Added script to save during textual inversion training. Issue 524 (hu…

…ggingface#645)

* Added script to save during training

* Suggested changes

examples/textual_inversion/textual_inversion.py

@@ -29,8 +29,21 @@
 logger = get_logger(__name__)
 
 
+def save_progress(text_encoder, placeholder_token_id, accelerator, args):
+    logger.info("Saving embeddings")
+    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
+    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
+    torch.save(learned_embeds_dict, os.path.join(args.output_dir, "learned_embeds.bin"))
+
+
 def parse_args():
     parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
     parser.add_argument(
         "--pretrained_model_name_or_path",
         type=str,
@@ -542,6 +555,8 @@ def main():
             if accelerator.sync_gradients:
                 progress_bar.update(1)
                 global_step += 1
+                if global_step % args.save_steps == 0:
+                    save_progress(text_encoder, placeholder_token_id, accelerator, args)
 
             logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
             progress_bar.set_postfix(**logs)
@@ -567,9 +582,7 @@ def main():
         )
         pipeline.save_pretrained(args.output_dir)
         # Also save the newly trained embeddings
-        learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
-        learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
-        torch.save(learned_embeds_dict, os.path.join(args.output_dir, "learned_embeds.bin"))
+        save_progress(text_encoder, placeholder_token_id, accelerator, args)
 
         if args.push_to_hub:
             repo.push_to_hub(