NVIDIA diffusion language model Nemotron TwoTower achieves 2.42x LLM inference throughput without a full retraining run, ...

Deploying DFlash block diffusion on NVIDIA hardware accelerates autoregressive LLMs during latency-sensitive inference.

Our long-term goal is to build efficient and reliable 2.5B diffusion-based decoding for document OCR. MinerU-Diffusion reframes document OCR as an inverse rendering problem and replaces slow, ...

Rather than generating text word by word, Google's experimental open-source model drafts entire passages simultaneously using diffusion, resulting in up to 4x faster inference.

Google has released DiffusionGemma, an experimental language model that generates text using a diffusion-based method, producing blocks of 256 tokens at once rather than generating text word by word.

Abstract: Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing ...

TL;DR: Text Prompt -> LLM as a Request Parser -> Intermediate Representation (such as an image layout) -> Stable Diffusion -> Image. [2023.8] Our repo has been largely improved: now we have a repo ...

Chinese AI models are rapidly closing the gap with U.S. frontier systems. This analysis examines what their growing ...

Abstract: Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating ...

Google's open-source diffusion language model generates 256 tokens in parallel and self-corrects, hitting 4x speed on one GPU at a cost to quality.