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Soft prompts and textual inversion, as I understand them, required considerable hardware resources and were effectively made obsolete by low precision LORA trai
by brucethemoose2 3y ago
Soft prompts and textual inversion, as I understand them, required considerable hardware resources and were effectively made obsolete by low precision LORA training.
But the text token merging people do for negative prompts is fast iirc.
Not that you are wrong. LLM prompting does seem quite primitive compared to zoo of encoding/prompting methods we have for SD... And some of it is directly applicable to LLMs. I never even thought about that before.
- Der_Einzige 3y agoAll of it is directly applicable to LLMs. Textual Inversion/Soft Prompts are not made "obsolete" at all by Loras. Textual Inversion is far faster to train, extremely effective on newer models like SDXL which have better clip encoding, and has neat properties of not messing with the general weights of the rest of the model, which is sometimes desirable.
- ijk 3y agoSoft prompts for language models are still viable, but the most accessible version of open source soft prompt training was a TPU-based implementation that depended on features of Google Colab that were no longer available. You can modify it to run locally on a GPU, though it is a bit slow to train. It provides different advantages when compared to a LoRA.