be used as-is, or with the placeholder name(s) replaced by trigger words to test character adapters (LoRA, LoKR, …) in erotic images.

To adapt the prompt style to the convention of a specific text-to-image model or do some substantial changes like setting an image style it is recommended to use an LLM to translate the relevant prompts of this dataset to the requirements of the intended use case.

Statistics

82880 prompts in captions.csv / captions.parquet.

Count of Persons

<table> <tbody> <tr> <td colspan="2" rowspan="2"></td> <th colspan="6">Females</th> </tr> <tr> <th>0</th> <th>1</th> <th>2</th> <th>3</th> <th>4</th> <th>5</th> </tr> <tr> <th rowspan="4">Males</th> <th>0</th> <td>0</td> <td>75978</td> <td>4217</td> <td>615</td> <td>42</td> <td>4</td> </tr> <tr> <th>1</th> <td>391</td> <td>1515</td> <td>56</td> <td>19</td> <td>6</td> <td>0</td> </tr> <tr> <th>2</th> <td>7</td> <td>8</td> <td>7</td> <td>6</td> <td>2</td> <td>0</td> </tr> <tr> <th>3</th> <td>0</td> <td>0</td> <td>6</td> <td>1</td> <td>0</td> <td>0</td> </tr> </tbody> </table>

Aspect Ratio

Nudity

Artistic Quality (0.1 bins)

Erotic Score (0.1 bins)

Pornographic Score (0.1 bins)

Garments (Clothing)

Additional Information

Licensing Information

This dataset is released under the CC-BY-4.0 license.

Citation Information

If you use this dataset, please cite it as follows:

@misc{erotic-image-prompts,
	author       = { Stable Llama },
	title        = { erotic-image-prompts (Revision f0a5808) },
	year         = 2026,
	url          = { https://huggingface.co/datasets/stablellama/erotic-image-prompts },
	doi          = { 10.57967/hf/10548 },
	publisher    = { Hugging Face }
}