#amdryzenai

Presenting Amuse 2.2 Beta: AMD Ryzen AI Image Quality Update and Stable Diffusion 3.5 Support.

Users of AMD Ryzen AI 300 Series CPUs and Radeon 7000 Series GPUs can download Amuse 2.2 Beta from the Amuse AI. Designed for AMD hardware, Amuse Beta is a dependency-free AI picture creation studio. Supporting Stability The current 2.2 Beta incorporates AI’s Stable Diffusion 3.5 (SD 3.5) family of models, including Medium, Large, and Large Turbo. Additionally, it features an improvement in picture quality for AMD’s Ryzen AI family of devices.

Support for Stable Diffusion 3.5

The 8.1 billion-parameter Stable Diffusion 3.5 Large model is part of the family. The base model provides advanced picture capabilities for 1 MP (~1024×1024) photos and is a powerful addition to the Stable Diffusion family. A condensed version of the same model, Stable Diffusion 3.5 Large Turbo, may produce a picture in just four stages at a little quality trade-off.

Another new model, Stable Diffusion 3.5 Medium, has 2.5 billion parameters and can produce pictures up to 2 Mega Pixels (~1536×1536). Being a smaller variant, it also supports a larger range of hardware and has a reduced memory footprint. Better text support and timely adherence are features of both the SD 3.5 Large and SD 3.5 Medium versions, which may be adjusted.

With the introduction of Amuse 2.2 Beta, AMD users may now access all of the Stable Diffusion 3.5 models. Amuse’s “minimum” mode, which loads the various components of an image generating model one after the other, enables the partial offload of bigger models that would not fit inside the device’s vRAM. Your RAM and disk read/write speeds have an impact on performance in this mode.

Image Quality Enhancements for AMD Ryzen AI Users

AMD Ryzen AI 300 Series CPUs have also seen significant improvements in image quality. Now completely supported are checkpoints and very efficient designs, such as Stable Diffusion Lightning, which will be the default “HQ” mode while Amuse is in EZ Mode. While offline and on the go, this upgrade significantly improves the quality of local AI picture synthesis on thin and light platforms.

Additionally, a 4x increase in native canvas size has been made to 1024×1024 pixels (~1 MP), which, when paired with AMD XDNA Super Resolution, produces an output of 2048×2048 pixels (~2 MP).Image Credit To AMD

These modifications allow Amuse 2.2 Beta’s new HQ model to produce high-quality photos with noticeably improved visual integrity and rapid adherence.

The images are much more cohesive and detailed. There is now a “boost quality” option in the HQ mode as well, which will often result in even higher image quality at the expense of longer creation times. On an AMD Ryzen AI 300 Series laptop, download Amuse and set the quality slider to “HQ” to test out this new HQ model.

Additionally, the AMD XDNA architecture that underpins Super Resolution features has been tuned to enable end-to-end upscaling at a far quicker rate than previously possible. Amuse is an extremely user-friendly AI toolkit that requires no technical expertise and automatically configures itself, especially when combined with the AI Filters and AI Design tab, which have strong AI assistants.

How to Use AMD Radeon Graphics Cards with Ryzen AI to Run Stable Diffusion 3.5 Medium, Large, and Turbo

How to install Stable Diffusion 3.5 on Ryzen AI 300 Series CPUs or AMD Radeon 7000 Series graphics cards:

Install Amuse after downloading it.

Accept the terms and conditions in their entirety.

Click the flask icon at the bottom to select “Advanced” mode. Select “Yes” when asked.

Select “Model Manager.”

In the model manager, look for “Stable Diffusion 3.5 Large Turbo.”

After selecting “Download,” watch for the download to complete.

Make sure “Text to Image” is chosen on the left after clicking on the “Image Generation” tab.

Choose the model you recently downloaded by using the “drop-down” menu: Large Turbo Stable Diffusion 3.5.

Click “Minimum Memory Mode” to continue in minimum memory mode if you are informed that the model is too big.

After writing your prompt, click “Generate.”

Large Language Models (LLMs) are not limited to programmers and developers; they are intended for all users. AMD has released simple-to-follow instructions for utilising LM Studio to run cutting-edge big language models on AI PCs with AMD Ryzen AI or AMD Radeon 7000 Series graphics cards, all without the need for coding knowledge. Today, they will compare the AMD Ryzen AI x86 platform options with those of its rival and see how well the two perform in practical applications.

The Neural Processing Unit (NPU) of the AMD Ryzen Mobile 7040 Series and AMD Ryzen Mobile 8040 Series processors is specifically developed to manage tasks related to growing artificial intelligence. The NPU’s 16 TOPs enable the user to run AI tasks as power-efficiently as possible. To find out more about AMD Ryzen AI PCs, watch this video.

