SageMaker JumpStart now provides Stable Diffusion and Bloom models

Starting today, Amazon SageMaker JumpStart provides two additional state-of-the-art foundational models, Bloom for text generation and Stable Diffusion for image generation. Customers can access newly added models through the SageMaker Python SDK APIs and SageMaker JumpStart UI inside SageMaker Studio.

Bloom can be used to complete the sentence or generate long paragraphs for 46 different languages, and generated text often appears indistinguishable from the human-written text. This release includes Bloom-560m, Bloom-1b1, and Bloom-1b7 models for text generation. Stable diffusion generates images from given text, and is known for its realistic images that closely resemble the input text.

Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker that offers 350+ built-in algorithms, pre-trained models, and pre-built solution templates to help customers get started with ML fast. Pre-trained models hosted in JumpStart are State-of-the-Art (SOTA) publicly available models from popular model hubs such as TensorFlow, PyTorch, Hugging Face and MXNet, and support popular ML tasks such as object detection, text classification, and text generation. To help data scientists and ML practitioners get started quickly and securely, contents are stored in AWS repository and come with training and inferencing scripts compatible with SageMaker features. Customers can fine-tune models using their own data or deploy as-is for inferencing.

All these models can be used in all regions where Amazon SageMaker is available.

To learn more about how to use each model, visit Bloom launch blog and Stable Diffusion launch blog. To browse all models available in SageMaker JumpStart, please visit SageMaker JumpStart ML Hub.