Fix torch_dtype in Kolors text encoder with transformers v4.49 (#10816)

* Fix `torch_dtype` in Kolors text encoder with `transformers` v4.49

* Default torch_dtype and warning

Author: hlky

examples/community/checkpoint_merger.py

@@ -92,9 +92,13 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
         token = kwargs.pop("token", None)
         variant = kwargs.pop("variant", None)
         revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         device_map = kwargs.pop("device_map", None)
 
+        if not isinstance(torch_dtype, torch.dtype):
+            torch_dtype = torch.float32
+            print(f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`.")
+
         alpha = kwargs.pop("alpha", 0.5)
         interp = kwargs.pop("interp", None)
 

src/diffusers/loaders/single_file.py

@@ -360,11 +360,17 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs) -> Self:
         cache_dir = kwargs.pop("cache_dir", None)
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
         is_legacy_loading = False
 
+        if not isinstance(torch_dtype, torch.dtype):
+            torch_dtype = torch.float32
+            logger.warning(
+                f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
+            )
+
         # We shouldn't allow configuring individual models components through a Pipeline creation method
         # These model kwargs should be deprecated
         scaling_factor = kwargs.get("scaling_factor", None)

src/diffusers/loaders/single_file_model.py

@@ -240,11 +240,17 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         subfolder = kwargs.pop("subfolder", None)
         revision = kwargs.pop("revision", None)
         config_revision = kwargs.pop("config_revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         quantization_config = kwargs.pop("quantization_config", None)
         device = kwargs.pop("device", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
+        if not isinstance(torch_dtype, torch.dtype):
+            torch_dtype = torch.float32
+            logger.warning(
+                f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
+            )
+
         if isinstance(pretrained_model_link_or_path_or_dict, dict):
             checkpoint = pretrained_model_link_or_path_or_dict
         else:

src/diffusers/models/modeling_utils.py

@@ -866,7 +866,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         local_files_only = kwargs.pop("local_files_only", None)
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         subfolder = kwargs.pop("subfolder", None)
         device_map = kwargs.pop("device_map", None)
         max_memory = kwargs.pop("max_memory", None)
@@ -879,6 +879,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         dduf_entries: Optional[Dict[str, DDUFEntry]] = kwargs.pop("dduf_entries", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
 
+        if not isinstance(torch_dtype, torch.dtype):
+            torch_dtype = torch.float32
+            logger.warning(
+                f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
+            )
+
         allow_pickle = False
         if use_safetensors is None:
             use_safetensors = True

src/diffusers/pipelines/pipeline_utils.py

@@ -685,7 +685,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         token = kwargs.pop("token", None)
         revision = kwargs.pop("revision", None)
         from_flax = kwargs.pop("from_flax", False)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
         custom_pipeline = kwargs.pop("custom_pipeline", None)
         custom_revision = kwargs.pop("custom_revision", None)
         provider = kwargs.pop("provider", None)
@@ -702,6 +702,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
+        if not isinstance(torch_dtype, torch.dtype):
+            torch_dtype = torch.float32
+            logger.warning(
+                f"Passed `torch_dtype` {torch_dtype} is not a `torch.dtype`. Defaulting to `torch.float32`."
+            )
+
         if low_cpu_mem_usage and not is_accelerate_available():
             low_cpu_mem_usage = False
             logger.warning(
@@ -1826,7 +1832,7 @@ def from_pipe(cls, pipeline, **kwargs):
         """
 
         original_config = dict(pipeline.config)
-        torch_dtype = kwargs.pop("torch_dtype", None)
+        torch_dtype = kwargs.pop("torch_dtype", torch.float32)
 
         # derive the pipeline class to instantiate
         custom_pipeline = kwargs.pop("custom_pipeline", None)

tests/pipelines/kolors/test_kolors.py

@@ -89,7 +89,9 @@ def get_dummy_components(self, time_cond_proj_dim=None):
             sample_size=128,
         )
         torch.manual_seed(0)
-        text_encoder = ChatGLMModel.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
+        text_encoder = ChatGLMModel.from_pretrained(
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+        )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
 
         components = {

tests/pipelines/kolors/test_kolors_img2img.py

@@ -93,7 +93,9 @@ def get_dummy_components(self, time_cond_proj_dim=None):
             sample_size=128,
         )
         torch.manual_seed(0)
-        text_encoder = ChatGLMModel.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
+        text_encoder = ChatGLMModel.from_pretrained(
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+        )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
 
         components = {

tests/pipelines/pag/test_pag_kolors.py

@@ -98,7 +98,9 @@ def get_dummy_components(self, time_cond_proj_dim=None):
             sample_size=128,
         )
         torch.manual_seed(0)
-        text_encoder = ChatGLMModel.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
+        text_encoder = ChatGLMModel.from_pretrained(
+            "hf-internal-testing/tiny-random-chatglm3-6b", torch_dtype=torch.bfloat16
+        )
         tokenizer = ChatGLMTokenizer.from_pretrained("hf-internal-testing/tiny-random-chatglm3-6b")
 
         components = {