Qualcomm has posted a video demo of AI image generation using Stable Diffusion on an Android smartphone. The company says it's the world's first on-device demonstration of its kind.
Stable Diffusion, a very popular foundation model, is a text-to-image generative AI model capable of creating photorealistic images given any text input within tens of seconds — pretty incredible. At over 1 billion parameters, Stable Diffusion had been primarily confined to running in the cloud, until now. This demo shows Stable Diffusion running on an Android smartphone for the very first time -- Qualcomm AI Research performed full-stack AI optimizations using the Qualcomm AI Stack to deploy it on the device.
For the Stable Diffusion demo, Qualcomm started with the FP32 version 1-5 open-source model from Hugging Face and made optimizations through quantization, compilation, and hardware acceleration, enabling it to run it on a phone powered by Snapdragon 8 Gen 2 Mobile Platform.
The company explains that on-device edge AI provides many benefits including reliability, latency, privacy, efficient use of network bandwidth, and overall cost.
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Running Stable Diffusion on a smartphone is just the start. All the full-stack research and optimization that went into making this possible will flow into the Qualcomm AI Stack. Our one technology roadmap allows us to scale and utilize a single AI stack that works across not only different end devices but also different models.
This means that the optimizations for Stable Diffusion to run efficiently on phones can also be used for other platforms like laptops, XR headsets, and virtually any other device powered by Qualcomm Technologies. Running all the AI processing in the cloud will be too costly, which is why efficient edge AI processing is so important. Edge AI processing ensures user privacy while running Stable Diffusion (and other generative AI models) since the input text and generated image never need to leave the device — this is a big deal for adoption of both consumer and enterprise applications. The new AI stack optimizations also mean that the time-to-market for the next foundation model that we want to run on the edge will also decrease. This is how we scale across devices and foundation models to make edge AI truly ubiquitous.
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Take a look at the video demo below and hit the link for the full writeup...
Read More
Stable Diffusion, a very popular foundation model, is a text-to-image generative AI model capable of creating photorealistic images given any text input within tens of seconds — pretty incredible. At over 1 billion parameters, Stable Diffusion had been primarily confined to running in the cloud, until now. This demo shows Stable Diffusion running on an Android smartphone for the very first time -- Qualcomm AI Research performed full-stack AI optimizations using the Qualcomm AI Stack to deploy it on the device.
For the Stable Diffusion demo, Qualcomm started with the FP32 version 1-5 open-source model from Hugging Face and made optimizations through quantization, compilation, and hardware acceleration, enabling it to run it on a phone powered by Snapdragon 8 Gen 2 Mobile Platform.
The company explains that on-device edge AI provides many benefits including reliability, latency, privacy, efficient use of network bandwidth, and overall cost.
---
Running Stable Diffusion on a smartphone is just the start. All the full-stack research and optimization that went into making this possible will flow into the Qualcomm AI Stack. Our one technology roadmap allows us to scale and utilize a single AI stack that works across not only different end devices but also different models.
This means that the optimizations for Stable Diffusion to run efficiently on phones can also be used for other platforms like laptops, XR headsets, and virtually any other device powered by Qualcomm Technologies. Running all the AI processing in the cloud will be too costly, which is why efficient edge AI processing is so important. Edge AI processing ensures user privacy while running Stable Diffusion (and other generative AI models) since the input text and generated image never need to leave the device — this is a big deal for adoption of both consumer and enterprise applications. The new AI stack optimizations also mean that the time-to-market for the next foundation model that we want to run on the edge will also decrease. This is how we scale across devices and foundation models to make edge AI truly ubiquitous.
---
Take a look at the video demo below and hit the link for the full writeup...
Read More