Unleashing the Power of GenAI: Stable Diffusion with Optimum-Intel and OpenVINO

Table of Contents

1. Introduction
2. What is Generative AI?
3. Challenges of Adopting Generative AI
4. The Paradigm of Hybrid AI
5. Open Vino: An Introduction
6. How Open Vino Accelerates Generative AI
7. Accelerating Stable Diffusion with Open Vino
8. Accelerating LLAMA 2.0 with Open Vino
9. Using Open Vino on Red Hat Open Shift
10. Performance Benchmarking on Intel Hardware

Introduction

In today's digital age, generative AI is revolutionizing the creation of high-quality content. However, adopting generative AI can be challenging due to high inference time and poor customer experience. As developers, we often face the dilemma of whether to use powerful machines like servers or data centers, or if we can leverage edge devices with optimized models. This article explores a third option: the paradigm of hybrid AI, which combines the strengths of both edge and cloud computing. Specifically, we will Delve into how Open Vino, an open-source toolkit for AI inference, can accelerate and deploy generative AI models on Intel hardware. This article covers topics such as the challenges of adopting generative AI, the capabilities of Open Vino, and how it can be used to optimize and deploy models on CPUs and GPUs. We will also explore specific examples of using Open Vino to accelerate Stable Diffusion and LLAMA 2.0 models, as well as its integration with the Red Hat Open Shift platform. Additionally, we will touch upon performance benchmarking on Intel hardware. So let's dive in and explore the world of generative AI and how Open Vino can be a game-changer in accelerating its deployment and execution.

What is Generative AI?

Generative AI is a branch of artificial intelligence that focuses on generating high-quality content such as images, text, and audio. Unlike traditional AI models that rely on pre-labeled data for classification or prediction, generative AI models learn to Create new data Based on Patterns and examples from existing datasets. These models leverage deep learning techniques, such as neural networks, to generate content that mimics human-like creativity. Generative AI has applications in various fields, including image synthesis, text generation, virtual reality, and more. The ability to generate realistic and original content has significant implications for industries like entertainment, design, and marketing.

Challenges of Adopting Generative AI

While generative AI offers immense potential, there are several challenges that developers and organizations face when adopting this technology.

1. Inference Time: Many generative AI models require significant computational resources and have long inference times. Running these models on resource-constrained hardware can result in poor performance and delay in generating outputs.

2. Customer Experience: Slow inference times can lead to a poor customer experience, especially in real-time applications like chatbots or content generation platforms. Users expect quick responses and immediate access to generated content. If the experience is laggy or delayed, it can discourage users from engaging with the application.

3. Model Size and Memory Footprint: Generative AI models can be large in size and require substantial memory to store and process data. This poses a challenge when deploying models on edge devices with limited storage and memory capacity.

4. Flexibility of Deployment: Different hardware architectures and platforms require optimized models for efficient deployment. Adapting generative AI models to run seamlessly on CPUs, GPUs, or specialized accelerators like NPUs and FPGAs can be complex and time-consuming.

The above challenges highlight the need for solutions that address these issues and enable developers to accelerate and deploy generative AI models efficiently.

The Paradigm of Hybrid AI

The paradigm of hybrid AI aims to combine the strengths of both edge and cloud computing in processing AI workloads. It offers a flexible approach that allows developers to leverage edge devices' real-time data processing capabilities, privacy of locally stored data, and cost efficiency, while also harnessing the benefits of the cloud, such as limitless compute resources on demand.

By adopting hybrid AI, developers can choose to process AI workloads using available or targeted system resources and accelerators on the edge or in the cloud. This paradigm offers the flexibility to switch between the cloud and edge, enabling efficient execution of generative AI models based on specific use cases and infrastructure requirements.

However, accomplishing this requires tools and frameworks that facilitate the conversion, optimization, and deployment of generative AI models across different hardware architectures and platforms. This is where Open Vino comes into play.

Open Vino: An Introduction

Open Vino is an open-source toolkit for AI inference developed by Intel. It provides a comprehensive set of tools and libraries that enable developers to convert AI models from different frameworks, such as TensorFlow or PyTorch, into an intermediate representation (IR) format. This IR format can then be optimized and deployed with performance improvements on a wide range of hardware, including CPUs (such as Intel Xeon and Core processors), GPUs (such as the Intel Arc 770), NPUs, and FPGAs.

Open Vino offers several key features that make it an efficient and versatile toolkit for accelerating and deploying generative AI models:

1. Model Conversion: Open Vino supports the conversion of AI models from popular frameworks to its optimized IR format. This enables seamless interoperability and portability across different frameworks and hardware architectures.

