Flux AI Images Refiner And Upscale With SDXL

Future Thinker @Benji
6 Aug 202404:59

TLDRThis video tutorial demonstrates refining and upscaling AI-generated images using the Flux diffusion model and the SDXL refiner. It addresses common issues like plastic-looking human characters and artifacts in elements like trees and leaves. The process involves initial image generation, tile upscaling, denoising, latent upscaling, and final upscaling with preferred models like Real Vis or Zavi Chroma XL for a more natural look. The video also hints at future content on creating AI video scenes with Flux.

Takeaways

  • 🎨 The video discusses refining and upscaling AI-generated images using the Flux model and SDXL.
  • 🔍 Flux diffusion models are effective for prompt instructions but can sometimes create artifacts on human characters, especially in hair and skin.
  • 🖼️ To refine these images, realistic checkpoint models like Real Viz or Zavi Chroma XL can be used in SDXL to improve skin and hair realism.
  • 🌳 The script also mentions refining elements like trees and leaves, which may have a plastic texture surface in the initial AI-generated images.
  • 📝 The process involves using a text-to-image group for Flux image generation and switching to VAE encode for image-to-image refinement.
  • 🔄 The video demonstrates a step-by-step process, starting with creating an image, then using tile upscaling, and refining with SDXL.
  • 🛠️ Tile upscaling is used to double the original image size before refining skin tones and hairstyles to avoid plastic-looking artifacts.
  • 🔍 The refiner in SDXL is adjusted with settings like Deno level to enhance the image quality during the latent stage.
  • ✨ The final step is to upscale the image using models to achieve a more natural look, especially for elements like leaves and flowers.
  • 🌟 The video provides examples of AI-generated images that look more natural after refinement with the SDXL image refiner and tile upscaling.
  • 📹 The creator plans to demonstrate creating AI video scenes using Flux in future videos.

Q & A

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

    -The main purpose of using SDXL in the video 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 aim to address?

    -The video aims to address issues such as artifacts on human characters, making them look plastic, especially on hair and skin, which are common problems with the Flux diffusion models.

  • What is a realistic checkpoint model in the context of refining AI images?

    -A realistic checkpoint model in this context refers to a pre-trained model in SDXL, such as RealViz or Zavi Chroma XL, used to enhance the realism of elements like human character skins in AI-generated images.

  • How does the process of refining an AI image with SDXL start?

    -The process starts by generating an image with the Flux diffusion model and then applying a tile upscale using tile diffusion and the tile control net upscale to double the original image size.

  • What is the role of the denoise level in the SDXL refiner group?

    -The denoise level in the SDXL refiner group is used to control the amount of noise reduction applied during the latent upscaling process, helping to refine the image and reduce artifact surfaces.

  • What is the significance of using the 'latent upscaling' with SDXL?

    -Latent upscaling with SDXL is significant as it allows for the enhancement of image details and resolution at the latent stage before the final upscaling, leading to a more refined and realistic image.

  • How does the final step of upscaling the AI image contribute to the overall image quality?

    -The final upscaling step contributes to the overall image quality by further increasing the resolution and clarity of the image, making it look more natural and reducing any remaining plastic or artifact styles.

  • What are some alternative SDXL checkpoint models mentioned in the video for refining AI images?

    -The video mentions RealViz and Zavi Chroma XL as alternative SDXL checkpoint models that can be used for refining AI images, with a personal preference for RealViz 4.

  • Why is it beneficial to bring the image data to SDXL instead of generating a high-resolution image directly in Flux?

    -Bringing the image data to SDXL is beneficial because generating a high-resolution image directly in Flux can be time-consuming. SDXL allows for more efficient enhancement and refinement of the image data.

  • What future application of Flux is hinted at in the video?

    -The video hints at future applications of Flux for creating AI video scenes, which will be covered in subsequent videos.

  • How does the video demonstrate the effectiveness of the SDXL refiner and tile upscaling?

    -The video demonstrates the effectiveness by showing before and after examples of AI images, highlighting the increased naturalness and reduction of plastic or artifact styles after refinement with the SDXL image refiner and tile upscaling.

Outlines

00:00

🎨 Refining AI-Generated Images with Upscaling Techniques

This paragraph introduces the process of refining and upscaling AI-generated images using the flux diffusion model. The focus is on addressing common issues such as plastic-looking human characters and artifacts on elements like hair, skin, trees, and leaves. The speaker proposes using realistic checkpoint models within the SdxL framework, such as RealViz or Zavi Chroma XL, to enhance the realism of these elements. The method involves initial image generation, tile upscaling to double the image size, refinement in the SdxL sampler to correct artifacts, and a final upscaling step. The paragraph also mentions testing text prompts for image generation and the use of tile diffusion and control net upscale for the process.

