This model was created as an experiment by simply aggregating the learned content of various models (this test using "Anima" models, chosen for their lightweight nature). The process involves iteratively combining components: given definitions like Model A (AA + AB + Base) and Model B (BB + AB + Base), the system generates a composite of AA + AB + BB + Base (essentially Model A plus BB). During addition, duplicate elements are excluded so that only unique components are merged.The diagram below illustrates the merging process. Although there was some backtracking along the way, the final result reflects this general workflow.<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e1dbb04f-f032-46c5-baaf-f85f29141482/original=true/e1dbb04f-f032-46c5-baaf-f85f29141482.jpeg" />While mechanically combining learned content does not guarantee high-quality images, it does enable the model to handle a broader range of concepts. My findings suggest that, although certain models were better left out to avoid undesirable interference, the resulting images were less prone to structural collapse than anticipated, making this a highly viable technique. Although the test excluded photorealistic models, the specific models used were selected somewhat arbitrarily, leading to unexpected stylistic outcomes; this implies that the art style could be controlled through careful model selection. It should also be possible to achieve even greater flexibility and ease of use by gathering and combining components equivalent to the "BB" module.

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