File size: 2,601 Bytes
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license: mit
task_categories:
- image-generation
language:
- en
size_categories:
- n<1K
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: model
dtype: string
- name: category
dtype: string
- name: sub_category
dtype: string
splits:
- name: train
num_bytes: 18397027.0
num_examples: 16
download_size: 18293528
dataset_size: 18397027.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Diffusion Studio Generations
This dataset contains 16 high-quality AI-generated images created using various diffusion models. Each image is accompanied by detailed prompts and model information, showcasing different styles and techniques in AI image generation.
## Dataset Description
- **Curated by:** shodiBoy
- **Language:** English
- **License:** MIT
- **Size:** 16 images
- **Task:** Image Generation
- **Repository:** diffusion_studio_generations
## Dataset Structure
Each entry in the dataset contains:
- `id`: Unique identifier for the entry
- `prompt`: Detailed text prompt used to generate the image
- `image`: Path to the generated image
- `model`: AI model used to generate the image
- `category`: Main category of the image (Sports, Fashion, Cinematic)
- `sub_category`: More specific classification
## Models Used
- **flux-1-1-pro**: Black Forest Labs FLUX.1 Pro model
- **GPT-IMAGE-1**: OpenAI's GPT Image Generation model
- **RECRAFT-V3**: Recraft V3 model
## Image Categories
The dataset includes various types of images:
- **Sports**: Alpine/skiing scenes, basketball action shots
- **Fashion**: Portraits, avant-garde fashion photography
- **Cinematic**: Sci-fi scenes, urban photography, vintage aesthetics
## Usage
This dataset can be used for:
- Training image generation models
- Prompt engineering research
- Model comparison studies
- Computer vision research
- Style analysis and categorization
## File Structure
```
dataset/
├── images/
│ └── chosen/ # Generated images (16 files)
├── metadata/ # Individual JSON files for each image
├── dataset.json # Main dataset file with all entries
├── dataset_info.json # Dataset metadata
└── README.md # This file
```
## Citation
If you use this dataset in your research, please cite:
```bibtex
@dataset{diffusion_studio_generations_2024,
author = {shodiBoy},
title = {Diffusion Studio Generations},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/shodiBoy/diffusion_studio_generations}
}
``` |