New Supir Workflow ComfyUI

20 Mar 202409:12

TLDRIn this AI fuzz video, Abigail demonstrates an advanced image upscaling workflow using the Super Resolution method with the latest version of the SuperAR software. She guides viewers through the process of installing SuperAR from GitHub, setting up the nodes, and using the model loader, encoder, and conditioner to enhance image quality. The video showcases the before and after results of upscaling, highlighting the software's ability to sharpen and smooth out images, making them less blurry and more detailed. Abigail encourages viewers to experiment with different settings for optimal results and to stay tuned for more AI content.


  • 🎨 The video discusses an AI-based image upscaling method using the 'Super' nodes.
  • πŸ” 'Super' has been improved by splitting into multiple nodes, offering better results.
  • πŸ’» The 'Super' nodes can be downloaded from GitHub and installed in the custom nodes folder or via Measure and Com.
  • πŸ–ΌοΈ The workflow begins with a model loader, followed by an encoder, and eventually a conditioner.
  • πŸ”„ The process involves connecting the nodes in a specific order to upscale the image quality.
  • πŸ› οΈ The video provides a step-by-step guide on setting up the nodes for the upscaling workflow.
  • πŸ“Έ A model input is required from the model loader, which is connected to the 'Super' sampler.
  • πŸ–ŒοΈ The 'Conditioner' node is used to refine the image with settings like 'high quality' and 'detailed photograph'.
  • πŸ” The 'Color Match' node helps to align the upscaled image with the original in terms of color and detail.
  • πŸ”„ The 'Comparison' node allows for a side-by-side view of the original and upscaled images.
  • πŸ‘ The video concludes that the 'Super' upscaling method significantly improves image quality, especially from blurry inputs.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about using an AI-based method called Super Resolution Upscale (Super) for image enhancement.

  • How has the Super method evolved?

    -The Super method has evolved from being a single node to having a suite of nodes that provide better results, similar to the demos provided.

  • Where can one find the Super nodes for download?

    -The Super nodes can be downloaded from a link on GitHub, which will be posted in the video description.

  • How does one install the Super nodes?

    -The Super nodes can be installed by cloning the GitHub link into the custom nodes folder or by using the Measure and Com interface to install it.

  • What are the initial steps in setting up the Super workflow?

    -The initial steps involve opening a blank project, adding nodes such as a model loader, encoder, and conditioner, and connecting them in the correct sequence.

  • What is the purpose of the model loader in the workflow?

    -The model loader is used to input the image into the Super sampler, which is the first step in the upscaling process.

  • How does the video demonstrate the effectiveness of the Super method?

    -The video demonstrates the effectiveness by showing a before and after comparison of an upscaled image, highlighting improvements in sharpness, detail, and overall quality.

  • What are some of the settings that can be adjusted in the Super workflow?

    -Some adjustable settings include the encoder tolerance, the size of the encoder, the type of sampler, and the conditioning text for the image.

  • What is the final output of the Super workflow?

    -The final output is an upscaled image that is sharper, clearer, and has improved detail compared to the original input image.

  • What is the recommendation for users interested in the Super method?

    -Users are encouraged to experiment with the different settings and nodes in the Super workflow to achieve the desired results and to subscribe to the channel for more AI-related content.



🎨 Introducing the Super Upscale Workflow

This paragraph introduces the audience to a video tutorial focused on an AI-based image upscaling technique called 'Super Upscale.' The speaker, Abigail, explains that the method has been improved by splitting it into several nodes and introduces a new suite of nodes that can be downloaded from GitHub. The process begins with opening a blank project in Comy and adding necessary nodes such as a model loader, encoder, and denoiser. The workflow starts with saving the noiser, setting up the superior sampler, and connecting the nodes in a specific sequence to achieve the desired upscaling effect. The speaker also mentions the importance of the conditioning node and the resizing of images for optimal results.


πŸ–ΌοΈ Enhancing Image Quality with Super Upscale

In this paragraph, Abigail demonstrates the practical application of the Super Upscale method by using a specific model and adjusting settings such as the image size and conditioning text. The speaker emphasizes the importance of the encoder, color match, and sampler settings in achieving high-quality results. The paragraph showcases the effectiveness of the upscaling technique by comparing the original and upscaled images, highlighting improvements in sharpness, detail, and overall image quality. Abigail encourages viewers to experiment with different settings to achieve the best results and concludes by expressing satisfaction with the Super Upscale tool, considering it a favorite among users.



