34 citations · 140 across the 26 of their papers we have counts for
8 papers · 1 filter
LEDITS++: Limitless Image Editing using Text-to-Image Models
Manuel Brack, Felix Friedrich, Katharina Kornmeier +4
Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research…
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge
Manuel Brack, Patrick Schramowski, Kristian Kersting
Text-conditioned image generation models have recently achieved astonishing image quality and alignment results. Consequently, they are employed in a fast-growing number of applica…
Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?
Manuel Brack, Felix Friedrich, Patrick Schramowski +1
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of ap…
MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation
Marco Bellagente, Manuel Brack, Hannah Teufel +11
The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in…
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
Lukas Struppek, Dominik Hintersdorf, Felix Friedrich +3
Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising conc…
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Felix Friedrich, Manuel Brack, Lukas Struppek +4
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly da…
SEGA: Instructing Text-to-Image Models using Semantic Guidance
Manuel Brack, Felix Friedrich, Dominik Hintersdorf +3
Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot ge…
AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation
Björn Deiseroth, Mayukh Deb, Samuel Weinbach +3
Generative transformer models have become increasingly complex, with large numbers of parameters and the ability to process multiple input modalities. Current methods for explainin…