ComfyUI SDXL Lightning Performance Test | How & Which to Use | 2,4,8 Steps
TLDRIn this video, the presenter, 'h', discusses the use of the Bite Dance SDXL Lightning on ComfyUI, which is considered a significant advancement in stable diffusion technology. The video explains that SDXL Lightning is a text-to-image generative model that employs a progressive adversarial diffusion distillation method. It is more memory-efficient and faster than SDXL Turbo, allowing for training on larger pixel sizes. The presenter demonstrates various step-based models (2, 4, and 8 steps) and their performance in terms of speed and quality. The video also explores the compatibility of SDXL Lightning with ControlNet and its potential use with other checkpoint models to reduce diffusion steps while maintaining output quality. The presenter's PC setup is mentioned for context, and a series of tests are conducted to compare the speed and quality of different models. The results show that the 4-step and 8-step models are generally acceptable, while the 2-step model is less stable. The video concludes with a discussion on the potential time savings and quality considerations when using SDXL Lightning, suggesting that it could be a worthwhile switch for those looking to improve their workflow.
Takeaways
- π SDXL Lightning is a significant advancement in stable diffusion technology, potentially reshaping the field.
- π SDXL Lightning uses a progressive adversarial diffusion distillation method, with a focus on latent space rather than pixel space.
- βοΈ The SDXL Lightning model has lower memory consumption and training time compared to SDXL Turbo, allowing for larger image sizes (1024x1024 pixels).
- 𧩠SDXL Lightning is compatible with ControlNet and can be used as a plugin on other checkpoint models to reduce diffusion steps.
- π The base models (1, 2, 4, and 8 steps) can be installed directly from the Bite Dance Hugging Face repository.
- π» The video creator's PC setup includes a 490 GPU with 24GB VRAM and 32GB DDR5 RAM, which may affect the performance speed.
- β±οΈ Speed tests show that the 2-step model is the fastest, taking around 0.6 seconds, while the 8-step model is still fast at 1.3 seconds per image.
- π¨ In terms of quality, the 4-step and 8-step models are deemed usable, whereas the 2-step model is considered unstable.
- π The video also tests the performance of SDXL Lightning with ControlNet, showing that it works effectively with the Lightning model.
- π€ The integration with the IP adapter and in-painting workflow is demonstrated, with the 4-step and 8-step models providing good results.
- β‘ The Lightning checkpoint model is noted for its high-speed performance and quality that is close to the original Juggernaut v9 checkpoint model.
- β For users who prioritize quality, the 8-step Laura or the v9 Lightning model are recommended for their balance of speed and output quality.
Q & A
What is the main topic of the video?
-The main topic of the video is about using SDXL Lightning on ComfyUI, a significant advancement in stable diffusion technology.
What is SDXL Lightning?
-SDXL Lightning is a text-to-image generative model that uses a progressive adversarial diffusion distillation method, running on latent space and offering less memory consumption and faster training times.
How does SDXL Lightning differ from SDXL Turbo?
-SDXL Lightning uses its own U-Net model running on latent space, while SDXL Turbo uses the encoder Dyo V2 as the discriminator backbone and operates on pixel space.
What are the advantages of using SDXL Lightning?
-SDXL Lightning allows for training on higher resolution images (1024x1024 pixels), faster training times, and is compatible with ControlNet and other checkpoint models to reduce diffusion steps while maintaining output quality.
How can users install the SDXL Lightning models?
-Users can install the SDXL Lightning models directly from the Bite Dance Hugging Face repository, which includes 1, 2, 4, and 8 step base models.
What are the system requirements mentioned in the video for running the SDXL Lightning models?
-The video mentions that the PC used for demonstration has a 490 GPU with 24GB VRAM, and a 32GB DDR5 RAM.
How does the speed of image generation vary with different step models?
-The speed increases as the number of steps decreases. 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 2-step model?
-The 2-step model generates images at a very fast speed but the quality is unstable and may not be suitable for all use cases.
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 recommended approach if one needs to prioritize quality?
-If quality is a priority, one should prototype with the step Laura for the V9 Lightning model first. If the desired result is achieved, it saves time. If not, one can revert to the checkpoint model.
What is the time efficiency gain when generating 1,000 images with SDXL Lightning compared to the base model?
-The time efficiency gain is approximately 6 seconds per image, which equates to around 1 hour and 40 minutes faster per 1,000 images.