2. Optimization Techniques: Open Vino incorporates various optimization techniques to improve model performance, such as model quantization, which reduces model size and memory footprint. This optimization allows for efficient deployment on resource-constrained edge devices.

3. Hardware Acceleration: Open Vino leverages the computational capabilities of Intel CPUs, GPUs, NPUs, and FPGAs to accelerate AI inference. By harnessing the power of specialized hardware, developers can achieve significant performance improvements and reduce inference time.

4. Red Hat Open Shift Integration: Open Vino is compatible with the Red Hat Open Shift platform, an open-source machine learning platform that provides tools for optimizing the complete life cycle of AI workflows. This integration enables developers to seamlessly deploy and manage generative AI models on Intel hardware using Open Vino.

In the following sections, we will explore how Open Vino can accelerate and optimize generative AI models, specifically focusing on examples with Stable Diffusion and LLAMA 2.0.

How Open Vino Accelerates Generative AI

Open Vino offers several techniques and tools that enable developers to accelerate generative AI models, reducing inference time and improving overall performance. Let's explore some of these techniques and how they contribute to enhancing model execution.

1. Quantization: One of the key techniques used by Open Vino is quantization, which involves reducing the precision of the model from 32-bit floating-point (FP32) to 16-bit floating-point (FP16). This reduction in precision helps in reducing the model size and memory footprint, allowing for more efficient deployment, especially on edge devices with limited resources.

2. Precision Calibration: Open Vino provides APIs, like the Hugging Face Optimum, that allow developers to calibrate model precision for specific hardware targets. By fine-tuning the model precision based on hardware capabilities, developers can achieve optimal performance and maximize resource utilization.

3. Hardware Acceleration: Open Vino leverages the computational power of Intel CPUs and GPUs, such as the Intel Xeon, Core processors, and the Intel Arc 770, to accelerate AI inference. By utilizing hardware-specific optimizations and Parallel processing capabilities, Open Vino can significantly reduce inference time and enable real-time generation of content.

4. Model Optimization: Open Vino performs various model optimizations, such as layer Fusion, kernel fusion, and memory optimization, to improve overall performance. These optimizations streamline model execution, reduce redundant computations, and enhance memory access patterns, resulting in faster inference and reduced computational overhead.

By leveraging these techniques and tools provided by Open Vino, developers can overcome the challenges associated with generative AI adoption and achieve efficient and accelerated execution of models.

Accelerating Stable Diffusion with Open Vino

Stable Diffusion is a popular generative AI model used for image generation. However, running Stable Diffusion on resource-constrained hardware can result in long inference times and compromised efficiency. Here's how Open Vino can accelerate Stable Diffusion:

1. Text Encoder: Stable Diffusion takes a text prompt and a negative prompt as input to guide the image generation process. The first step is to execute the text encoder, which creates a condition for generating an image from the text prompt.

2. Denoising: Stable Diffusion uses a denoising algorithm called the "Unet model" to progressively denoise the latent image representation. This step-by-step denoising enhances the quality and fidelity of the generated images. However, the Unet model can be computationally expensive to run.

3. Auto Encoder: In the final step, Stable Diffusion decodes the latent space into an image using an autoencoder. The autoencoder converts the learned representation back into a visually appealing and coherent image.

Open Vino helps optimize each of these three models in the Stable Diffusion pipeline, reducing their size and memory footprint through quantization to FP16 from FP32. With just a few lines of code, developers can achieve quantization and improve the efficiency of model execution on both CPUs and GPUs. This optimization allows for faster image generation and more efficient deployment of Stable Diffusion for text-image generation use cases.

Accelerating LLAMA 2.0 with Open Vino

LLAMA 2.0 is another popular generative AI model that focuses on text generation. It is licensable from Meta and offers state-of-the-art capabilities in producing coherent and informative textual content. With Open Vino, developers can accelerate LLAMA 2.0 and optimize its execution on Intel hardware.

Open Vino enables model quantization and precision calibration for LLAMA 2.0, resulting in a model that can fit within a memory footprint of approximately 9 GB. By quantizing the model with INT8 precision, developers can achieve a balance between model size and inference time, making it suitable for deployment on edge devices.

Once quantized, developers can Interact with the LLAMA model, ask it questions, and observe the latency of its responses. This interaction demonstrates the real-time capabilities of accelerated LLAMA 2.0, showcasing its usefulness in applications like chatbots, virtual assistants, and content generation platforms.

Using Open Vino on Red Hat Open Shift

One of the key advantages of Open Vino is its integration with the Red Hat Open Shift platform. Red Hat Open Shift is an open-source machine learning platform that provides a comprehensive set of tools for optimizing the complete life cycle of AI workflows.

