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Found 7 papers, 5 papers with code
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines
Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process.
Unified Concept Editing in Diffusion Models
Text-to-image models suffer from various safety issues that may limit their suitability for deployment.
ReFACT: Updating Text-to-Image Models by Editing the Text Encoder
Text-to-image models are trained on extensive amounts of data, leading them to implicitly encode factual knowledge within their parameters.
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e. g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e. g., "a pack of blue roses").
BLIND: Bias Removal With No Demographics
Common methods to mitigate biases require prior information on the types of biases that should be mitigated (e. g., gender or racial bias) and the social groups associated with each data sample.
Choose Your Lenses: Flaws in Gender Bias Evaluation
In this position paper, we assess the current paradigm of gender bias evaluation and identify several flaws in it.
How Gender Debiasing Affects Internal Model Representations, and Why It Matters
Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations.
