Better AI Hands. How to Install Smea Dyn for Stable Diffusion.

Sebastian Kamph
15 Apr 202412:01

TLDRThe video introduces a new sampler for Stable Fusion, promising improved hand and limb depictions in generated images. The sampler, based on Oiler's approach, mitigates structural and limb collapse issues, particularly in large images. A comparison of various samplers demonstrates the new method's effectiveness, though it's not perfect. The installation process is outlined, and the video explores the sampler's performance with different settings and image dimensions, highlighting areas for further development.

Takeaways

  • 🤖 Introduction of a new sampler for Stable Fusion aimed at improving the depiction of hands and limbs in generated images.
  • 🔍 Comparison of various samplers, including Oilers, Mia, and DPM Plus+ 2 MSD, based on their ability to render hands and fingers correctly.
  • 📈 The new samplers, particularly the Mia and die variants, show promise in delivering better hand and finger separation, though not perfect.
  • 🚀 The Oiler smia smme EA D sampler is based on Oilers' approach, designed to reduce structural and limb collapse in large images.
  • 💻 Installation instructions for the new sampler are provided, involving the use of an automatic update and extension installation from a URL.
  • 📱 The effects of the new samplers are more pronounced in SD 1.5 than in sdlx, with the Oiler di being roughly equivalent to Oiler a.
  • 🌐 The smia die sampler consumes more computational resources, approximately 1.25 times more than other methods.
  • 📊 Use of the XYZ plot script for detailed comparisons of different samplers and seeds, allowing for a more granular analysis of image quality.
  • 🖼️ Experimentation with different image resolutions and aspect ratios to identify issues related to limb and body part generation.
  • 🔧 Application of fixes such as the high-risk fix and ad tailor to improve the quality of generated images, particularly in relation to hands and faces.
  • 🔄 Generation of new images with modified settings and prompts to test the consistency and effectiveness of the new samplers in various scenarios.

Q & A

  • What is the main purpose of the new sampler for Stable Fusion discussed in the video?

    -The main purpose of the new sampler for Stable Fusion is to fix issues with hands and weird limbs in generated images.

  • How does the video demonstrate the effectiveness of the new sampler?

    -The video demonstrates the effectiveness of the new sampler by comparing images generated with different samplers, focusing on the accuracy of hand depictions and limb structure.

  • What are some of the samplers compared in the video?

    -Some of the samplers compared in the video include Oiler A, Oilers Mia, DPM Plus+ 2 MSD, and DD.

  • What is the claim made by the developers of the new sampler?

    -The developers claim that the new sampler can significantly reduce structural and limb collapse in large images and produce superior hand depictions compared to existing sampling methods.

  • How does the video show the process of installing the new sampler?

    -The video shows the installation process by guiding through copying a URL to the clipboard, accessing the Stable Fusion extensions, installing from URL, and applying the changes after restarting the UI.

  • What are the performance differences between the new samplers and the existing ones?

    -The new samplers, particularly the smia die sampler, consume more computational resources, approximately 1.25 times more than the existing ones. The effects of the new samplers are also more pronounced in SD 1.5 than in sdlx.

  • How does the video address the issue of inconsistent results with the old samplers?

    -The video addresses the issue by showing examples where the old samplers produced inconsistent results, such as varying numbers of fingers or broken limbs, and then contrasts these with the more consistent results from the new samplers.

  • What additional tools does the video mention for further analysis of the images?

    -The video mentions the use of an XYZ plot available in the scripts for further analysis and comparison of the images.

  • What are the recommendations for using the new samplers with different image dimensions?

    -The video suggests using a high-risk fix for non-square or non-standard image dimensions to ensure the correct depiction of people and limbs, and also recommends using additional tools like ad tailor for fine-tuning.

  • What is the conclusion drawn from the tests conducted in the video?

    -The conclusion is that the new samplers show promise in improving hand depictions and reducing limb collapse, but they are still in development and may not work perfectly in all scenarios. The video encourages viewers to share their experiences and findings with the new samplers.

Outlines

00:00

🤖 Introduction to New Sampler for Stable Fusion

The paragraph introduces a new sampler for Stable Fusion, a tool that aims to improve the generation of images, particularly in fixing issues with hands and limbs. The speaker discusses various samplers, including Oiler, Mia, and DPM Plus+ 2 MSD, and compares their effectiveness through different seeds. The focus is on the claim that the new sampler can significantly reduce structural and limb collapse in large images, and the speaker provides a brief tutorial on how to install and use the new sampler.

