Flux AI Images Refiner And Upscale With SDXL
TLDRThis video tutorial demonstrates how to refine and upscale AI-generated images using Flux models with SDXL. It addresses common issues like plastic-looking human characters and artifacts on elements like hair, skin, trees, and leaves. The process involves using tile upscaling, denoising, and a refiner to enhance image quality. Viewers are shown how to apply this method to a light bulb with flowers prompt, resulting in a more realistic and less artifact-ridden image. The video also mentions the use of checkpoints like Real Vis or Zavi Chroma XL for better refinement and hints at future content on creating AI video scenes with Flux.
Takeaways
- 🌟 The video discusses refining and upscaling AI images generated by the Flux model using SDXL.
- 🔍 Flux diffusion models are effective for prompt instructions but can create artifacts on human characters, making them look plastic.
- 🎨 To refine the images, realistic checkpoint models like RealViz or Zavi Chroma XL can be used in SDXL to enhance human character skins and elements like trees and leaves.
- 🖼️ The process begins with text-to-image generation in the Flux model, then refining the image using tile upscaling and the SDXL refiner group.
- 🔧 Tile upscaling doubles the original image size using tile diffusion and control net upscale.
- ✨ Denoising level can be adjusted in the refiner to reduce plastic-looking hair or artifact surfaces on the image elements.
- 🛠️ Latent upscaling with SDXL is performed, which can make a significant difference in the image's naturalness.
- 🌿 The video provides an example of refining an image of a light bulb with flowers inside, addressing artifact styles in elements like flowers and leaves.
- 🔄 The final step involves upscaling the image with models to achieve a more natural look, reducing plastic or artifact styles.
- 📈 The presenter shares a preference for RealVis or Zavi Chroma XL models for their refining capabilities.
- 🔄 The video demonstrates a workaround for enhancing Flux-generated images by bringing the data to SDXL, given the lack of control net or other extensions for Flux currently.
- 🎥 Upcoming videos will cover creating AI video scenes using Flux to generate images.
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 address issues such as skin artifacts and to enhance the realism of elements like human characters, trees, and leaves.
What are some common issues with Flux diffusion models when generating images?
-Flux diffusion models sometimes create artifacts on human characters, making them look plastic, especially in areas like hair and skin.
Which models are suggested for refining human character skins and other elements in the video?
-The video suggests using realistic checkpoint models in SDXL such as RealViz or Zavi Chroma XL to refine human character skins and elements like trees and leaves.
What is the process of refining an image from the Flux diffusion model as described in the video?
-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, and finally upscaling the image as the last step.
What is a 'text to Image Group' and how is it used in the video?
-A 'text to Image Group' is a feature in the AI image generation process where text prompts are used to generate images. In the video, it is used to create an initial image that will then be refined using the SDXL process.
What does the video suggest for the initial text prompt to test the AI image generation?
-The video suggests using a text prompt like 'a light bulb with flowers inside sitting on the ground' to test the AI image generation and observe any artifacts in the elements like flowers and leaves.
How does the video describe the tile upscaling process?
-The tile upscaling process is described as doubling the image size from the first generation using tile diffusion and the tile control net, followed by refining in the SDXL refiner group.
What settings are mentioned in the video for the latent upscaling with SDXL?
-The video mentions increasing the Deno level slightly to 0.55 and performing latent upscaling with SDXL as the basic settings for refining the image.
What are the preferred SDXL checkpoint models mentioned by the speaker in the video?
-The speaker prefers RealViz or Zavi Chroma XL models, specifically mentioning RealViz 4 as the model they usually use.
What is the alternative approach discussed in the video for enhancing images without control net or other extensions for Flux?
-The alternative approach is to bring the image data to SDXL and enhance it using the refiner and tile upscaling process, rather than relying on control net or other extensions for Flux.
What does the video suggest for future content related to Flux AI image generation?
-The video suggests that in the next videos, they will create AI video scenes using Flux to generate images, which will be another topic covered.
Outlines
🖼️ 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 focus is on addressing common issues such as skin artifacts on human characters, which can make them appear plastic, particularly in hair and skin textures. To refine these images, the script discusses using realistic checkpoint models within the Stable Diffusion XL (sdxl) framework, such as Real-Viz or Zavi Chroma XL, to enhance the realism of human skin and other elements like trees and leaves. The paragraph also outlines the steps for using a text-to-image group for flux image generation, including switching to VAE encoding for image-to-image transformations, and the importance of refining the image with tile upscaling and the sdxl sampler to avoid plastic-looking artifacts. The process concludes with an upscaler to finalize the AI image, aiming for a more natural appearance.
Mindmap
Keywords
💡Flux AI Images
💡SDXL
💡Upscaling
💡Tile Upscaling
💡Denoising
💡Checkpoint Models
💡Real Vis
💡Zavi Chroma XL
💡Artifacts
💡Text to Image
💡Image Refinement
Highlights
Introduction to refining and upscaling AI images generated by Flux using SDXL.
Flux image generation models can sometimes create artifacts on human characters, making them look plastic, 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 with tile diffusion and tile control net upscale to refine the image.
Refining the image in the SDXLK sampler to remove plastic-looking hair or artifact surfaces.
Upscaling the final AI image as the last step in the refinement process.
Testing text prompts for image generation, such as a light bulb with flowers inside.
The initial result may not look realistic, indicating the need for further refinement.
Using the SDXL refiner group for tile upscaling to double the original image size.
Adjusting the Deno level to 0.55 for latent upscaling with SDXL to improve image quality.
Comparing the difference between the original image and the one after latent upscaling.
The preference for using Real Vis or Zavi Chroma XL models for refining images.
The time-saving benefit of using SDXL for image refinement instead of generating high-resolution images in Flux.
Working around the lack of control net or extensions for Flux by using SDXL to enhance images.
Preview of upcoming videos on creating AI video scenes using Flux for image generation.
Examples of images looking more natural after refinement with the SDXL image refiner and tile upscaling.
Conclusion of the video with a summary of the process and a teaser for the next videos.