diff --git a/src/diffusers/quantizers/bitsandbytes/utils.py b/src/diffusers/quantizers/bitsandbytes/utils.py
index a9771b368a86..e150281e81ce 100644
--- a/src/diffusers/quantizers/bitsandbytes/utils.py
+++ b/src/diffusers/quantizers/bitsandbytes/utils.py
@@ -139,10 +139,12 @@ def replace_with_bnb_linear(model, modules_to_not_convert=None, current_key_name
             models by reducing the precision of the weights and activations, thus making models more efficient in terms
             of both storage and computation.
     """
-    model, has_been_replaced = _replace_with_bnb_linear(
-        model, modules_to_not_convert, current_key_name, quantization_config
-    )
+    model, _ = _replace_with_bnb_linear(model, modules_to_not_convert, current_key_name, quantization_config)
 
+    has_been_replaced = any(
+        isinstance(replaced_module, (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt))
+        for _, replaced_module in model.named_modules()
+    )
     if not has_been_replaced:
         logger.warning(
             "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
@@ -283,16 +285,18 @@ def dequantize_and_replace(
     modules_to_not_convert=None,
     quantization_config=None,
 ):
-    model, has_been_replaced = _dequantize_and_replace(
+    model, _ = _dequantize_and_replace(
         model,
         dtype=model.dtype,
         modules_to_not_convert=modules_to_not_convert,
         quantization_config=quantization_config,
     )
-
+    has_been_replaced = any(
+        isinstance(replaced_module, torch.nn.Linear) for _, replaced_module in model.named_modules()
+    )
     if not has_been_replaced:
         logger.warning(
-            "For some reason the model has not been properly dequantized. You might see unexpected behavior."
+            "Some linear modules were not dequantized. This could lead to unexpected behaviour. Please check your model."
         )
 
     return model
diff --git a/tests/quantization/bnb/test_4bit.py b/tests/quantization/bnb/test_4bit.py
index 29a3e212c48d..6ae46f352c19 100644
--- a/tests/quantization/bnb/test_4bit.py
+++ b/tests/quantization/bnb/test_4bit.py
@@ -63,6 +63,8 @@ def get_some_linear_layer(model):
 if is_bitsandbytes_available():
     import bitsandbytes as bnb
 
+    from diffusers.quantizers.bitsandbytes.utils import replace_with_bnb_linear
+
 
 @require_bitsandbytes_version_greater("0.43.2")
 @require_accelerate
@@ -364,6 +366,18 @@ def test_bnb_4bit_errors_loading_incorrect_state_dict(self):
 
             assert key_to_target in str(err_context.exception)
 
+    def test_bnb_4bit_logs_warning_for_no_quantization(self):
+        model_with_no_linear = torch.nn.Sequential(torch.nn.Conv2d(4, 4, 3), torch.nn.ReLU())
+        quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+        logger = logging.get_logger("diffusers.quantizers.bitsandbytes.utils")
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            _ = replace_with_bnb_linear(model_with_no_linear, quantization_config=quantization_config)
+        assert (
+            "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
+            in cap_logger.out
+        )
+
 
 class BnB4BitTrainingTests(Base4bitTests):
     def setUp(self):
diff --git a/tests/quantization/bnb/test_mixed_int8.py b/tests/quantization/bnb/test_mixed_int8.py
index a5e38f931e09..1049bfecbaab 100644
--- a/tests/quantization/bnb/test_mixed_int8.py
+++ b/tests/quantization/bnb/test_mixed_int8.py
@@ -68,6 +68,8 @@ def get_some_linear_layer(model):
 if is_bitsandbytes_available():
     import bitsandbytes as bnb
 
+    from diffusers.quantizers.bitsandbytes import replace_with_bnb_linear
+
 
 @require_bitsandbytes_version_greater("0.43.2")
 @require_accelerate
@@ -317,6 +319,18 @@ def test_device_and_dtype_assignment(self):
         # Check that this does not throw an error
         _ = self.model_fp16.to(torch_device)
 
+    def test_bnb_8bit_logs_warning_for_no_quantization(self):
+        model_with_no_linear = torch.nn.Sequential(torch.nn.Conv2d(4, 4, 3), torch.nn.ReLU())
+        quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+        logger = logging.get_logger("diffusers.quantizers.bitsandbytes.utils")
+        logger.setLevel(30)
+        with CaptureLogger(logger) as cap_logger:
+            _ = replace_with_bnb_linear(model_with_no_linear, quantization_config=quantization_config)
+        assert (
+            "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
+            in cap_logger.out
+        )
+
 
 class Bnb8bitDeviceTests(Base8bitTests):
     def setUp(self) -> None: