ComfyUI SDXL Lightning Performance Test | How & Which to Use | 2,4,8 Steps

Data Leveling
11 Mar 202410:14

TLDRIn this video, the presenter, H, discusses the use of Bite Dance's SDXL Lightning on ComfyUI, which is considered a significant advancement in stable diffusion technology. The SDXL Lightning employs a progressive adversarial diffusion distillation method, which allows for training on higher resolution images with less memory and time consumption compared to SDXL Turbo. The video covers the installation of 1, 2, 4, and 8-step base models from the Bik Dan Hugging Face repository and their performance in terms of speed and quality. The presenter also tests the compatibility of SDXL Lightning with ControlNet and the IP adapter, demonstrating the potential for faster and more efficient image generation while maintaining quality. The video concludes with a comparison of the different models' speeds and quality, suggesting that the 8-step Laura or the Juggernaut v9 Lightning model are preferable for high-speed and quality output. The presenter encourages viewers to share their thoughts on switching to SDXL Lightning and to subscribe for more informative content.

Takeaways

  • 📈 **SDXL Lightning Performance**: The SDXL Lightning is a significant advancement in stable diffusion, offering faster image generation with less memory consumption and training time.
  • 🔍 **Progressive Adversarial Diffusion Distillation**: SDXL Lightning uses a unique method for text-to-image generation, which is also employed in SDXL Turbo but with key differences in model architecture.
  • 🔑 **Model Compatibility**: SDXL Lightning is compatible with ControlNet and can be used as a plug-in for other checkpoint models to reduce diffusion steps while maintaining quality.
  • 🚀 **Installation Process**: The base models (1, 2, 4, and 8 steps) can be installed directly from the Bite Dance Hugging Face repository, with no need to install U-Net models separately.
  • 💻 **System Requirements**: The presenter's PC configuration is mentioned for context, highlighting that performance may vary based on the user's hardware.
  • ⏱️ **Speed Test Results**: The 2-step model demonstrated the fastest generation time at approximately 0.6 seconds, while the 4-step and 8-step models were also fast at 0.9 and 1.3 seconds respectively.
  • 🎨 **Quality Assessment**: The 4-step and 8-step models were deemed usable for image generation, with the 2-step model being less stable.
  • 🔌 **Lora Integration**: When using Lora with the Juggernaut v9 checkpoint model, the 4-step and 8-step models showed improved quality over the 2-step model.
  • 🤖 **ControlNet Functionality**: The Lightning model was confirmed to work well with ControlNet, adhering to the depth of the base image in the generated outputs.
  • 🧩 **IP Adapter Compatibility**: The Lightning model and Lora models were tested with an IP adapter, showing that they can be a direct replacement in existing workflows.
  • 🖌️ **In-Painting Test**: For in-painting tasks, increasing the CFG value was necessary for better color results with the Lightning version or Lora models.
  • ⏲️ **Time Efficiency**: There's a significant speed increase when using SDXL Lightning models, with a 70-80% faster generation time compared to the base model, which translates to substantial time savings at scale.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the use of bite dance, specifically the SDXL Lightning feature on ComfyUI, and its impact on stable diffusion in text to image generative models.

  • What is the SDXL Lightning method?

    -SDXL Lightning is a text to image generative model that uses a progressive adversarial diffusion distillation method. It operates on latent space with its own UNet model, which allows for less memory consumption and faster training times compared to SDXL Turbo.

  • What are the differences between SDXL Lightning and SDXL Turbo?

    -SDXL Lightning uses its own UNet model running on latent space, while SDXL Turbo uses the encoder dyo V2 as the discriminator backbone and operates on pixel space. Lightning can perform training on 1024x1024 pixels, whereas Turbo is limited to 512x512 pixels.

  • What are the benefits of using SDXL Lightning?

    -SDXL Lightning offers faster training times and lower memory consumption. It is also compatible with ControlNet and can be used as a plug-in on other checkpoint models to reduce the steps required in the diffusion process while maintaining an acceptable final output.

  • How can users install the SDXL Lightning models?

    -Users can install the SDXL Lightning models directly from the bite dance Hugging Face repository, which offers 1, 2, 4, and 8 step base models.

  • What are the system requirements mentioned in the video for running the models?

    -The video mentions that the presenter's PC is running on a 490 GPU with 24GB vRAM and a 32GB DDR5 RAM. The speed shown in the video might not be the same for all viewers, and they can downscale it based on their own system capabilities.

