diff --git a/examples/textual_inversion/textual_inversion_flax.py b/examples/textual_inversion/textual_inversion_flax.py
index bb9884b3..e184eb0c 100644
--- a/examples/textual_inversion/textual_inversion_flax.py
+++ b/examples/textual_inversion/textual_inversion_flax.py
@@ -121,6 +121,12 @@ def parse_args():
         default=5000,
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
     parser.add_argument(
         "--learning_rate",
         type=float,
@@ -136,6 +142,13 @@ def parse_args():
     parser.add_argument(
         "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
     )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
     parser.add_argument(
         "--lr_scheduler",
         type=str,
@@ -420,9 +433,9 @@ def main():
     placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
 
     # Load models and create wrapper for stable diffusion
-    text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
-    vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
-    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+    text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder",revision=args.revision)
+    vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae",revision=args.revision)
+    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet",revision=args.revision)
 
     # Create sampling rng
     rng = jax.random.PRNGKey(args.seed)
@@ -619,6 +632,12 @@ def main():
 
             if global_step >= args.max_train_steps:
                 break
+            if global_step % args.save_steps == 0:
+                learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
+                    placeholder_token_id
+                ]
+                learned_embeds_dict = {args.placeholder_token: learned_embeds}
+                jnp.save(os.path.join(args.output_dir, "learned_embeds-"+str(global_step)+".npy"), learned_embeds_dict)
 
         train_metric = jax_utils.unreplicate(train_metric)