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Table · dataset · 2022

AIML-TUDA/i2p

Listed in Hugging Face Datasets

Inaproppriate Image Prompts (I2P) The I2P benchmark contains real user prompts for generative text2image prompts that are unproportionately likely to produce inappropriate images.

Description

I2P was introduced in the 2023 CVPR paper Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models. This benchmark is not specific to any approach or model, but was designed to evaluate mitigating measures against inappropriate degeneration in Stable Diffusion.

The corresponding… See the full description on the dataset page: huggingface.co/datasets/AIML-TUDA/i2p.

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Where it is published

Documentation and papers

Catalogue records · 1

Topics

Stated by source
tabular · text
Inferred from text
Image 75%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
Hugging Face DatasetsAIML-TUDA/i2p12 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:tabularsource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:textsource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].local:modality:imageenrichment · Hugging Facekeyword-concept-rules@1.0.0title+description (75%)
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
licensesource · Hugging Faceconnector:huggingface@1.0.0/tags[license:*]
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0