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--- |
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language: |
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- en |
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license: mit |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- text-to-image |
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- image-to-image |
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pretty_name: NSFW Prompts |
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dataset_info: |
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features: |
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- name: model |
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dtype: string |
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- name: prompt |
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dtype: string |
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- name: negative_prompt |
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dtype: string |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 647548943 |
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num_examples: 851568 |
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download_size: 0 |
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dataset_size: 647548943 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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tags: |
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- uncensored |
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- nsfw |
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- art |
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- not-for-all-audiences |
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- diffusers |
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- image generation |
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--- |
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# Dataset Card for "stable-diffusion-prompts-uncensored" |
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## Not SAFE for public - Definately Unfiltered |
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This dataset comes from prompts shared from images' metadata on Civitai. Not for the faint of heart. |
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Thanks to Civitai.com for all the models, building a playground, allowing fine tuning of models, and generally being a good influence on model building and generation. |
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The purpose of this dataset is to allow for analysis of prompts and feature analysis in prompts and negative prompts. |
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This could be for: |
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- similarity |
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- effective prompting |
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- prompt alignment or misalignment |
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- statistical research on prompts and categories |
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- popularity of image generation approaches |
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- mimimalism prompts with certain models |
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- matching generated prompts to images for LLAVA purposes |
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- mimimizing prompts for better context usage |
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- social research on interest level and creative approaches |
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- modeling based on prompts for automating prompt generation strategy |
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- modeling of categorical interest and similarity |
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- modeling of evolution of prompts based on model versioning |
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A seperate upload will include metadata statistics such as cry count, laugh count, etc. for semantic analysis based on prompt length and content. |
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