5 papers
InfSplign: Inference-Time Spatial Alignment of Text-to-Image Diffusion Models
Sarah Rastegar, Violeta Chatalbasheva, Sieger Falkena +5
Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced to two fa…
Unified Control for Inference-Time Guidance of Denoising Diffusion Models
Maurya Goyal, Anuj Singh, Hadi Jamali-Rad
Aligning diffusion model outputs with downstream objectives is essential for improving task-specific performance. Broadly, inference-time training-free approaches for aligning diff…
CoDe: Blockwise Control for Denoising Diffusion Models
Anuj Singh, Sayak Mukherjee, Ahmad Beirami +1
Aligning diffusion models to downstream tasks often requires finetuning new models or gradient-based guidance at inference time to enable sampling from the reward-tilted posterior.…
MAGMA: Manifold Regularization for MAEs
Alin Dondera, Anuj Singh, Hadi Jamali-Rad
Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative…
GeNIe: Generative Hard Negative Images Through Diffusion
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet +2
Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more…