up

src/diffusers/__init__.py

@@ -13,6 +13,7 @@ from .utils import (
     is_k_diffusion_available,
     is_librosa_available,
     is_note_seq_available,
+    is_nunchaku_available,
     is_onnx_available,
     is_opencv_available,
     is_optimum_quanto_available,
@@ -99,6 +100,18 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["quantizers.quantization_config"].append("TorchAoConfig")
 
+try:
+    if not is_torch_available() and not is_accelerate_available() and not is_nunchaku_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from .utils import dummy_nunchaku_objects
+
+    _import_structure["utils.dummy_nunchaku_objects"] = [
+        name for name in dir(dummy_nunchaku_objects) if not name.startswith("_")
+    ]
+else:
+    _import_structure["quantizers.quantization_config"].append("NunchakuConfig")
+
 try:
     if not is_torch_available() and not is_accelerate_available() and not is_optimum_quanto_available():
         raise OptionalDependencyNotAvailable()
@@ -791,6 +804,14 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .quantizers.quantization_config import QuantoConfig
 
+    try:
+        if not is_nunchaku_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from .utils.dummy_optimum_quanto_objects import *
+    else:
+        from .quantizers.quantization_config import NunchakuConfig
+
     try:
         if not is_onnx_available():
             raise OptionalDependencyNotAvailable()

src/diffusers/utils/dummy_nunchaku_objects.py

@@ -0,0 +1,17 @@
+# This file is autogenerated by the command `make fix-copies`, do not edit.
+from ..utils import DummyObject, requires_backends
+
+
+class NunchakuConfig(metaclass=DummyObject):
+    _backends = ["nunchaku"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["nunchaku"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["nunchaku"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["nunchaku"])