Outlines
π Introduction to Bite Dance's SDXL Lightning
The video introduces SDXL Lightning, a text-to-image generative model that is considered a significant advancement in stable diffusion. The presenter has read the associated paper and provides a summary of the method, which involves a progressive adversarial diffusion distillation technique. SDXL Lightning is differentiated from SDXL Turbo by its use of a custom UNet model operating in latent space, which allows for less memory consumption and faster training times, supporting higher resolution images. The video also mentions compatibility with ControlNet and the ability to use as a plug-in to reduce diffusion steps. The presenter outlines the installation process for the models from the Bite Dance Hugging Face repository and provides a brief overview of the system requirements and expected performance based on their own PC setup. A speed test and quality comparison of the base model and various step-based models are demonstrated, highlighting the trade-offs between speed and image quality.
π Testing SDXL Lightning Models and Features
The presenter conducts a series of tests to evaluate the performance and quality of SDXL Lightning models. They compare the base model output with the output from different step-based models, noting the significant speed improvements with the two-step model, though with some quality concerns. The four-step and eight-step models are found to be more stable and usable. The video also explores the use of SDXL Lightning with ControlNet, demonstrating its ability to follow the depth of an image. The presenter further tests the models with an IP adapter and in-painting workflow, showing that the four-step and eight-step Laura models perform well in terms of speed and quality. The lightning version, while slower, offers comparable quality to the original checkpoint model. The video concludes with a discussion on the potential time savings when using these models at scale and encourages viewers to share their thoughts and experiences.
π Conclusion and Call for Feedback
The video concludes with a summary of the findings, emphasizing the speed and quality benefits of the eight-step Laura and Juggernaut v9 Lightning models. The presenter suggests that for those who prioritize quality, the step Laura or v9 Lightning model could be a good starting point for prototyping. They also highlight the potential time savings when generating a large number of images. The presenter invites viewers to share their opinions on whether they would switch to SDXL Lightning and to provide feedback if they encounter any difficulties following the video. The video ends with an encouragement to continue learning and improving.
Mindmap
Keywords
π‘ComfyUI
π‘SDXL Lightning
π‘Progressive Adversarial Diffusion Distillation
π‘U-Net Model
π‘ControlNet
π‘Checkpoint Models
π‘Steps
π‘CFG
π‘In-Painting
π‘Juggernaut v9
π‘Quality vs. Speed
Highlights
Sdxl Lightning is a text-to-image generative model that uses a progressive adversarial diffusion distillation method.
Compared to Sdxl Turbo, Lightning uses its own Unet model running on latent space, which reduces memory consumption and training time.
Lightning can perform training on 1024x1024 pixels, whereas Turbo is limited to 512x512.
The model is compatible with ControlNet and can be used as a plug-in to reduce diffusion steps while maintaining output quality.
The 1, 2, 4, and 8 step base models can be installed directly from the B Dan Hugging Face repository.
The one-step model is experimental with more unstable quality and is not tested in the video.
The 2-step model generates images in about 0.6 seconds, offering a significant speed increase.
The 4-step model generates images in 0.9 seconds, and the 8-step model in 1.3 seconds, both offering fast performance.
The 4-step and 8-step models are considered usable, while the 2-step model is deemed unstable.
The Juggernaut v9 checkpoint model, when used with a 2-step Laura, has questionable quality but generates images quickly.
Increasing the CFG to 2.0 for the 4-step Laura improves color quality, with an acceptable generation time of 0.9 seconds.
The 8-step Laura model generates high-quality images in about 1.1 seconds.
The Juggernaut v9 Lightning checkpoint model closely matches the original checkpoint's quality with a generation time of around 2 seconds.
ControlNet is confirmed to work with the Lightning model, maintaining the base image's depth in generated images.
The IP adapter workflow can directly replace the model in existing processes without loss of functionality.
The 4-step and 8-step Laura models, as well as the Lightning version, achieve a certain degree of likeness to the reference face.
In-painting with the 8-step Laura model takes about 2.3 seconds, with the Lightning version taking around 3.2 seconds.
The average time taken for image generation increases with the number of steps, but speed increases by 70-80% compared to the base model.
For quality-focused work, the 8-step Laura or the v9 Lightning model are recommended for their balance of speed and quality.
At scale, using the Lightning models can save significant time, such as 1 hour and 40 minutes for every 1,000 images.