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@ -858,9 +858,9 @@ def do_textual_inversion_training(
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# Let's make sure we don't update any embedding weights besides the newly added token
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index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
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with torch.no_grad():
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accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
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index_no_updates
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] = orig_embeds_params[index_no_updates]
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accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
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orig_embeds_params[index_no_updates]
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)
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# Checks if the accelerator has performed an optimization step behind the scenes
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if accelerator.sync_gradients:
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