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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Image Generation Through Text Prompt
By:
Latest Version:
v1
This solution generates an image from scratch incorporating elements described in the text prompt.
Product Overview
Text-guided Image generation helps in creating high-quality ethical images from scratch from a text description. Text-to-image uses AI to understand your words and convert them to a unique image each time. Deep generative neural networks concepts like image synthesis and latent diffusion models are used to create completely new images with reduced memory and computational cost. It can also create images with several designs, Arts, and styles by giving relevant text input. The model also uses ML-based content moderation techniques to consider the ethical aspects of generated images by sending appropriate warnings. If Not Safe For Work (NSFW) is more than a fixed threshold or a blank image is generated then the model throws an error stating to give an appropriate input prompt or to change the random seed.
Key Data
Version
By
Type
Model Package
Highlights
The solution can be used for generating synthetic images with more designs and styles potentially being added to the image. The model also considers the ethical aspects of image generation and gives NSFW (Not Safe For Work) warnings appropriately. However, the content input by the user and output generated by the listing needs to be duly verified for quality and ethical concerns before using integrating with other applications.
This guided image synthesis can be applied to use cases like data augmentation, in which synthetic Images can be generated by giving text prompts. This reduces manual effort and improves productivity in cross-functional industries, some of which are metaverse, online content generation, Creative/Digital media, wildlife photography, designing UX/UI, etc.
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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.
Contact us to request contract pricing for this product.
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$5.00/hr
running on ml.p3.2xlarge
Model Batch Transform$10.00/hr
running on ml.p2.xlarge
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$3.825/host/hr
running on ml.p3.2xlarge
SageMaker Batch Transform$1.125/host/hr
running on ml.p2.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.p3.8xlarge | $5.00 | |
ml.p2.xlarge | $5.00 | |
ml.g4dn.4xlarge | $5.00 | |
ml.p3.2xlarge Vendor Recommended | $5.00 | |
ml.g4dn.16xlarge | $5.00 | |
ml.g4dn.8xlarge | $5.00 | |
ml.p2.8xlarge | $5.00 | |
ml.g4dn.12xlarge | $5.00 | |
ml.p2.16xlarge | $5.00 | |
ml.p3.16xlarge | $5.00 | |
ml.g4dn.xlarge | $5.00 | |
ml.g4dn.2xlarge | $5.00 |
Usage Information
Model input and output details
Input
Summary
Usage Methodology for the algorithm: 1) The input must be 'Input.zip' file. 2) The zip file should contain Input file which includes a .json file. 3) The name of the .json file should be "parameters" which is case sensitive. 4) Name of the folder inside the zip file should be “Input” which is case-sensitive 5) check the instructions and sample endpoint in the sample jupyter file provided.
Input MIME type
application/zipSample input data
Output
Summary
The output will be a .jpeg file or a text prompt in case of warnings.
Limitations for output type
If your output contains NSFW (Not Safe For Work) content, then an image or text will generate. Suggestions to change the input parameters.
Output MIME type
image/jpeg, text/plainSample output data
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Image Generation Through Text Prompt
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AWS Infrastructure
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Learn MoreRefund Policy
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