Mindmap

Keywords

💡Flux AI Images

Flux AI Images refers to the images generated by the Flux AI model, which is a type of artificial intelligence capable of creating visual content based on textual descriptions. In the context of the video, the main focus is on refining and upscaling these AI-generated images to improve their quality and realism. The script mentions that Flux AI models are effective for prompt instructions but sometimes create artifacts, particularly on human characters.

💡SDXL

SDXL is an acronym for Stable Diffusion XL, which is a high-resolution image upscaling model used in the video to enhance the quality of AI-generated images. It is employed to perform tile upscaling and refine the images by adjusting the denoising level, thus improving the details and textures of elements like skin, hair, and leaves, which often appear plastic or artifact-laden in the original Flux AI images.

💡Upscaling

Upscaling in the context of the video refers to the process of increasing the resolution of an image while maintaining or improving its quality. The script discusses using the SDXL model for tile upscaling to double the original image size and then refining it to achieve a more natural look, especially for elements that may have appeared unrealistic in the initial Flux AI image generation.

💡Artifacts

Artifacts in the video script are the unintended visual imperfections or distortions that sometimes appear in AI-generated images, such as plastic-looking hair or skin textures. The video aims to address these issues by using the SDXL refiner to reduce or eliminate such artifacts, resulting in a more realistic final image.

💡Realistic Checkpoint Models

Realistic checkpoint models, such as Real Vis or Zavi Chroma XL mentioned in the script, are specific versions or configurations of the SDXL model that are designed to enhance the realism of AI-generated images. These models are used to refine human character skins and other elements, aiming to make them appear more lifelike and less artificial.

💡Tile Upscale

Tile upscaling is a technique described in the video where the image is divided into tiles, each of which is upscaled individually before being reassembled into a larger image. This method is used in conjunction with the SDXL model to increase the resolution of the image while also refining its details.

💡Denoising

Denoising in the video script refers to the process of reducing noise or artifacts in an image, which can make it appear more realistic and less grainy. The script mentions adjusting the denoising level to 0.55 during the latent upscaling process with SDXL, which helps in refining the image by reducing unwanted visual noise.

💡Latent Upscaling

Latent upscaling is a term used in the video to describe the process of increasing the resolution of an image in its latent space, which is a lower-dimensional representation of the image data. This is done using the SDXL model to refine the image before it is brought back into its original pixel space, resulting in a higher-quality final image.

💡Text to Image

Text to Image is a concept in AI image generation where a textual description is used as input to create an image. The script mentions a 'Text to Image Group for Flux image generation,' indicating that the Flux AI model can generate images based on textual prompts provided by the user.

💡Image to Image

Image to Image is another mode of AI image generation where an existing image is used as a starting point to create a new image. The script suggests that users can switch the 'empty latent image to vae encode' to perform image-to-image generation, which is an alternative to text-to-image generation.

💡Control Net

Control Net is mentioned in the script as a tool that is not currently available for use with Flux. It implies a feature or extension that would provide additional control over the image generation process, but the video suggests working around its absence by using SDXL to enhance the images instead.

Highlights

Refining and upscaling AI images generated by Flux using SDXL.

Using SDXL to fix skin artifacts in AI-generated images.

The challenge of plastic-looking hair and skin in Flux diffusion models.

Utilizing realistic checkpoint models like Real Viz or Zavi Chroma XL in SDXL for better human character skins.

Refining elements like trees and leaves to avoid plastic texture surfaces.

Process of refining images from the Flux diffusion model using tile upscaling and control net upscale.

Running the refiner to adjust denoise levels and perform latent upscaling with SDXL.

The result of a light bulb with flowers inside generated by Flux diffusion model.

Upscaling the image size using tile diffusion and control net upscale.

Adjusting denoise levels to 0.55 for refining the image in SDXL.

The difference between the original and the latent upscaled image.

Upscaling the final AI image to enhance naturalness.

Preference for Real Vis or Zavi Chroma XL models in SDXL for realistic results.

Testing upscale on Flux-generated images to avoid long generation times.

Working with image data in SDXL to fix issues from Flux without control net extensions.

Demonstration of AI video scenes creation using Flux in upcoming videos.

Examples of more natural-looking images after refinement with SDXL.

Conclusion of the video with a preview of future content.

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