【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。

Shinano Matsumoto・晴れ時々ガジェット
7 Jan 202311:57

TLDRThe video script discusses the process of creating an AI-based drawing model using 30 original images. It guides the user through setting up a DreamBooth model on Google Drive, selecting the appropriate Stable Diffusion version, and uploading images for training. The script emphasizes the importance of diverse image variations to avoid biases in the model's output. It also touches on the differences between the free and paid plans, suggesting the latter for a stress-free experience. The tutorial includes tips on adjusting settings for optimal results and hints at the potential need for additional training steps for better outcomes.

Takeaways

  • 🎨 The script discusses creating an AI-based drawing model using a service called DreamBooth, which allows customization with original images.
  • 🖼️ Users are instructed to prepare 30 original images for the model, ensuring variety in backgrounds, poses, and outfits to avoid bias in the AI's learning process.
  • 🔗 The process involves accessing Google Drive and using a specific model from Stable Diffusion 1.5 or 2.1, with the former being more user-friendly.
  • 📱 Users need to provide an API token and can either use a pre-existing model or upload their own by providing a link or path.
  • 📋 The script provides detailed steps on setting up the model, including selecting the version, agreeing to terms, and configuring training settings.
  • 🔄 The training process can be done in batches, with an initial free plan allowing for up to 1500 steps, after which a paid plan is recommended for more extensive training.
  • ⏰ The training time for 3000 steps is approximately 50 minutes, depending on the user's system and plan.
  • 📌 It's important to monitor storage space on Google Drive, as a minimum of 3GB is required, though more is recommended to avoid issues.
  • 🔄 The script mentions the possibility of saving checkpoints during training, which can be useful for resuming or adjusting the model later.
  • 🚀 The final model file is saved within the user's Google Drive, in a folder named after the model, and can be accessed or downloaded for future use.
  • 💡 The script emphasizes the importance of testing the model after training and adjusting it as needed to achieve the desired output.

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 recommended Google Drive storage capacity for this process?

    -The recommended Google Drive storage capacity is at least half of the 15 GB free space, which means around 7.5 GB, but having more is better to ensure smooth operation.

  • How many images are needed to create the AI drawing model?

    -To create the AI drawing model, one needs to prepare 30 original images.

  • What should be considered when selecting the images for the model?

    -The images should showcase various aspects of the subject, such as different poses, expressions, and backgrounds, to avoid the model learning unwanted elements like a specific landmark that appears in all the images.

  • What are the differences between the Stable Diffusion 1.5 and 2.1 versions?

    -The 1.5 version is more suitable for general use as it 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 those who want to mimic specific artists' styles.

  • How can one obtain the token needed for the process?

    -The token can be obtained from one's account settings and then pasted into the required field in the process.

  • What is the significance of the 'Concept Image' in the process?

    -The 'Concept Image' is used to teach the model ambiguous elements like fog, backlight, and other朦胧 features. However, it is not used in this process as it is not needed.

  • How long does it take to train the model with 3000 steps?

    -It takes approximately 50 minutes to train the model with 3000 steps, depending on the system's performance and the complexity of the images.

  • What happens if the training process is stopped midway?

    -If the training process is stopped midway, the model may not learn properly, and the output might not be satisfactory. It is recommended to use the paid plan to avoid interruptions and ensure a smoother training process.

  • How can one save the trained model?

    -The trained model can be saved by selecting the 'Save Checkpoint' option and deciding the steps at which the model will be saved. The files will be saved in the designated folder within the user's Google Drive.

  • What should one do if the output is not satisfactory after the initial training?

    -If the output is not satisfactory, one can add more training steps by using the 'Additional Training' feature, setting the text encoder to 0, and executing the process again.

Outlines

00:00

🖌️ Introduction to AI Art Generation

The paragraph introduces the concept of using AI for art generation, specifically mentioning the Fast Stable Table Diffusion model. It discusses the creation of an AI art model using 30 original images and the importance of updates in the series. The process involves accessing Google Drive, with a recommendation of having at least 15GB of free space. The user is guided through the steps of granting access, copying the 'Dream Booth' to the drive, and understanding the basic usage of the tool. The paragraph emphasizes the significant changes from the prototype and invites the viewer to follow a link in the video description to access the GitHub page for further details.

05:01

📸 Preparing Images for AI Training

This paragraph delves into the specifics of preparing 30 images for training the AI model. It advises on the variety of images needed, such as full-body shots and different poses, to effectively train the AI without incorporating unwanted elements like backgrounds. The text warns against including consistent background elements, as they may become part of the AI's learning and affect the output. It also touches on the importance of agreeing to terms and settings, and the option to use a custom model if the user has one. The paragraph concludes with instructions on selecting and uploading the images for the AI to learn from.

10:01

🔧 Customizing and Training the AI Model

The third paragraph focuses on the customization and training process of the AI model. It provides instructions on selecting the version of the Stable Diffusion model, emphasizes the importance of consistency in the selection process, and guides the user through the steps of setting up the training environment. The paragraph explains how to adjust learning rates and the number of training steps, with a recommendation for a minimum of 3000 steps for satisfactory results. It also discusses the option to save checkpoints during training and the importance of considering the available drive space. The user is prepared for the testing phase after training, with tips on how to access and use the trained model effectively.

