Remove code snippets containing `is_safetensors_available()` (#4521)

* [WIP] Remove code snippets containing `is_safetensors_available()`

* Modifying `import_utils.py`

* update pipeline tests for safetensor default

* fix test related to cached requests

* address import nits

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

examples/community/checkpoint_merger.py

@@ -2,14 +2,8 @@ import glob
 import os
 from typing import Dict, List, Union
 
+import safetensors.torch
 import torch
-
-from diffusers.utils import is_safetensors_available
-
-
-if is_safetensors_available():
-    import safetensors.torch
-
 from huggingface_hub import snapshot_download
 
 from diffusers import DiffusionPipeline, __version__
@@ -229,14 +223,14 @@ class CheckpointMergerPipeline(DiffusionPipeline):
                     update_theta_0 = getattr(module, "load_state_dict")
                     theta_1 = (
                         safetensors.torch.load_file(checkpoint_path_1)
-                        if (is_safetensors_available() and checkpoint_path_1.endswith(".safetensors"))
+                        if (checkpoint_path_1.endswith(".safetensors"))
                         else torch.load(checkpoint_path_1, map_location="cpu")
                     )
                     theta_2 = None
                     if checkpoint_path_2:
                         theta_2 = (
                             safetensors.torch.load_file(checkpoint_path_2)
-                            if (is_safetensors_available() and checkpoint_path_2.endswith(".safetensors"))
+                            if (checkpoint_path_2.endswith(".safetensors"))
                             else torch.load(checkpoint_path_2, map_location="cpu")
                         )
 

scripts/convert_original_audioldm_to_diffusers.py

@@ -38,7 +38,7 @@ from diffusers import (
     PNDMScheduler,
     UNet2DConditionModel,
 )
-from diffusers.utils import is_omegaconf_available, is_safetensors_available
+from diffusers.utils import is_omegaconf_available
 from diffusers.utils.import_utils import BACKENDS_MAPPING
 
 
@@ -824,9 +824,6 @@ def load_pipeline_from_original_audioldm_ckpt(
     from omegaconf import OmegaConf
 
     if from_safetensors:
-        if not is_safetensors_available():
-            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
         from safetensors import safe_open
 
         checkpoint = {}

scripts/convert_tiny_autoencoder_to_diffusers.py

 import argparse
 
-from diffusers.utils import is_safetensors_available
-
-
-if is_safetensors_available():
-    import safetensors.torch
-else:
-    raise ImportError("Please install `safetensors`.")
+import safetensors.torch
 
 from diffusers import AutoencoderTiny
 

src/diffusers/commands/fp16_safetensors.py

@@ -27,7 +27,7 @@ import torch
 from huggingface_hub import hf_hub_download
 from packaging import version
 
-from ..utils import is_safetensors_available, logging
+from ..utils import logging
 from . import BaseDiffusersCLICommand
 
 
@@ -68,12 +68,7 @@ class FP16SafetensorsCommand(BaseDiffusersCLICommand):
         self.local_ckpt_dir = f"/tmp/{ckpt_id}"
         self.fp16 = fp16
 
-        if is_safetensors_available():
         self.use_safetensors = use_safetensors
-        else:
-            raise ImportError(
-                "When `use_safetensors` is set to True, the `safetensors` library needs to be installed. Install it via `pip install safetensors`."
-            )
 
         if not self.use_safetensors and not self.fp16:
             raise NotImplementedError(

src/diffusers/loaders.py

@@ -22,6 +22,7 @@ from pathlib import Path
 from typing import Callable, Dict, List, Optional, Union
 
 import requests
+import safetensors
 import torch
 import torch.nn.functional as F
 from huggingface_hub import hf_hub_download
@@ -34,16 +35,12 @@ from .utils import (
     deprecate,
     is_accelerate_available,
     is_omegaconf_available,
-    is_safetensors_available,
     is_transformers_available,
     logging,
 )
 from .utils.import_utils import BACKENDS_MAPPING
 
 
-if is_safetensors_available():
-    import safetensors
-
 if is_transformers_available():
     from transformers import CLIPTextModel, CLIPTextModelWithProjection, PreTrainedModel, PreTrainedTokenizer
 
@@ -261,14 +258,10 @@ class UNet2DConditionLoadersMixin:
         network_alphas = kwargs.pop("network_alphas", None)
         is_network_alphas_none = network_alphas is None
 
-        if use_safetensors and not is_safetensors_available():
-            raise ValueError(
-                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
-            )
-
         allow_pickle = False
+
         if use_safetensors is None:
-            use_safetensors = is_safetensors_available()
+            use_safetensors = True
             allow_pickle = True
 
         user_agent = {
@@ -757,14 +750,9 @@ class TextualInversionLoaderMixin:
         weight_name = kwargs.pop("weight_name", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
 
