update

docs/source/en/quantization/torchao.md

@@ -126,7 +126,7 @@ image = pipe(prompt, num_inference_steps=30, guidance_scale=7.0).images[0]
 image.save("output.png")
 ```
 
-Some quantization methods, such as `uint4wo`, cannot be loaded directly and may result in an `UnpicklingError` when trying to load the models, but work as expected when saving them. In order to work around this, one can load the state dict manually into the model. Note, however, that this requires using `weights_only=False` in `torch.load`, so it should be run only if the weights were obtained from a trustable source.
+If you are using `torch<=2.6.0`, some quantization methods, such as `uint4wo`, cannot be loaded directly and may result in an `UnpicklingError` when trying to load the models, but work as expected when saving them. In order to work around this, one can load the state dict manually into the model. Note, however, that this requires using `weights_only=False` in `torch.load`, so it should be run only if the weights were obtained from a trustable source.
 
 ```python
 import torch

src/diffusers/models/model_loading_utils.py

@@ -54,7 +54,6 @@ _CLASS_REMAPPING_DICT = {
     }
 }
 
-
 if is_accelerate_available():
     from accelerate import infer_auto_device_map
     from accelerate.utils import get_balanced_memory, get_max_memory, offload_weight, set_module_tensor_to_device

src/diffusers/quantizers/torchao/torchao_quantizer.py

@@ -23,7 +23,14 @@ from typing import TYPE_CHECKING, Any, Dict, List, Union
 
 from packaging import version
 
-from ...utils import get_module_from_name, is_torch_available, is_torch_version, is_torchao_available, logging
+from ...utils import (
+    get_module_from_name,
+    is_torch_available,
+    is_torch_version,
+    is_torchao_version,
+    is_torchao_available,
+    logging,
+)
 from ..base import DiffusersQuantizer
 
 
@@ -62,6 +69,38 @@ if is_torchao_available():
     from torchao.quantization import quantize_
 
 
+def _update_torch_safe_globals():
+    safe_globals = [
+        (torch.uint1, "torch.uint1"),
+        (torch.uint2, "torch.uint2"),
+        (torch.uint3, "torch.uint3"),
+        (torch.uint4, "torch.uint4"),
+        (torch.uint5, "torch.uint5"),
+        (torch.uint6, "torch.uint6"),
+        (torch.uint7, "torch.uint7"),
+    ]
+    try:
+        from torchao.dtypes import NF4Tensor
+        from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl
+        from torchao.dtypes.uintx.uint4_layout import UInt4Tensor
+        from torchao.dtypes.uintx.uintx_layout import UintxAQTTensorImpl, UintxTensor
+
+        safe_globals.extend([UintxTensor, UInt4Tensor, UintxAQTTensorImpl, Float8AQTTensorImpl, NF4Tensor])
+
+    except (ImportError, ModuleNotFoundError) as e:
+        logger.warning(
+            "Unable to import `torchao` Tensor objects. This may affect loading checkpoints serialized with `torchao`"
+        )
+        logger.debug(e)
+
+    finally:
+        torch.serialization.add_safe_globals(safe_globals=safe_globals)
+
+
+if is_torch_version(">=", "2.6") and is_torchao_available() and is_torchao_version(">=", "0.7.0"):
+    _update_torch_safe_globals()
+
+
 logger = logging.get_logger(__name__)
 
 

src/diffusers/utils/__init__.py

@@ -92,6 +92,7 @@ from .import_utils import (
     is_torch_xla_available,
     is_torch_xla_version,
     is_torchao_available,
+    is_torchao_version,
     is_torchsde_available,
     is_torchvision_available,
     is_transformers_available,

src/diffusers/utils/import_utils.py

@@ -849,6 +849,21 @@ def is_gguf_version(operation: str, version: str):
     return compare_versions(parse(_gguf_version), operation, version)
 
 
+def is_torchao_version(operation: str, version: str):
+    """
+    Compares the current torchao version to a given reference with an operation.
+
+    Args:
+        operation (`str`):
+            A string representation of an operator, such as `">"` or `"<="`
+        version (`str`):
+            A version string
+    """
+    if not _is_torchao_available:
+        return False
+    return compare_versions(parse(is_torch_version), operation, version)
+
+
 def is_k_diffusion_version(operation: str, version: str):
     """
     Compares the current k-diffusion version to a given reference with an operation.