[Core] Remove TF import checks (#4968)

[TF] Remove tf

src/diffusers/utils/__init__.py

@@ -69,7 +69,6 @@ from .import_utils import (
     is_onnx_available,
     is_scipy_available,
     is_tensorboard_available,
-    is_tf_available,
     is_torch_available,
     is_torch_version,
     is_torchsde_available,

src/diffusers/utils/import_utils.py

@@ -62,43 +62,6 @@ else:
     logger.info("Disabling PyTorch because USE_TORCH is set")
     _torch_available = False
 
-
-_tf_version = "N/A"
-if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
-    _tf_available = importlib.util.find_spec("tensorflow") is not None
-    if _tf_available:
-        candidates = (
-            "tensorflow",
-            "tensorflow-cpu",
-            "tensorflow-gpu",
-            "tf-nightly",
-            "tf-nightly-cpu",
-            "tf-nightly-gpu",
-            "intel-tensorflow",
-            "intel-tensorflow-avx512",
-            "tensorflow-rocm",
-            "tensorflow-macos",
-            "tensorflow-aarch64",
-        )
-        _tf_version = None
-        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
-        for pkg in candidates:
-            try:
-                _tf_version = importlib_metadata.version(pkg)
-                break
-            except importlib_metadata.PackageNotFoundError:
-                pass
-        _tf_available = _tf_version is not None
-    if _tf_available:
-        if version.parse(_tf_version) < version.parse("2"):
-            logger.info(f"TensorFlow found but with version {_tf_version}. Diffusers requires version 2 minimum.")
-            _tf_available = False
-        else:
-            logger.info(f"TensorFlow version {_tf_version} available.")
-else:
-    logger.info("Disabling Tensorflow because USE_TORCH is set")
-    _tf_available = False
-
 _jax_version = "N/A"
 _flax_version = "N/A"
 if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
@@ -308,10 +271,6 @@ def is_torch_available():
     return _torch_available
 
 
-def is_tf_available():
-    return _tf_available
-
-
 def is_flax_available():
     return _flax_available
 

utils/check_repo.py

@@ -23,7 +23,7 @@ from difflib import get_close_matches
 from pathlib import Path
 
 from diffusers.models.auto import get_values
-from diffusers.utils import ENV_VARS_TRUE_VALUES, is_flax_available, is_tf_available, is_torch_available
+from diffusers.utils import ENV_VARS_TRUE_VALUES, is_flax_available, is_torch_available
 
 
 # All paths are set with the intent you should run this script from the root of the repo with the command
@@ -421,10 +421,6 @@ def get_all_auto_configured_models():
         for attr_name in dir(diffusers.models.auto.modeling_auto):
             if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"):
                 result = result | set(get_values(getattr(diffusers.models.auto.modeling_auto, attr_name)))
-    if is_tf_available():
-        for attr_name in dir(diffusers.models.auto.modeling_tf_auto):
-            if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"):
-                result = result | set(get_values(getattr(diffusers.models.auto.modeling_tf_auto, attr_name)))
     if is_flax_available():
         for attr_name in dir(diffusers.models.auto.modeling_flax_auto):
             if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"):
@@ -462,8 +458,6 @@ def check_all_models_are_auto_configured():
     missing_backends = []
     if not is_torch_available():
         missing_backends.append("PyTorch")
-    if not is_tf_available():
-        missing_backends.append("TensorFlow")
     if not is_flax_available():
         missing_backends.append("Flax")
     if len(missing_backends) > 0: