[Model Card] standardize T2I Adapter Sdxl model card

examples/t2i_adapter/train_t2i_adapter_sdxl.py

@@ -49,6 +49,7 @@
 )
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.torch_utils import is_compiled_module
 
@@ -195,7 +196,7 @@ def import_model_class_from_model_name_or_path(
         raise ValueError(f"{model_class} is not supported.")
 
 
-def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=None):
+def save_model_card(repo_id: str, image_logs: dict = None, base_model: str = None, repo_folder: str = None):
     img_str = ""
     if image_logs is not None:
         img_str = "You can find some example images below.\n"
@@ -209,27 +210,25 @@ def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_folder=N
             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-xl
-- stable-diffusion-xl-diffusers
-- text-to-image
-- diffusers
-- t2iadapter
-inference: true
----
-    """
-    model_card = f"""
+    model_description = f"""
 # t2iadapter-{repo_id}
 
 These are t2iadapter weights trained on {base_model} with new type of conditioning.
 {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-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "t2iadapter"]
+    model_card = populate_model_card(model_card, tags=tags)
+
+    model_card.save(os.path.join(repo_folder, "README.md"))
 
 
 def parse_args(input_args=None):