Double Your Stable Diffusion Inference Speed with RTX Acceleration TensorRT: A Comprehensive Guide

The paragraph introduces the release of NVIDIA's newest driver and an extension for the Stable, Diffusion Automatic1111 Web UI interface. It highlights the significant speed improvements achieved with the GeForce RTX 4090, up to 2 times faster, and promises a tutorial on installing and using this extension. The importance of following the video is emphasized for those seeking up to 70% speed improvements. Comparisons are provided to demonstrate the performance gains, especially when using the RTX 3090 TI. The video also mentions the use of more VRAM but justifies it with the substantial speed improvements. The tutorial includes a Github readme file and encourages viewers to star the repository, follow the creator on various platforms, and subscribe to the channel.

This paragraph discusses the installation process of the TensorRT extension, starting with an automatic installation method using a .bat file for fresh installations of the Automatic1111 Web UI. It also touches on the necessity of having the latest Game Ready driver from NVIDIA for optimal performance. The paragraph compares the performance between different versions of the NVIDIA driver on the RTX 3090 TI GPU and suggests that there are no significant speed changes observed. The manual installation process is also outlined, which includes using command line arguments and cloning the extension into the extension folder. The importance of upgrading to the latest version of cuDNN for manual installations is stressed, with detailed instructions provided on how to do so.

The paragraph explains the process of generating TensorRT versions of the models to be used, including the Realistic Vision version 5.1 and the best VAE files. It discusses the speed of generating images without TensorRT and the expected improvements with it. The paragraph also addresses some issues encountered with the extension, noting that it is still under active development and not yet fully stable. The manual installation process is reiterated, with a focus on the steps for non-Patreon supporters. The paragraph also provides guidance on setting up the Web UI to use the generated TensorRT models and troubleshooting tips for common errors.

This section details the process of generating TensorRT files for different models, including the Realistic Vision version 5.1 and SDXL base 1.0. It explains the importance of the model.json file for using the TensorRT files and the process of updating this file each time a new TensorRT file is generated. The paragraph also discusses the generation of ONNX files, the use of command line arguments to avoid errors during TensorRT file generation, and the suggestion to use xFormers. It highlights the development branch of the Automatic1111 Web UI and the necessity of switching to it for certain functionalities, such as SDXL TensorRT. The paragraph concludes with a demonstration of the speed improvements achieved with TensorRT and the successful generation of images.

The paragraph presents a speed comparison between generating images with and without TensorRT, showcasing significant improvements in speed. It also discusses the generation of TensorRT for SDXL and the use of pre-compiled models available to Patreon supporters. The paragraph emphasizes the potential for even greater performance with static shapes and the importance of dynamic ranges for varying resolutions and batch sizes. It also touches on the potential for using TensorRT with custom models and LoRAs, and the process for generating TensorRT versions for these. The paragraph concludes with a call to action for viewers to support the creator through Patreon, Github, and other platforms, and provides a brief overview of the creator's activities and offerings.

This paragraph focuses on the creator's personal experience training a custom model and the intention behind using a low-quality dataset for SDXL DreamBooth. It discusses the potential for achieving impressive results even with subpar data, and the expectation of better outcomes with higher quality datasets. The paragraph then explores the combination of this custom model with LoRAs, specifically the SDXL Pixel Art XL LoRA from CivitAI. It outlines the process of downloading, installing, and using LoRAs with the model, and the expected speed improvements. However, it also acknowledges some issues encountered with the LoRA integration, particularly with the SDXL model, and the need to report these bugs to the developers for future fixes.

The paragraph delves into troubleshooting the integration of LoRAs with TensorRT, particularly focusing on the SDXL model. It describes the process of generating TensorRT profiles for the SDXL base model and the subsequent generation of the TensorRT LoRA. The paragraph highlights the necessity of restarting the Web UI after adding new LoRAs and the potential need to switch between different branches of the Web UI to resolve errors. It also discusses the manual selection of LoRA TensorRT for effective use and the observed differences in output. The paragraph concludes with a note on the ongoing development and potential bugs, and the creator's commitment to reporting these issues for resolution.

In the final paragraph, the creator expresses gratitude for the community's support and encourages viewers to engage with the content by starring the Github repository, forking it, and watching it. The creator also highlights the importance of Patreon support and provides a range of platforms where viewers can follow and support the creator's work. The paragraph mentions the creator's full-time dedication to AI and various activities, including consultation, side projects, and model training. It concludes with an invitation for collaboration and a promise of future tutorials.