STABLE DIFFUSION - Tone Mapping Miracle Might Move Mountains - Playing with the CFG Scale in ComfyUI

Pixovert
7 Aug 202305:45

TLDRThe speaker shares insights on using the CFG scale in ComfyUI with Stable Fusion, highlighting its potential and challenges. They discuss a modification based on research from ByteDance that enhances image generation, maintaining vibrant colors without high CFG negatives. The speaker also mentions an updated course that delves into prompt engineering, CFG, and their interactions, inviting users to join and explore this emerging technology.

Takeaways

  • 🔍 The speaker was researching ComfyUI and Stable Fusion and made interesting discoveries about the behavior of the CFG scale.
  • 🌟 CFG scale, or Classifier Free Guidance scale, has its strengths and weaknesses that the speaker explored.
  • 💡 The speaker found a way to fix some of the problems associated with the CFG scale, leading to improved results.
  • 🖼️ Multiple images were generated using the same prompt but different seeds, showcasing the variety of outputs possible.
  • 🌈 The use of two samplers with the CFG scale resulted in images with amazing contrast and quality.
  • 🛠️ The CFG scale typically breaks down at high levels around 15 or 16, but the speaker's modification allows for better performance.
  • 🚀 The modification is based on research from ByteDance and involves a simple modifier between the model and the sampler.
  • 📈 The speaker's initial goal was to make the CFG scale respect the prompt more, but they shifted focus to playing with the scale itself.
  • 📚 The speaker offers a course that covers ComfyUI, prompts, CFGs, and other related topics, recently updated with a new section on prompt engineering.
  • 🎉 The speaker is optimistic about the potential of this new technology and invites others to learn more through their course.
  • 🔧 There are different proposals for fixing the CFG, but the speaker is encouraged by the early results of their experimentation.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the discovery and exploration of the CFG scale in the context of a comfy UI and stable Fusion, and how it can be improved to produce better results.

  • What does the CFG scale stand for?

    -The CFG scale stands for Classifier Free Guidance scale, which is a parameter that influences the behavior of AI models in generating images based on prompts.

  • What problem does the speaker initially encounter with the CFG scale?

    -The speaker initially encounters a problem where the CFG scale produces undesirable results, with the images becoming too vibrant and not respecting the prompt, especially at higher levels around 15 or 16, and becoming nonsensical at level 30.

  • How does the speaker modify the CFG scale?

    -The speaker modifies the CFG scale by introducing a simple basic modifier that goes between the model and the sampler, which changes the behavior of the sampler and is based on research from ByteDance.

  • What is the outcome of the modification to the CFG scale?

    -The modification leads to the creation of images with vibrant colors and improved contrast, without the negative effects typically associated with high CFG values. It allows for the generation of images that the speaker had not been able to create before.

  • What is the significance of the research from ByteDance?

    -The research from ByteDance suggests that stable diffusion uses a flawed noise schedule in sample steps and offers solutions to fix this issue, which is the basis for the modification the speaker applied to the CFG scale.

  • What is the current status of this modification?

    -The modification is currently in an experimental phase and not yet available for professional use. The speaker mentions that an extension based on this research might be released in the future.

  • How does the speaker suggest one can learn more about this technology?

    -The speaker suggests that one can learn more about this technology by enrolling in his course on comfy UI and stable Fusion, which has recently been updated to include a new section on prompt engineering and how CFG works with prompts and steps.

  • What is the discount code for the course mentioned in the video?

    -The video does not provide a specific discount code; it only mentions that there is a discount available for signing up for the course.

  • What are the different proposals for fixing the CFG?

    -The video does not detail the different proposals for fixing the CFG, but it mentions that there are a couple of them, and the speaker is particularly pleased with the results of the approach he has been experimenting with.

Outlines

00:00

🤖 Discovering CFG Scale Optimization

The speaker shares their findings on optimizing the Classifier Free Guidance (CFG) scale while researching a comfortable user interface and stable Fusion. They discuss the behavior of the CFG scale, its effectiveness, and the issues encountered at higher settings. The speaker stumbled upon a method to fix common CFG problems, resulting in a variety of impressive images generated from the same prompt but with different seeds. The key discovery was that inserting a tone mapper between the sampler and the model could change the CFG behavior, leading to the creation of contrasting images with unique qualities. The speaker initially aimed to make the CFG respect the prompt more, but then shifted focus to experimenting with the CFG scale, which led to fascinating results. The modification is based on research from ByteDance, addressing issues in stable diffusion and its mathematics. The speaker also mentions an updated course that delves into prompt engineering, CFG, and their interactions, with a new section on clip skipping.