πŸ’‘AI upscale method

The AI upscale method refers to the process of using artificial intelligence to enhance the resolution and quality of an image. In the context of the video, this method is employed to improve the quality of a photo by using a series of nodes and algorithms. The AI upscale method is central to the video's theme as it demonstrates how technology can be utilized to refine and clarify visual content.


Superar is a suite of nodes used in the AI upscaling process. It represents an improvement over previous versions by offering more refined and better results. The term is significant in the video as it is the core tool used to achieve the desired outcome of image enhancement. The video instructs viewers on how to download and install Superar to achieve improved upscaling results.

πŸ’‘Model loader

A model loader is a component used in the AI workflow to input the original image into the system. It is a crucial starting point for the upscaling process as it sets the foundation for all subsequent operations. In the video, the model loader is used to feed the image into the Superar suite for further processing.


An encoder in the context of AI image processing is a node or tool that compresses and transforms the input image into a format that can be efficiently processed by the AI algorithms. It is an essential part of the upscaling workflow as it prepares the image for further enhancement. The video details the use of an encoder to process the image before it is sent to the next stages of upscaling.


A denoiser is a tool used to reduce or eliminate noise from an image, resulting in a clearer and smoother final output. In the video, the denoiser is a key component in the AI upscaling process, as it helps to refine the image by removing any unwanted artifacts or distortions. The denoiser is used after the initial encoding and is connected to the superior encode node to improve image quality.


A sampler in the context of AI image processing is a node that generates a sample or version of the processed image based on the input and the parameters set. It is an integral part of the upscaling process as it produces the final output that the user sees. The video highlights the importance of the sampler in achieving the desired upscaling results.


A conditioner in the AI upscaling workflow is a node that refines the image further by adjusting specific settings or parameters. It is used to fine-tune the image quality and achieve a more detailed and high-resolution output. The video emphasizes the use of a conditioner to enhance the image after the initial upscaling process.

πŸ’‘Color match

Color matching is the process of adjusting the colors of an image to ensure consistency and accuracy. In the AI upscaling context, it is used to make sure that the upscaled image has a similar color tone to the original, maintaining the visual integrity of the content. The video details the use of a color match node to achieve a harmonious color balance between the original and the upscaled image.

πŸ’‘Comparison node

A comparison node is a tool used to compare two images side by side. In the context of the video, it is used to evaluate the effectiveness of the AI upscaling process by showing the original and the upscaled image for direct comparison. This helps to visually assess the improvements made to the image quality.


Settings in the context of the AI upscaling video refer to the various parameters and configurations that can be adjusted within the nodes to control the output of the upscaling process. These settings are crucial as they allow for customization and optimization of the final image quality. The video encourages viewers to experiment with different settings to achieve the best results.


A workflow in the video represents the sequence of steps or operations used to upscale an image using AI. It involves the use of various nodes and tools, each with specific functions, connected in a particular order to process the image from start to finish. The workflow is central to the video's message as it guides the viewer through the process of upscaling an image using the AI method.


The video discusses an AI-based upscale method using the Super Resolution technique.

Super was improved by splitting into several nodes, offering better results.

The SuperAR can be downloaded from GitHub for enhanced performance.

The workflow starts with a model loader and ends with an upscaled image.

An encoder and conditioner are essential components of the workflow.

The video provides a step-by-step guide on setting up the nodes for the workflow.

The use of a comparison node allows viewers to see the before and after results of the image upscaling.

The video demonstrates the process using a photo of a person on a couch.

The upscaling process sharpens and smooths out blurry areas, enhancing image quality.

The final image is presented in a higher resolution of 1536 by 1536 pixels.

The video encourages viewers to experiment with different settings for optimal results.

The presenter shares their preference for the Super Resolution upscaler over other methods.

The video concludes with a positive endorsement of the Super Resolution technique and its impact on AI image processing.

The presenter invites viewers to engage with the content by commenting and subscribing.

The video ends with a personal note from the presenter, showing a casual and relatable side.