  • How does the speed of image generation vary with the number of steps in the SDXL Lightning models?

    -The speed of image generation decreases as the number of steps increases. The base model takes around 4 seconds, the 2-step model takes about 0.6 seconds, the 4-step model takes 0.9 seconds, and the 8-step model takes around 1.3 seconds per image.

  • What is the quality of the images generated by the SDXL Lightning models?

    -The 4-step and 8-step models are considered acceptable for generating usable images. The 2-step model's quality is unstable and may not always produce satisfactory results.

  • How does the use of ControlNet with SDXL Lightning perform?

    -ControlNet works well with SDXL Lightning, as it is able to follow the depth of the base image effectively in the generated images.

  • What is the impact of using the IP adapter with SDXL Lightning models?

    -The IP adapter can be used with SDXL Lightning models, and the 4-step and 8-step Laura models, in particular, are able to achieve a certain degree of likeness to the reference face with good results and fast generation times.

  • What is the presenter's recommendation for users who need to balance speed and quality?

    -The presenter recommends the 8-step Laura or the Juggernaut v9 Lightning model for users who need a balance of speed and quality, as they can generate high-quality images at a very fast speed.

  • What is the potential time saved when generating 1,000 images with SDXL Lightning models?

    -At scale, generating 1,000 images with SDXL Lightning models can save around 6 seconds per image, equating to approximately 1 hour and 40 minutes faster per 1,000 images compared to the base model.

Outlines

00:00

😀 Introduction to Bite Dance SDXL Lightning in Comfy UI

The video begins with the host, H, welcoming viewers back to the channel and introducing the topic of the day: Bite Dance's SDXL Lightning, a significant advancement in stable diffusion technology. H has read the associated paper and explains that SDXL Lightning is a text-to-image generative model that employs a unique progressive adversarial diffusion distillation method. This method is also used in SDXL Turbo, but the key difference is that Lightning operates in latent space with its own U-Net model, while Turbo uses an encoder and operates in pixel space. As a result, Lightning can train on higher resolution images (1024x1024) and with less memory and time consumption. The video outlines the installation process of the models from the BikDan Hugging Face repository and their compatibility with ControlNet and other checkpoint models. H also shares the system specifications of their PC, which will be used to demonstrate the speed of the models. A speed test is conducted on various models, with the two-step model showing particularly impressive results. The quality of the generated images is also discussed, with the four-step and eight-step models deemed usable, while the two-step model is considered unstable.

05:02

🎨 Testing SDXL Lightning with ControlNet and IP Adapter

The host proceeds to test the SDXL Lightning models with ControlNet to verify if it can follow the depth of an image as expected. The results are positive, with the two-step model showing rapid results, although the quality is questionable. The four-step and eight-step models perform well, with the eight-step model taking approximately 1.4 seconds to generate an image. The host also tests the models with an IP adapter, ensuring that the existing workflow can be directly replaced with the new model. The base checkpoint model is compared with the two-step, four-step, and eight-step Laura models, with the latter two providing good results in terms of both speed and quality. The lightning version of the model is tested last, showing a significant increase in speed compared to the base model, although it takes about twice as long as the eight-step Laura model. The host concludes that the eight-step Laura or the v9 lightning model are preferable for high-speed generation while maintaining quality, and suggests that for quality-focused work, one should prototype with the step Laura for the v9 lightning model first.

10:02

🖌️ In-Painting Test and Conclusion

The final segment of the video involves an in-painting test using the same workflow as in previous Comfy UI in-painting videos. The host attempts to change the outfit of a character to an X-Men Wolverine costume. The base checkpoint model's performance is compared with the two-step, four-step, and eight-step Laura models. The two-step model does not meet expectations, but the four-step and eight-step models provide acceptable results within a reasonable time frame. The lightning version is also tested, showing a time increase to around 3.2 seconds. The host emphasizes the importance of increasing the CFG value for in-painting with the lightning version or Laura models to achieve better color results. The video concludes with a comparison of the average time taken by each model, highlighting a 70-80% increase in speed over the base model. The host encourages viewers to share their thoughts on switching to SDXL Lightning and to like and subscribe for more content. They also invite viewers to comment if they face any difficulties, promising to help and reminding them to keep improving.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is a term referring to a class of machine learning models that are capable of generating images from textual descriptions. In the context of the video, it represents a significant advancement in AI-generated imagery and is central to the discussion of the SDXL Lightning models.

