
Faster Learning for Diffusion Models: Pretrained embeddings accelerate diffusion transformers’ learning
Diffusion transformers learn faster when they can look at embeddings generated by a pretrained model like DINOv2.
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Diffusion transformers learn faster when they can look at embeddings generated by a pretrained model like DINOv2.

Students benefit from tutoring, but training tutors is expensive. A study shows that large language models can boost tutors’ effectiveness in real time.

Diffusion models usually take many noise-removal steps to produce an image, which takes time at inference. There are ways to reduce the number of steps, but the resulting systems are less effective. Researchers devised a streamlined approach that doesn’t sacrifice output quality.

Google updated its open-weights family of large language models to include versions that handle image and video inputs.

Fine-tuning small language models has been gaining traction over the past half year.

The Batch AI News and Insights: Fine-tuning small language models has been gaining traction over the past half year.
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