23 citations · 29 across the 7 of their papers we have counts for
9 papers
Test-time scaling of diffusions with flow maps
Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich +4
A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics o…
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
Xuanchi Ren, Yifan Lu, Tianshi Cao +13
Collecting and annotating real-world data for safety-critical physical AI systems, such as Autonomous Vehicle (AV), is time-consuming and costly. It is especially challenging to ca…
Align Your Flow: Scaling Continuous-Time Flow Map Distillation
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
Diffusion- and flow-based models have emerged as state-of-the-art generative modeling approaches, but they require many sampling steps. Consistency models can distill these models…
VideoPanda: Video Panoramic Diffusion with Multi-view Attention
Kevin Xie, Amirmojtaba Sabour, Jiahui Huang +5
High resolution panoramic video content is paramount for immersive experiences in Virtual Reality, but is non-trivial to collect as it requires specialized equipment and intricate…
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond. A crucial drawback of DMs is their slow sam…
EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
Koichi Namekata, Amirmojtaba Sabour, Sanja Fidler +1
Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks. However, generating fine-grained segme…
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation
Yassaman Ommi, Matin Yousefabadi, Faezeh Faez +3
Graph data structures are fundamental for studying connected entities. With an increase in the number of applications where data is represented as graphs, the problem of graph gene…
Dynamic Averaging Load Balancing on Cycles
Dan Alistarh, Giorgi Nadiradze, Amirmojtaba Sabour
We consider the following dynamic load-balancing process: given an underlying graph with nodes, in each step , one unit of load is created, and placed at a randoml…
Asynchronous Decentralized SGD with Quantized and Local Updates
Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies +2
Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, but also introduces new challenges in terms of synchronization costs. To t…