【最新版】fast DreamBoothでオリジナルモデルを作る方法。Stable Diffusion v2.1対応です。
TLDRThe video script introduces a process for creating an AI-based portrait model using 30 original images. It guides viewers through setting up a Fast Stable Table Diffusion model, accessing Google Drive, and using a free plan for up to 1500 steps. The script emphasizes the importance of diverse image variations for accurate learning and suggests a paid plan for stress-free, deeper training. Detailed instructions are provided for model customization, including selecting the version of the Stable Diffusion model, uploading images, and adjusting settings for optimal results.
Takeaways
- 🎨 The script discusses creating an AI-based drawing model using a service called FastStableTableDiffusion, which allows users to generate images based on their own original artwork.
- 🖼️ Users are instructed to prepare 30 original images to be used as the basis for training the AI model, emphasizing the importance of variety in these images to avoid biases in the output.
- 🔗 The process involves linking Google Drive to the service, ensuring sufficient storage space (at least 3GB recommended), and granting necessary permissions for access.
- 📂 The script provides a step-by-step guide on how to navigate through the interface, including selecting the desired model version (1.5 or 2.1), which affects the style and quality of the generated images.
- 🖌️ The user is advised to agree to the terms and conditions, and to obtain a token from their account settings to proceed with the model training.
- 🔄 The script mentions the option to upload images directly or to use an existing folder in Google Drive, with a note on resizing images to 512x512 pixels if they do not match this dimension.
- 📸 It is highlighted that the images used for training should not have consistent backgrounds or elements (like the Tokyo Tower) that could be inadvertently learned and included in the generated images.
- 📚 The concept of 'Concept Image' is introduced, which is used to teach the model about ambiguous elements like fog or backlight, but it is noted that it is not necessary for this tutorial.
- 🔄 The script details the training process, including setting the learning rate and deciding on the number of steps for training, with a recommendation to start with 3000 steps for a free plan user.
- 🔍 The user is guided on how to test the model after training, using the trained model files and inputting prompts to generate images, with an expectation that 1500 steps might yield satisfactory results.
- ⏪ The script also covers how to add additional training steps if the initial results are not satisfactory, and how to save checkpoints during the training process.
Q & A
What is the main topic of the video script?
-The main topic of the video script is about creating an AI-based drawing model using one's original images with the Fast Stable Table Diffusion model.
What is the 'ドリームブース' mentioned in the script?
-The 'ドリームブース' refers to a feature or platform that allows users to create and train AI models using their own images, in this context, for generating drawings or artwork.
How many original images are required to create the AI drawing model?
-To create the AI drawing model, one needs to prepare 30 original images.
What is the purpose of the Google Drive capacity recommendation in the script?
-The script recommends having about 15 GB of Google Drive capacity to ensure smooth processing and storage of the AI model and related data.
What are the differences between the Stable Diffusion 1.5 and 2.1 versions mentioned in the script?
-The Stable Diffusion 1.5 version is more suitable for general use and is easier to handle, while the 2.1 version has stronger adult filters and may have fewer artist names available, making it less suitable for users who want to mimic specific artists' styles.
How does the script instruct users to select images for the AI model training?
-The script instructs users to select 30 images with diverse variations such as different poses, backgrounds, and outfits to avoid the AI model learning specific elements like a particular background or hairstyle.
What is the significance of the 'Save Checkpoint' option in the script?
-The 'Save Checkpoint' option allows users to save the model at specific steps during the training process, which can be useful for resuming training or for creating multiple versions of the model at different stages.
How long does it take to train the AI model with 3000 steps according to the script?
-According to the script, training the AI model with 3000 steps takes approximately 50 minutes.
What happens if the user wants to continue training the model after the initial training?
-If the user wants to continue training the model after the initial training, they can add more steps to the model by using the 'Add Training' button and setting the number of additional steps.
How does the script address the issue of the model not learning properly?
-The script suggests that if the output is not as expected, indicating that the model has not learned properly, the user should stop the training, make necessary adjustments, and then restart the training process.
