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Blaze.1 Portrait is a flux-based adapter designed to generate high-quality, realism-driven super-portraits. This advanced model underwent rigorous training using a diverse set of images to ensure exceptional output in portrait generation. With 72 variations and a total of 1080 images per variation, the training process involved meticulous curation and processing of 14-bit raw Camera Uncompressed Format (CUF) images. For comprehensive insights, refer to the detailed documentation on training procedures, data curation, and parameter optimization.

The model was trained with an overall image count of 1080 images, repeated across variations to create a total dataset of 1100 repetition per image.

Training was conducted for 4 epochs per set, resulting in a cumulative total of 38,000 steps, with the image labeler Florence2-en (natural language & English).

The dataset consists of highly realistic images, meticulously curated for optimal model performance. Smaller adapter versions were initially trained on batches, and high-resolution images were later generated from these adapters for further training.

Pre-training upscaling techniques were employed to enhance pixel clarity, ensuring superior image fidelity during model inference.

Image compression details:

Blaze.1 Portrait was trained using state-of-the-art hardware:

Total machine runtime: 8 hours 14 minutes

To generate images using Blaze.1 Portrait, utilize the trigger phrase:

Incorporating this trigger in your prompts ensures optimal model performance and high-quality portrait generation.

Weights for Blaze.1 Portrait are available in Safetensors format. You can download them from the Files & Versions tab using the link below: