[Single File] Allow loading T5 encoder in mixed precision

src/diffusers/loaders/single_file.py

@@ -555,7 +555,4 @@ def load_module(name, value):
 
         pipe = pipeline_class(**init_kwargs)
 
-        if torch_dtype is not None:
-            pipe.to(dtype=torch_dtype)
-
         return pipe

src/diffusers/loaders/single_file_utils.py

@@ -1808,4 +1808,17 @@ def create_diffusers_t5_model_from_checkpoint(
 
     else:
         model.load_state_dict(diffusers_format_checkpoint)
+
+    use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (torch_dtype == torch.float16)
+    if use_keep_in_fp32_modules:
+        keep_in_fp32_modules = model._keep_in_fp32_modules
+    else:
+        keep_in_fp32_modules = []
+
+    if keep_in_fp32_modules is not None:
+        for name, param in model.named_parameters():
+            if any(module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in keep_in_fp32_modules):
+                # param = param.to(torch.float32) does not work here as only in the local scope.
+                param.data = param.data.to(torch.float32)
+
     return model

tests/single_file/single_file_testing_utils.py

@@ -201,6 +201,20 @@ def test_single_file_components_with_diffusers_config_local_files_only(
 
         self._compare_component_configs(pipe, single_file_pipe)
 
+    def test_single_file_setting_pipeline_dtype_to_fp16(
+        self,
+        single_file_pipe=None,
+    ):
+        single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(
+            self.ckpt_path, torch_dtype=torch.float16
+        )
+
+        for component_name, component in single_file_pipe.components.items():
+            if not isinstance(component, torch.nn.Module):
+                continue
+
+            assert component.dtype == torch.float16
+
 
 class SDXLSingleFileTesterMixin:
     def _compare_component_configs(self, pipe, single_file_pipe):
@@ -378,3 +392,17 @@ def test_single_file_format_inference_is_same_as_pretrained(self, expected_max_d
         max_diff = numpy_cosine_similarity_distance(image.flatten(), image_single_file.flatten())
 
         assert max_diff < expected_max_diff
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(
+        self,
+        single_file_pipe=None,
+    ):
+        single_file_pipe = single_file_pipe or self.pipeline_class.from_single_file(
+            self.ckpt_path, torch_dtype=torch.float16
+        )
+
+        for component_name, component in single_file_pipe.components.items():
+            if not isinstance(component, torch.nn.Module):
+                continue
+
+            assert component.dtype == torch.float16

tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py

@@ -180,3 +180,12 @@ def test_single_file_components_with_diffusers_config_local_files_only(self):
                 local_files_only=True,
             )
         super()._compare_component_configs(pipe, pipe_single_file)
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(self):
+        controlnet = ControlNetModel.from_pretrained(
+            "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
+        )
+        single_file_pipe = self.pipeline_class.from_single_file(
+            self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+        )
+        super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)

tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py

@@ -181,3 +181,12 @@ def test_single_file_components_with_diffusers_config_local_files_only(self):
                 local_files_only=True,
             )
         super()._compare_component_configs(pipe, pipe_single_file)
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(self):
+        controlnet = ControlNetModel.from_pretrained(
+            "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
+        )
+        single_file_pipe = self.pipeline_class.from_single_file(
+            self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+        )
+        super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)

tests/single_file/test_stable_diffusion_controlnet_single_file.py

@@ -169,3 +169,12 @@ def test_single_file_components_with_diffusers_config_local_files_only(self):
                 local_files_only=True,
             )
         super()._compare_component_configs(pipe, pipe_single_file)
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(self):
+        controlnet = ControlNetModel.from_pretrained(
+            "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"
+        )
+        single_file_pipe = self.pipeline_class.from_single_file(
+            self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+        )
+        super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)

tests/single_file/test_stable_diffusion_xl_adapter_single_file.py

@@ -200,3 +200,11 @@ def test_single_file_components_with_original_config_local_files_only(self):
                 local_files_only=True,
             )
         self._compare_component_configs(pipe, pipe_single_file)
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(self):
+        adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
+
+        single_file_pipe = self.pipeline_class.from_single_file(
+            self.ckpt_path, adapter=adapter, torch_dtype=torch.float16
+        )
+        super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)

tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py

@@ -195,3 +195,12 @@ def test_single_file_components_with_diffusers_config_local_files_only(self):
                 local_files_only=True,
             )
         super()._compare_component_configs(pipe, pipe_single_file)
+
+    def test_single_file_setting_pipeline_dtype_to_fp16(self):
+        controlnet = ControlNetModel.from_pretrained(
+            "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
+        )
+        single_file_pipe = self.pipeline_class.from_single_file(
+            self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
+        )
+        super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe)