AI training – KREA private beta
TLDRVictor, the co-founder of KREA, introduces a straightforward process for training AI models using the KREA platform. He explains the importance of a unified style or concept in the images uploaded for training, provides examples of effective datasets, and emphasizes the need for high-resolution images. Victor also discusses the ability to remove unsuitable images and demonstrates how to use a trained model to generate content, showcasing the adaptability of the AI to user prompts while maintaining the learned style.
Takeaways
- 🚀 Get started with AI training by signing up on the KREA dashboard.
- 📌 Click the AI training button to begin the process of training your AI model.
- 🆕 To train a new model, provide a title, description, and upload relevant images.
- 🎨 Ensure the images have a common style or concept for effective AI learning.
- 🌐 Examples include a product line like Crux or a set of Sci-Fi retro images.
- 🔍 Review and remove low-quality, repeated, or irrelevant images for better training.
- 📸 Use high-resolution images, preferably over 1000 pixels, for optimal results.
- 🛠️ The training process should take no more than one or two hours.
- 🔗 Reach out on Discord or via email if the training status doesn't change.
- 🎨 Utilize the trained AI model in the generate tool for creative outputs.
- 🌈 The AI model can adapt its style based on the data set and the input prompt.
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 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, be of high resolution (at least 512x512 pixels, ideally over a thousand pixels), and not be low quality, repeated, or unrelated to the data set.
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 to recognize and generate variations of that product.
What is an example of a good data set with a common style?
-A set of Sci-Fi retro images that share the same visual style, such as color schemes and textures, would be a good data set for the AI to learn a specific style.
How can you ensure the quality of the training data set?
-You can remove any images that are of low quality, repeated, or do not match the desired data type, and ensure all images are high resolution.
What happens after you click 'Train New'?
-You will be taken to a new page where you can input a title, description, and upload the images for your AI training data set.
What is the estimated time for the entire AI training process?
-The whole training process should not take more than one or two hours.
How can you use the trained AI model?
-After training, you can access the model through the generate tool, select it, and use it to create new content based on the style or concept it learned from the data set.
What should you do if the training status does not change?
-If the status does not change, you should reach out to KREA support either on Discord or through email, and refresh the page for the status to update.
How do the title and description fields function in the AI training process?
-The title and description serve as labels for you to recognize the model you trained. They do not affect the training process itself but help you identify the models in your projects.
Outlines
🚀 Getting Started with AI Training
Victor, the co-founder, introduces the process of training your own AI model using the Korea platform. He guides users through the initial steps, starting from the dashboard, to the AI training section. He emphasizes the importance of having a common style or concept in the images uploaded for training. Victor provides examples of good datasets, such as images of a specific product in various versions or a collection of images sharing a unique style. He also mentions the ability to remove unsuitable images and the necessity of high-resolution images for effective training. Lastly, he explains the purpose of the title and description fields, which are currently for user recognition but may affect training in the future.
🎨 Exploring AI Model Applications
In this paragraph, Victor demonstrates the practical application of an AI model trained with a specific dataset. He accesses the AI engine and selects a previously trained model based on 'clowns'. By inputting a prompt like 'happy', he showcases how the AI generates content that captures the stylistic properties of the training dataset while attempting to incorporate the suggested emotion. Victor also explains that the AI will try to follow the prompt while being influenced by the style of the training dataset. He invites users to reach out with questions and encourages them to share their creations, highlighting the creative potential of the AI training process.
Mindmap
Keywords
💡AI training
💡Korea dashboard
💡Common style
💡Common concept
💡High resolution images
💡Data set
💡Remove low-quality images
💡Custom AI engine
💡Generate tool
💡Progress percentage
💡Happy clowns
Highlights
Introduction to AI training with KREA private beta by Victor, the co-founder.
Accessing the AI training feature through the KREA dashboard after signing up.
The necessity of a common style or concept in the images uploaded for AI training.
Example of a good data set: images from the same product line, like different versions of a product bar.
Another example of a good data set: images sharing the same style, like Sci-Fi retro images.
The ability to remove low-quality, repeated, or irrelevant images from the data set.
Recommendation for high-resolution images (at least 512x512 pixels) for effective training.
Showcase of a data set created by artist Voltron, combining a common concept and style.
Adding a title and description for the AI model training, which currently serves as labels for recognition.
Starting the AI model training process by clicking 'train new' and waiting for the training to begin.
The training progress is shown as a percentage and should take no more than one or two hours.
Instructions to contact support if the training status does not change and to refresh the page for updates.
Demonstration of using a trained AI model by accessing a project and the generate tool.
Selecting a previously trained model and using it to generate new content based on a prompt.
The AI's ability to retain stylistic properties from the data set while following the input prompt.
Adjusting the prompt to change the style and mood of the generated content, such as using a 'pink palette'.
The AI training process allows for creative exploration and the generation of unique content.
Invitation to reach out with questions and encouragement to share creations made with the AI model.