-        if use_safetensors and not is_safetensors_available():
-            raise ValueError(
-                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
-            )
-
         allow_pickle = False
         if use_safetensors is None:
-            use_safetensors = is_safetensors_available()
+            use_safetensors = True
             allow_pickle = True
 
         user_agent = {
@@ -1014,14 +1002,9 @@ class LoraLoaderMixin:
         unet_config = kwargs.pop("unet_config", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
 
-        if use_safetensors and not is_safetensors_available():
-            raise ValueError(
-                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
-            )
-
         allow_pickle = False
         if use_safetensors is None:
-            use_safetensors = is_safetensors_available()
+            use_safetensors = True
             allow_pickle = True
 
         user_agent = {
@@ -1853,7 +1836,7 @@ class FromSingleFileMixin:
 
         torch_dtype = kwargs.pop("torch_dtype", None)
 
-        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
+        use_safetensors = kwargs.pop("use_safetensors", None)
 
         pipeline_name = cls.__name__
         file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
@@ -2050,7 +2033,7 @@ class FromOriginalVAEMixin:
 
         torch_dtype = kwargs.pop("torch_dtype", None)
 
-        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
+        use_safetensors = kwargs.pop("use_safetensors", None)
 
         file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
         from_safetensors = file_extension == "safetensors"
@@ -2223,7 +2206,7 @@ class FromOriginalControlnetMixin:
 
         torch_dtype = kwargs.pop("torch_dtype", None)
 
-        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
+        use_safetensors = kwargs.pop("use_safetensors", None)
 
         file_extension = pretrained_model_link_or_path.rsplit(".", 1)[-1]
         from_safetensors = file_extension == "safetensors"

src/diffusers/models/modeling_utils.py

@@ -21,6 +21,7 @@ import re
 from functools import partial
 from typing import Any, Callable, List, Optional, Tuple, Union
 
+import safetensors
 import torch
 from torch import Tensor, device, nn
 
@@ -36,7 +37,6 @@ from ..utils import (
     _get_model_file,
     deprecate,
     is_accelerate_available,
-    is_safetensors_available,
     is_torch_version,
     logging,
 )
@@ -56,9 +56,6 @@ if is_accelerate_available():
     from accelerate.utils import set_module_tensor_to_device
     from accelerate.utils.versions import is_torch_version
 
-if is_safetensors_available():
-    import safetensors
-
 
 def get_parameter_device(parameter: torch.nn.Module):
     try:
@@ -296,9 +293,6 @@ class ModelMixin(torch.nn.Module):
             variant (`str`, *optional*):
                 If specified, weights are saved in the format `pytorch_model.<variant>.bin`.
         """
-        if safe_serialization and not is_safetensors_available():
-            raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")
-
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -454,14 +448,9 @@ class ModelMixin(torch.nn.Module):
         variant = kwargs.pop("variant", None)
         use_safetensors = kwargs.pop("use_safetensors", None)
 
-        if use_safetensors and not is_safetensors_available():
-            raise ValueError(
-                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
-            )
-
         allow_pickle = False
         if use_safetensors is None:
-            use_safetensors = is_safetensors_available()
+            use_safetensors = True
             allow_pickle = True
 
         if low_cpu_mem_usage and not is_accelerate_available():

src/diffusers/pipelines/pipeline_utils.py

@@ -52,7 +52,6 @@ from ..utils import (
     is_accelerate_available,
     is_accelerate_version,
     is_compiled_module,
-    is_safetensors_available,
     is_torch_version,
     is_transformers_available,
     logging,
@@ -899,7 +898,7 @@ class DiffusionPipeline(ConfigMixin):
         offload_state_dict = kwargs.pop("offload_state_dict", False)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
         variant = kwargs.pop("variant", None)
-        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)
+        use_safetensors = kwargs.pop("use_safetensors", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
         # 1. Download the checkpoints and configs
@@ -1311,14 +1310,9 @@ class DiffusionPipeline(ConfigMixin):
         use_onnx = kwargs.pop("use_onnx", None)
         load_connected_pipeline = kwargs.pop("load_connected_pipeline", False)
 