By leveraging Open Vino on the Red Hat Open Shift platform, developers can:

1. Train Models: Red Hat Open Shift Data Science enables developers to train AI and machine learning models easily. It provides a streamlined workflow to preprocess data, train models, and evaluate performance.

2. Serve Models: With Red Hat Open Shift, developers can serve trained models as web services, enabling seamless integration with other applications or platforms.

3. Monitor and Manage Models: Red Hat Open Shift ensures easy monitoring and management of AI and machine learning models. Developers can track model performance, monitor resource utilization, and Scale deployments based on workload demands.

Additionally, Red Hat Open Shift comes with a variety of benefits, including less time spent managing AI infrastructure, tested AI/ML tooling, and support for Intel-certified operators and plugins. This integration with Open Vino enables efficient deployment and execution of generative AI models on Intel hardware, delivering high-performance and scalable solutions.

Performance Benchmarking on Intel Hardware

To ensure optimal performance and identify potential bottlenecks in generative AI workflows, developers can utilize performance benchmarking using the container playground built with Red Hat Open Shift. This playground provides access to REST APIs that facilitate performance testing and profiling of AI workloads on different Intel hardware, including CPUs and GPUs.

Benchmarking generative AI workloads helps developers understand and optimize resource utilization, identify areas for improvement, and fine-tune models for enhanced performance. By leveraging the performance benchmarking capabilities on Intel hardware, developers can deliver robust and efficient generative AI solutions.

Conclusion

Generative AI has immense potential in transforming how content is created and generated. However, adopting generative AI can be challenging due to high inference time and limited deployment options. The paradigm of hybrid AI, combining the strengths of edge and cloud computing, offers a flexible approach to process AI workloads efficiently.

Open Vino, an open-source toolkit for AI inference, is a powerful tool that enables developers to accelerate and deploy generative AI models on Intel hardware. With features like model conversion, optimization techniques, and hardware acceleration, Open Vino streamlines the deployment process and improves model performance.

In this article, we explored the challenges of adopting generative AI, the capabilities of Open Vino, and its integration with the Red Hat Open Shift platform. We also delved into specific examples of accelerating Stable Diffusion and LLAMA 2.0 models using Open Vino and highlighted performance benchmarking on Intel hardware.

By leveraging the capabilities of Open Vino and harnessing the power of Intel hardware, developers can unlock the full potential of generative AI, delivering high-quality and real-time content generation for various applications and industries.

Highlights

• Generative AI is revolutionizing content creation but has challenges like high inference time and poor customer experience.
• The paradigm of hybrid AI combines edge and cloud computing for optimized AI workloads.
• Open Vino is an open-source toolkit that accelerates and deploys generative AI models on Intel hardware.
• Open Vino optimizes models through quantization and precision calibration for CPUs and GPUs.
• Stable Diffusion and LLAMA 2.0 are examples of generative AI models accelerated by Open Vino.
• Red Hat Open Shift integrates with Open Vino for efficient deployment and management of generative AI models.
• Performance benchmarking on Intel hardware allows developers to optimize resource utilization and fine-tune models.

FAQ

Q: Can Open Vino be used with other hardware accelerators besides Intel CPUs and GPUs? A: Yes, Open Vino supports a wide range of hardware accelerators, including NPUs and FPGAs, enabling developers to leverage specialized hardware for optimized AI inference.

Q: How does Open Vino handle the conversion of models from different frameworks? A: Open Vino provides tools and libraries to convert AI models from popular frameworks like TensorFlow or PyTorch into its intermediate representation (IR) format. This IR format allows for seamless interoperability and portability across different frameworks and hardware architectures.

Q: Can Open Vino be used to optimize other types of generative AI models besides Stable Diffusion and LLAMA 2.0? A: Yes, Open Vino can be used to optimize and accelerate a wide variety of generative AI models. Its quantization and optimization techniques can be applied to different types of models to improve their performance and efficiency.

Q: Is Open Vino limited to running generative AI models on edge devices or can it also be used in cloud environments? A: Open Vino can be used in both edge and cloud environments. Its flexibility allows developers to switch between running AI workloads on edge devices or in the cloud, depending on their specific use cases and infrastructure requirements.

Q: Does Open Vino require extensive coding knowledge to optimize and deploy generative AI models? A: While some coding knowledge is required, Open Vino provides a comprehensive set of tools, APIs, and examples that make it accessible for developers with varying levels of expertise. The official Open Vino documentation and community support can assist developers in getting started and overcoming any challenges they may face.