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---
license: mit
tags:
- t5
- embeddings
- prompts
- feature-extraction
language:
- en
pretty_name: "T5-XXL Embeddings for Image Generation Prompts"
task_categories:
- feature-extraction
- text-to-image
---
# T5-XXL Embeddings for Image Generation Prompts
This dataset contains 4096-dimensional text embeddings for prompts from the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
## Model Used
The embeddings were generated using a T5-XXL encoder model. Specifically, the weights from `t5xxl_fp8_e4m3fn_scaled.safetensors` were used.
**Important Note:** While the source model weights were in a FP8 format, the embeddings in this dataset have been calculated and stored in full **`float32`** precision to ensure numerical stability and maximum quality.
## Data Fields
- `Prompt`: A `string` containing the original text prompt.
- `vector`: A `list` of `float32` numbers, representing the 4096-dimensional embedding of the prompt.
## Data Splits
- `train`: Contains 73718 prompts and their corresponding vectors.
- `test`: Contains 8192 prompts and their corresponding vectors, sampled from the end of the original train set.
## Usage
You can load the dataset easily using the `datasets` library:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("JusteLeo/t5-xxl-embedding")
# Access the splits
train_data = dataset['train']
test_data = dataset['test']
# Example
print(train_data[0]['Prompt'])
print(len(train_data[0]['vector']))
# Output: 4096
```
### Citation and Credits
This dataset of embeddings was created by [JusteLeo](https://huggingface.co/JusteLeo). If you use this dataset in your work, please consider citing this repository.
#### Original Data
The text prompts used as a basis for this dataset are from the `Gustavosta/Stable-Diffusion-Prompts` dataset. A huge thank you to Gustavosta for providing the initial data.
- **Original Prompt Dataset:** [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)
#### Model
The embeddings were generated using an encoder based on the T5-XXL architecture.
- **Base Model Architecture:** [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) by Google.
- **Specific Encoder Weights:** The model was loaded with specific `fp8` quantized weights from the `t5xxl_fp8_e4m3fn_scaled.safetensors` file.
### License
This dataset is licensed under the [MIT License](LICENSE). |