05:00

🔍 Comparative Analysis of Samplers in Stable Fusion

This paragraph delves into a comparative analysis of different samplers in Stable Fusion, particularly focusing on their ability to generate images without common issues such as weird limbs or multiple heads. The speaker conducts tests with various resolutions and seeds, and observes the results. The discussion includes the challenges faced with non-square images and the impact of different samplers on the quality of the generated images. The speaker also notes the differences in visual style and the potential need for additional fixes like highest fix or ad tailor to improve the final output.

10:01

🚀 Testing New Samplers and Their Effectiveness

The speaker continues to test the new samplers, exploring their effectiveness in generating images with correct details, especially hands. Various tests are conducted, including high-risk fixes and the use of default negatives to mitigate weird limb issues. The results show a difference in the quality of images produced by different samplers, with some showing promise and others requiring further development. The speaker also discusses the potential for future improvements and versions of the new samplers, encouraging viewers to share their experiences and findings.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is an AI model used for generating images from textual descriptions. It is known for its ability to create detailed and diverse visual content. In the video, the focus is on improving the performance of Stable Diffusion, particularly in rendering hands and limbs accurately, which has been a common issue in AI-generated images.

💡Sampler

A sampler in the context of AI image generation refers to a method or algorithm used to select and combine different elements to create a new image. The video introduces a new sampler called 'Oiler smia smme EA D sampler' that aims to improve the quality of hand and limb depictions in images produced by Stable Diffusion.

💡Limb Collapse

Limb collapse is a term used to describe the visual artifact where limbs or other body parts in AI-generated images appear distorted, disconnected, or incorrectly structured. The video aims to find solutions to this problem by testing different sampling methods in Stable Diffusion.

💡Fingers

Fingers are a crucial detail in human hands that can be challenging for AI image generation models to render correctly. The video script discusses the sampler's ability to depict fingers accurately, which is often a point of focus when assessing the quality of AI-generated human figures.

💡Computational Resources

Computational resources refer to the hardware and software capabilities required to perform complex tasks, such as running AI models. In the context of the video, some samplers may consume more computational resources than others, affecting the processing time and efficiency of image generation.

💡Automatic 1111

Automatic 1111 is not a standard term and might be a typo or a specific reference in the video's context. It could potentially refer to an automatic update mechanism or a version number of a software related to Stable Diffusion. The video mentions that the new sampler will be available in Automatic 1111, suggesting it's a platform or tool used to manage and update AI models.

💡XY Z Plot

The XY Z plot is a method used to visualize and compare different variables or results in a structured way. In the context of the video, it seems to be a script feature that allows the creator to organize and display different samplers and their outcomes in a grid format for easy comparison.

💡High-Risk Fix

High-Risk Fix likely refers to a technique or setting within the AI image generation process that attempts to correct or fix common issues such as limb collapse or distorted body parts, but may also introduce new artifacts or unexpected changes to the image. The video discusses using a High-Risk Fix in combination with certain samplers to improve the quality of the generated images.

💡DPM Plus+ 2 MSD

DPM Plus+ 2 MSD is mentioned as one of the samplers used for comparison in the video. It's likely a specific configuration or version of a sampler within the Stable Diffusion model. The video compares this sampler to others in terms of its ability to generate accurate hand and limb structures in AI-generated images.

💡Ad Tailor

Ad Tailor, as mentioned in the video, seems to refer to a feature or script used to refine or adjust specific elements of AI-generated images, such as the hands or face. It's a tool or method aimed at improving the quality and accuracy of certain details in the generated content.

💡Textual Inversion

Textual inversion might refer to a technique used in the AI image generation process where certain textual prompts or descriptions are manipulated to achieve a desired outcome in the generated image. This could involve changing the structure or focus of the prompt to correct issues or to guide the AI model more effectively.

Highlights

A new sampler for Stable Fusion aims to fix hands and weird limbs.

The sampler is based on Oilers approach, designed to generate superior imagery.

It significantly mediates the structural and limb collapse in large images.

The sampler produces superior hand depictions compared to existing methods.

The sampler is available soon in automatic 1111 and can be installed via a URL.

The effects of the sampler are not as pronounced in SD 1.5 as in SD 1.0.

The smia die sampler consumes approximately 1.25 times more computational resources.

The sampler can be applied by installing from URL and restarting the UI.

The XYZ plot script can be used for comparing different samplers and seeds.

The sampler can handle non-square aspect ratios, but may still produce anomalies.

The sampler shows promise in generating images with correct finger counts and less limb collapse.

The visual style of the new samplers is notably different from previous versions.

The sampler can be combined with other fixes such as high-risk fix and ad tailor for better results.

The development of the sampler is ongoing, with potential for future improvements.

The sampler is expected to have DPM versions available in the future.

The video invites viewers to share their experiences and settings that worked flawlessly for them.

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