34 citations · 137 across the 37 of their papers we have counts for
10 papers · 1 filter
No Safe Dose: How Training Data Drives Unsafe Image Generation
Felix Friedrich, Lukas Helff, Niharika Hegde +2
Text-to-image models trained on large-scale data often inevitably ingest unsafe content. While some people observe input-output amplifications, it remains unclear whether and how t…
How to Train your Text-to-Image Model: Evaluating Design Choices for Synthetic Training Captions
Manuel Brack, Sudeep Katakol, Felix Friedrich +4
Training data is at the core of any successful text-to-image models. The quality and descriptiveness of image text are crucial to a model's performance. Given the noisiness and inc…
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +7
Effective human-AI interaction relies on AI's ability to accurately perceive and interpret human emotions. Current benchmarks for vision and vision-language models are severely lim…
LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models
Lukas Helff, Felix Friedrich, Manuel Brack +2
This paper introduces LlavaGuard, a suite of VLM-based vision safeguards that address the critical need for reliable guardrails in the era of large-scale data and models. To this e…
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…
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…
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…
The Stable Artist: Steering Semantics in Diffusion Latent Space
Manuel Brack, Patrick Schramowski, Felix Friedrich +2
Large, text-conditioned generative diffusion models have recently gained a lot of attention for their impressive performance in generating high-fidelity images from text alone. How…
Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis
Lukas Struppek, Dominik Hintersdorf, Felix Friedrich +3
Models for text-to-image synthesis, such as DALL-E~2 and Stable Diffusion, have recently drawn a lot of interest from academia and the general public. These models are capable of p…