SDXS: 90 millisecond renders with comfyUI
TLDRThe video discusses the challenges of slow loading times in KY due to a high number of custom nodes and offers a solution. By reinstalling KY and using the verbose command line parameter, the video demonstrates how to significantly improve rendering speed with SDXS, achieving rapid image generation despite lower quality. The workflow is shared for users to experiment with, highlighting the benefits of a clean KY install over a cluttered one.
Takeaways
- 🚀 The video discusses improving the render times and performance of a complex KY (Kerchief) setup with many custom nodes and models.
- 🔍 It highlights the issue of slow GUI load times in the browser, which can take up to 5 seconds, affecting both interface and rendering performance.
- 🛠️ The speaker suggests using developer tools to identify and disable problematic functions, but acknowledges this as a temporary and inconvenient solution.
- 🌟 SDXS (an AI model) is introduced as a fast rendering technology, albeit with average image quality, producing many 'bears' per second in the example given.
- 🔄 A strategy to improve performance is to disable previews in the manager, but this is not ideal as previews are useful for users.
- 💻 The speaker plans to replace the existing slow server instance by downloading a fresh copy of KY and setting up a new environment.
- 📁 The importance of correctly configuring the 'extra model path.yml' file is emphasized to direct KY to the correct model source folder.
- 📈 The new setup significantly improves load times, with the server starting in microseconds compared to the previous milliseconds.
- 📊 The use of the verbose command line parameter allows users to understand what KY is doing, including the selection of models from specified paths.
- 🎨 The video demonstrates the dynamic generation of images with SDXS, changing prompts in real-time and achieving fast render speeds of 10 milliseconds per image.
- 🏎️ The comparison shows a drastic improvement in render times, from 0.19 to 0.10 seconds per image, by using a clean KY install and SDXS technology.
Q & A
What is the main issue discussed in the transcript?
-The main issue discussed is the slow loading time and poor performance of the KY interface, particularly when dealing with a high number of custom nodes and models.
What is the proposed solution to the performance problem?
-The proposed solution is to download a fresh copy of KY, properly configure it to use the existing model files, and utilize the verbose command line parameter for better visibility into its operations.
How does the speaker suggest improving the rendering speed?
-The speaker suggests using SDXS technology for faster image generation and batch processing to improve rendering speed.
What is the significance of the 'verbose' command line parameter?
-The 'verbose' command line parameter allows users to see detailed information about what the program is doing, which can help in troubleshooting and understanding the workflow.
What is the role of the 'extra model path.yml' file?
-The 'extra model path.yml' file is used to specify the paths from which KY should load its models, which is crucial when transferring models from one installation to another.
How does disabling previews in the manager help with performance?
-Disabling previews can reduce the load on system resources, as it avoids the overhead of rendering and displaying preview images, thus improving performance.
What is the average time taken to generate an image in the old and new KY installations?
-In the old installation, it took an average of 0.19 to 0.20 seconds, while in the new installation, it took about 0.09 to 0.10 seconds.
What is the benefit of using a clean KY installation?
-A clean installation without extra custom nodes and extensions results in faster load times and better overall performance, allowing for quicker access to the interface and rendering.
How can users optimize their workflow with the new KY installation?
-Users can optimize their workflow by ensuring that the new KY installation is properly configured to use their existing models and by leveraging the verbose parameter for better visibility and control over the program's operations.
What is the potential maximum frame rate for SDXS technology?
-In theory, SDXS technology can achieve up to 100 frames per second for image generation.
How does the speaker demonstrate the dynamic nature of SDXS?
-The speaker demonstrates the dynamic nature of SDXS by changing the prompt in real-time and showing how the generated images update instantly to reflect the new input.
Outlines
🚀 Optimizing KY with Custom Nodes and Performance Issues
The paragraph discusses the challenges of dealing with a high number of custom nodes and IP adapters in KY, which can lead to slow loading times and performance issues. The speaker shares their personal experience with an existing confy install that takes a long time to load, particularly in the browser, and how it affects both the interface and rendering performance. They mention using developer tools to identify problematic functions and provide an example of using SD XS, a technology that generates images quickly but with varying quality. The speaker then suggests a solution to the problem, which involves killing the current server instance, downloading a fresh copy of KY, and setting up the new install to use the existing model folder, effectively combining the benefits of a clean install with the user's custom models. This results in significantly improved load times and performance, allowing the speaker to run a queue with a batch size of one and still achieve fast results.
🎨 Enhancing Image Generation with SD XS and Auto Queue
In this paragraph, the focus is on the capabilities of SD XS for image generation and how to enhance the workflow for better performance. The speaker explains how changing the prompt to a cute dog results in a rapid generation of dog images and highlights the dynamic nature of the technology, which allows for real-time changes to the prompt. They also discuss the speed of image generation, mentioning the ability to save images directly and the impact of the config GUI autoq overhead on the generation rate. The speaker then demonstrates changing the prompt to a cat with wings flying in the sky and notes how the image updates in real-time. They address the trade-off between speed and quality, acknowledging that while the images may not be high quality, the generation speed is impressive. The speaker concludes by comparing the performance of the old install with the new, clean install, showing a significant improvement in image generation speed, and encourages users to experiment with the workflow even if they do not have a fast computer.
Mindmap
Keywords
💡SDXS
💡KY
💡GUI
💡Custom Nodes
💡Performance
💡Server Instance
💡Config UI
💡YAML File
💡Verbose Command Line Parameter
💡Batch Processing
💡Latent Images
Highlights
SDXS technology allows for rapid image generation with a one-step diffusion process.
Performance issues can arise from a high number of custom nodes and models in an interface.
Disabling previews in the manager can be a temporary solution for slow interface performance.
A fresh installation of KY can significantly improve load times and overall performance.
Using the verbose command line parameter provides insights into KY's operations and model sourcing.
The new KY install can select models from specified folders, streamlining the process.
SDXS can generate images at an impressive rate, even with a less-than-perfect quality.
Batch processing with SDXS can greatly increase the speed of image generation.
The auto queue feature in SDXS facilitates faster image generation.
Changing the prompt in real-time allows for dynamic image generation with SDXS.
SDXS can achieve high frame rates, theoretically up to 100 frames per second.
A clean KY install with SDXS support can reduce image generation time to 0.09-0.10 seconds.
The difference between a clean and cluttered KY install is substantial in terms of performance.
The described workflow is available on float for users to experiment with, regardless of their computer's speed.