AI training – KREA private beta
TLDRVictor, co-founder of KREA, introduces a user-friendly AI training platform in this video. He demonstrates the process of training an AI model by uploading images with a common style or concept. Victor emphasizes the importance of high-resolution images and provides examples of effective datasets. He guides viewers through the training process, from uploading images to starting the training job, which typically takes one to two hours. Finally, he showcases the AI's ability to generate content based on the trained model, adjusting to prompts like 'happy' and 'pink palette', illustrating the AI's adaptability and creativity.
Takeaways
- 😀 Victor, the co-founder of KREA, demonstrates how to train an AI model using the KREA platform.
- 🛠️ Users start by accessing the KREA dashboard and navigating to the AI training section.
- 📝 To train a new model, users must provide a title, description, and upload a set of images.
- 🎨 A good AI training dataset should have a common style or concept across all images.
- 📐 Examples include images of the same product in different versions or images with a consistent style.
- 🔍 Users can remove low-quality, repeated, or irrelevant images from the dataset.
- 📸 High-resolution images are recommended, ideally over 1000 pixels in size.
- 🏷️ Titles and descriptions are for user reference and do not currently affect the training process.
- 🔄 Once everything is set, users can initiate the training process, which typically takes 1-2 hours.
- 🔍 If the training status doesn't update, users should contact KREA support through Discord or email.
- 🎨 After training, users can generate new content using their custom AI model with different prompts.
- 🤖 The AI model captures stylistic properties from the training dataset and can adapt to new prompts.
Q & A
What is the purpose of the video by Victor, the co-founder of KREA?
-The purpose of the video is to demonstrate the ease of training one's own AI model using the KREA platform.
Where does one begin the process of AI model training on the KREA dashboard?
-The process begins by clicking the 'AI Training' button on the KREA dashboard and then selecting 'Train New'.
What are the necessary components for a good AI training dataset according to Victor?
-A good AI training dataset should have either a common style or a common concept across all images, ensuring consistency for effective learning.
Can you provide an example of a common concept dataset as mentioned in the video?
-An example of a common concept dataset is images of the same product, such as Crux, in different versions.
What does Victor suggest as an example of a dataset with a common style?
-An example of a dataset with a common style is a set of Sci-Fi retro images that represent different things but share the same visual style.
What is the importance of image quality and resolution in the training process?
-High-resolution images, ideally more than a thousand pixels, are crucial for the AI to learn accurately. Victor suggests at least 512 by 512 pixels.
How can users ensure their dataset is consistent and relevant for training?
-Users can remove any low-quality, repeated, or irrelevant images from their dataset to maintain consistency and relevance.
What is the role of the title and description in the AI training process as explained by Victor?
-The title and description serve as labels for the user to recognize the model they trained with. Currently, they do not affect the training but may do so in the future.
What should users do if the training status does not change after clicking 'Start Training Job'?
-If the status does not change, users should reach out to KREA via Discord or email and refresh the page for the status to update.
How long should the AI training process take according to the video?
-The AI training process should not take more than one or two hours.
Can you describe how Victor demonstrates the use of a trained AI model in the video?
-Victor accesses one of his projects, uses the 'Generate Tool' and selects a custom AI engine trained with a dataset of clowns. He types 'happy' and generates multiple outputs to showcase the model's ability to follow prompts and retain stylistic properties.
Outlines
😀 Introduction to AI Model 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 guides users through signing up, accessing the AI training section, and beginning the training process by uploading images with a common style or concept. Victor emphasizes the importance of high-resolution images and provides examples of effective datasets, such as images of the same product in different versions or images with a consistent style. He also explains that users can remove unwanted images and that the title and description fields are for user reference to identify their models. The training process is described as taking approximately one to two hours, with instructions to contact support if issues arise.
🎨 Using Trained AI Models for Custom Generations
After explaining the training process, Victor demonstrates how to use a trained AI model for custom image generation. He navigates to the AI engine, selects a previously trained model featuring 'clowns,' and inputs a prompt to generate images. The model successfully generates images that capture the stylistic properties of the training dataset, even when tasked with creating a 'happy' version of the abstract clowns. Victor shows that while the style is largely defined by the dataset, the AI attempts to adhere to the user's prompt. He encourages viewers to experiment with the platform and offers support for any questions they may have.
Mindmap
Keywords
💡AI training
💡KREA dashboard
💡Common style
💡Common concept
💡Data set
💡Image quality
💡Training progress
💡Custom AI engine
💡Generate tool
💡Prompt
Highlights
Victor introduces himself as a co-founder at KREA and demonstrates how to train an AI model.
The KREA dashboard is the first screen users see after signing up.
To train an AI model, users must navigate to AI training and click 'train new'.
A good AI training requires a common style or concept across all images.
Examples of a good dataset include images of a single product in different versions or images with a shared style.
Users can remove low-quality, repeated, or irrelevant images from the training set.
Images should be at least 512x512 pixels in resolution, ideally over a thousand pixels.
Voltron's artwork serves as an example of a dataset with both a common concept and style.
The title and description fields are for user reference and do not affect the training process.
Once everything is set, users can start the training job by clicking 'train'.
Training progress is indicated by a percentage and should not take more than one or two hours.
If the training status does not change, users should contact KREA support.
Victor demonstrates using a trained model by accessing one of his projects.
The 'generate tool' allows users to input prompts and generate AI outputs.
The AI engine can generate outputs based on the style and concept of the training dataset.
Users can customize the AI output by adjusting the prompt, such as specifying a 'pink palette'.
The AI attempts to follow the user's prompt while maintaining the dataset's style.
Victor encourages users to reach out with questions and share their creations.