It can be used to replace part of an image guided by prompt. Update (March 2023): You can now Stable Diffusion inapainting model in JumpStart. To learn more, please see Upscale images with Stable Diffusion in Amazon SageMaker JumpStart blog and the Introduction to JumpStart – Enhance image quality guided by prompt notebook. You can upscale images with Stable Diffusion models in JumpStart. Negative prompt: Yellow colorĪlthough primarily used to generate images conditioned on text, Stable Diffusion models can also be used for other tasks such as inpainting, outpainting, and generating image-to-image translations guided by text. The following images are in response to the inputs: (i) dogs playing poker, (ii) A colorful photo of a castle in the middle of a forest with trees, and (iii) A colorful photo of a castle in the middle of a forest with trees. The following images are in response to the inputs “a photo of an astronaut riding a horse on mars,” “a painting of new york city in impressionist style,” and “dog in a suit.” The following are some examples of input texts and the corresponding output images generated by Stable Diffusion. Finally, the de-noised output is decoded into the pixel space. Then, a series of noise addition and noise removal operations are performed in the latent space with a U-Net architecture. The text must first be embedded into a latent space using a language model. For instance, Stable Diffusion is a latent diffusion where the model learns to recognize shapes in a pure noise image and gradually brings these shapes into focus if the shapes match the words in the input text. These models can also generate images from text alone by conditioning the generation process on the text. This de-noising process generates a realistic image. Stable Diffusion is a text-to-image model that empowers you to create photorealistic applications.Ī diffusion model trains by learning to remove noise that was added to a real image. Generative AI technology is improving rapidly, and it’s now possible to generate text and images simply based on text input. ![]() In this post, we provide an overview of how to deploy and run inference with Stable Diffusion in two ways: via JumpStart’s user interface (UI) in Amazon SageMaker Studio, and programmatically through JumpStart APIs available in the SageMaker Python SDK. You can use Stable Diffusion to design products and build catalogs for ecommerce business needs or to generate realistic art pieces or stock images. Stable Diffusion is an image generation model that can generate realistic images given a raw input text. JumpStart is the machine learning (ML) hub of SageMaker that provides hundreds of built-in algorithms, pre-trained models, and end-to-end solution templates to help you quickly get started with ML. Today, we announce that Stable Diffusion 1 and Stable Diffusion 2 are available in Amazon SageMaker JumpStart. ![]() March 2023: This post was reviewed and updated with support for Stable Diffusion inpainting model.
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