It must be used with a model checkpoint file. Like textual inversion, you cannot use a LoRA model alone. LoRA is an excellent solution to the storage problem. Because of the large size, It is hard to maintain a collection with a personal computer. Stable Diffusion users who like experimenting with models can tell you how quickly their local storage fills up. LoRA sits in between: Their file sizes are manageable (2 – 200 MBs), and the training power is decent. Textual inversions are tiny (about 100 KBs), but you can’t do as much. Dreambooth is powerful but results in large model files (2-7 GBs). What’s the big deal about LoRA? LoRA offers a good trade-off between file size and training power. LoRA (Low-Rank Adaptation) is a training technique for fine-tuning Stable Diffusion models.īut we already have training techniques such as Dreambooth and textual inversion.
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