【Stable Diffusion】Loraモデル学習をGoogle Colabで作る方法解説。Kohya LoRA Dreambooth使用。【ジェネレーティブAI】
TLDRThis tutorial outlines the process of creating a Lora model using Google Colab, specifically with the Kohya LoRA Dreambooth tool. It details the steps from model selection, customization, and training using a variety of images to refine the model's output. The script emphasizes the importance of unique prompts and diverse image sets to enhance the model's learning capabilities. Additionally, it provides troubleshooting tips and explains how to utilize the trained model for generating images, highlighting the iterative nature of the process for achieving desired results.
Takeaways
- 📚 The tutorial is about creating a Lora model using Google Colab and the KOHYA SD script.
- 🔧 The Lora model size is 4.8 megs, but it's upgraded to 8 megs for the tutorial.
- 🛠️ The process involves using the KOHYA tool, which simplifies the model training process.
- 🔗 A link to the Dream Booth is provided in the video description for easy access.
- 💡 The training process requires the user to input a unique prompt for the model.
- 🖼️ Users should provide diverse images for the model to learn from, avoiding repetition in poses or outfits.
- 📌 It's important to maintain high accuracy in tagging and avoid unrelated files to ensure the model learns correctly.
- 📈 The tutorial explains how to adjust settings such as batch size and learning steps for optimal training.
- 🔄 The model training involves epochs and steps, with clear instructions on how to calculate them.
- 📋 The script outlines the importance of a clear and understandable prompt for effective model training.
- ⏱️ The training and testing process is time-efficient, with sessions typically lasting less than 10 minutes.
Q & A
What is the size of the original Lora model mentioned in the script?
-The original Lora model mentioned in the script is 4.8 megs in size.
What is KOHYA in the context of the script?
-KOHYA refers to a tool or a script created by users that is being utilized in the process of learning and applying the Lora model.
How long does the process of executing the Lora study typically take?
-The process usually takes around four minutes to execute.
What is the purpose of mounting Google Drive in the process?
-Mounting Google Drive allows the user to access and use the drive for storing and retrieving necessary files for the Lora model application.
What type of models can be chosen for stable diffusion in the script?
-The user can choose between animated models and live-action models for stable diffusion.
What is the significance of the prompt when training the model?
-The prompt is significant as it guides the model on what to learn and focus on, helping to shape the output of the model.
How many images are recommended for training the model?
-The number of images can vary, but the user has found 8 images to work well for their purposes.
Why is it important to use images of the subject in different poses, clothes, and moods?
-Using diverse images helps the model learn and understand the subject matter better, avoiding repetition and ensuring a broader understanding.
What happens if the same word is used for learning as is already present in the model?
-Using the same word that is already present in the model can lead to the original word being covered and not working properly, hence unique words should be used.
What is the role of the batch size in the learning process?
-The batch size determines the number of sheets studied at the same time per study. A larger batch size is suitable for stronger systems, while a smaller size is for weaker systems.
How is the number of learning steps calculated?
-The number of learning steps is calculated by multiplying the number of epochs by the number of sheets to study. For example, 20 epochs multiplied by 8 sheets results in 160 learning steps.
Outlines
🤖 Introduction to Lora Training and Model Sizes
This paragraph introduces the process of training using Lora and the different model sizes involved. It begins with discussing the original 4.8-megabyte model and the transition to an 8-megabyte model for the training process. The speaker briefly mentions the Lora study and the tool's basic functionality, including the use of a script provided by KOHYA. The paragraph also touches on the importance of collaboration and the potential for using the tool with more extensive collaboration in the future. The speaker provides a link to Dream Booth in the video description for further exploration.
📸 Image Tagging and Model Selection for Training
The second paragraph delves into the specifics of image tagging and model selection for the training process. It discusses the automatic tagging of images provided, the importance of including diverse images to avoid repetition in poses, clothing, and mood. The speaker advises on the avoidance of common tags that may lead to the model being overwhelmed, such as 'Biden', to ensure the learning process remains effective. The paragraph also covers the process of selecting models for training, including the use of VAE and Stable Diffusion models, and the need for a unique prompt for each model to maintain accuracy and avoid confusion.
🚀 Training Execution and Testing
In the final paragraph, the focus shifts to the execution of the training process and subsequent testing. The speaker explains the steps involved in starting the learning process, including the preparation of the model and the setting of various parameters such as batch size and learning steps. The paragraph also discusses the importance of patience, as the learning process can be time-consuming. After training, the speaker describes how to conduct a test to ensure the model has learned effectively, emphasizing the need for a diverse set of prompts and the potential for further refinement through additional training epochs.
Mindmap
Keywords
💡Lora
💡Google Colab
💡Kohya
💡Dreambooth
💡Model Size
💡Token
💡Stable Diffusion
💡VAE
💡Prompt
💡Training Data
💡Epochs
Highlights
The tutorial explains how to create a Lora model using Google Colab, specifically with the Kohya LoRA Dreambooth.
The Lora model size is 4.8 megs, but it is increased to 8 megs for the process.
Kohya has created a simplified SD script for users to utilize in the Lora study process.
The tutorial provides a link to Dream Booth, which can be copied to the Google Drive for use.
Users need to paste their own token for certain steps in the process.
The model selection includes options for animated or live-action models, and the possibility to use Stable Diffusion 2.0.
Users can customize the training data destination and the prompt for their study.
It is recommended to use unique and descriptive names for the study and to avoid common words for better results.
The images for training should be diverse in pose, clothing, and mood to improve learning accuracy.
The tutorial advises against including background elements like the Tokyo Tower to prevent them from being learned as part of the subject.
There is a warning about the potential for electric shocks, although it is not a serious concern.
The process includes automatically tagging the images provided for better learning.
The tutorial explains how to adjust the settings for batch size and learning steps based on the user's capabilities.
The number of epochs and the extension of the saved model are also customizable.
A test is conducted after the learning process to verify its effectiveness.
The tutorial mentions the possibility of additional training epochs for further refinement of the model.
The final output is compared to the initial model to show the improvements made through the Lora learning process.