【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】
TLDRIn this informative video, the presenter, Alice, introduces viewers to the world of SuperMerger, a tool for creating custom merge models in AI art generation. She discusses licensing considerations for distribution on platforms like CIVITAI, highlighting the importance of understanding and adhering to the terms. The video offers a step-by-step guide on installing SuperMerger, selecting and merging models, and adjusting parameters for image generation. Alice demonstrates the process using popular models like Majicmixrealistic and epiCRealism, and explores advanced features like hierarchy merging and random merging. The video concludes with tips on naming and saving custom models, encouraging viewers to create their own unique AI art models.
Takeaways
- 🌟 Introduction to SuperMerger for creating custom merge models without distributing them publicly.
- 📜 Importance of checking licenses for models distributed on platforms like CIVITAI, such as CreativeML Open RAIL-M and stable diffusion.
- 🚫 Restrictions on commercial use and sharing of merge models, especially with Attribution-nonCommercial-NoDerive licenses.
- 🔍 Detailed explanation of model licensing, including the rights to output and ethical considerations.
- 🛠️ Installation process of SuperMerger, which requires stable diffusion web ui and specific version compatibility.
- 🎨 Tutorial on creating a merge model, including selecting models, setting merge ratios, and generating images.
- 🔄 Demonstration of the Weight sum merge mode and its impact on the final image generation.
- 📊 Use of XYZ plot for comparing merge models and understanding the influence of different layers on the output.
- 🌐 Hierarchy merging, which adjusts the merging ratio for each U-Net layer to fine-tune the image generation process.
- 🎲 Random merge option for exploring a variety of merge ratios and outcomes without manual adjustment.
- 🔧 Various calculation modes and their effects on image generation, with examples and comparisons provided.
- 💡 Encouragement for users to experiment with SuperMerger and create their own original models, emphasizing its accessibility and potential for customization.
Q & A
What is the main purpose of using SuperMerger as discussed in the video?
-The main purpose of using SuperMerger, as discussed in the video, is to create a merged model by combining different AI models, which can then be used for generating images with unique characteristics by adjusting the merging ratios and parameters.
What is the significance of licensing when distributing AI-generated models?
-Licensing is significant when distributing AI-generated models because it determines how the models can be used, shared, and commercialized. It's important to carefully check the licenses associated with the models to ensure ethical and legal use, especially when planning to distribute or sell products created with these models.
How does the video address the concept of model merging?
-The video addresses model merging by explaining the process of combining different AI models to create a new model with unique features. It discusses the use of SuperMerger for this purpose, including the selection of models, adjustment of merging ratios, and the generation of images based on the merged model.
What are the key components of the SuperMerger process as outlined in the video?
-The key components of the SuperMerger process outlined in the video include selecting the models to be merged (Model A and Model B), choosing the merge mode (such as Weight sum), setting the calculation mode (default normal), and adjusting the alpha value to control the influence of each model in the merge.
How does the video demonstrate the practical use of SuperMerger?
-The video demonstrates the practical use of SuperMerger by walking through the steps of merging two models, Majicmixrealistic and epiCRealism, to create a new model. It shows how to adjust the alpha value for a 1:1 merge, generate an image from the merged model, and save the model for future use.
What are the considerations for using different calculation modes in SuperMerger?
-Different calculation modes in SuperMerger, such as normal, add, and multiply, affect how the models are merged and the resulting characteristics of the generated images. The choice of calculation mode can influence the balance between the features of the individual models in the final merged output.
How does the video address the concept of hierarchy merging?
-Hierarchy merging is addressed in the video as a method of adjusting the merging ratio for each U-Net layer, which allows for more nuanced control over the influence of each model on different aspects of the generated image. The video explains how to use this feature to focus the influence of one model on specific layers, such as the face or body.
What is the role of the XYZ plot in SuperMerger?
-The XYZ plot in SuperMerger is used for comparing the effects of different merge ratios (alpha values) on the generated images. It allows users to generate a grid of images with varying ratios, which can help in understanding how different settings impact the final output and in making informed decisions about model merging.
What are the additional features of SuperMerger that the video highlights?
-The video highlights additional features of SuperMerger such as the ability to save the merged model for future use, recall previous merge settings from history, and use presets for hierarchical merging. It also mentions the possibility of merging three models and the inclusion of elemental merging and LoRA merge mode for more advanced users.
How does the video conclude regarding the use of SuperMerger?
-The video concludes that SuperMerger is a powerful tool that allows users to create original models by merging different AI models. It emphasizes that despite the complexity of some features, the process is accessible and encourages users to explore model merging to create unique outputs.
