112 citations · 552 across the 46 of their papers we have counts for
12 papers · 1 filter
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham +5
Fragment-based drug discovery, in which molecular fragments are assembled into new molecules with desirable biochemical properties, has achieved great success. However, many fragme…
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Giannis Daras, Weili Nie, Karsten Kreis +4
Using image models naively for solving inverse video problems often suffers from flickering, texture-sticking, and temporal inconsistency in generated videos. To tackle these probl…
Multi-student Diffusion Distillation for Better One-step Generators
Yanke Song, Jonathan Lorraine, Weili Nie +2
Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student…
Energy-Based Diffusion Language Models for Text Generation
Minkai Xu, Tomas Geffner, Karsten Kreis +5
Despite remarkable progress in autoregressive language models, alternative generative paradigms beyond left-to-right generation are still being actively explored. Discrete diffusio…
Truncated Consistency Models
Sangyun Lee, Yilun Xu, Tomas Geffner +4
Consistency models have recently been introduced to accelerate sampling from diffusion models by directly predicting the solution (i.e., data) of the probability flow ODE (PF ODE)…
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
Siyi Gu, Minkai Xu, Alexander Powers +6
Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recen…
CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
Dejia Xu, Weili Nie, Chao Liu +4
Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often…
DiffUHaul: A Training-Free Method for Object Dragging in Images
Omri Avrahami, Rinon Gal, Gal Chechik +4
Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a sce…
Compositional Text-to-Image Generation with Dense Blob Representations
Weili Nie, Sifei Liu, Morteza Mardani +3
Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose…
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
Sihyun Yu, Weili Nie, De-An Huang +3
Video diffusion models have recently made great progress in generation quality, but are still limited by the high memory and computational requirements. This is because current vid…
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Zizheng Pan, Bohan Zhuang, De-An Huang +5
Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model. In this paper, we int…
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Shengchao Liu, Chengpeng Wang, Jiarui Lu +5
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained…