[LoRA] Freezing the model weights #2245
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Freeze the model weights since we don't need to calculate grads for them.
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The documentation is not available anymore as the PR was closed or merged. |
patrickvonplaten
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patrickvonplaten
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This makes sense to me! @patil-suraj @williamberman could you also take a quick look?
| unet.requires_grad_(False) | ||
| vae.requires_grad_(False) | ||
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| params = itertools.chain( |
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Why not just to text_encoder.requires_grad_(False) ?
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+1, , this way of freezing is only needed for the textual inversion
here we can use text_encoder.requires_grad_(False)
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Also cc @sayakpaul - I think we can freeze everything here just like we do for dreambooth_lora |
+1 |
patil-suraj
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patil-suraj
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Thanks a lot for the PR, looks good, just left one comment about freezing text encoder.
| unet.requires_grad_(False) | ||
| vae.requires_grad_(False) | ||
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| params = itertools.chain( |
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+1, , this way of freezing is only needed for the textual inversion
here we can use text_encoder.requires_grad_(False)
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
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Thanks a lot @erkams ! |
* [LoRA] Freezing the model weights Freeze the model weights since we don't need to calculate grads for them. * Apply suggestions from code review Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Apply suggestions from code review --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Suraj Patil <surajp815@gmail.com>
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As in original lora repository freezing the weights of the models helps us to use less memory