How This Model Came to Be: Why Epsilon: Same as Delta, just a test for new dataset and it is good. The outputs are also very different (compare to Delta). Dataset: The dataset for training this model was sourced from HakuBooru, comprising 5.2 million images selected from the danbooru2023 dataset.[2][3] A selection process was employed to choose 1 million posts from IDs 0 to 2,000,000, another 2 millions from IDs 2,000,000 to 4,999,999, and all posts after ID 5,000,000, totaling 5.35 million posts. After filtering out deleted posts, gold account posts and those without images (which could be GIFs or MP4s), the final dataset comprised 5.2 million images. The selection was essentially random, but a fixed seed was utilized to ensure reproducibility. Further Process: - Shuffle tags: The order of general tags was shuffled in each step. - Tag dropout: Randomly, 15% of general tags were dropped in each step. ----- Training: The training of Kohaku XL Epsilon was facilitated by the LyCORIS project and the trainer from kohya-ss/sd-scripts. [1][4] Algorithm: LoKr[7] The model was trained using the LoKr algorithm with full matrix triggered and a factor of 2~8 for different modules. The aim was to demonstrate the applicability of LoRA/LyCORIS in training base models. The original LoKr file size is under 800MB, and the TE was not frozen. The original LoKr file also be provided as "delta-lokr" version. For detailed settings, refer to the LyCORIS config file from Kohaku XL Delta.