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---
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license: mit
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tags:
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- t5
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- embeddings
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- prompts
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- feature-extraction
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language:
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- en
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pretty_name: "T5-XXL Embeddings for Image Generation Prompts"
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task_categories:
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- feature-extraction
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- text-to-image
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---
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# T5-XXL Embeddings for Image Generation Prompts
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This dataset contains 4096-dimensional text embeddings for prompts from the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
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## Model Used
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The embeddings were generated using a T5-XXL encoder model. Specifically, the weights from `t5xxl_fp8_e4m3fn_scaled.safetensors` were used.
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**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.
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## Data Fields
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- `Prompt`: A `string` containing the original text prompt.
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- `vector`: A `list` of `float32` numbers, representing the 4096-dimensional embedding of the prompt.
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## Data Splits
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- `train`: Contains 73718 prompts and their corresponding vectors.
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- `test`: Contains 8192 prompts and their corresponding vectors, sampled from the end of the original train set.
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## Usage
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You can load the dataset easily using the `datasets` library:
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```python
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("JusteLeo/t5-xxl-embedding")
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# Access the splits
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train_data = dataset['train']
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test_data = dataset['test']
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# Example
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print(train_data[0]['Prompt'])
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print(len(train_data[0]['vector']))
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# Output: 4096
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```
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### Citation and Credits
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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.
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#### Original Data
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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.
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- **Original Prompt Dataset:** [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)
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#### Model
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The embeddings were generated using an encoder based on the T5-XXL architecture.
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- **Base Model Architecture:** [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) by Google.
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- **Specific Encoder Weights:** The model was loaded with specific `fp8` quantized weights from the `t5xxl_fp8_e4m3fn_scaled.safetensors` file.
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### License
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This dataset is licensed under the [MIT License](LICENSE). |
