ComfyUI SDXL Lightning dual workflow (UNET, LORA)

AIFuzz
21 Feb 202407:03

TLDRIn this AI fuzz video, the host introduces the new SDXL Lightning model, a text-image generation model that is remarkably fast and efficient. The model is capable of producing high-quality 1024-pixel images through a one-step, two-step, four-step, or eight-step process, with the latter two being the focus as the one-step and two-step processes are not yet functional with Comfy UI. The video demonstrates the model's speed and quality using the UNET and LORA workflows, both of which are available for download. The host uses the Epic Realism XL as a base model and tests the model's performance on a laptop with an RTX super 2070 and 8GB of VRAM. The results are impressive, with the model generating images rapidly and with good quality. The video concludes with the host's enthusiasm for the model's speed and performance, and he offers a workflow for download, encouraging viewers to subscribe and support his channel for more AI insights.

Takeaways

  • 🌟 The video introduces the new 'sdxl lightning' model, which is known for its fast text-to-image generation capabilities.
  • ⚡ The model can produce high-quality 1024-pixel images quickly, with options for one-step, two-step, four-step, and eight-step processes.
  • 🚧 Currently, the one-step and two-step processes are not operational with Comfy UI, pending updates from Comfy.
  • 📂 There are two workflows available for use with the model: the UNET workflow and the LORA workflow, which involve placing models in different folders.
  • 📁 The user demonstrates setting up a workflow with both UNET and LORA loaders to compare their performance side by side.
  • 💻 The video showcases the model's speed on a relatively low-spec machine, such as a laptop with an RTX super 2070 and 8GB of VRAM.
  • 🔧 The user uses 'Epic Realism XL' as the base model and emphasizes the importance of matching the sampler and scheduler settings to the defaults.
  • 🖼️ The generated images are of good quality, and the user suggests that an upscaler could be added for further enhancement.
  • ⏱️ Speed is highlighted as a key feature, with the model generating images rapidly even on the user's modest hardware.
  • 🛠️ The user mentions that they will make the workflow available for download and encourages viewers to subscribe for updates.
  • 🎨 The video ends with a call to action for viewers to support the creator on Patreon and hints at exciting upcoming news.

Q & A

  • What is the main strength of the SDXL Lightning model discussed in the video?

    -The main strength of the SDXL Lightning model is its speed. It is a lightning-fast text-image generation model capable of producing high-quality 1024 pixel images in just a few steps.

  • Which steps of the SDXL Lightning model are currently not working with Comfy?

    -The one-step and two-step processes of the SDXL Lightning model are currently not working with Comfy, as they are waiting for an update from Comi.

  • What are the two different workflows available for the SDXL Lightning model?

    -The two different workflows for the SDXL Lightning model are the UNET workflow, where models are placed into the models slun folder, and the LORA workflow, where models are placed into the models SL Laur folder.

  • What is the base model used for the Epic Realism XL in the video?

    -The base model used for Epic Realism XL in the video is the SDXL Lightning four-step model, with both UNET and LORA loaders being utilized.

  • What are the default settings for the K sampler in the SDXL Lightning model?

    -The default settings for the K sampler in the SDXL Lightning model start with four steps CFGs at one sampler named ULER and the scheduler must be set to SGM (Sub-Generator Multiplexer) with uniform distribution.

  • How does the video demonstrate the speed of the SDXL Lightning model?

    -The video demonstrates the speed of the SDXL Lightning model by showing real-time image generation without any editing or cutting. The model quickly goes through the UNET and LORA processes, generating images rapidly even on a relatively low-spec machine.

  • What is the recommended approach if one wants to upscale the generated images?

    -If one wants to upscale the generated images, they can add an S upscaler to the generated images for improved quality.

  • What is the status of the one-step and two-step processes in terms of functionality?

    -The one-step and two-step processes are not yet functional and are awaiting updates to become operational.

  • What is the significance of the scheduler being set to SGM in the SDXL Lightning model?

    -The scheduler being set to SGM (Sub-Generator Multiplexer) is very important as it ensures that the settings match the default, which is crucial for the proper functioning of the model.

  • What does the video suggest about the quality of the images generated by the SDXL Lightning model?

    -The video suggests that the images generated by the SDXL Lightning model are of high quality, despite the speed of generation. The host is pleased with the results and considers them fairly nice.

  • How can viewers get access to the workflow discussed in the video?

    -The workflow discussed in the video will be made available for download. Viewers are encouraged to subscribe to the channel and follow updates to get access to the workflow.

  • What additional resources does the video host offer for those interested in supporting their work?

    -The video host offers a Patreon page for those who wish to show support. They also mention having exciting news coming up, suggesting that patrons might get early access or additional insights.

Outlines

00:00

🚀 Introduction to the SDXL Lightning Model

The speaker introduces the SDXL Lightning, a text-image generation model that is highly efficient and quick. They discuss the model's capabilities, including its ability to produce high-quality 1024-pixel images through a one-step to eight-step process. The one-step and two-step processes are mentioned as not yet functional with Comfy UI, but the four-step and eight-step processes are operational. The speaker outlines two different workflows for using the model: the U-Net workflow and the lower workflow, both of which involve placing the models into specific folders. The speaker also provides a brief guide on downloading and setting up the model, including the use of positive and negative prompts and the importance of the K sampler and scheduler settings. A demonstration of the model's speed is given, showing the generation process in real-time.