Mindmap

Keywords

💡FastStableTableDiffusion

FastStableTableDiffusion is a term that refers to a specific model used in AI image generation. It is a variant of the Stable Diffusion model optimized for faster processing times while maintaining a high quality of generated images. In the context of the video, it is used as the foundation for creating custom AI art models based on original images provided by the user. The script mentions different versions such as 1.5 and 2.1, with 1.5 being more user-friendly and 2.1 offering higher quality but potentially including adult content filters.

💡DreamBooth

DreamBooth is a concept related to AI and machine learning where a model is trained on a specific set of images to generate new content based on those inputs. In the video, the user is guided through the process of creating a custom AI model using their own original images, which is similar to the idea of a 'booth' or 'workspace' where the AI learns and adapts to the provided content. The term is used to illustrate the interactive and personalized nature of the AI model creation process.

💡GitHub

GitHub is a web-based hosting service for version control and collaboration that is used by developers. It allows individuals and teams to work on projects and share code with others. In the video, GitHub is mentioned as a platform where users can find the necessary links and resources to access and use the DreamBooth AI model. It serves as a repository for the tools and guides needed to proceed with the AI art generation process.

💡Google Drive

Google Drive is a cloud storage service that allows users to store and share files online. In the context of the video, it is used to store the AI model and the original images that the user provides. The script emphasizes the importance of having sufficient storage capacity on Google Drive to accommodate the files associated with the AI model creation process.

💡AI Art Generation

AI Art Generation refers to the process of creating visual art through artificial intelligence, where the AI learns from a set of input images and generates new images based on patterns and styles identified. In the video, the main theme revolves around guiding users through the steps of creating an AI model that can generate art using their own original images, highlighting the intersection of technology and creativity.

💡Training Steps

Training steps refer to the number of iterations the AI model undergoes during the learning process. Each step represents a cycle of learning where the model adjusts its parameters to better achieve the desired output. In the video, the user is informed about the number of training steps available in the free plan and the possibility of upgrading to a paid plan for more steps, which can lead to a better-trained and higher-quality AI model.

💡Token

In the context of AI and machine learning, a token is a representation of a piece of data, such as an image or a text snippet, that the model uses for training. In the video, the user is instructed to obtain a token from their settings, which is then used to access and train the AI model with the provided images. The token serves as a key to unlock the computational resources needed for the AI to process and generate the art.

💡Model Path

The model path refers to the location or URL where the AI model file is stored. It is necessary for the system to know where to find and load the model for training or generating art. In the video, users with a custom model may be asked to provide the model path so that the system can utilize it for the AI art generation process.

💡Image Selection

Image selection is the process of choosing the specific images that will be used to train the AI model. These images serve as the basis for the AI to learn from and generate new content. In the video, the user is instructed to prepare 30 original images that will be used to train the AI in creating art that reflects the user's desired style or subject.

💡Prompt

In the context of AI art generation, a prompt is a text input that guides the AI in generating a specific type of image. It serves as a description of what the user wants the AI to create. In the video, the user is advised to use a consistent prompt for the 30 images they provide to ensure that the AI model can accurately learn and produce the desired art.

💡Concept Image

A concept image is a visual representation used to convey a general idea or theme that the AI should learn and incorporate into its generated art. These images may depict abstract concepts like fog, backlighting, or sunrise, which are used to teach the AI about different visual effects and styles. In the video, the concept image is mentioned as an optional tool for users who want to introduce more nuanced visual elements into their AI-generated art.

💡Learning Rate

The learning rate is a hyperparameter in machine learning models that determines how much the model adjusts its internal parameters with each training step. A higher learning rate means more significant changes are made during training, which can lead to faster learning but also a higher risk of not converging to the optimal solution. In the video, the user is advised to select a learning rate that balances between the number of training steps and the quality of the learning process.

Highlights

The introduction of an AI-based model for creating original artwork using personal images.

The process of creating an AI drawing model using 30 prepared original images.

The importance of having a sufficient Google Drive storage capacity for the process, preferably around 15GB.

Accessing Google Drive and granting necessary permissions for the AI process to begin.

The selection between two versions of Stable Diffusion for different artistic styles and content preferences.

The process of obtaining and inserting a token for personal settings and model access.

The instruction on preparing 30 images with varied backgrounds, poses, and outfits to avoid bias in the AI learning.

The caution against including consistent elements like specific landmarks in all images, as it may affect the output.

The step-by-step guidance on uploading images and setting up the AI model for training.

The explanation of the training process, including the selection of steps and learning rates.

The option to save checkpoints during the training process and the impact of the frequency on file size.

The testing of the AI model after training completion and the evaluation of the results.

The provision of a link to the GitHub page for detailed instructions and resources.

The mention of the free plan limitations and the benefits of upgrading to a paid plan for a stress-free experience.

The detailed description of the model's capabilities and potential applications in creating personalized artwork.

The emphasis on the iterative nature of the training process and the possibility of refining the model through additional training steps.