Discover Web Stable Diffusion: an AI project that brings stable diffusion models to web browsers

Source: https://mlc.ai/web-stable-diffusion/

Recently, artificial intelligence (AI) models have shown dramatic improvements. The open source movement has made it easy for programmers to combine different open source models to create new applications.

Stable diffusion enables automatic generation of photorealistic and other images from text input. Because these models are typically large and computationally intensive, all required computations are forwarded to servers (GPUs) when building web applications that use them. Additionally, most workloads need a specific family of GPUs to run popular deep learning frameworks on.

The Machine Learning Compilation (MLC) team presents a project as an effort to change the current situation and increase biodiversity in the environment. They believed there were many benefits to shifting the computation to the customer, such as lower service provider costs and more personalized experiences and security.

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According to the team, ML models should be able to be transported to a location without the necessary GPU-accelerated Python frameworks. AI frameworks typically rely heavily on optimized computational libraries from hardware vendors. Therefore backup is important to start over. To maximize returns, unique variants should be generated based on each customer’s infrastructure specifications.

The proposed stable web syndication injects the regular syndication model directly into the browser and runs directly through the client GPU on the users laptop. Everything is handled locally within the browser and never touches a server. According to the team, this is the world’s first stable browser-based deployment.

Here, machine learning build technology plays a central role (MLC). PyTorch, Hugging Face diffusers and tokenizers, rust, wasm and WebGPU are some of the open source technologies on which the proposed solution is based. Apache TVM Unity, a fascinating work-in-progress within Apache TVM, is the foundation upon which the main stream is built.

The team used the Hugging Face Speaker Libraries Stable Diffusion Models from Track v1-5.

Key model components are captured in an IRM module in TVM using TorchDynamo and Torch FX. TVM’s IRModule can generate executable code for each function, allowing them to be implemented in any environment capable of running at least the minimal TVM runtime (javascript is one of them).

They use TensorIR and MetaSchedule to create scripts that automatically generate efficient code. These transforms are locally optimized to generate GPU-optimized shaders using the devices’ native GPU runtimes. They provide a repository for these changes, allowing for future builds to be produced without tuning.

They build static memory scheduling optimizations to optimize memory reuse on multiple levels. The TVM web runtime uses Emscripten and typescript to facilitate form deployment generation.

Also, they use the wasm port of the Hugging Face Rust tokenizer library.

Except for the final step, which creates a 400-loc JavaScript app to tie everything together, the entire workflow is done in Python. The introduction of new models is an exciting by-product of this type of participatory development.

The open source community is what makes this possible. In particular, the team relies on TVM Unity, the latest and coolest addition to the TVM project, which provides such first interactive MLC development experiences in Python, allowing them to build further optimizations in Python and gradually release the app on the web . TVM Unity also facilitates the rapid composition of new ecosystem solutions.


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Tanushree Shenwai is a Consulting Intern at MarktechPost. She is currently pursuing her B.Tech from Indian Institute of Technology (IIT), Bhubaneswar. She is passionate about Data Science and has a keen interest in the application scope of Artificial Intelligence in various fields. She is passionate about exploring new advancements in technologies and their real-life application.

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