Flux AI Images Refiner And Upscale With SDXL

Future Thinker @Benji
6 Aug 202404:59

TLDRThis video tutorial demonstrates how to refine and upscale AI-generated images using Flux AI models with the help of SDXL. It addresses common issues like plastic-looking hair and skin artifacts by employing realistic checkpoint models like Real VIz or Zavi Chroma XL. The process involves initial image generation, tile upscaling, refining with increased Deno level, and final upscaling for a more natural result. The tutorial also hints at future content on creating AI video scenes with Flux.

Takeaways

  • 🔍 The video discusses refining and upscaling AI images generated by Flux using the SDXL tool.
  • 🎨 Flux image generation models are being fine-tuned to improve image quality, particularly in human characters.
  • 🤖 Skin artifacts, such as plastic-looking hair and skin, are common issues with Flux diffusion models that the video aims to address.
  • 🖼️ Realistic checkpoint models like Real Viz or ZaVi Chroma XL are suggested for refining human character skins and elements like trees and leaves.
  • 📝 The process involves using a text-to-image group for Flux image generation and switching to VAE encode for image-to-image refinement.
  • 🔧 A tile upscale technique is used to double the original image size before refining with the SDXL refiner group.
  • 🛠️ Denoising level adjustments and latent upscaling with SDXL are part of the refinement process to enhance image details.
  • 🌟 The video provides a step-by-step guide on how to refine and upscale AI images, including a demonstration with a text prompt.
  • 🌱 The example of a light bulb with flowers inside shows the potential artifacts in the initial Flux diffusion model.
  • 🌐 The final upscaled image is expected to look more natural, with fewer plastic or artifact styles, especially in complex elements like leaves and flowers.
  • 🔄 The video concludes with a mention of future content, including creating AI video scenes using Flux for image generation.

Q & A

  • What is the main purpose of using the SDXL in the video script?

    -The main purpose of using SDXL in the video script is to refine and upscale AI-generated images from the Flux models, particularly to fix skin artifacts and enhance the realism of human characters, trees, and leaves.

  • What issues with the Flux diffusion models does the video address?

    -The video addresses the issue of artifacts on human characters in Flux diffusion models, which can make them look plastic, especially in areas like hair and skin.

  • What are the two specific realistic checkpoint models mentioned in the script for refining human character skins?

    -The two specific realistic checkpoint models mentioned for refining human character skins are Real Viz and Zavi Chroma XL.

  • Can you explain the process of refining an image from the Flux diffusion model as described in the script?

    -The process involves using tile upscaling with tile diffusion and tile control net to double the original image size, then refining the skin tone and hairstyles in the SDXL sampler to avoid plastic-looking hair or artifact surfaces, and finally upscaling the image as the last step.

  • What is the significance of using a 'tile upscale' in the refining process?

    -The significance of using a 'tile upscale' is to increase the resolution of the image before further refinement, which helps in enhancing the details and reducing the plastic or artifact-like appearance of elements in the image.

  • What is the role of the 'refiner' in the SDXL process mentioned in the script?

    -The role of the 'refiner' in the SDXL process is to perform latent upscaling, which involves adjusting the Deno level to refine the image and make it look more realistic by reducing artifacts.

  • What settings are adjusted during the latent upscaling with SDXL as described in the script?

    -During the latent upscaling with SDXL, the Deno level is slightly increased to 0.55 to perform the refinement, and these settings can be adjusted based on the desired level of denoise or upscale in the latent stage.

  • Why is it preferable to upscale the image with models in SDXL rather than generating a high-resolution image directly in Flux?

    -Upscaling the image with models in SDXL is preferable because generating a high-resolution image directly in Flux can be time-consuming. By bringing the image data to SDXL, the image can be fixed and enhanced more efficiently.

  • What are the personal preferences of the speaker regarding the SDXL checkpoint models for refining images?

    -The speaker personally prefers using Real Vis or Zavi Chroma XL models for refining images, and they often use the Real Vis 4 model.

  • What is the next step or plan mentioned in the video script after refining and upscaling images with SDXL?

    -The next step or plan mentioned in the video script is to create AI video scenes using Flux to generate images, which will be covered in future videos.

  • How does the speaker describe the final outcome of the images after refinement with the SDXL image refiner and tile upscaling?

    -The speaker describes the final outcome of the images as looking much more natural after refinement with the SDXL image refiner and tile upscaling, with fewer plastic or artifact styles on surfaces like leaves and flowers.