AMD Ryzen AI Laptop

For instance, an AMD Ryzen AI-equipped laptop costs $899, whereas a competing x86 device costs $999. With a 120Hz frame rate, 2.8k resolution, and an OLED IMAX upgraded screen, the AMD AI PC is more affordable. The rival SKU is limited to a regular IPS panel with a 60 Hz frame rate and a 1.2k screen. Along with having twice the SSD storage, the AMD laptop has a lower TDP of 15W compared to the competition’s significantly higher TDP of 28W.

What about performance, then? Large language models may be deployed and used using LM Studio, one of the most widely used apps for consumers, and research has shown that the AMD AI PC performs better.

The AMD Ryzen 7 7840U 15W CPU outperforms the competition with a specimen sample prompt, achieving up to 17% quicker tokens per second than the widely used Mistral 7b model. In Llama v2 Chat 7b, the AMD Ryzen AI processor also delivers an average 79% quicker time-to-first-token. For LLMs operated in a daily environment, AMD suggests a 4-bit K M quantization; for jobs requiring the highest level of precision, such as coding, a 5-bit K M quantization is advised.

Additionally included is a discussion of how their performance (measured in tokens-per-second and time-to-first-token) stacks up against rivals at different quantization settings. It should be noted that AMD does not advise using Q8 or Q2 quantizations due to the former’s extreme slowness and the latter’s significant confusion loss. This aligns with suggestions made by colleagues in the sector.

They also evaluated the Llama v2 Chat 7b model, and the time-to-first-token and tokens-per-second metrics showed comparable outcomes:

Large language models may greatly boost productivity, and you can now execute them entirely locally thanks to Ryzen AI.

AMD is dedicated to developing AI and bringing forward its widespread advantages. AMD’s AI PCs make it possible for everyone to benefit from the expansion of AI consumer applications. Users may choose from a variety of x86 platforms. AMD Ryzen AI laptops, on the other hand, are not only very affordable, but they also offer customers a leading value proposition due to their ability to run consumer LLM programmes like LM Studio at next-level performance while using half the TDP.

Future AI PCs Improve with AMD

Users are the owners of the key to unlocking amazing AI experiences with 2nd generation AI PCs powered by AMD Ryzen AI.

AMD Ryzen AI Transforms AI Computers for All

With an AI PC from AMD, you can experience the power of personal computing at your fingertips. This opens up new possibilities for productivity, teamwork, and creativity, enabling you to keep more in touch with the outside world.

AI Resources for All Trades

Give yourself the tools to build the future as a developer with a fresh AI application, a creator with captivating content, or a business owner with the means to streamline processes and increase productivity at work.

Daily Digital Encounters, Elevated

Boost your relationship with Windows Studio Effects’ AI-powered capabilities and get better visuals thanks to its AI-generated graphics.

AI for Privacy of Data

Discover the fascinating ways artificial intelligence (AI) may improve your daily life while preserving the accessibility of your personal information.

AMD Ryzen AI engine

With AMD Ryzen AI support, Microsoft Windows Studio Effects is supported. AI Technology from AMD Ryzen that adapts to your needs the AMD Ryzen AI engine provides a way for consumers to be prepared for the workloads and applications in AI that are developing daily at an incredible rate.

AMD Ryzen AI Software

The tools and runtime libraries for optimising and implementing AI inference on AMD Ryzen AI-powered PCs are included in the AMD Ryzen AI Software. The neural processing unit (NPU) included in the AMD XDNA architecture the first specifically designed AI processing silicon on a Windows x86 processor may be used by apps using Ryzen AI software. With the ONNX Runtime and Vitis AI Execution Provider (EP), developers can now create and implement models learned in PyTorch or TensorFlow and execute them directly on laptops powered by Ryzen AI.

While using a CPU or GPU alone to run AI models may quickly deplete battery life, Ryzen AI laptops allow AI models to run on the inbuilt NPU, freeing up CPU and GPU resources for other computational activities. As a result, developers can execute concurrent apps and private, on-device LLM AI tasks effectively and locally, while also greatly extending battery life. Additionally, developers can start constructing their apps in minutes and fully use Ryzen AI’s AI acceleration capabilities thanks to Hugging Face’s expanding pre-trained model zoo, which supports a broad range of models, and its simple one-click installation method.

Ryzen AI AMD, Flow of Development

There are three simple steps to creating AI apps using Ryzen AI:

Train

A model in PyTorch, TensorFlow, or ONNX models is chosen or developed by the user, and it is then trained on the cloud.

Quantize

Several quantization processes are supported by Ryzen AI software development. Alternatively, developers may utilise Microsoft Olive with the Vitis AI quantizer as a plug-in, or they can use the AMD Vitis AI quantizer to quantize the model into INT8 and store it in ONNX format.

Implement

Using Ryzen AI hardware, ONNX Runtime Vitis AI EP efficiently divides, assembles, and runs the quantized ONNX models, optimising workloads to guarantee peak performance while using minimal power.