【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】
TLDRThe video script provides a detailed guide on creating a Lora model using Google Colab, specifically with the Kohya LoRA Dreambooth tool. It explains the process of training the model, including the model size, the use of SD script, and the importance of unique prompts for better learning. The script also covers the steps for uploading to Google Drive, selecting model types, and the training process with tips on image selection and avoiding common mistakes. It concludes with testing the trained model and suggestions for further refinement.
Takeaways
- 📚 The tutorial is about creating a Lora model with Stable Diffusion using Google Colab and Kohya's Dreambooth.
- 🔧 The Lora model size is 4.8 megs, but it's upscaled to 8 megs for the process.
- 🛠️ Kohya has provided a simplified SD script for users to utilize in the tutorial.
- 🔗 Users are instructed to copy the provided Dream Booth link to their Google Drive for the next steps.
- ⏳ The process takes around four minutes to execute, depending on the system's performance.
- 🔑 A personal token is required and should be pasted to run the script successfully.
- 💾 Mounting Google Drive is necessary for the process, and the model can be downloaded from there.
- 🎨 Users can choose between animation or live-action models for Stable Diffusion 2.0.
- 🖋️ Prompts should be unique and descriptive, avoiding common terms that may cause confusion or overlap.
- 📸 Images for training should be diverse in pose, clothing, and mood to improve learning accuracy.
- 🚫 The tutorial advises against including unrelated elements in the background, such as the Tokyo Tower, to prevent interference with learning.
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 script created by the community to facilitate the process of working with Stable Diffusion models.
How long does the process of executing the script 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 store data needed for the model training process.
What type of models can be chosen for stable diffusion?
-There are animated models and live-action models to choose from for stable diffusion.
What is the significance of the prompt when training the model?
-The prompt is a descriptive word or phrase that guides the model's learning process, helping it to understand the subject matter better.
Why is it important to use different poses, clothes, and moods in the images for training?
-Using diverse images helps the model to learn and understand the subject matter in various contexts, improving its accuracy and adaptability.
What does VAE stand for and what is its role in the process?
-VAE stands for Variational Autoencoder, which is a type of generative model used to help improve the quality of the generated images during the training process.
How does the batch size affect the training process?
-The batch size determines the number of images the model studies at the same time per study. A larger batch size may lead to faster training but requires more computational resources.
What is the purpose of the epoch in the training process?
-An epoch refers to a complete pass of the entire data set during the training process. Multiple epochs help the model to learn more effectively by repeating the training process.
What happens after the model is trained?
-After training, the model is saved in the designated output directory, and the user can run tests to check its performance and make further adjustments if needed.
Outlines
🤖 Introduction to Lora Training and Model Size
The paragraph discusses the process of learning using Lora and the transformation of the original model through training. It mentions the initial model size of 4.8 megs and its upgrade to 8 megs for application. The concept of the Lora study is introduced, highlighting the creation of a basic tool for the process. The speaker expresses surprise at the capabilities of the tool and suggests its potential for collaboration. The paragraph also covers the use of a script, the importance of model and token usage, and the execution process, which takes approximately four minutes. Additionally, it touches on the customization of the study with personal prompts and the inclusion of unique, diverse images for effective learning.
📸 Enhancing Learning Accuracy with Image Tagging
This paragraph delves into the importance of image tagging for improving learning accuracy. It describes the automatic tagging of images provided, with examples like 'One girl, solo, long hair' and 'One Dog, Run, Green lawn'. The speaker discusses the capabilities of handling live-action images and suggests adjusting thresholds for better tag accuracy. The paragraph also covers the selection of models for training, the naming of projects, and the preparation of VAE folders. It further explains the detailed setup of learning parameters, such as batch size, learning steps, and epochs, emphasizing the need for careful adjustment to achieve optimal learning results.
🚀 Completing the Training and Testing Phases
The final paragraph covers the completion of the learning process, which is described as being relatively short, not exceeding 4 minutes. It discusses the handling of potential errors and the saving of the model after training. The speaker then moves on to the testing phase, where the effectiveness of the training is evaluated. The process involves copying the model path, inputting prompts, and using both positive and negative prompts for testing. The paragraph also addresses additional training with the Lora model, the use of seeds for consistent outputs, and the potential for repeated learning sessions. The speaker concludes by noting the brevity of each session and the potential for further experimentation and refinement.
Mindmap
Keywords
💡Lora
💡Google Colab
💡Kohya
💡Dreambooth
💡Model Size
💡Stable Diffusion
💡VAE
💡Prompt
💡Token
💡Epoch
💡Batch Size
💡Learning Steps
Highlights
The tutorial explains how to create a Lora model using Google Colab, specifically with the Kohya LoRA Dreambooth.
The model size is 4.8 megs, which will be increased to 8 megs for the process.
Kohya has created a simplified SD script for users to utilize in their projects.
The process involves mounting Google Drive for ease of access and use.
Users can choose between different types of models such as animated or live-action models.
The tutorial emphasizes the importance of using unique and descriptive prompts for the study.
Images for training should be diverse in pose, clothing, and mood to improve model accuracy.
The tutorial advises against including background elements like the Tokyo Tower to avoid unwanted learning.
The process includes automating the coloring of images with transparent backgrounds.
The tutorial explains how to tag images automatically for better learning outcomes.
Users can handle live-action images with specific settings and adjustments.
The training setup includes selecting the base model and adjusting various parameters like batch size and learning steps.
The tutorial covers how to save models after training and how to test the trained model.
Additional training can be done by adding more epochs and using a seed for consistent results.
The process is designed to be repeated with minor adjustments for continuous improvement.