05:00

🚀 Exciting Advances in CFG and Stable Diffusion

The speaker continues discussing the CFG scale and its impact on image generation, highlighting the excitement around new findings and potential solutions for the issues with high CFG values. They mention a specific lecture that focuses on CFG, prompts, clip skipping, and sample steps, explaining how these elements interact. The speaker invites the audience to join the course, which has been updated and now includes a discount for new sign-ups. They express optimism about the future release of this technology and share their enthusiasm for the promising results seen so far, including different proposals to fix the CFG challenges.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is a term used in the context of AI and machine learning, referring to a specific type of model that generates images or other media based on input data. In the video, the speaker is discussing their research and findings related to this technology, particularly how it can be manipulated and improved through the use of the CFG scale and ComfyUI.

💡ComfyUI

ComfyUI seems to be a user interface designed to make the interaction with AI models more comfortable and intuitive. The speaker talks about their research on ComfyUI in conjunction with Stable Diffusion, indicating that it is an important aspect of their work and the video's content.

💡CFG Scale

The CFG scale, or Classifier Free Guidance scale, is a parameter used in AI models like Stable Diffusion to guide the generation process without the use of classifiers. The speaker discusses the challenges and potential solutions related to the CFG scale, and how it affects the output of the AI.

💡Tone Mapping

Tone mapping is a technique used to adjust an image's brightness or contrast to make it suitable for a specific output device or medium. In the context of the video, the speaker describes using a tone mapper to modify the behavior of the CFG scale, resulting in improved image generation.

💡Prompt

In the context of AI and machine learning, a prompt is the input data or text given to the model to guide its output. The speaker discusses the use of prompts in relation to the CFG scale and Stable Diffusion, and their desire to make the model pay more attention to the prompt.

💡Sampler

A sampler in the context of AI models like Stable Diffusion is a component that generates outputs based on input data and parameters. The speaker talks about using two different samplers and how their interaction with the CFG scale and tone mapper resulted in varied and improved image outputs.

💡Research

Research in this context refers to the investigation and study conducted by the speaker and other experts to improve the functionality of AI models like Stable Diffusion. The speaker mentions research from ByteDance, a company known for its work in AI and machine learning.

💡Noise Schedule

A noise schedule is a specific algorithm or set of parameters used in the generation process of AI models to introduce controlled randomness. The speaker critiques the noise schedule used in Stable Diffusion as flawed and discusses how the proposed modifications address this issue.

💡Course

The speaker refers to a course they have created that covers topics related to AI model usage, configuration, and optimization. This course includes information on prompts, CFG scale, and other aspects of working with AI models like Stable Diffusion.

💡Extension

In this context, an extension refers to a software add-on or modification that enhances or alters the functionality of a base program. The speaker talks about an experimental extension for Stable Diffusion that incorporates their research findings and modifications to the CFG scale.

💡Vibrant Colors

Vibrant colors refer to bright, rich, and intense hues. In the video, the speaker is pleased with the outcome of using the modified CFG scale, as it results in images with vibrant colors without the negative effects typically associated with high CFG values.

Highlights

Discovered interesting behavior of the CFG scale in ComfyUI and Stable Fusion research.

CFG scale sometimes works well and sometimes doesn't, affecting the output.

Found a way to fix problems with CFG, leading to improved results.

All images shown use the same prompt, demonstrating variability.

The variety of images produced is stunning, with one featuring god rays.

Initial difficulty with the CFG extension led to a breakthrough.

CFG scale normally breaks around level 15-16 in ComfyUI, becoming unusable by level 30.

Modification of the CFG scale allowed for continued use beyond typical limitations.

Two samplers with CFG scale modification produced amazing contrast.

Achieved images never created before with the help of the CFG scale modification.

The prompt, a lament about humanity and AI, was not initially respected by CFG.

Decided to focus on playing with the CFG scale rather than respecting the prompt.

The modification is a simple basic modifier based on research from ByteDance.

Stable diffusion uses a flawed noise schedule in sample steps.

Researchers at ByteDance suggested solutions to the issues with stable diffusion.

The new modification allows for vibrant colors without negative effects of high CFGs.

The paper discussing these findings was published just a couple of weeks ago.

An extension based on this research is in the experimental phase and not yet for professional use.

A course has been updated to include new sections on prompt engineering, CFG, and their interactions.

A discount code is available for those interested in the course to learn more about these technologies.

There are different proposals for fixing the CFG, and the presenter is excited about the current results.

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