AI training – KREA private beta

KREA
13 Sept 202306:46

TLDRVictor, a co-founder at KREA, introduces a straightforward process for training personal AI models using KREA's platform. He explains the importance of a unified style or concept in the images uploaded for training and demonstrates how to navigate the AI training section, select and upload images, and begin the training process. High-resolution images are recommended for optimal results. Victor showcases examples of effective datasets and concludes by illustrating how a trained model can generate content based on user prompts, maintaining the stylistic elements learned from the dataset.

Takeaways

  • 📝 The video is a tutorial on training AI models using KREA platform.
  • 🚀 To start, sign up on the KREA dashboard and navigate to the AI training section.
  • 🆕 Click 'train new' to begin the process of training a new AI model.
  • 📸 For effective training, images should share a common style or concept.
  • 🎨 Examples of good datasets include images of the same product line or images in a consistent style.
  • 🗑️ Low quality, repeated, or irrelevant images can be removed from the dataset.
  • 📷 High-resolution images are recommended, preferably over 1,000 pixels.
  • 🎨 Voltron's artwork exemplifies a dataset with both a common style and concept.
  • 🏷️ Title and description are used as labels for recognizing your trained models.
  • 🚀 After setup, click 'start training job' and monitor the progress which should take 1-2 hours.
  • 🎨 Trained models can be utilized in the generate tool to produce content with the learned style and according to prompts.

Q & A

  • What is the first step to train an AI model with KREA?

    -The first step is to sign up with KREA and then navigate to the AI training section by clicking the corresponding button on the dashboard.

  • What are the requirements for the images uploaded for AI training?

    -The images should either share a common style or a common concept. They should also be high resolution, ideally more than a thousand pixels, to ensure effective training.

  • How does the AI learn from the training data set?

    -The AI learns by recognizing patterns in the images, such as common styles or concepts. It can then apply these learned characteristics to generate new content based on different prompts.

  • What happens if the training status does not change?

    -If the status does not change, the user should contact KREA support either through Discord or email and refresh the page for the status to update.

  • How long does the AI training process typically take?

    -The entire AI training process should not take more than one or two hours.

  • What is the purpose of the title and description for the AI training data set?

    -The title and description serve as labels to help the user recognize the model they trained. They do not affect the training process itself.

  • How can users ensure their AI model is trained with the desired data?

    -Users can review and remove any low-quality, repeated, or irrelevant images from the data set to ensure the AI model is trained with the kind of data they want.

  • What is an example of a good data set with a common concept?

    -A good example of a data set with a common concept would be images of the same product line, such as different versions of a product bar.

  • What is an example of a good data set with a common style?

    -A good example of a data set with a common style would be a collection of Sci-Fi retro images, where all images share the same visual style and aesthetic.

  • How can the AI model be used after it has been trained?

    -After the AI model has been trained, users can use it in the generate tool by selecting the custom option and choosing the trained model to create new content based on their prompts.

  • What was the result of using the AI model trained with the clown data set?

    -The AI model was able to generate content that captured the stylistic properties of the clown data set, even when given new prompts like 'happy', and could adjust the style according to the input, such as using a pink palette.

Outlines

00:00

🚀 Getting Started with AI Training

In this paragraph, Victor introduces viewers to the process of training their own AI model using the Korea platform. He explains that users begin by navigating to the AI training section and clicking 'train new.' This leads to a page where users can title their project, describe it, and upload relevant images. Victor emphasizes the importance of having a common style or concept in the images for effective AI learning. He provides examples of good datasets, such as images of a single product in various versions or a collection of images sharing a specific style. Additionally, he mentions the ability to remove low-quality or irrelevant images and suggests using high-resolution images for better training results. Victor also explains that while titles and descriptions are currently for recognition purposes, they may impact training in the future.

05:03

🎨 Exploring AI Model Applications

In this section, Victor demonstrates the application of a trained AI model by accessing one of his projects that used a dataset of 'clowns.' He uses the Korea platform's 'generate tool' to show how the AI can produce content that captures the stylistic properties of the training dataset. Victor experiments with adding a 'happy' emotion and a 'pink palette' to the prompt, resulting in AI-generated images that maintain the clown style with the requested adjustments. This illustrates how the AI model can follow user input while being constrained by the style and concept of the training dataset. Victor encourages viewers to engage with the platform and share their creations, highlighting the creative potential of AI training on the Korea dashboard.

