Flux AI Images Refiner And Upscale With SDXL

Introduction

In recent discussions, we explored the capabilities of flux image generation models. Today, we will delve into the process of fine-tuning and upscaling images produced by these models. We will be utilizing the SDXL model for tile upscaling and addressing skin artifacts commonly found in the generated images. Additionally, we will introduce an upscaling technique to enhance the final AI images.

After testing the flux diffusion model over the past few days, it has become evident that while they excel at following prompt instructions, they can produce artifacts on human characters. This often results in an unrealistic, plastic appearance, particularly on hair and skin. To overcome this issue, we can employ realistic checkpoint models in SDXL, which include tools like RealVis or Zavi Chroma XL. These models can help refine the skin tones and hairstyles, as well as improve the texture of other elements, such as trees and leaves, which can also appear plasticky.

Let’s begin by executing some prompts within the flux image generation framework. One option is to switch from an empty latent image to a VAE encode, allowing us to perform image-to-image transformations. Our main goal is to refine the outputs from the flux diffusion model. We will create an initial image based on the prompt of a light bulb filled with flowers, positioned on the ground.

As we examine the result, we may notice that while the generated image is not yet realistic, we can proceed with the refinement process. The first step is to apply a tile upscale, which doubles the resolution of the original image from the first generation. Next, we will employ the SDXL refiner to improve the image further. Increasing the denoising level slightly to 0.55 will help us achieve a more refined appearance when performing latent upscaling.

These settings are quite basic and can be modified depending on how much denoising or upscaling you wish to apply during the latent stage. The impact of latent upscaling can be observed by comparing the enhanced image with the original. Although the difference may not be drastic with this particular image, many times it results in more natural textures for leaves and flowers, with reduced artifacts.

When selecting which SDXL models to use, I find that RealVis or Zavi Chroma XL provide the most satisfying results. The advantage of testing an upscale on images generated by flux is significant; relying solely on a sampler in flux to produce high-resolution images can be time-consuming. Instead, we can transition our image data to SDXL for a more expedient process of enhancement.

This quick overview demonstrates how we can mitigate some issues associated with flux diffusion models, especially in the absence of ControlNet or other extensions for flux at this time. By transferring the image data to SDXL, we can significantly enhance the output. In future videos, we will explore the potential for creating AI video scenes based on images generated through flux.

Here are some additional examples that illustrate how much more natural images can appear after refinement with the SDXL image refiner and tile upscaling.

Keywords

Flux AI, image generation, refinement, upscaling, SDXL, tile diffusion, artifacts, RealVis, Zavi Chroma XL, skin refinement, human characters, AI images.

FAQ

1. What is the main purpose of using SDXL with flux-generated images?
The primary goal of using SDXL is to refine and upscale the images produced by flux diffusion models, especially to address common artifacts found in human characters, making them appear more realistic.

2. Which models are recommended for refining images?
RealVis and Zavi Chroma XL are the preferred models for refining the skin tones and hairstyles, along with improving other elements like leaves and flowers.

3. How do you upscale an image using SDXL?
To upscale an image using SDXL, you can apply a tile upscale to double the resolution and then utilize the SDXL refiner with specific settings to enhance the output further.

4. Are there any artifacts that can occur in flux-generated images?
Yes, flux diffusion models often produce artifacts on human characters, resulting in a plastic appearance, particularly affecting hair and skin.

5. Will the enhancements always make a visible difference in the images?
While the difference may not always be significant in every image, many cases show improved natural textures, especially for elements like leaves and flowers after refinement.