Version 5 represents another significant enhancement.
Version 5 Training:
Version 5 is trained at twice the resolution of versin 4 and four times the resolution used in versions 1-3.
Version 5 underwent 91 epochs of training , 9 fewer then version 4 but more steps due to the larger dataset.
A higher rank, 16, was utilized to enhance the model’s ability to discern intricate details.
Version 5 Dataset:
The source images for Version 5 were retrieved from the original sources (no AI-generated images were utilized in the dataset). Each image is either a high-quality, exceptionally high-resolution source image or a composite image created by meticulously combining high-quality, exceptionally high-resolution source images with extensive photoshopping techniques to produce a high-quality digital artwork tailored to showcase specific visual elements for the LORA’s training.
Version 5 is an expanded version of Version 4, encompassing a wider range of surreal imagery and more detailed examples.
I created Version 5 to include images with twice the resolution of Versions 4. Four times the resolution of Versions 2, and Versions 1. This enhancement ensures that smaller details are in resolution. (I manually scale the images to control the specific scaling algorithm that best aligns with the intended use case, guaranteeing optimal image quality in the dataset.) No images were upscaled, only downscaled from higher-resolution source images.
I was really worried that version 5 would come out poorly because it again doubles the resolution of the dataset and the resolution the LORA is trained at. It also doubles the rank (to 16) and increases the number of points in images in the dataset (almost double).
I have seen many articles, websites, etc., that warn of problems when the dataset gets this large, causing issues and the risk of teaching the LORA unintended lessons (it learns more details at the higher ranks, so if you have smaller quality issues with your dataset that didn't get picked up at the lower rank, they may be learned at the higher ranks). It is also easier to overfit the model if you do not have a diverse enough dataset or you train the model for too long.
Testing has yielded impressive results, and I am extremely pleased with the results.
About this version
Version 5 represents another significant enhancement.
Version 5 Training:
Version 5 is trained at twice the resolution of versin 4 and four times the resolution used in versions 1-3.
Version 5 underwent 91 epochs of training , 9 fewer then version 4 but more steps due to the larger dataset.
A higher rank, 16, was utilized to enhance the model’s ability to discern intricate details.
Version 5 Dataset:
The source images for Version 5 were retrieved from the original sources (no AI-generated images were utilized in the dataset). Each image is either a high-quality, exceptionally high-resolution source image or a composite image created by meticulously combining high-quality, exceptionally high-resolution source images with extensive photoshopping techniques to produce a high-quality digital artwork tailored to showcase specific visual elements for the LORA’s training.
Version 5 is an expanded version of Version 4, encompassing a wider range of surreal imagery and more detailed examples.
I created Version 5 to include images with twice the resolution of Versions 4. Four times the resolution of Versions 2, and Versions 1. This enhancement ensures that smaller details are in resolution. (I manually scale the images to control the specific scaling algorithm that best aligns with the intended use case, guaranteeing optimal image quality in the dataset.) No images were upscaled, only downscaled from higher-resolution source images.
I was really worried that version 5 would come out poorly because it again doubles the resolution of the dataset and the resolution the LORA is trained at. It also doubles the rank (to 16) and increases the number of points in images in the dataset (almost double).
I have seen many articles, websites, etc., that warn of problems when the dataset gets this large, causing issues and the risk of teaching the LORA unintended lessons (it learns more details at the higher ranks, so if you have smaller quality issues with your dataset that didn't get picked up at the lower rank, they may be learned at the higher ranks). It is also easier to overfit the model if you do not have a diverse enough dataset or you train the model for too long.
Testing has yielded impressive results, and I am extremely pleased with the results.