5 papers
Abra: Scaling Diffusion Image Training
Kyle Chickering, Wei-An Lin, Swayam Bhanded +5
Compute-optimal scaling laws guide the training of frontier language models yet remain largely unexplored for visual generation. We present a systematic scaling law study for text-…
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
Kai Zhao, Yanjun Zhao, Jiaming Song +4
Large language models (LLMs) empowered by chain-of-thought reasoning have achieved impressive accuracy on complex tasks but suffer from excessive inference costs and latency when a…
Self-NPO: Data-Free Diffusion Model Enhancement via Truncated Diffusion Fine-Tuning
Fu-Yun Wang, Keqiang Sun, Yao Teng +4
Diffusion models have demonstrated remarkable success in various visual generation tasks, including image, video, and 3D content generation. Preference optimization (PO) is a promi…
Decentralized Diffusion Models
David McAllister, Matthew Tancik, Jiaming Song +1
Large-scale AI model training divides work across thousands of GPUs, then synchronizes gradients across them at each step. This incurs a significant network burden that only centra…
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models
NVIDIA, :, Yuval Atzmon +29
We introduce Edify Image, a family of diffusion models capable of generating photorealistic image content with pixel-perfect accuracy. Edify Image utilizes cascaded pixel-space dif…