Sold by: MphasisÂ
Deployed on AWS
This solution re-draws or remove selected portion of existing images to incorporate new entity/objects described in the text prompt.
Overview
Text-guided Image Variation helps in creating high-quality ethical images by adding or omitting the elements in a given masked area/region of interest of input images based on the text provided. The solution is capable of generating photo-realistic images given any text input, with the capability of editing the pictures by using a masked image. The solution uses diffusion models to create the same image but completely changed entities/elements in the selected regions with reduced memory and computational cost. Suppose, to remove certain elements/objects from the image, provide the masked image of the object to be edited with an input 'empty' and leave the rest to us. It can also enhance the resolution or change the design of the image as per the text input. The solution uses ML-based content moderation techniques to consider the ethical aspects of generated images by sending appropriate warnings.
Highlights
- The solution can also be used for targeted image upscaling/editing, in which the resolution of an image is increased, with more designs and styles potentially being added to the image. The solution renders something entirely new in any part of an existing picture. The model also considers the ethical aspects of image generation and gives NSFW (Not Safe For Work) warnings appropriately. 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 the visual features of image data are changed to create more data of a similar kind. 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.
- Mphasis Synth Studio is an Enterprise Synthetic Data solution for generating high-quality synthetic data that can help derive and monetize trustworthy business insights while preserving privacy and protecting data subjects. Build reliable and high-accuracy models when you have no or low data. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $5.00/request |
Vendor refund policy
Currently, we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
First Version
Additional details
Inputs
- Summary
- The input must be a zip file named 'input.zip' (case sensitive).
- The zip file contains a parameters.json and a maximum of 4 folders with names matching the 'id' provided in the parameters.json file.
- The folders contain input & masked images named as 'input.png' and 'mask.png’ (case sensitive).
- The parameters.json (case sensitive) should contain the key, and value pairs: 'id' (should match the folder names), 'prompt', 'manual_seed'.
- Maximum of 4 images (folders) can be processed.
- Limitations for input type
- The solution can take up to 4 images and generate 3 different edited variations of each image.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Text-Guided-Image-Editing/blob/main/Input/input.zip
https://github.com/Mphasis-ML-Marketplace/Text-Guided-Image-Editing/blob/main/Input/input.zip
Resources
Vendor resources
Support
Vendor support
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