05:01

🔍 Speed Test and Image Quality Assessment

The speaker conducts a series of speed tests on the SDXL Lightning model to evaluate its performance. They mention an issue with generating not safe for work (NSFW) images and adjust the prompts to avoid this. The speaker emphasizes the impressive speed of the model, even on their relatively modest hardware setup, which includes an RTX super 2070 with 8GB of VRAM. They also discuss the image quality produced by the model, noting that it is satisfactory and can be further improved with an upscaler. The speaker concludes by encouraging viewers to download the workflow, subscribe to their channel, and support them on Patreon for more updates on AI advancements.

Mindmap

Keywords

💡SDXL Lightning

SDXL Lightning refers to a new model for text-image generation that is characterized by its high speed. In the context of the video, it is emphasized as being 'lightning quick', indicating that it can produce high-quality images rapidly. This model is central to the video's theme, as the presenter discusses its capabilities and speed in generating images.

💡Text-Image Generation

Text-image generation is the process by which a system or model converts textual descriptions into visual images. In the video, the SDXL Lightning model is specifically designed for this purpose, showcasing its ability to create high-quality 1024-pixel images from textual prompts. This is a core concept as the entire video revolves around demonstrating and discussing this process.

💡UNET

UNET is a term mentioned in the video that likely refers to a specific workflow or method used in the context of the SDXL Lightning model. The presenter discusses a 'UNET workflow' where the models are placed into a certain folder, indicating that it is a part of the process for utilizing the model. It is one of the two workflows mentioned, making it significant for understanding the operational aspect of the model.

💡LORA

LORA, similar to UNET, is another workflow mentioned in the video. It is a method of organizing and using the models within the system. The presenter chooses to test both UNET and LORA workflows side by side to compare their performance. This keyword is important as it represents an alternative approach to using the SDXL Lightning model.

💡High-Quality Images

The term 'high-quality images' is used to describe the output of the SDXL Lightning model. The video emphasizes that despite its speed, the model can still generate images that are of high quality, with a resolution of 1024 pixels. This is a key selling point for the model and is repeatedly highlighted throughout the video.

💡One-Step, Two-Step, Four-Step, and Eight-Step Process

These terms refer to the different stages or processes involved in generating an image with the SDXL Lightning model. The video mentions that while the one-step and two-step processes are not yet functional, the four-step and eight-step processes are operational. The presenter uses these steps to demonstrate the speed and efficiency of the model.

💡Comfi UI

Comfi UI is the user interface or platform being used in the video to demonstrate the SDXL Lightning model. It is the environment where the presenter sets up the workflows and conducts the tests. Comfi UI is significant as it is the interface through which the audience can understand how to interact with the SDXL Lightning model.

💡Epic Realism XL

Epic Realism XL is mentioned as the base model that the presenter is using for their tests with the SDXL Lightning model. It serves as a comparison point to demonstrate the capabilities and improvements of the SDXL Lightning model. This keyword is important as it provides a benchmark for the performance of the new model.

💡C Samplers

C Samplers are part of the settings within the Comfi UI that the presenter discusses. They are used in conjunction with the K sampler, which is the default sampler provided. The presenter mentions that there are four steps CFGs at one sampler named ULER, which is crucial for the correct operation of the model. Understanding C Samplers is important for users looking to configure the model settings.

💡SGM Scheduler

The SGM Scheduler is a setting within the Comfi UI that needs to be set to 'uniform' for the SDXL Lightning model to function correctly. It is a crucial part of the configuration process and is mentioned as being 'very important' by the presenter. This keyword is significant for users who need to ensure their settings are correctly matched for optimal performance.

💡Speed Test

Speed test is a repeated theme in the video where the presenter is measuring and demonstrating how quickly the SDXL Lightning model can generate images. The presenter conducts several speed tests to emphasize the model's efficiency and rapid image generation capabilities. This keyword encapsulates the main advantage and focus of the model being discussed.

Highlights

Introducing the new SDXL Lightning model, which is exceptionally fast in text-image generation.

The model can produce high-quality 1024-pixel images in a few steps.

One-step and two-step processes are not yet functional with Comfy, awaiting updates.

The four-step and eight-step processes are operational and utilize two different workflows: UNET and LORA.

Downloading the model files involves selecting either the UNET or LORA workflow based on user preference.

The presenter downloaded only the four-step and eight-step models for testing purposes.

ComfyUI setup includes both UNET and LORA loaders for side-by-side comparison.

Epic Realism XL is used as the base model for the demonstrations.

The K sampler is set to 4 steps with the name 'uler' and the scheduler set to 'sgm' for uniform sampling.

The first generation might be slower due to initial downloading of resources.

The presenter's machine specifications include an RTX super 2070 and 8GB of VRAM.

The model loaded quickly despite the presenter's modest hardware setup.

The output images from both UNET and LORA are of good quality and can be further enhanced with an upscaler.

The speed of image generation is a key feature, even on less powerful systems.

The presenter experienced non-safe-for-work images and adjusted the prompts accordingly.

The workflow demonstrated is available for download and is considered topnotch in the AI community.

The presenter encourages viewers to subscribe and support their Patreon for more knowledge sharing.

The video concludes with a teaser for exciting upcoming news.

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