💡SDXL Lightning

SDXL Lightning is a specific text-to-image generative model that uses a progressive adversarial diffusion distillation method. It is highlighted in the video for its efficiency and ability to generate high-resolution images more quickly than its predecessors.

💡Progressive Adversarial Diffusion Distillation

This is a method used in AI image generation that involves a multi-step process to refine the generated image. It's mentioned as a core technique that enables SDXL Lightning to perform well, with the video contrasting it with the SDXL Turbo's use of a different encoder.

💡UNet Model

UNet is a type of convolutional neural network architecture that is commonly used in image segmentation tasks. In the video, it is noted that SDXL Lightning uses its own UNet model, which operates on latent space, allowing for reduced memory consumption and faster training times.

💡ControlNet

ControlNet is a term used in the video to describe a feature that allows for the manipulation of specific aspects of the generated image, such as depth. It is shown to be compatible with SDXL Lightning, enhancing the model's versatility.

💡Checkpoint Model

A Checkpoint model refers to a saved state of a neural network that can be reloaded for further training or inference. In the context of the video, the presenter discusses installing checkpoint models for SDXL Lightning and how they are used in the process.

💡Steps in Diffusion Process

The number of steps in the diffusion process refers to the iterations the model goes through to generate an image. The video explores different step-based models (2, 4, 8 steps) and their impact on speed and quality of the generated images.

💡CFG

CFG, likely shorthand for 'configuration' in this context, is a parameter that can be adjusted in the model to affect the quality and characteristics of the generated images. The video shows how changing the CFG value can influence the output.

💡In-Painting

In-Painting is a technique used in image editing where missing or selected parts of an image are filled in. The video demonstrates how SDXL Lightning can be used for in-painting tasks, such as changing the outfit of a character in an image.

💡IP Adapter

IP Adapter, as used in the video, refers to a tool or method for integrating or adapting one AI model to work with another system or workflow. It is tested to ensure compatibility and efficiency when replacing the base model with SDXL Lightning.

💡Performance Test

A performance test, as conducted in the video, is an evaluation of how well a system or model operates under specific conditions. The presenter performs tests on different SDXL Lightning models to measure their speed and quality of image generation.

Highlights

Sdxl Lightning is a text to image generative model using a progressive adversarial diffusion distillation method.

SDXL Lightning uses its own U-Net model running on latent space, resulting in lower memory consumption and training time.

SDXL Lightning can perform training on 1024x1024 pixels, compared to SDXL Turbo's 512x512 pixels.

The model is compatible with ControlNet and can be used as a plugin on other checkpoint models to reduce diffusion steps.

Four different base models (1, 2, 4, and 8 steps) are available for installation from the BikDan Hugging Face repository.

The one-step model is experimental with unstable quality and is not tested in the video.

The two-step model generates images in about 0.6 seconds, offering significant speed improvements.

The four-step model takes 0.9 seconds per image and is considered acceptable for usability.

The eight-step model generates images in about 1.3 seconds, maintaining fast performance.

The video demonstrates that four-step and eight-step models are capable of generating usable outputs.

The two-step model's quality is unstable, similar to the one-step model.

When using SDXL Lightning with the Juggernaut v9 checkpoint model, the image generation time is around 7 seconds.

Increasing the CFG to 2.0 improves color quality for the two-step model.

The four-step Laura model generates images in about 0.9 seconds with acceptable quality.

The eight-step Laura model takes around 1.1 seconds and provides very good quality.

The SDXL Lightning checkpoint model, fused with the four-step Laura, achieves quality close to the original in about 2 seconds.

All models struggle with complex prompts like 'cinematic photo of a monkey holding a subscribe sign'.

ControlNet works well with the Lightning model, following the depth of the base image.

The two-step Laura does not work with the IP adapter, but the four-step and eight-step models perform well.

In-painting with the four-step Laura model takes about 1.8 seconds and achieves a reasonable result.

For in-painting, increasing the CFG value to two or higher is necessary for better color representation.

The eight-step Laura model takes around 2.3 seconds for in-painting, offering good results.

The Lightning version for in-painting takes about 3.2 seconds, providing a high degree of likeness to the reference face.

Speed increases by 70-80% with SDXL Lightning models compared to the base model.

For generating 1,000 images, using SDXL Lightning could save approximately 1 hour and 40 minutes.

The eight-step Laura or the Juggernaut v9 Lightning model are recommended for high-speed generation with comparable quality.