Standardize model card for Controlnet flax

examples/controlnet/train_controlnet_flax.py

@@ -48,6 +48,7 @@
     FlaxUNet2DConditionModel,
 )
 from diffusers.utils import check_min_version, is_wandb_available, make_image_grid
+from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 
 
 # To prevent an error that occurs when there are abnormally large compressed data chunk in the png image
@@ -145,28 +146,33 @@ def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=N
             make_image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png"))
             img_str += f"![images_{i})](./images_{i}.png)\n"
 
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- controlnet
-- jax-diffusers-event
-inference: true
----
-    """
-    model_card = f"""
+    model_description = f"""
 # controlnet- {repo_id}
 
 These are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \n
 {img_str}
 """
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
+
+    model_card = load_or_create_model_card(
+        repo_id_or_path=repo_id,
+        from_training=True,
+        license="creativeml-openrail-m",
+        base_model=base_model,
+        model_description=model_description,
+        inference=True,
+    )
+
+    tags = [
+        "stable-diffusion",
+        "stable-diffusion-diffusers",
+        "text-to-image",
+        "diffusers",
+        "controlnet",
+        "jax-diffusers-event",
+    ]
+    model_card = populate_model_card(model_card, tags=tags)
+
+    model_card.save(os.path.join(repo_folder, "README.md"))
 
 
 def parse_args():