Fix dtype model loading

src/diffusers/modeling_utils.py

@@ -472,6 +472,21 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             model = cls.from_config(config, **unused_kwargs)
 
             state_dict = load_state_dict(model_file)
+            dtype = set(v.dtype for v in state_dict.values())
+
+            if len(dtype) > 1 and torch.float32 not in dtype:
+                raise ValueError(
+                    f"The weights of the model file {model_file} have a mixture of incompatible dtypes {dtype}. Please"
+                    f" make sure that {model_file} weights have only one dtype."
+                )
+            elif len(dtype) > 1 and torch.float32 in dtype:
+                dtype = torch.float32
+            else:
+                dtype = dtype.pop()
+
+            # move model to correct dtype
+            model = model.to(dtype)
+
             model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
                 model,
                 state_dict,

tests/models/test_models_unet_1d.py

@@ -63,8 +63,8 @@ def test_outputs_equivalence(self):
         super().test_outputs_equivalence()
 
     @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
-    def test_from_pretrained_save_pretrained(self):
-        super().test_from_pretrained_save_pretrained()
+    def test_from_save_pretrained(self):
+        super().test_from_save_pretrained()
 
     @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
     def test_model_from_pretrained(self):
@@ -183,8 +183,8 @@ def test_outputs_equivalence(self):
         super().test_outputs_equivalence()
 
     @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
-    def test_from_pretrained_save_pretrained(self):
-        super().test_from_pretrained_save_pretrained()
+    def test_from_save_pretrained(self):
+        super().test_from_save_pretrained()
 
     @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
     def test_model_from_pretrained(self):

tests/pipelines/versatile_diffusion/test_versatile_diffusion_mega.py

@@ -42,7 +42,7 @@ def tearDown(self):
         gc.collect()
         torch.cuda.empty_cache()
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/test_modeling_common.py

@@ -27,7 +27,7 @@
 
 
 class ModelTesterMixin:
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
         model = self.model_class(**init_dict)
@@ -57,6 +57,24 @@ def test_from_pretrained_save_pretrained(self):
         max_diff = (image - new_image).abs().sum().item()
         self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
 
+    def test_from_save_pretrained_dtype(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        for dtype in [torch.float32, torch.float16, torch.bfloat16]:
+            if torch_device == "mps" and dtype == torch.bfloat16:
+                continue
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.to(dtype)
+                model.save_pretrained(tmpdirname)
+                new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=True)
+                assert new_model.dtype == dtype
+                new_model = self.model_class.from_pretrained(tmpdirname, low_cpu_mem_usage=False)
+                assert new_model.dtype == dtype
+
     def test_determinism(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
         model = self.model_class(**init_dict)

tests/test_pipelines.py

@@ -656,7 +656,7 @@ def test_warning_unused_kwargs(self):
 
         assert cap_logger.out == "Keyword arguments {'not_used': True} not recognized.\n"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         # 1. Load models
         model = UNet2DModel(
             block_out_channels=(32, 64),

tests/test_scheduler.py

@@ -333,7 +333,7 @@ def check_over_forward(self, time_step=0, **forward_kwargs):
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         kwargs = dict(self.forward_default_kwargs)
 
         num_inference_steps = kwargs.pop("num_inference_steps", None)
@@ -860,7 +860,7 @@ def check_over_configs(self, time_step=0, **config):
 
                 assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         pass
 
     def check_over_forward(self, time_step=0, **forward_kwargs):
@@ -1037,7 +1037,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         pass
 
     def check_over_forward(self, time_step=0, **forward_kwargs):
@@ -1717,7 +1717,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         pass
 
     def check_over_forward(self, time_step=0, **forward_kwargs):

tests/test_scheduler_flax.py

@@ -126,7 +126,7 @@ def check_over_forward(self, time_step=0, **forward_kwargs):
 
             assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         kwargs = dict(self.forward_default_kwargs)
 
         num_inference_steps = kwargs.pop("num_inference_steps", None)
@@ -408,7 +408,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         kwargs = dict(self.forward_default_kwargs)
 
         num_inference_steps = kwargs.pop("num_inference_steps", None)
@@ -690,7 +690,7 @@ def check_over_configs(self, time_step=0, **config):
 
             assert jnp.sum(jnp.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
-    def test_from_pretrained_save_pretrained(self):
+    def test_from_save_pretrained(self):
         pass
 
     def test_scheduler_outputs_equivalence(self):