-        if use_safetensors and not is_safetensors_available():
-            raise ValueError(
-                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetensors"
-            )
-
         allow_pickle = False
         if use_safetensors is None:
-            use_safetensors = is_safetensors_available()
+            use_safetensors = True
             allow_pickle = True
 
         allow_patterns = None

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -50,7 +50,7 @@ from ...schedulers import (
     PNDMScheduler,
     UnCLIPScheduler,
 )
-from ...utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available, logging
+from ...utils import is_accelerate_available, is_omegaconf_available, logging
 from ...utils.import_utils import BACKENDS_MAPPING
 from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from ..paint_by_example import PaintByExampleImageEncoder
@@ -1225,9 +1225,6 @@ def download_from_original_stable_diffusion_ckpt(
     from omegaconf import OmegaConf
 
     if from_safetensors:
-        if not is_safetensors_available():
-            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
         from safetensors.torch import load_file as safe_load
 
         checkpoint = safe_load(checkpoint_path, device="cpu")
@@ -1650,9 +1647,6 @@ def download_controlnet_from_original_ckpt(
     from omegaconf import OmegaConf
 
     if from_safetensors:
-        if not is_safetensors_available():
-            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
-
         from safetensors import safe_open
 
         checkpoint = {}

src/diffusers/utils/__init__.py

@@ -64,7 +64,6 @@ from .import_utils import (
     is_note_seq_available,
     is_omegaconf_available,
     is_onnx_available,
-    is_safetensors_available,
     is_scipy_available,
     is_tensorboard_available,
     is_tf_available,

src/diffusers/utils/import_utils.py

@@ -306,10 +306,6 @@ def is_torch_available():
     return _torch_available
 
 
-def is_safetensors_available():
-    return _safetensors_available
-
-
 def is_tf_available():
     return _tf_available
 

tests/models/test_modeling_common.py

@@ -60,10 +60,6 @@ class ModelUtilsTest(unittest.TestCase):
     def tearDown(self):
         super().tearDown()
 
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_accelerate_loading_error_message(self):
         with self.assertRaises(ValueError) as error_context:
             UNet2DConditionModel.from_pretrained("hf-internal-testing/stable-diffusion-broken", subfolder="unet")
@@ -100,14 +96,15 @@ class ModelUtilsTest(unittest.TestCase):
         if torch_device == "mps":
             return
 
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = False
+        use_safetensors = False
 
         with tempfile.TemporaryDirectory() as tmpdirname:
             with requests_mock.mock(real_http=True) as m:
                 UNet2DConditionModel.from_pretrained(
-                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
+                    "hf-internal-testing/tiny-stable-diffusion-torch",
+                    subfolder="unet",
+                    cache_dir=tmpdirname,
+                    use_safetensors=use_safetensors,
                 )
 
             download_requests = [r.method for r in m.request_history]
@@ -116,7 +113,10 @@ class ModelUtilsTest(unittest.TestCase):
 
             with requests_mock.mock(real_http=True) as m:
                 UNet2DConditionModel.from_pretrained(
-                    "hf-internal-testing/tiny-stable-diffusion-torch", subfolder="unet", cache_dir=tmpdirname
+                    "hf-internal-testing/tiny-stable-diffusion-torch",
+                    subfolder="unet",
+                    cache_dir=tmpdirname,
+                    use_safetensors=use_safetensors,
                 )
 
             cache_requests = [r.method for r in m.request_history]
@@ -124,8 +124,6 @@ class ModelUtilsTest(unittest.TestCase):
                 "HEAD" == cache_requests[0] and len(cache_requests) == 1
             ), "We should call only `model_info` to check for _commit hash and `send_telemetry`"
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_weight_overwrite(self):
         with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context:
             UNet2DConditionModel.from_pretrained(

tests/pipelines/test_pipelines.py

@@ -472,15 +472,13 @@ class DownloadTests(unittest.TestCase):
                     assert False, "Parameters not the same!"
 
     def test_download_from_variant_folder(self):
-        for safe_avail in [False, True]:
-            import diffusers
-
-            diffusers.utils.import_utils._safetensors_available = safe_avail
-
-            other_format = ".bin" if safe_avail else ".safetensors"
+        for use_safetensors in [False, True]:
+            other_format = ".bin" if use_safetensors else ".safetensors"
             with tempfile.TemporaryDirectory() as tmpdirname:
                 tmpdirname = StableDiffusionPipeline.download(
-                    "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname
+                    "hf-internal-testing/stable-diffusion-all-variants",
+                    cache_dir=tmpdirname,
+                    use_safetensors=use_safetensors,
                 )
                 all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
@@ -492,21 +490,18 @@ class DownloadTests(unittest.TestCase):
                 # no variants
                 assert not any(len(f.split(".")) == 3 for f in files)
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_download_variant_all(self):
-        for safe_avail in [False, True]:
-            import diffusers
-
-            diffusers.utils.import_utils._safetensors_available = safe_avail
-
-            other_format = ".bin" if safe_avail else ".safetensors"
-            this_format = ".safetensors" if safe_avail else ".bin"
+        for use_safetensors in [False, True]:
+            other_format = ".bin" if use_safetensors else ".safetensors"
+            this_format = ".safetensors" if use_safetensors else ".bin"
             variant = "fp16"
 
             with tempfile.TemporaryDirectory() as tmpdirname:
                 tmpdirname = StableDiffusionPipeline.download(
-                    "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
+                    "hf-internal-testing/stable-diffusion-all-variants",
+                    cache_dir=tmpdirname,
+                    variant=variant,
+                    use_safetensors=use_safetensors,
                 )
                 all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
@@ -520,21 +515,18 @@ class DownloadTests(unittest.TestCase):
                 assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files)
                 assert not any(f.endswith(other_format) for f in files)
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_download_variant_partly(self):
-        for safe_avail in [False, True]:
-            import diffusers
-
-            diffusers.utils.import_utils._safetensors_available = safe_avail
-
-            other_format = ".bin" if safe_avail else ".safetensors"
-            this_format = ".safetensors" if safe_avail else ".bin"
+        for use_safetensors in [False, True]:
+            other_format = ".bin" if use_safetensors else ".safetensors"
+            this_format = ".safetensors" if use_safetensors else ".bin"
             variant = "no_ema"
 
             with tempfile.TemporaryDirectory() as tmpdirname:
                 tmpdirname = StableDiffusionPipeline.download(
-                    "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant
+                    "hf-internal-testing/stable-diffusion-all-variants",
+                    cache_dir=tmpdirname,
+                    variant=variant,
+                    use_safetensors=use_safetensors,
                 )
                 all_root_files = [t[-1] for t in os.walk(tmpdirname)]
                 files = [item for sublist in all_root_files for item in sublist]
@@ -551,13 +543,8 @@ class DownloadTests(unittest.TestCase):
                 assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3
                 assert not any(f.endswith(other_format) for f in files)
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_download_broken_variant(self):
-        for safe_avail in [False, True]:
-            import diffusers
-
-            diffusers.utils.import_utils._safetensors_available = safe_avail
+        for use_safetensors in [False, True]:
             # text encoder is missing no variant and "no_ema" variant weights, so the following can't work
             for variant in [None, "no_ema"]:
                 with self.assertRaises(OSError) as error_context:
@@ -566,6 +553,7 @@ class DownloadTests(unittest.TestCase):
                             "hf-internal-testing/stable-diffusion-broken-variants",
                             cache_dir=tmpdirname,
                             variant=variant,
+                            use_safetensors=use_safetensors,
                         )
 
                 assert "Error no file name" in str(error_context.exception)
@@ -573,7 +561,10 @@ class DownloadTests(unittest.TestCase):
             # text encoder has fp16 variants so we can load it
             with tempfile.TemporaryDirectory() as tmpdirname:
                 tmpdirname = StableDiffusionPipeline.download(
-                    "hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant="fp16"
+                    "hf-internal-testing/stable-diffusion-broken-variants",
+                    use_safetensors=use_safetensors,
+                    cache_dir=tmpdirname,
+                    variant="fp16",
                 )
 
                 all_root_files = [t[-1] for t in os.walk(tmpdirname)]
@@ -584,8 +575,6 @@ class DownloadTests(unittest.TestCase):
                 assert len(files) == 15, f"We should only download 15 files, not {len(files)}"
                 # only unet has "no_ema" variant
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_local_save_load_index(self):
         prompt = "hello"
         for variant in [None, "fp16"]:
@@ -961,10 +950,6 @@ class PipelineFastTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = True
-
     def dummy_image(self):
         batch_size = 1
         num_channels = 3
@@ -1319,14 +1304,13 @@ class PipelineFastTests(unittest.TestCase):
             assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
 
     def test_no_safetensors_download_when_doing_pytorch(self):
-        # mock diffusers safetensors not available
-        import diffusers
-
-        diffusers.utils.import_utils._safetensors_available = False
+        use_safetensors = False
 
         with tempfile.TemporaryDirectory() as tmpdirname:
             _ = StableDiffusionPipeline.from_pretrained(
-                "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname
+                "hf-internal-testing/diffusers-stable-diffusion-tiny-all",
+                cache_dir=tmpdirname,
+                use_safetensors=use_safetensors,
             )
 
             path = os.path.join(
@@ -1341,8 +1325,6 @@ class PipelineFastTests(unittest.TestCase):
             # pytorch does
             assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin"))
 
-        diffusers.utils.import_utils._safetensors_available = True
-
     def test_optional_components(self):
         unet = self.dummy_cond_unet()
         pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler")