4 accepted papers
Latent generative modeling has become the standard strategy for Diffusion Transformers (DiTs), but the autoencoder has barely evolved. Most DiTs still use the legacy VAE encoder, which introduces several limitations: large convolutional backbones that compromise architectural simplicity, low-dimensi…
Unified multimodal models aim to input and output both vision and language data within a single system. In this work, we explore the design space of Unified Multimodal Pretraining through a controlled, from-scratch study. We find that leveraging a single high-dimensional semantic encoder (e.g. SigLI…
This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image synthesis---specifically, the deep fusion of large language models (LLMs) with diffusion transformers (DiTs) for multimodal…
Diffusion models build a new milestone for image generation yet raising public concerns, for they can be fine-tuned on unauthorized images for customization. Protection based on adversarial attacks rises to encounter this unauthorized diffusion customization, by adding protective watermarks to image…