Hello everyone and welcome to a new video! Today's video is a continuation of the previous one about upscaling with ComfyUI images. In today's article, I’ll show you a handy trick to add more details to your image before you upscale it by 4X.
As shown below, you can see the difference in the workflow I’ll discuss. The standard upscaling we did yesterday is fast and efficient, but this new method is a bit slower and provides much more detailed images. You can observe enhanced details in the ship's decorations, the lighting, and the wave patterns. Overall, the image on the right showcases more refined and clearer details compared to the one on the left.
In this guide, I will walk you through the refined workflow:
I am using the Control Box, which is free to download on my Ko-fi page. The link is in the description. This tool helps organize all my workflow essentials and control everything from one place. For image generation, I am using the AGI Excel model, available for silver and gold members. It provides precise and high-quality images. In this example, I’ll create an image of a cat using Prompt Engineer as my AI expert.
Here’s the step-by-step process:
The combination of 1.5X latent upscale plus the 4X upscale results in a beautifully detailed final image, reaching a resolution of 6,144 by 6,144 pixels.
Using this method, the enhanced smaller details such as decorations, lighting, and reflections are significantly improved. However, one major drawback is that it is slower than the conventional method. I use an 8GB VRAM graphics card, which extends the generation time.
Let's create a cat image to compare both outcomes:
Normal Image:
Enhanced Image:
I hope you find this method valuable for creating highly detailed images. It may be slower, but the quality payoff is more than worth it. If you have any questions, feel free to reach out on my Ko-fi page. Make sure to subscribe for future videos. Thank you for reading!
Q1: What is the benefit of using the latent space for upscaling images?
Q2: How does the Control Box help in the workflow?
Q3: Why is the suggested method slower?
Q4: What configuration is recommended for best results?
Q5: Where can I find the tools and models used in this method?
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