【絶対できる】Supermergerの階層マージを使いこなして、myマージモデルを作ろう【stable diffusion】
TLDRThe video script introduces the use of SuperMerger to create a custom merge model for image generation, emphasizing the importance of licensing considerations. It guides viewers through the process of installing SuperMerger, selecting models for merging, and adjusting merge ratios. The video also explores various merge functions, including hierarchical merging and random merging, and compares different calculation modes. The presenter demonstrates how to generate and save custom merge models, encouraging viewers to create their own unique models and subscribe for more content.
Takeaways
- 🌟 Introduction to SuperMerger as a tool for creating custom merge models without distributing them widely.
- 📜 Importance of checking licenses when considering distribution of merged models, especially regarding CreativeML Open RAIL-M and stable diffusion licenses.
- 🚫 Restrictions on commercial use and sharing of certain merge models due to licensing, such as those with Attribution-nonCommercial-NoDerive.
- 🔍 Detailed examination of the licenses for various models, including the CreativeML Open RAIL-M and stable diffusion's non-claim on output rights.
- 🛠️ Installation process of SuperMerger, which requires stable diffusion web ui, and its compatibility with specific versions.
- 🎨 Explanation of creating a merge model using SuperMerger, including selecting models, determining merge ratios, and generating images.
- 🔧 Utilization of different merge modes like Weight sum and calculation modes for fine-tuning the output of the merged model.
- 📊 Use of XYZ plot for comparing merge models and understanding the impact of different alpha values on the final image.
- 🌐 Hierarchy merging as a method to adjust merging ratios for each U-Net layer, affecting different parts of the generated image.
- 🎲 Random merge option 'let the dice roll' for performing random hierarchy merging, offering a variety of outcomes.
- 🔗 Additional resources and articles for further understanding of model merging, including Tofu no Kakera's detailed explanations.
Q & A
What is the main purpose of using SuperMerger as discussed in the script?
-The main purpose of using SuperMerger is to create a merged model by combining different existing models, which can then be used for generating images without the need to create a new file each time, thus saving storage space and allowing for easy experimentation with various model combinations.
What is the significance of licensing when distributing a merged model?
-Licensing is significant because it dictates how the merged model can be used, shared, and commercialized. It's important to carefully check and understand the licensing terms to ensure ethical and legal use of the model, especially when planning to distribute or sell products generated using the model.
How does the script suggest handling models with restrictive licenses?
-The script suggests avoiding the use of models with restrictive licenses that prohibit commercial use or sharing of the merged models. It's better to choose models with permissive licenses, like CreativeML Open RAIL-M, which allow for more flexibility in usage and distribution.
What is the role of the 'Weight sum' mode in SuperMerger?
-The 'Weight sum' mode in SuperMerger is used to determine the merging ratio of the two models. It allows users to adjust the alpha value to control the influence of model B, thereby customizing the characteristics of the generated images.
How can users utilize the 'History' tab in SuperMerger?
-The 'History' tab in SuperMerger allows users to review and recall previous merge models. By clicking on the Load history tab, users can see the models that have been merged so far and use the ID of a specific model to recreate that merge configuration for further image generation.
What is the purpose of the XYZ plot in SuperMerger?
-The XYZ plot in SuperMerger is a tool for comparing merge models by varying the alpha values. It generates a grid of images that showcase the visual differences resulting from different merge ratios, helping users understand how changes in the model's influence affect the final image.
What is hierarchy merging in the context of SuperMerger?
-Hierarchy merging is a method in SuperMerger that allows users to adjust the merging ratio for each U-Net layer. This细致的 control over the merging process can result in more nuanced and targeted changes to the generated images, as different layers of the network can be responsible for different aspects of the image.
How does the 'Random mode' in SuperMerger work?
-The 'Random mode' in SuperMerger performs hierarchy merging using random alpha values. This method introduces an element of chance, allowing users to quickly explore a wide range of merge possibilities and potentially discover interesting and unexpected image outcomes.
What is the importance of understanding calculation modes in SuperMerger?
-Understanding calculation modes in SuperMerger is important because different modes can significantly affect the final output of the merged model. Calculation modes determine how the elements from the merged models are combined, and experimenting with these modes can lead to better control over the image generation process.
How can users save their merged models in SuperMerger?
-Users can save their merged models in SuperMerger by opening the Save Settings tab and checking the 'save model' option. They can also choose to save the model as a safetensors file and give their merged model a custom name for easy reference and future use.
What additional features does SuperMerger offer for model merging?
