size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
- config_name: quality_augmentation
  features:
  - name: model_a
    dtype: string
  - name: model_b
    dtype: string
  - name: preference
    dtype: float64
  - name: prompt
    dtype: 'null'
  - name: category
    dtype: 'null'
  - name: subcategory
    dtype: 'null'
  - name: quality_generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_quality_augmentation
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_quality_augmentation
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 1927
    num_examples: 1
  download_size: 18614
  dataset_size: 1927
- config_name: style_augmentation
  features:
  - name: model_a
    dtype: string
  - name: model_b
    dtype: string
  - name: preference
    dtype: float64
  - name: prompt
    dtype: 'null'
  - name: category
    dtype: 'null'
  - name: subcategory
    dtype: 'null'
  - name: style_generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_style_augmentation
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_style_augmentation
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 1407
    num_examples: 1
  download_size: 14965
  dataset_size: 1407
configs:
- config_name: quality_augmentation
  data_files:
  - split: train
    path: quality_augmentation/train-*
- config_name: style_augmentation
  data_files:
  - split: train
    path: style_augmentation/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for stylish_image_preferences_v2

This dataset has been created with [distilabel](https://distilabel.argilla.io/).


The pipeline script was uploaded to easily reproduce the dataset:
[generate.py](https://huggingface.co/datasets/dvilasuero/stylish_image_preferences_v2/raw/main/generate.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/dvilasuero/stylish_image_preferences_v2/raw/main/generate.py"
```


## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/stylish_image_preferences_v2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/stylish_image_preferences_v2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: quality_augmentation </summary><hr>

```json
{
    "category": null,
    "distilabel_metadata": {
        "raw_input_quality_augmentation": [
            {
                "content": "\nYou are an expert at refining prompts for image generation models. Your task is to enhance the given prompt by adding descriptive details and quality-improving elements, while maintaining the original intent and core concept.\n\nFollow these guidelines:\n1. Preserve the main subject and action of the original prompt.\n2. Add specific, vivid details to enhance visual clarity.\n3. Incorporate elements that improve overall image quality and aesthetics.\n4. Keep the prompt concise and avoid unnecessary words.\n5. Use modifiers that are appropriate for the subject matter.\n\nExample modifiers (use as reference, adapt based on some aspect that\u0027s suitable for the original prompt):\n- Lighting: \"soft golden hour light\", \"dramatic chiaroscuro\", \"ethereal glow\"\n- Composition: \"rule of thirds\", \"dynamic perspective\", \"symmetrical balance\"\n- Texture: \"intricate details\", \"smooth gradients\", \"rich textures\"\n- Color: \"vibrant color palette\", \"monochromatic scheme\", \"complementary colors\"\n- Atmosphere: \"misty ambiance\", \"serene mood\", \"energetic atmosphere\"\n- Technical: \"high resolution\", \"photorealistic\", \"sharp focus\"\n\nThe enhanced prompt should be short, concise, direct, avoid unnecessary words and written as it was a human expert writing the prompt.\n\nOutput only one enhanced prompt without any additional text or explanations.\n\n## Original Prompt\nNone\n\n## Quality-Enhanced Prompt",
                "role": "user"
            }
        ],
        "raw_output_quality_augmentation": "A majestic lion standing tall on a rocky outcrop at sunset, bathed in soft golden hour light, with a dramatic sky and rich textures in the fur, composition using the rule of thirds, high resolution, photorealistic."
    },
    "model_a": "SDXL-Lightning (8 steps)",
    "model_b": "Fooocus (Refined SDXL LCM)",
    "model_name": "Qwen/Qwen2.5-72B-Instruct",
    "preference": 0.0,
    "prompt": null,
    "quality_generation": "A majestic lion standing tall on a rocky outcrop at sunset, bathed in soft golden hour light, with a dramatic sky and rich textures in the fur, composition using the rule of thirds, high resolution, photorealistic.",
    "subcategory": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/stylish_image_preferences_v2", "quality_augmentation")
```


</details>

<details><summary> Configuration: style_augmentation </summary><hr>

```json
{
    "category": null,
    "distilabel_metadata": {
        "raw_input_style_augmentation": [
            {
                "content": "\nYou are an expert at refining prompts for image generation models. Your task is to enhance the given prompt by transforming it into a specific artistic style, technique, or genre, while maintaining the original core concept.\n\nFollow these guidelines:\n1. Preserve the main subject and action of the original prompt.\n2. Transform the prompt into a distinctive visual style (e.g., impressionism, surrealism, cyberpunk, art nouveau).\n3. Incorporate style-specific elements and techniques.\n4. Keep the prompt concise and avoid unnecessary words.\n5. Use modifiers that are appropriate for the chosen style.\n\nYou should use the following style, technique, genre to enhance the prompt:\nNone\n\nThe enhanced prompt should be short, concise, direct, avoid unnecessary words and written as it was a human expert writing the prompt.\n\nOutput only one style-enhanced prompt without any additional text or explanations.\n\n## Original Prompt\nNone\n\n## Style-Enhanced Prompt",
                "role": "user"
            }
        ],
        "raw_output_style_augmentation": "A lone cyberpunk figure stands atop a neon-lit skyscraper, overlooking a bustling futuristic cityscape, raindrops reflecting off the wet streets and illuminated signs."
    },
    "model_a": "SDXL-Lightning (8 steps)",
    "model_b": "Fooocus (Refined SDXL LCM)",
    "model_name": "Qwen/Qwen2.5-72B-Instruct",
    "preference": 0.0,
    "prompt": null,
    "style_generation": "A lone cyberpunk figure stands atop a neon-lit skyscraper, overlooking a bustling futuristic cityscape, raindrops reflecting off the wet streets and illuminated signs.",
    "subcategory": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/stylish_image_preferences_v2", "style_augmentation")
```


</details>




