【やってみよう！】LoRAの使い方&簡単な作り方（kohya's GUI）
TLDRThe video script offers a comprehensive guide on using LoRA (Low-Rank Adaptation) for character, costume, and background generation in AI models. It explains the basics of LoRA, how to download and apply it, and the importance of trigger words. The tutorial also covers saving LoRA files and adjusting their intensity. Additionally, it provides a step-by-step process for creating custom LoRA using a GUI developed by bmaltais, including preparing images, editing captions, and training the LoRA model. The video concludes with a practical demonstration of generating images with the newly created LoRA, encouraging viewers to explore AI-generated content creation.
Takeaways
- 📚 LoRA stands for Low-Rank Adaptation and is a technique for adding additional information to existing models to generate images with fixed characters, outfits, and backgrounds.
- 🚀 By mastering LoRA, users can generate images of their favorite characters, clothing, and backgrounds with ease.
- 💾 LoRA files can be downloaded from platforms like CIVITAI and Hugging Face and saved in the Lora folder within the stable diffusion web UI folder.
- 🖼️ To display LoRA thumbnails in the web UI, save a PNG or JPEG image with the same name as the LoRA file in the Lora folder.
- 🔄 Commercial use of LoRA files is indicated by a checkbox in the summary section of the LoRA file.
- 🔧 Applying LoRA requires a trigger word, which can be found in the Trigger Words section on the LoRA page and copied into the prompt box.
- 🎨 The intensity of LoRA can be adjusted by changing the numerical value at the end of the activation prompt, which affects how strongly the LoRA characteristics are reflected in the generated image.
- 🔍 Different models may require adjustments to generate accurate LoRA images, with anime-style models being more likely to produce accurate results.
- 💡 LoRA trigger words can be saved for easy use by entering them in the Activation text field in the Lora tab.
- 🛠️ Creating an original LoRA can be done using a GUI developed by bmaltais, which simplifies the kohya-style LoRA creation process.
- 📸 For creating a new LoRA, gather images of the subject, adjust them to the recommended size, and remove backgrounds to focus on the character features.
- 📈 The learning process for LoRA involves setting up folders for input and output, naming them appropriately, and using a tool like Dataset-tag-editor to edit captions for the images.
Q & A
What does LoRA stand for and what is its purpose?
-LoRA stands for Low-Rank Adaptation, and it is a technique used to add additional information to a model, allowing for the generation of images with specific characters, clothing, and backgrounds.
How can you download a LoRA file?
-You can download a LoRA file from platforms like CIVITAI or Hugging Face. In the script, the user chooses to download a free LoRA from CIVITAI.
Where should you save the downloaded LoRA file?
-After downloading, the LoRA file should be saved in the 'models' folder within the 'stable diffusion web UI' directory, specifically in a subfolder named 'Lora'.
What is the purpose of the trigger words in LoRA?
-Trigger words are necessary to apply the LoRA file. They are copied from the LoRA page and pasted into the prompt box to activate the LoRA during the image generation process.
How can you adjust the intensity of the LoRA when generating images?
-The intensity of the LoRA can be adjusted by changing the numerical value written at the end of the activation prompt. Higher values will make the LoRA's characteristics more prominent, while lower values will make them more subtle.
What is the process for saving the LoRA trigger words?
-To save the trigger words, click on the hammer and spanner icon next to the LoRA file in the 'Lora' tab. Enter the trigger words in the 'Activation text' field and click 'Save'.
How does the video script guide the creation of an original LoRA?
-The script guides the user through the use of a GUI developed by bmaltais, which simplifies the process of creating a kohya-style LoRA. It involves installing prerequisites like Python and Visual Studio, setting up the GUI, and preparing folders and images for learning.
What are the recommended image sizes for LoRA learning?
-The recommended image size for LoRA learning is 512×512 pixels or larger. In the script, the user decides to统一 the image size to 768×768 pixels.
How does the script address the issue of unwanted tags in the caption file?
-Unwanted tags, which are features not desired for learning, are removed using the 'Batch Edit Captions' feature in the Dataset-tag-editor tool. The user selects the tags to remove and applies the changes to the filtered images.
What is the role of the 'Mixed precision' and 'Save precision' settings in LoRA creation?
-These settings determine the precision used for training and saving the LoRA file. 'bf16' is selected for RTX30xx series and above, while 'fp16' is chosen for other graphics cards.
How can you ensure that the created LoRA files are saved at specific intervals during training?
-The 'Save every N epochs' setting in the Parameters tab allows you to specify how often LoRA files are saved during the training process, based on the number of epochs completed.
