set max_shard_size to None for pipeline save_pretrained (#9447)

* update default max_shard_size

* add None check to fix tests

---------

Co-authored-by: YiYi Xu <yixu310@gmail.com>

Author: 2 people

src/diffusers/pipelines/pipeline_utils.py

@@ -189,7 +189,7 @@ def save_pretrained(
         save_directory: Union[str, os.PathLike],
         safe_serialization: bool = True,
         variant: Optional[str] = None,
-        max_shard_size: Union[int, str] = "10GB",
+        max_shard_size: Optional[Union[int, str]] = None,
         push_to_hub: bool = False,
         **kwargs,
     ):
@@ -205,7 +205,7 @@ class implements both a save and loading method. The pipeline is easily reloaded
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
             variant (`str`, *optional*):
                 If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
-            max_shard_size (`int` or `str`, defaults to `"10GB"`):
+            max_shard_size (`int` or `str`, defaults to `None`):
                 The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
                 lower than this size. If expressed as a string, needs to be digits followed by a unit (like `"5GB"`).
                 If expressed as an integer, the unit is bytes. Note that this limit will be decreased after a certain
@@ -293,7 +293,8 @@ def is_saveable_module(name, value):
                 save_kwargs["safe_serialization"] = safe_serialization
             if save_method_accept_variant:
                 save_kwargs["variant"] = variant
-            if save_method_accept_max_shard_size:
+            if save_method_accept_max_shard_size and max_shard_size is not None:
+                # max_shard_size is expected to not be None in ModelMixin
                 save_kwargs["max_shard_size"] = max_shard_size
 
             save_method(os.path.join(save_directory, pipeline_component_name), **save_kwargs)