38 summarised stories about Diffusion Models, each linking back to the original source. Browse all topics →
OpenAI 1 year ago 12
Researchers improved continuous-time consistency models to match the sample quality of leading diffusion models while requiring only two sampling steps instead of many. The models achieved this through simplified architecture, enhanced training stability, and improved scaling methods. This reduces the computational cost of image generation, making the approach more practical for deployment.
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16 October 2026
AI’s center of gravity this week moved toward something less glamorous than headlines about “smarter” models: scaling the stuff models actually learn from. AI4Bharat and NVIDIA released Nemotron-pretraining-Indic-v1, backed by an open, GPU-accelerated synthetic data generation pipeline for 22 Indian languages. The corpus is massive—about 2.8 trillion tokens—split across 2,601.0 billion native-script tokens and 224.6 billion romanized tokens. The practical pitch is even sharper: builders can reuse not just the data, but the full prompt/quality/repair recipe, then continue-pretraining to push results on Nemotron 3.5 Super VL, with the promise that an Indic continued-pretraining stage helps benchmarks.
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