[SD3 dreambooth lora] smol fix to checkpoint saving #9993
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linoytsaban
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sayakpaul
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Nov 24, 2024
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| elif isinstance(model, type(unwrap_model(text_encoder_one))): # or text_encoder_two | ||
| # check hidden size to distinguish between text_encoder_one and two | ||
| hidden_size = unwrap_model(model).config.hidden_size | ||
| if hidden_size == 768: | ||
| text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model) | ||
| elif hidden_size == 1280: | ||
| text_encoder_two_lora_layers_to_save = get_peft_model_state_dict(model) |
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Works for me. We cannot distinguish with the classes here because both have the same class. Maybe this reasoning as a comment?
lawrence-cj
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Nov 26, 2024
* smol change to fix checkpoint saving & resuming (as done in train_dreambooth_sd3.py) * style * modify comment to explain reasoning behind hidden size check
sayakpaul
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Dec 23, 2024
* smol change to fix checkpoint saving & resuming (as done in train_dreambooth_sd3.py) * style * modify comment to explain reasoning behind hidden size check
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minor change to fix saving of text-encoder layers in lora training script, as we have in place in the dreambooth training script.
related to #8955