What is the significance of the 'Clip skip' setting in the Advanced tab of the Parameters?
-The 'Clip skip' setting is used to adjust the learning process for specific types of content. In the script, it is set to 2 for creating an anime-style LoRA, which may help in achieving better results for this type of content.
Outlines
📚 Introduction to LoRA and Its Simple Creation Method
This paragraph introduces the concept of LoRA (Low-Rank Adaptation) and its application in generating personalized characters, outfits, and backgrounds. It explains that LoRA is a technique for adding additional information to existing models, allowing for the generation of images with fixed character, outfit, and background elements. The video aims to teach viewers how to master LoRA by providing a step-by-step guide on downloading and using LoRA files from platforms like CIVITAI and Hugging Face. It also touches on the importance of understanding the commercial use rights associated with LoRA files.
🛠️ Customizing and Applying LoRA with Different Models
The paragraph details the process of selecting and applying LoRA to different models, including both anime and real-life models. It guides the viewer through the steps of selecting models, generating images, and adjusting LoRA to achieve desired results. The section also discusses saving LoRA trigger words and the importance of using the correct model for optimal LoRA application. Additionally, it provides a brief overview of changing the model using the script option in the web UI.
🎨 Creating a Personalized LoRA Using GUI
This section provides a comprehensive guide on creating an original LoRA using a GUI developed by bmaltais, which simplifies the kohya-style LoRA creation process. It outlines the prerequisites for using the GUI, including the installation of Python, Git, and Visual Studio packages. The paragraph then walks through the installation process of Visual Studio packages and the setup of the kohya's GUI, emphasizing the importance of following the instructions carefully to ensure successful installation. It also discusses the preparation of necessary folders for LoRA learning and the collection of images from Pinterest to be used for LoRA learning.
🖼️ Preparing Images and Captions for LoRA Learning
The paragraph focuses on preparing images and captions for LoRA learning. It explains the process of gathering images, adjusting their size and background using Canva, and saving them in a structured folder system. The section also covers the use of the Dataset-tag-editor tool for editing captions, which involves removing unwanted tags and adding trigger words to guide the LoRA learning process. The goal is to create a dataset that effectively captures the desired features for the LoRA to learn.
🚀 Executing LoRA Training and Evaluating Results
This part of the script describes the actual execution of LoRA training using the prepared images and captions. It details the setup process in the kohya's GUI, including selecting the source model, specifying image and output folders, and adjusting various parameters for the training process. The paragraph also addresses common issues such as the 'No bitsandbytes' error and provides solutions. Finally, it discusses the evaluation of the trained LoRA by generating images with different settings and comparing the results to find the optimal balance of steps and intensity.
🎥 Conclusion and Encouragement for Further Exploration
The concluding paragraph summarizes the video's content, highlighting the practical use of LoRA for generating personalized images. It encourages viewers to apply the knowledge gained from the video to create their favorite images and to explore further by subscribing to the channel for more information on AI, 3DCG, and video creation. The video ends with a call to action for viewers to like, subscribe, and comment on their thoughts and questions.
Mindmap
Keywords
💡LoRA
💡Trigger Words
💡Stable Diffusion
💡Commercial Use
💡Negative Prompt
💡Image Generation
💡Model Selection
💡Meta Data
💡kohya's GUI
💡Visual Studio Package
💡GitHub
💡Canva
💡Dataset-tag-editor
Highlights
Introduction to LoRA (Low-Rank Adaptation) and its capabilities.
LoRA allows for the addition of extra information to models for character reproduction and fixed image generation.
Downloading free LoRA files from CIVITAI for practical demonstration.
Instructions on saving and viewing LoRA files within the stable diffusion web UI.
Details on commercial use permissions for LoRA files.
Explanation of using trigger words with LoRA files for effective image generation.
Demonstration of generating images with and without LoRA application.
Adjusting LoRA intensity to modify the output image characteristics.
Changing models while applying LoRA to observe differences in image generation.
Guidelines for saving LoRA trigger words for easy future use.
Introduction to creating an original LoRA using kohya's GUI.
Prerequisites for installing kohya's GUI, including Python and Visual Studio package installation.
Step-by-step process for setting up kohya's GUI for LoRA creation.
Preparation of folders and images for LoRA learning.
Using Canva to adjust image sizes and backgrounds for optimal LoRA learning efficiency.
Editing image captions using Dataset-tag-editor for effective LoRA training.
Starting the LoRA training process with kohya's GUI and reviewing the generated LoRA files.
Applying the newly created LoRA to generate images resembling a specific character.
Conclusion and encouragement for viewers to try creating their own images using LoRA.