AI training – KREA private beta
TLDRVictor, a co-founder at KREA, introduces a straightforward method 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 use the AI training feature. High-resolution images are recommended for better results. He showcases examples of effective datasets and how they influence the AI's output, emphasizing the adaptability of the AI to user prompts while maintaining the style of the training data.
Takeaways
- 📝 Start by signing up on the KREA 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 specific artistic style.
- 🔍 Review and remove any low-quality, repeated, or irrelevant images from your dataset.
- 📏 Ensure high-resolution images are used, preferably 512x512 pixels or more.
- 🎨 Voltron's artwork exemplifies a dataset with both a common style and concept.
- 🏷️ Add a title and description for clarity and recognition of your trained model.
- 🚀 Once ready, start the training job and monitor the progress percentage.
- 🛠️ Reach out for support if the training status doesn't change, and refresh the page for updates.
- 🎨 After training, use the AI engine with your custom model to generate content based on your 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 navigate to the AI training section by clicking the respective 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, and they should be of high resolution, preferably more than a thousand pixels.
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, which allows it to generate new content based on those learned characteristics.
What is an example of a good data set with a common concept?
-A good example is a set of images from the same product line, like different versions of a product bar, which would help the AI learn about the product and its variations.
How can the AI training process be improved?
-It can be improved by removing low-quality, repeated, or irrelevant images from the data set to ensure the AI is trained with the most relevant and high-quality data.
What happens after a new model is trained?
-Once the model is trained, it appears on the dashboard, and the user can start using it for generating new content by inputting different prompts in the generate tool.
How long does the AI training process typically take?
-The entire AI training process should not take more than one or two hours.
What should a user do if the training status does not change?
-If the status does not change, the user should reach out to KREA support either on Discord or through email and refresh the page for the status to update.
How can the style of the generated content be influenced?
-The style of the generated content is defined by the data set used for training the AI, but the user can also input specific prompts to guide the style in a particular direction, such as using a specific color palette.
What is the role of the title and description in AI training?
-The title and description serve as labels for the user to recognize the model they trained. They do not affect the training process but help in identifying the model for future use.
Outlines
🚀 Introduction to AI Training with Korea
Victor, the co-founder of Korea, introduces viewers to the process of training their own AI model using the Korea platform. He explains that users start on the Korea dashboard and must go to AI training to begin. The first step is to click 'train new' which 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 training. He provides examples of good datasets, such as images of the same product in various versions or images sharing a common stylistic theme. He also mentions the ability to remove low-quality or irrelevant images and stresses the need for high-resolution images for optimal training results. Lastly, Victor shares an example of a successful dataset created in collaboration with an artist named Voltron, which combines a common concept and style in the images.
🎨 Using Trained AI Models for Custom Generations
In this segment, Victor demonstrates how to use a previously trained AI model on the Korea platform. He accesses the AI engine and selects the custom option to view his past AI trainings. He chooses the model trained with the clown dataset and uses it to generate new images by typing in prompts such as 'happy'. Victor shows how the AI captures the stylistic properties of the original dataset, even when trying to convey different emotions or themes. He further illustrates this by adding a 'pink palette' prompt and receiving images in the same stylistic theme but with a pink color scheme. The summary highlights the flexibility of the AI in following user prompts while maintaining the essence of the trained dataset, inviting users to explore their creativity with the platform.
Mindmap
Keywords
💡AI training
💡KREA dashboard
💡Data set
💡Common style
💡Common concept
💡High resolution images
💡Remove low quality images
💡Training progress
💡Custom AI engine
💡Generate tool
💡Problem following
Highlights
Introduction to AI training with KREA private beta
Signing up and accessing the KREA dashboard
Starting the AI model training process
Importance of a common style or concept in image datasets
Example 1: Training with images of a single product line (Crux)
Example 2: Training with images sharing a common style (Sci-Fi retro)
Removing low-quality or irrelevant images from the dataset
Recommendation for high-resolution images for training
Example 3: Training with artwork that has both a common concept and style
Adding a title and description for the training dataset
Starting the AI model training job
Monitoring the training progress and reaching out for support if needed
Accessing a trained AI model and using it in a project
Demonstration of using a trained model with a custom prompt
Results: AI-generated content reflecting the style of the training dataset
Adjusting the prompt for different stylistic outcomes (e.g., pink palette)
Conclusion: The versatility of AI training with KREA and its potential for creative applications