What advice does the video give on selecting models for merging?
-The video advises users to select models for merging based on their licensing terms and personal use cases. It suggests choosing models that do not have license issues and are easy to use freely, such as Majicmixrealistic, and to be cautious of models with strict licenses or those that prohibit commercial use and sharing.
Outlines
🌟 Introduction to Supermerger and Licensing Considerations
The video begins with an introduction to the use of Supermerger for creating custom merge models, similar to majicmix, and distribution through CIVITAI. The importance of checking licenses for distribution is emphasized, with a brief overview of the types of models available on CIVITAI, including epiCRealism and CreativeML Open RAIL-M. The video also touches on the licensing terms for stable diffusion and the potential for commercial use of model outputs. The complexity of understanding licenses is acknowledged, and viewers are encouraged to comment with feedback or corrections.
🛠️ Supermerger Setup and Basic Usage
This section details the process of setting up Supermerger, which requires the stable diffusion web UI. Instructions for installation are provided, including copying and pasting the URL into the Extensions tab for installation. The video then moves on to demonstrate the creation of a merge model, with a focus on combining Japanese real and foreign real models to achieve a half-real, transcendental beauty. The use of Majicmixrealistic and epiCRealism models is highlighted, and the process of merging models using the Weight sum mode is explained, including the adjustment of alpha values and the generation of images.
🔍 Exploring Merge Ratios and Image Comparison with XYZ Plot
The paragraph discusses the exploration of merge ratios using the XYZ plot feature in Supermerger. It explains how to compare different merge ratios by adjusting alpha values and generating images in a grid format. The use of random seed values for generating diverse images and the convenience of recalling previous merge models through the History tab are also covered. The video then delves into hierarchy merging, explaining the concept of U-Net layers and how adjusting the merging ratio at each layer can affect the final image. The process of using hierarchy merging is demonstrated, with a focus on the influence of different layers on the image's features.
🎨 Fine-Tuning Image Characteristics through Hierarchical Merging
This section continues the discussion on hierarchical merging, showcasing the impact of different layers on the image's appearance. The video presents various presets and their effects on the merge, such as GRAD_V and GRAD_A, and compares the results. It also explores the influence of the shallow and middle layers on facial features and skin texture. The video then examines the impact of OUT layers on the overall image and compares different merging strategies, including WRAP12 and RING10 3. The goal is to achieve a balance between the characteristics of both models used in the merge.
🎲 Random Hierarchical Merging and Calculation Modes
The video introduces the concept of random hierarchical merging, allowing users to explore a variety of merge ratios without manual adjustment. The 'let the dice roll' feature is highlighted for its ease of use and the ability to generate diverse images quickly. The video also discusses different calculation modes, such as smoothAdd and smoothAdd,MT, and their impact on image generation. The use of XYZ plots to compare calculation modes is demonstrated, and the video concludes with a recommendation to refer to Tofu no Kakera's detailed explanation of calculation modes for further understanding.
🏁 Conclusion and Final Thoughts
The video concludes with a summary of the key points covered, including the setup and use of Supermerger, the exploration of merge ratios, and the intricacies of hierarchical merging. The presenter encourages viewers to subscribe to the channel and like the video, and expresses satisfaction with the merge results achieved throughout the video. The video ends with a farewell and an anticipation for the next video installment.
Mindmap
Keywords
💡Supermerger
💡CIVITAI
💡License
💡Checkpoint
💡Merging
💡Weight Sum
💡Alpha Value
💡XYZ Plot
💡Hierarchy Merging
💡Safetensors
💡Calculation Mode
Highlights
Introduction to SuperMerger for creating custom merge models.
Mention of the importance of checking licenses before distributing models.
Explanation of the difference between a trained model and a merge model.
Discussion on the licensing of CreativeML Open RAIL-M and stable diffusion models.
The process of selecting and merging models using CIVITAI's platform.
Instructions for installing and using SuperMerger with stable diffusion web UI.
Creation of a half-real, transcendental beauty model using merge.
Demonstration of the SuperMerger interface and basic usage.
Explanation of merge modes, including Weight sum and its parameters.
How to save a merge model and its generated images without consuming storage space.
Introduction to hierarchy merging and its impact on different layers of the model.
Use of presets for hierarchical merging and their effects on the final image.
Comparison of different calculation modes and their visual outcomes.
The ability to merge three models and the various merging methods available.
Elemental merging and analysis features of SuperMerger for model comparison.
Encouragement for viewers to merge models and create their own original models.
Closing remarks, call to action for subscribing and liking the video.