Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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Stable Diffusion XL Beta 0.9
By:
Latest Version:
20230725
Get a preview of SDXL from Stability AI, the leading model for creating and editing images.
Product Overview
SDXL Beta 0.9 offers a preview to the largest open source image model from Stability AI. SDXL produces high resolution images at native 1024px resolution. With SDXL Beta 0.9, Stability introduces a brand new 2-stage architecture for image generative AI models, and integrates the largest CLIP model. The result: stunning quality in image generation with minimal need for prompt engineering.
Key Data
Version
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Type
Model Package
Highlights
The foundation model for images: All text to image, image to image, inpainting, and outpainting workflows are handled by the official SDXL model. SDXL introduces a new SOTA architecture for image generation, comprising a 3.5B parameter base model stage and a 6.6B parameter ensemble pipeline.
Unprecedented quality and ease of use: Native 1024x1024 image generation with cinematic photorealism and fine detail. The most advanced text generation within images. Fine-tuned to create complex compositions with basic natural language prompting, thanks to the largest CLIP model in production.
Beta: SDXL 0.9 is a beta version of SDXL 1.0. While it is a powerful image generation model, for increased professionalism in image output, consider using SDXL 1.0
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.00/inference
running on any instance
Model Batch Transform$0.00/hr
running on ml.g4dn.12xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$1.515/host/hr
running on ml.g5.2xlarge
SageMaker Batch Transform$4.89/host/hr
running on ml.g4dn.12xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
Additional infrastructure cost, taxes or fees may apply.
Usage Information
Model input and output details
Input
Summary
This model accepts JSON input aligned with the Stability REST API as well as protocol buffers aligned with the Stability GRPC API .
Limitations for input type
Input images must be base64 encoded in JSON or supplied via protobuf.
Input MIME type
application/jsonSample input data
{
"cfg_scale": 7,
"height": 1024,
"width": 1024,
"steps": 50,
"seed": 42,
"sampler": "K_DPMPP_2M",
"text_prompts": [
{
"text": "A photograph of fresh pizza with basil and tomatoes, from a traditional oven",
"weight": 1
}
]
}
Output
Summary
Model output is available as base64 inside JSON, binary serialized protocol buffers, or binary PNG
Output MIME type
application/json, image/pngSample output data
{
"result": "success",
"artifacts": [
{
"base64": "...very long string...",
"finishReason": "SUCCESS",
"seed": 1050625087
},
{
"base64": "...very long string...",
"finishReason": "CONTENT_FILTERED",
"seed": 1229191277
}
]
}
Sample notebook
Additional Resources
End User License Agreement
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Support Information
AWS Infrastructure
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Learn MoreRefund Policy
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