Now we're adding results from all the RTX GPUs, from the RTX 2060 all the way up to the RTX 4090, using the TensorRT optimizations.įor AMD and Intel GPUs, there are forks of the A1111 webui available that focus on DirectML and OpenVINO, respectively. Instructions are at that link, and we've previous tested Stable Diffusion TensorRT performance against the base model without tuning if you want to see how things have improved over time. But there are still additional steps required to extract improved performance, using the latest TensorRT extensions. Getting things to run on Nvidia GPUs is as simple as downloading, extracting, and running the contents of a single Zip file. The easiest way to get Stable Diffusion running is via the Automatic1111 webui project. Not all AI projects have received the same level of effort as Stable Diffusion, but this should at least provide a fairly insightful look at what the various GPU architectures can manage with AI workloads given proper tuning and effort. We've been poking at Stable Diffusion for over a year now, and while earlier iterations were more difficult to get running - never mind running well - things have improved substantially. We've benchmarked Stable Diffusion, a popular AI image generator, on the 45 of the latest Nvidia, AMD, and Intel GPUs to see how they stack up. How fast are consumer GPUs for doing AI inference using Stable Diffusion? That's what we're here to investigate. While more advanced tools like ChatGPT can require large server installations with lots of hardware for training, running an already-trained network for inference can be done on your PC, using its graphics card. Using trained networks to create images, videos, and text has become not just a theoretical possibility but is now a reality. Stable Diffusion and other AI-based image generation tools like Dall-E and Midjourney are some of the most popular uses of deep learning right now.
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