Snowpixel

Creating custom AI models can unlock new creative possibilities in image generation and manipulation, but the process requires careful planning and execution. This guide walks you through Snowpixel's model training system, from selecting and naming your model to generating stunning custom images.

Model Naming and Type

The model creation process begins with selecting and naming the model. In this example, the user created a model named "Wednesday," which indicates that the subject is a human character. The process requires at least 10 images per model, with each photo focusing on a single subject in various contexts and lighting conditions.

Image preparation is crucial for optimal training. Phone photos should be cropped before uploading, and images can be in any dimension (landscape, portrait, square) and format (jpg, png). The training process takes between 5 to 20 minutes to fine-tune the model, after which it becomes accessible through the "Personal Models" section of the application. Users can then select their model by name from a dropdown menu and generate prompts using specific phrases related to the model's subject. For instance, "Wednesday on Mount Everest" demonstrates how users can create unique scenarios to test their custom model's capabilities.

Image Requirements

For optimal model training, at least 10 images are required, each featuring a single subject in various contexts and lighting conditions. The model is trained to recognize the subject across different environments and lighting setups. All images should contain only one person or object to ensure accurate training results.

Phone photos must be cropped before uploading, as mentioned in the user guide. The system accepts images in landscape, portrait, or square formats, with both jpg and png file types supported. During the upload process, images not in square format are automatically converted to a square crop by the system.

The training process typically takes between 5 to 20 minutes, depending on the complexity of the images and the specific requirements of the model. Once trained, users can access their custom model through the "Personal Models" section, where it appears in a dropdown menu. To generate prompts, users select their model and enter specific phrases related to the model's subject, as demonstrated with the "Wednesday on Mount Everest" example.

Upload and Training

A maximum of 60 images can be uploaded for each model, with no size limitations per individual photo. The system automatically processes all images in square format, ensuring consistent training data regardless of original aspect ratio. Users can upload multiple sets of images simultaneously if desired, though each set must contain between 10 and 60 images.

During the upload process, the system applies automated optimizations to ensure image quality while maintaining processing efficiency. This includes adjusting contrast and color balance to improve recognition accuracy across different lighting conditions. The training algorithm analyzes metadata from each photo, including EXIF information and file properties, to enhance model performance during the fine-tuning phase.

Upon completion of the training cycle, users receive a confirmation message indicating their custom model is ready for use. The development team continuously monitors system performance and regularly updates training algorithms to maintain optimal model accuracy and response times.

Model Access and Use

After the training phase, models are stored in the "Personal Models" section of the application. They appear as options in a dropdown menu, allowing users to select their custom model for generating prompts. To use the model, users input specific phrases related to the model's subject. For instance, "Wednesday on Mount Everest" demonstrates how users can create unique scenarios to test their custom model's capabilities.

When generating prompts, users can select from multiple trained models in the "Personal Models" dropdown menu. The application processes the selected model's name and associated training data to generate appropriate responses or outputs based on the input phrase. This feature enables users to create custom scenarios or settings for their model while maintaining consistent performance across different contextual uses.