AI training – KREA private beta
TLDRVictor, a co-founder, introduces a platform for training AI models with a common style or concept through image uploads. He emphasizes the importance of high-resolution images and demonstrates the training process, including dataset preparation and model application. The video showcases the customization capabilities of the AI, as it adapts to new prompts while retaining the stylistic elements of the trained data set.
Takeaways
- 📝 Start by signing up on the Korea dashboard to access AI training features.
- 🎯 To train an AI model, click the AI training button and then select 'train new'.
- 📸 For effective training, upload images with a common style or concept.
- 🖼️ Examples of good datasets include images of the same product line or images sharing a stylistic theme.
- 🚫 During training, you can remove low-quality, repeated, or irrelevant images.
- 📷 Ensure images are high resolution, preferably over 1000 pixels, for optimal training results.
- 🏷️ Add a title and description for clarity and recognition of your trained model.
- 🔄 Training a model should take no longer than one or two hours.
- 🆘 If the training status doesn't update, contact support via Discord or email and refresh the page.
- 🎨 After training, use the generate tool to apply your AI model to create new content based on your prompts.
- 🌟 The AI model retains the stylistic properties and themes from the training dataset while trying to adhere to the user's prompt.
Q & A
What is the main purpose of the video?
-The main purpose of the video is to demonstrate the ease of training one's own AI model using Korean, by walking through the process step by step.
What is the first step in training an AI model according to the video?
-The first step is to sign up on the Korea dashboard and click the AI training button.
What are the requirements for uploading images for AI training?
-The images should either have a common style or a common concept. They should also be high resolution, preferably more than a thousand pixels.
How does the AI model learn from the training data set?
-The AI model learns by recognizing the common style or concept present in the images uploaded for training, allowing it to generate content that adheres to those parameters.
What is the significance of having a high-quality data set for AI training?
-A high-quality data set ensures that the AI model can effectively learn and produce accurate and stylistically consistent results.
How long does the AI training process typically take?
-The AI training process should not take more than one or two hours.
What should one do if the AI training status does not change?
-If the status does not change, one should reach out to the support team either on Discord or through email and refresh the page for the status to update.
How can one use the trained AI model?
-After training, one can access the AI engine through the generate tool and select the custom option to use the trained model for generating content based on the prompts given.
What is the role of the title and description in AI training?
-The title and description serve as labels to help the user recognize the model they trained. They do not affect the training process but may be used for future enhancements.
Can the AI model be used for different prompts while maintaining the trained style?
-Yes, the AI model can be used for different prompts while still maintaining the stylistic properties and characteristics learned from the training data set.
What is an example of a successful data set with both a common concept and style?
-An example is the data set of abstract clones or abstract animals with the same colors, textures, and concept, created by the artist Voltron.
Outlines
🚀 Getting Started with AI Training
Victor, the co-founder, introduces the process of training an AI model with Korean. He guides through the initial steps on the Korea dashboard, emphasizing the need for a common style or concept in the images uploaded for training. The importance of high-resolution images and the ability to remove unsuitable images is also highlighted. Victor provides examples of good datasets, including images from the same product line and those sharing a common style. He mentions the potential for future changes in the training process and the current use of titles and descriptions for model recognition.
🎨 Exploring AI Model Applications
The video continues with Victor demonstrating the application of a trained AI model, specifically one that was trained with images of clowns. He explains how the AI has learned the stylistic properties from the dataset and can generate content based on a given prompt, such as 'happy'. Victor adjusts the prompt to include a 'pink palette' and shows how the AI can adapt to create a happy clown with a pink color scheme. He emphasizes the AI's ability to follow instructions while being influenced by the style of the training data set. The segment concludes with an invitation for viewers to engage with the platform and share their creations.
Mindmap
Keywords
💡AI model
💡Korea dashboard
💡AI training
💡Common style
💡Common concept
💡Dataset
💡High resolution
💡Custom AI engine
💡Training job
💡Progress percentage
💡Generate tool
Highlights
Victor is a co-founder introducing the process of training an AI model with Korean.
The first step is to sign up on the Korea dashboard to access AI training features.
To train a new AI model, click 'train new' after navigating to the AI training section.
A good AI training requires images with a common style or concept.
Examples of good datasets include images from the same product line or images sharing a common style.
The AI will learn from the dataset to create variations of the common concept or style.
Low quality, repeated, or irrelevant images can be removed from the training dataset.
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 used as labels for recognizing the trained models.
Once the training process starts, it should take no more than one or two hours to complete.
The training progress is displayed as a percentage and can be monitored on the dashboard.
If the status does not change, users are advised to contact support through Discord or email.
Victor demonstrates using a trained model by accessing a project and the generate tool.
The AI engine can generate content based on the style and concept of the trained dataset.
The AI follows the prompt while maintaining the stylistic properties of the dataset.
Users can experiment with different prompts to create unique outputs based on their trained models.
The video serves as a guide for users to understand and utilize the AI training platform effectively.