update

src/diffusers/__init__.py

@@ -31,7 +31,7 @@
     "loaders": ["FromOriginalModelMixin"],
     "models": [],
     "pipelines": [],
-    "quantizers.quantization_config": ["BitsAndBytesConfig", "TorchAoConfig", "GGUFQuantizationConfig"],
+    "quantizers.quantization_config": ["BitsAndBytesConfig", "GGUFQuantizationConfig", "TorchAoConfig"],
     "schedulers": [],
     "utils": [
         "OptionalDependencyNotAvailable",
@@ -428,8 +428,7 @@
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from .utils import \
-        dummy_torch_and_transformers_and_k_diffusion_objects  # noqa F403
+    from .utils import dummy_torch_and_transformers_and_k_diffusion_objects  # noqa F403
 
     _import_structure["utils.dummy_torch_and_transformers_and_k_diffusion_objects"] = [
         name for name in dir(dummy_torch_and_transformers_and_k_diffusion_objects) if not name.startswith("_")
@@ -442,8 +441,7 @@
     if not (is_torch_available() and is_transformers_available() and is_sentencepiece_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from .utils import \
-        dummy_torch_and_transformers_and_sentencepiece_objects  # noqa F403
+    from .utils import dummy_torch_and_transformers_and_sentencepiece_objects  # noqa F403
 
     _import_structure["utils.dummy_torch_and_transformers_and_sentencepiece_objects"] = [
         name for name in dir(dummy_torch_and_transformers_and_sentencepiece_objects) if not name.startswith("_")
@@ -456,8 +454,7 @@
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from .utils import \
-        dummy_torch_and_transformers_and_onnx_objects  # noqa F403
+    from .utils import dummy_torch_and_transformers_and_onnx_objects  # noqa F403
 
     _import_structure["utils.dummy_torch_and_transformers_and_onnx_objects"] = [
         name for name in dir(dummy_torch_and_transformers_and_onnx_objects) if not name.startswith("_")
@@ -492,8 +489,7 @@
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from .utils import \
-        dummy_transformers_and_torch_and_note_seq_objects  # noqa F403
+    from .utils import dummy_transformers_and_torch_and_note_seq_objects  # noqa F403
 
     _import_structure["utils.dummy_transformers_and_torch_and_note_seq_objects"] = [
         name for name in dir(dummy_transformers_and_torch_and_note_seq_objects) if not name.startswith("_")

src/diffusers/quantizers/quantization_config.py

@@ -395,19 +395,29 @@ def to_diff_dict(self) -> Dict[str, Any]:
         return serializable_config_dict
 
 
+@dataclass
 class GGUFQuantizationConfig(QuantizationConfigMixin):
-    def __init__(self, compute_dtype=None, quant_storage=None, modules_to_not_convert=None):
+    """This is a config class for GGUF Quantization techniques.
+
+    Args:
+        compute_dtype: (`torch.dtype`, defaults to `torch.float32`):
+            This sets the computational type which might be different than the input type. For example, inputs might be
+            fp32, but computation can be set to bf16 for speedups.
+
+    """
+
+    def __init__(self, compute_dtype: torch.dtype = None):
         self.quant_method = QuantizationMethod.GGUF
         self.compute_dtype = compute_dtype
-        self.quant_storage = quant_storage
         self.pre_quantized = True
-        self.modules_to_not_convert = modules_to_not_convert
+
+        # TODO: (Dhruv) Add this as an init argument when we can support loading unquantized checkpoints.
+        self.modules_to_not_convert = []
 
         if self.compute_dtype is None:
             self.compute_dtype = torch.float32
 
-        if self.quant_storage is None:
-            self.quant_storage = torch.uint8
+
 @dataclass
 class TorchAoConfig(QuantizationConfigMixin):
     """This is a config class for torchao quantization/sparsity techniques.

src/diffusers/utils/import_utils.py

@@ -479,6 +479,8 @@ def is_imageio_available():
 
 def is_gguf_available():
     return _is_gguf_available
+
+
 def is_torchao_available():
     return _is_torchao_available
 
@@ -622,7 +624,8 @@ def is_torchao_available():
 """
 
 TORCHAO_IMPORT_ERROR = """
-{0} requires the torchao library but it was not found in your environment. You can install it with pip: `pip install torchao`
+{0} requires the torchao library but it was not found in your environment. You can install it with pip: `pip install
+torchao`
 """
 
 BACKENDS_MAPPING = OrderedDict(

src/diffusers/utils/testing_utils.py

@@ -487,6 +487,7 @@ def decorator(test_case):
             correct_gguf_version, f"Test requires gguf with the version greater than {gguf_version}."
         )(test_case)
 
+
 def require_torchao_version_greater(torchao_version):
     def decorator(test_case):
         correct_torchao_version = is_torchao_available() and version.parse(