-SuperMerger offers additional features such as elemental merging, LoRA, and merge mode, which provide users with various methods to combine models. It also includes an analysis tool to examine the similarity between checkpoints for each layer, giving users a more in-depth understanding of how the models interact.
Outlines
🌟 Introduction to Supermerger and Licensing Considerations
The paragraph introduces the use of Supermerger for creating a merge model, similar to majicmix, and emphasizes the importance of checking licenses for distribution. It outlines the process of selecting a Checkpoint trained model from CIVITAI's model status and discusses the licensing terms for stable diffusion and other models like CreativeML Open RAIL-M and Deliberate. The speaker mentions the restrictions on commercial use and sharing of merge models, and advises viewers to choose models listed on CIVITAI for safe usage.
🛠️ Supermerger Installation and Basic Usage
This section provides a step-by-step guide on installing Supermerger, which requires stable diffusion web ui. It explains the process of entering the page, copying the URL, and installing the extension. The speaker also mentions the compatibility requirements with Automatic1111 ver1.5 or later. The paragraph then delves into creating a merge model, specifically a half-real, transcendental beauty by using Majicmixrealistic and epiCRealism models. It details the process of selecting models, determining merge modes, and setting alpha values for the Weight sum mode.
🎨 Merging Models and Generating Images
The speaker discusses the process of merging models using Supermerger, including selecting Model A and Model B, adjusting merge ratios, and generating images. It highlights the ability to use various settings for image generation and the option to save the merged model for future use. The paragraph also explains how to recall previous merge models and the convenience of not creating a file every time a merge is performed. Additionally, it introduces the use of the XYZ plot for comparing merge models and the importance of understanding the U-Net structure for effective hierarchy merging.
🔄 Hierarchy Merging and Its Impact on Image Generation
This part focuses on hierarchy merging, which involves changing the merging ratio for each U-Net layer to influence different parts of the generated image. The speaker references a table by Mr. Tofu no Kakera that explains which layer affects which part of the image. The paragraph describes the process of adjusting alpha values for different layers and the impact on the final image. It also explores presets for hierarchy merging and their effects on the image, demonstrating how different settings can lead to varying results in terms of appearance and style.
🔧 Exploring the Effects of Different Layers and Calculation Modes
The speaker investigates the influence of various layers on the image generation process. It discusses the impact of the OUT part on the face and how IN layers contribute to the overall image. The paragraph also compares different calculation modes, such as smoothAdd and smoothAdd,MT, and their effects on the final image. The speaker experiments with random merging and the use of the XYZ plot to compare calculation modes, providing insights into the complexity and nuances of image generation through merging models.
🎥 Conclusion and Encouragement for Model Creation
In the concluding paragraph, the speaker wraps up the video by encouraging viewers to subscribe to the channel and like the content. It reflects on the day's activities of merging models and the satisfaction of achieving the desired image. The speaker also mentions the potential of Supermerger for elemental merging and analysis, and encourages viewers to create their own original models, reassuring them that merging models is not as complex as it may seem.
Mindmap
Keywords
💡Supermerger
💡Merging Models
💡Licensing
💡Checkpoints
💡Weight Sum
💡Hierarchical Merging
💡XYZ Plot
💡Safetensors
💡Attribution-nonCommercial-NoDerive
💡Upscaler
💡Random Merging
💡Calculation Modes
Highlights
Introduction to Supermerger for creating custom merge models.
The importance of checking licenses before distributing models, such as CreativeML Open RAIL-M and stable diffusion.
Explanation of the relationship between the distributed model and the merge model, based on CreativeML Open RAIL-M.
The process of selecting and merging models using CIVITAI's platform.
Discussion on the commercial use and ethical considerations of models like Deliberate and Majicmix realistic.
Instructions for installing supermerger and its requirements, such as stable diffusion web ui.
Step-by-step guide on creating a merge model, including selecting models and merge modes.
Explanation of the Weight sum merge mode and its impact on the final image.
Demonstration of how to generate an image using the merged model and adjust settings for the generated image.
The ability to save the merged model for future use without creating a file each time.
Introduction to useful functions of SuperMerger, such as the Merge button and the History tab.
How to use the XYZ plot for comparing merge models and understanding the impact of different alpha values.
Exploration of hierarchy merging, which adjusts the merging ratio for each U-Net layer.
Practical example of hierarchy merging using presets like GRAD_V and GRAD_A for image generation.
Discussion on the effects of different layers on the final image, such as OUT12 5 and RING10 3 presets.
Comparison of calculation modes and their impact on image generation using XYZ plots.
The option to merge three models and the various merging methods available.
Final thoughts on the ease of using SuperMerger for model merging and creating original models.