diff --git a/src/diffusers/loaders/single_file_model.py b/src/diffusers/loaders/single_file_model.py
index 16bd0441072a..b53647d47630 100644
--- a/src/diffusers/loaders/single_file_model.py
+++ b/src/diffusers/loaders/single_file_model.py
@@ -22,6 +22,7 @@
 from typing_extensions import Self
 
 from .. import __version__
+from ..models.model_loading_utils import _caching_allocator_warmup, _determine_device_map, _expand_device_map
 from ..quantizers import DiffusersAutoQuantizer
 from ..utils import deprecate, is_accelerate_available, is_torch_version, logging
 from ..utils.torch_utils import empty_device_cache
@@ -297,6 +298,7 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         device = kwargs.pop("device", None)
         disable_mmap = kwargs.pop("disable_mmap", False)
+        device_map = kwargs.pop("device_map", None)
 
         user_agent = {"diffusers": __version__, "file_type": "single_file", "framework": "pytorch"}
         # In order to ensure popular quantization methods are supported. Can be disable with `disable_telemetry`
@@ -403,19 +405,8 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         with ctx():
             model = cls.from_config(diffusers_model_config)
 
-        checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
-
-        if _should_convert_state_dict_to_diffusers(model.state_dict(), checkpoint):
-            diffusers_format_checkpoint = checkpoint_mapping_fn(
-                config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
-            )
-        else:
-            diffusers_format_checkpoint = checkpoint
+        model_state_dict = model.state_dict()
 
-        if not diffusers_format_checkpoint:
-            raise SingleFileComponentError(
-                f"Failed to load {mapping_class_name}. Weights for this component appear to be missing in the checkpoint."
-            )
         # Check if `_keep_in_fp32_modules` is not None
         use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
             (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")
@@ -428,6 +419,26 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
         else:
             keep_in_fp32_modules = []
 
+        # Now that the model is loaded, we can determine the `device_map`
+        device_map = _determine_device_map(model, device_map, None, torch_dtype, keep_in_fp32_modules, hf_quantizer)
+        if device_map is not None:
+            expanded_device_map = _expand_device_map(device_map, model_state_dict.keys())
+            _caching_allocator_warmup(model, expanded_device_map, torch_dtype, hf_quantizer)
+
+        checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
+
+        if _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint):
+            diffusers_format_checkpoint = checkpoint_mapping_fn(
+                config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
+            )
+        else:
+            diffusers_format_checkpoint = checkpoint
+
+        if not diffusers_format_checkpoint:
+            raise SingleFileComponentError(
+                f"Failed to load {mapping_class_name}. Weights for this component appear to be missing in the checkpoint."
+            )
+
         if hf_quantizer is not None:
             hf_quantizer.preprocess_model(
                 model=model,
diff --git a/tests/single_file/test_model_flux_transformer_single_file.py b/tests/single_file/test_model_flux_transformer_single_file.py
index a7e07e517f4d..8290c339b931 100644
--- a/tests/single_file/test_model_flux_transformer_single_file.py
+++ b/tests/single_file/test_model_flux_transformer_single_file.py
@@ -69,3 +69,11 @@ def test_checkpoint_loading(self):
             del model
             gc.collect()
             backend_empty_cache(torch_device)
+
+    def test_device_map_cuda(self):
+        backend_empty_cache(torch_device)
+        model = self.model_class.from_single_file(self.ckpt_path, device_map="cuda")
+
+        del model
+        gc.collect()
+        backend_empty_cache(torch_device)