Outlines

00:00

🎨 Refining AI-Generated Images with Flux and Upscaling Techniques

This paragraph introduces the process of refining and upscaling AI-generated images using the Flux diffusion model. The video script discusses the challenges of artifacts in human characters, particularly in hair and skin, which can appear plastic. To address this, the script suggests using realistic checkpoint models within the Stable Diffusion XL (sdxl) framework, such as Real Vis or Zavi Chroma XL, to enhance the realism of human character skins. The paragraph also covers the use of tile upscaling to improve the quality of elements like trees and leaves, which can have an unnatural texture. The script provides a step-by-step guide on using prompts for text-to-image generation with the Flux model, followed by upscaling and refining the generated image using sdxl techniques to achieve a more realistic result.

Mindmap

Keywords

💡Flux AI Images

Flux AI Images refers to the artificial intelligence-based image generation models developed by the company Flux. These models are capable of creating images from textual descriptions, which is a significant theme in the video. The script mentions refining and upscaling these images, indicating the process of improving the quality and resolution of AI-generated images.

💡SDXL

SDXL is an acronym for 'Stable Diffusion XL', a high-resolution image upscaling tool. In the context of the video, it is used for enhancing the quality of Flux AI-generated images by fixing artifacts and improving details. The script specifically mentions using SDXL for tile upscaling and refining human character skins, which shows its importance in the image refinement process.

💡Upscaling

Upscaling is the process of increasing the resolution of an image or video. In the video script, upscaling is a key step after refining the AI-generated images to make them look more realistic. The script describes using an upscaler to increase the final image resolution, which is crucial for achieving higher image quality.

💡Artifacts

In the context of image generation, artifacts refer to unintended visual elements or distortions that can occur in the generated images, such as unnatural textures or shapes. The script discusses how Flux AI Images sometimes create artifacts on human characters, particularly in hair and skin, which detracts from the realism of the images.

💡Realistic Checkpoint Models

Realistic checkpoint models are used in image refinement to improve the authenticity of the generated images. The script mentions using these models in SDXL to refine human character skins and elements like trees and leaves, which can have a plastic texture surface. These models help in making the AI-generated images appear more lifelike.

💡Tile Upscaling

Tile upscaling is a technique used to increase the size of an image by dividing it into tiles and processing each tile individually. The script describes using tile diffusion and tile control net upscale to double the original image size, which is a part of the refinement process before further refining the image in SDXL.

💡Denoising

Denoising is the process of reducing noise or artifacts in an image to improve its clarity and quality. In the script, increasing the denoise level to 0.55 is mentioned as part of the latent upscaling process with SDXL, which helps in refining the image by reducing unwanted visual noise.

💡Latent Upscaling

Latent upscaling is a method of enhancing an image's resolution at a stage before it is fully rendered, working with the underlying data that represents the image. The script explains that this is done with SDXL by adjusting settings to achieve the desired level of denoising and upscaling in the image's latent stage.

💡Control Net

Although not explicitly detailed in the script, a control net is generally a tool used in AI image generation to guide the model's output, ensuring certain features or elements are present or styled in a specific way. The mention of 'control net upscale' suggests its use in conjunction with tile upscaling to refine the image details.

💡Real Vis

Real Vis is mentioned in the script as a preferred realistic checkpoint model used in SDXL for refining images. It is one of the models that the video creator uses to enhance the realism of AI-generated images, particularly in the refinement of human character skins.

💡Zavi Chroma XL

Zavi Chroma XL is another realistic checkpoint model mentioned in the script, which is used alongside Real Vis for refining the details in AI-generated images. It is part of the set of tools that help in achieving a more natural look in the final image output.

Highlights

Refining and upscaling AI-generated images with Flux using the SDXL tool.

Flux image generation models can create artifacts on human characters, especially on hair and skin.

Using realistic checkpoint models in SDXL to refine human character skins and elements like trees and leaves.

The process involves using tile upscaling and the tile control net upscale to refine the image.

Fixing plastic-looking hair or artifact surfaces on image elements with the SDXL refiner.

Upscaling the final AI image as the last step in the refining process.

Testing the process with a text prompt to generate an image of a light bulb with flowers inside.

The initial result of the light bulb image is not realistic and requires further refinement.

Applying tile upscale to double the original image size before refining.

Adjusting the Deno level to 0.55 for latent upscaling in SDXL.

Comparing the difference between the original and latent upscaled images.

Using models to upscale the image and save it for a more natural look.

Preference for using Real Vis or Zavi Chroma XL models for refining.

The time-consuming process of generating high-resolution images with the Flux sampler.

Working around the lack of control net or extensions for Flux by using SDXL.

Demonstrating the enhancement of Flux-generated images with SDXL in a quick video.

Upcoming videos will cover creating AI video scenes using Flux to generate images.

Examples of images that look more natural after refinement with the SDXL image refiner and tile upscaling.