Sold by: MphasisÂ
Deployed on AWS
GenAI solution to redraw existing images to incorporate new elements described in the text prompt.
Overview
Text-guided Image Variation leverages the synergy of advanced Generative AI models to create high-quality ethical images either by incorporating or excising components in alignment with the specified textual directives. The solution uses deep generative neural architectures, employing cutting-edge methodologies like image synthesis and latent diffusion models. The system is adept at amplifying the resolution or metamorphosing the aesthetic of the imagery, contingent upon the textual cues provided. To ensure the generation of ethically conformant images, the solution is fortified with ML-driven content moderation techniques. Specifically, if the NSFW (Not Safe For Work) metric escalates beyond a threshold, or a void image is the output, the model interjects with an error prompt. This alert advises for modification of the input prompt or suggests a change in the random seed, thereby ensuring the adherence to content propriety and generation of contextually appropriate images.
Highlights
- The solution can also be used for image upscaling, in which the resolution of an image is increased, 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. 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 Platform 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.
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/host/hour |
|---|---|---|
ml.p2.xlarge Inference (Batch) Recommended | Model inference on the ml.p2.xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.p3.2xlarge instance type, real-time mode | $5.00 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $10.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.p3.8xlarge Inference (Real-Time) | Model inference on the ml.p3.8xlarge instance type, real-time mode | $5.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $5.00 |
ml.g4dn.4xlarge Inference (Real-Time) | Model inference on the ml.g4dn.4xlarge instance type, real-time mode | $5.00 |
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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
Usage Methodology for the algorithm:
- The input must be 'Input.zip' file.
- The zip file should contain Input file which includes .jpg/.png image and .json file.
- The name of the .jpg/.png should be "input" and the name of the .json file should be "parameters" which is case sensitive.
- Name of the folder inside the zip file should be “Input” which is case-sensitive
- check the instructions and sample endpoint in the sample jupyter file provided.
- Limitations for input type
- Should provide both .jpg/.png and .json files. The code only supports jpg/png formats.
- Input MIME type
- application/zip
https://github.com/Mphasis-ML-Marketplace/Synth-Studio-text-guided-image-variation/blob/main/Input/Input.zip
https://github.com/Mphasis-ML-Marketplace/Synth-Studio-text-guided-image-variation/blob/main/Input/Input.zip
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Support
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