Mindmap

Keywords

💡AI training

AI training refers to the process of teaching an artificial intelligence model to recognize patterns, understand data, and make decisions or predictions based on that data. In the context of the video, it involves the user uploading images that share a common style or concept to train an AI model that can generate content with similar characteristics. The training process is depicted as straightforward, allowing users to create their own AI models by following a few simple steps.

💡Korea dashboard

The Korea dashboard is the primary interface or control panel that users interact with when using the KREA AI training platform. It is the first screen users see after signing up, and it is where they can initiate the AI model training process by clicking the appropriate buttons and following the guided steps. The dashboard serves as the central hub for managing and monitoring AI training activities.

💡Common style

A common style refers to a consistent visual or thematic approach that is shared across a set of images or data. In the context of AI training, having a common style in the uploaded images helps the AI model learn and recognize the specific aesthetic or thematic elements that define the dataset. This enables the AI to generate new content that maintains the same stylistic properties as the training data.

💡Common concept

A common concept refers to a shared idea or theme that is present across a set of images or data. When training an AI model, having a common concept in the images helps the AI understand the subject matter or the overarching theme that ties the dataset together. This allows the AI to generate content that is relevant and coherent with the original dataset's theme.

💡High resolution images

High resolution images are those with a greater number of pixels, which results in a more detailed and clearer visual representation. In the context of AI training, using high resolution images is important because it provides the AI model with more information and detail to learn from, leading to more accurate and realistic outputs in the generated content.

💡Remove low-quality images

Removing low-quality images refers to the process of eliminating images from the dataset that do not meet the desired standards in terms of clarity, relevance, or consistency with the training objectives. This step is crucial in AI training as it helps to maintain the quality of the training data, ensuring that the AI model focuses on learning from the most representative and high-quality examples.

💡AI engine

The AI engine is the core component of an AI system that drives its functionality, enabling it to process data, recognize patterns, and generate outputs based on the training it has received. In the context of the video, the AI engine is what users interact with to generate new content using the trained models, applying the learned styles and concepts to create new images or outputs.

💡Custom AI trainings

Custom AI trainings refer to the personalized creation and development of AI models based on specific datasets provided by the user. These custom-trained models are tailored to understand and replicate the unique styles, concepts, or characteristics of the images uploaded by the user, allowing for a more targeted and relevant generation of new content.

💡Generate tool

The generate tool is an interface or feature within the AI platform that allows users to produce new content using the trained AI models. This tool typically requires users to input specific prompts or instructions, and it utilizes the trained AI model to create and display the generated outputs based on the input.

💡Progress percentage

The progress percentage is a visual representation of how much of the AI training process has been completed. It is usually displayed as a numerical value on a progress bar, indicating the proportion of the training task that has been accomplished. This feature allows users to monitor the status of their AI model training and provides an estimate of the time remaining until the training is finished.

💡Reach out

Reaching out refers to the act of contacting or communicating with a support team or other individuals for assistance, feedback, or to ask questions. In the context of the video, it encourages users to get in touch with the KREA platform's support if they encounter any issues or have questions during the AI training process.

Highlights

Victor, co-founder at KREA, introduces a video tutorial on training AI models with KREA.

The KREA dashboard is the first screen users see after signing up.

To train an AI model, users must navigate to the AI training section and click 'train new'.

A good AI training requires a common style or concept across all uploaded images.

Examples of effective datasets include images of a single product in various versions or images sharing a consistent style.

Users can remove low-quality, repeated, or irrelevant images from their datasets.

High-resolution images of at least 512x512 pixels are recommended for training.

The artist Voltron's work exemplifies a dataset with both a common concept and style.

Titles and descriptions are labels to help users recognize their trained models.

Once a model is trained, it can be used in the generate tool under the AI engine section.

The AI model can produce results that capture the stylistic properties of the training dataset.

The AI model attempts to follow the user's prompt while maintaining the dataset's style.

Users can adjust the style by adding specific descriptors, such as 'pink palette'.

The AI model's output is highly influenced by the quality and nature of the training dataset.

KREA encourages users to reach out with questions and to share their creations.

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