What is the recommendation for users who encounter limitations with the free plan?
-The script recommends that users who encounter limitations with the free plan consider upgrading to a paid plan for a stress-free experience and the ability to train the model with more steps.
Outlines
🎨 Introducing AI Art Model with DreamBooth
The paragraph introduces the process of creating an AI art model using DreamBooth, which involves preparing 30 original images and utilizing an AI sketching model. It discusses the updates made to the series and the transformation of the initial prototype. The user is guided through a video description link to a GitHub page for further instructions on copying the DreamBooth to a Google Drive with a recommended capacity of at least 15GB. The paragraph also outlines the basic usage of the tool, starting from granting access to Google Drive, selecting the appropriate version of Stable Diffusion (1.5 for easier handling), and obtaining an API token for further customization.
🖼️ Preparing Images and Settings for AI Training
This paragraph delves into the specifics of preparing 30 images for the AI model, emphasizing the importance of variety in the images to avoid bias in the training outcome. It advises on the inclusion of different backgrounds, poses, and expressions to ensure a comprehensive learning experience for the AI. The paragraph also cautions against including consistent elements like the Tokyo Tower in every image, as this could lead to unwanted outputs. Additionally, it touches on the concept of a 'prompt' for the AI and the process of uploading and selecting images for the training session.
🚀 Executing the Training and Testing the AI Model
The final paragraph focuses on the execution of the AI model training, detailing the steps to follow, including adjusting settings for learning rate and the number of training steps. It provides guidance on selecting the appropriate version of the model (1.5 for this example) and emphasizes the importance of saving checkpoints at specific intervals. The paragraph also discusses the testing of the trained model, suggesting that a minimum of 1500 steps should yield satisfactory results, although further training may be necessary for refinement. It concludes with advice on downloading the trained model for future use and the option to add more training if the initial results are not satisfactory.
Mindmap
Keywords
💡FastStableTableDiffusion
💡DreamBooth
💡AI Art Generation
💡Google Drive
💡Training Steps
💡Token
💡Instance Image
💡Prompt
💡Model Download
💡Learning Rate
💡Save Checkpoint
💡Concept Image
Highlights
The introduction of a new AI-based image generation model that utilizes a user's original images to create unique art.
The user is requested to prepare 30 original images to be used as a base for the AI image generation model.
The AI model is based on the FastStableTableDiffusion framework, which is continuously updated and improved.
A detailed explanation of how to access and use Google Drive for storing and managing the image data required for the AI model.
The importance of having sufficient storage capacity on Google Drive, with a recommendation of at least 15GB to avoid issues during the process.
A step-by-step guide on how to grant access to Google Drive and integrate it with the AI model for image processing.
The process of selecting the appropriate version of the Stable Diffusion model, with options like 1.5 or 2.1, each with its own characteristics and applications.
The impact of choosing different Stable Diffusion versions on the output, such as the inclusion of adult filters and the availability of certain artist styles.
Instructions on how to download and use the Stable Diffusion model, including the need for an API token and the proper settings for optimal results.
The significance of preparing a diverse set of 30 images that includes various poses, backgrounds, and outfits to train the AI model effectively.
A cautionary note about the potential for background elements, like the Tokyo Tower, to become part of the AI's learning process and influence the output.
The explanation of the concept of 'Hanging Face Talk' and its role in the AI model's training process.
The process of selecting and uploading images for the AI model, including the option to automatically resize images to 512x512 pixels.
The importance of naming the images in a consistent manner to avoid confusion during the AI model's training and generation process.
A brief overview of the 'Concept Image' and its role in training the AI model to understand and replicate certain visual elements or effects.
The detailed instructions on how to adjust the learning rate and the number of training steps for the AI model, emphasizing the balance between speed and quality.
The explanation of the 'Save Checkpoint' feature and its utility in saving the model's progress at specific intervals during the training process.
The final steps to test the trained AI model, including the use of the 'Use Custom Path' option and the potential need for additional training steps.
The provision of a URL for users to access and download their trained AI model for future use and experimentation.