[Textual Inversion] Do not update other embeddings (#1665)

examples/textual_inversion/textual_inversion.py

@@ -548,6 +548,9 @@ def main():
     progress_bar.set_description("Steps")
     global_step = 0
 
+    # keep original embeddings as reference
+    orig_embeds_params = text_encoder.get_input_embeddings().weight.data.clone()
+
     for epoch in range(args.num_train_epochs):
         text_encoder.train()
         for step, batch in enumerate(train_dataloader):
@@ -585,20 +588,15 @@ def main():
                 loss = F.mse_loss(model_pred, target, reduction="none").mean([1, 2, 3]).mean()
                 accelerator.backward(loss)
 
-                # Zero out the gradients for all token embeddings except the newly added
-                # embeddings for the concept, as we only want to optimize the concept embeddings
-                if accelerator.num_processes > 1:
-                    grads = text_encoder.module.get_input_embeddings().weight.grad
-                else:
-                    grads = text_encoder.get_input_embeddings().weight.grad
-                # Get the index for tokens that we want to zero the grads for
-                index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
-                grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
-
                 optimizer.step()
                 lr_scheduler.step()
                 optimizer.zero_grad()
 
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
+                with torch.no_grad():
+                    text_encoder.get_input_embeddings().weight[index_no_updates] = orig_embeds_params[index_no_updates]
+
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:
                 progress_bar.update(1)