8 citations · 24 across the 16 of their papers we have counts for
11 papers · 1 filter
AlignGuard: Scalable Safety Alignment for Text-to-Image Generation
Runtao Liu, I Chieh Chen, Jindong Gu +6
Text-to-image (T2I) models are widespread, but their limited safety guardrails expose end users to harmful content and potentially allow for model misuse. Current safety measures a…
Video Motion Transfer with Diffusion Transformers
Alexander Pondaven, Aliaksandr Siarohin, Sergey Tulyakov +2
We propose DiTFlow, a method for transferring the motion of a reference video to a newly synthesized one, designed specifically for Diffusion Transformers (DiT). We first process t…
MALT: Improving Reasoning with Multi-Agent LLM Training
Sumeet Ramesh Motwani, Chandler Smith, Rocktim Jyoti Das +6
Large Language Models (LLMs) often produce answers with a single chain-of-thought, which restricts their ability to explore reasoning paths or self-correct flawed outputs in comple…
MatchDiffusion: Training-free Generation of Match-cuts
Alejandro Pardo, Fabio Pizzati, Tong Zhang +4
Match-cuts are powerful cinematic tools that create seamless transitions between scenes, delivering strong visual and metaphorical connections. However, crafting match-cuts is a ch…
Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing
Ekaterina Iakovleva, Fabio Pizzati, Philip Torr +1
Text-based editing diffusion models exhibit limited performance when the user's input instruction is ambiguous. To solve this problem, we propose (SANE)…
Model Merging and Safety Alignment: One Bad Model Spoils the Bunch
Hasan Abed Al Kader Hammoud, Umberto Michieli, Fabio Pizzati +4
Merging Large Language Models (LLMs) is a cost-effective technique for combining multiple expert LLMs into a single versatile model, retaining the expertise of the original ones. H…
Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen +22
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic chan…
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen +21
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for thes…
Latent Guard: a Safety Framework for Text-to-image Generation
Runtao Liu, Ashkan Khakzar, Jindong Gu +3
With the ability to generate high-quality images, text-to-image (T2I) models can be exploited for creating inappropriate content. To prevent misuse, existing safety measures are ei…
On Pretraining Data Diversity for Self-Supervised Learning
Hasan Abed Al Kader Hammoud, Tuhin Das, Fabio Pizzati +3
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed comp…
SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
Hasan Abed Al Kader Hammoud, Hani Itani, Fabio Pizzati +3
We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synth…