Move buffers to device (#10523)

* Move buffers to device

* add test

* named_buffers

src/diffusers/loaders/single_file_model.py

@@ -362,13 +362,15 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
 
         if is_accelerate_available():
             param_device = torch.device(device) if device else torch.device("cpu")
+            named_buffers = model.named_buffers()
             unexpected_keys = load_model_dict_into_meta(
                 model,
                 diffusers_format_checkpoint,
                 dtype=torch_dtype,
                 device=param_device,
                 hf_quantizer=hf_quantizer,
                 keep_in_fp32_modules=keep_in_fp32_modules,
+                named_buffers=named_buffers,
             )
 
         else:

src/diffusers/models/model_loading_utils.py

@@ -20,7 +20,7 @@
 from array import array
 from collections import OrderedDict
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Dict, Iterator, List, Optional, Tuple, Union
 
 import safetensors
 import torch
@@ -193,6 +193,7 @@ def load_model_dict_into_meta(
     model_name_or_path: Optional[str] = None,
     hf_quantizer=None,
     keep_in_fp32_modules=None,
+    named_buffers: Optional[Iterator[Tuple[str, torch.Tensor]]] = None,
 ) -> List[str]:
     if device is not None and not isinstance(device, (str, torch.device)):
         raise ValueError(f"Expected device to have type `str` or `torch.device`, but got {type(device)=}.")
@@ -254,6 +255,20 @@ def load_model_dict_into_meta(
             else:
                 set_module_tensor_to_device(model, param_name, device, value=param)
 
+    if named_buffers is None:
+        return unexpected_keys
+
+    for param_name, param in named_buffers:
+        if is_quantized and (
+            hf_quantizer.check_if_quantized_param(model, param, param_name, state_dict, param_device=device)
+        ):
+            hf_quantizer.create_quantized_param(model, param, param_name, device, state_dict, unexpected_keys)
+        else:
+            if accepts_dtype:
+                set_module_tensor_to_device(model, param_name, device, value=param, **set_module_kwargs)
+            else:
+                set_module_tensor_to_device(model, param_name, device, value=param)
+
     return unexpected_keys
 
 

src/diffusers/models/modeling_utils.py

@@ -913,6 +913,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                             " those weights or else make sure your checkpoint file is correct."
                         )
 
+                    named_buffers = model.named_buffers()
+
                     unexpected_keys = load_model_dict_into_meta(
                         model,
                         state_dict,
@@ -921,6 +923,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                         model_name_or_path=pretrained_model_name_or_path,
                         hf_quantizer=hf_quantizer,
                         keep_in_fp32_modules=keep_in_fp32_modules,
+                        named_buffers=named_buffers,
                     )
 
                     if cls._keys_to_ignore_on_load_unexpected is not None:

tests/quantization/bnb/test_mixed_int8.py

@@ -20,7 +20,14 @@
 import pytest
 from huggingface_hub import hf_hub_download
 
-from diffusers import BitsAndBytesConfig, DiffusionPipeline, FluxTransformer2DModel, SD3Transformer2DModel, logging
+from diffusers import (
+    BitsAndBytesConfig,
+    DiffusionPipeline,
+    FluxTransformer2DModel,
+    SanaTransformer2DModel,
+    SD3Transformer2DModel,
+    logging,
+)
 from diffusers.utils import is_accelerate_version
 from diffusers.utils.testing_utils import (
     CaptureLogger,
@@ -302,6 +309,33 @@ def test_device_and_dtype_assignment(self):
         _ = self.model_fp16.cuda()
 
 
+class Bnb8bitDeviceTests(Base8bitTests):
+    def setUp(self) -> None:
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        mixed_int8_config = BitsAndBytesConfig(load_in_8bit=True)
+        self.model_8bit = SanaTransformer2DModel.from_pretrained(
+            "Efficient-Large-Model/Sana_1600M_4Kpx_BF16_diffusers",
+            subfolder="transformer",
+            quantization_config=mixed_int8_config,
+        )
+
+    def tearDown(self):
+        del self.model_8bit
+
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_buffers_device_assignment(self):
+        for buffer_name, buffer in self.model_8bit.named_buffers():
+            self.assertEqual(
+                buffer.device.type,
+                torch.device(torch_device).type,
+                f"Expected device {torch_device} for {buffer_name} got {buffer.device}.",
+            )
+
+
 class BnB8bitTrainingTests(Base8bitTests):
     def setUp(self):
         gc.collect()