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Copy file name to clipboardExpand all lines: examples/advanced_diffusion_training/README_flux.md
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To activate pivotal tuning for both encoders, add the flag `--enable_t5_ti`.
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* When not fine-tuning the text encoders, we ALWAYS precompute the text embeddings to save memory.
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***pure textual inversion** - to support the full range from pivotal tuning to textual inversion we introduce `--train_transformer_frac` which controls the amount of epochs the transformer LoRA layers are trained. By default, `--train_transformer_frac==1`, to trigger a textual inversion run set `--train_transformer_frac==0`. Values between 0 and 1 are supported as well, and we welcome the community to experiment w/ different settings and share the results!
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***token initializer** - similar to the original textual inversion work, you can specify a token of your choosing as the starting point for training. By default, when enabling `--train_text_encoder_ti`, the new inserted tokens are initialized randomly. You can specify a token in `--initializer_token` such that the starting point for the trained embeddings will be the embeddings associated with your chosen `--initializer_token`.
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***token initializer** - similar to the original textual inversion work, you can specify a concept of your choosing as the starting point for training. By default, when enabling `--train_text_encoder_ti`, the new inserted tokens are initialized randomly. You can specify a token in `--initializer_concept` such that the starting point for the trained embeddings will be the embeddings associated with your chosen `--initializer_concept`.
💡note that if you didn't enable `--enable_t5_ti`, you only load the embeddings to the CLIP encoder
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> [!NOTE]#1if `--enable_t5_ti` wasn't passed, we only load the embeddings to the CLIP encoder.
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> [!NOTE]#2 the number of tokens (i.e. <s0>,...,<si>) is either determined by `--num_new_tokens_per_abstraction` or by `--initializer_concept`. Make sure to update inference code accordingly :)
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