diff --git a/src/diffusers/pipelines/flux2/pipeline_flux2.py b/src/diffusers/pipelines/flux2/pipeline_flux2.py
index b1645b4ae244..9421fef37d53 100644
--- a/src/diffusers/pipelines/flux2/pipeline_flux2.py
+++ b/src/diffusers/pipelines/flux2/pipeline_flux2.py
@@ -60,6 +60,26 @@
 UPSAMPLING_MAX_IMAGE_SIZE = 768**2
 
 
+def _warn_if_bnb_int8_bfloat16(text_encoder, transformer) -> None:
+    """
+    Warns if the text encoder or transformer is loaded with bitsandbytes 8-bit quantization and bfloat16 dtype.
+
+    This combination causes precision loss during MatMul operations resulting in corrupted outputs.
+    """
+    for name, module in [("text_encoder", text_encoder), ("transformer", transformer)]:
+        if module is None:
+            continue
+        if getattr(module, "is_loaded_in_8bit", False):
+            module_dtype = getattr(module, "dtype", None)
+            if module_dtype == torch.bfloat16:
+                logger.warning(
+                    f"`{name}` is loaded with bitsandbytes 8-bit quantization and `torch.bfloat16` dtype. "
+                    "This combination causes precision loss during MatMul operations and can result in corrupted "
+                    "or noisy outputs for FLUX models. It is highly recommended to use NF4 4-bit quantization "
+                    "instead (e.g. `load_in_4bit=True, bnb_4bit_quant_type='nf4', bnb_4bit_compute_dtype=torch.bfloat16`)."
+                )
+
+
 # Adapted from
 # https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L68
 def format_input(
@@ -300,6 +320,8 @@ def __init__(
         self.system_message_upsampling_i2i = SYSTEM_MESSAGE_UPSAMPLING_I2I
         self.upsampling_max_image_size = UPSAMPLING_MAX_IMAGE_SIZE
 
+        _warn_if_bnb_int8_bfloat16(text_encoder, transformer)
+
     @staticmethod
     def _get_mistral_3_small_prompt_embeds(
         text_encoder: Mistral3ForConditionalGeneration,
@@ -579,12 +601,11 @@ def encode_prompt(
     ):
         device = device or self._execution_device
 
-        if prompt is None:
-            prompt = ""
-
-        prompt = [prompt] if isinstance(prompt, str) else prompt
-
         if prompt_embeds is None:
+            if prompt is None:
+                prompt = ""
+            prompt = [prompt] if isinstance(prompt, str) else prompt
+
             prompt_embeds = self._get_mistral_3_small_prompt_embeds(
                 text_encoder=self.text_encoder,
                 tokenizer=self.tokenizer,
@@ -595,6 +616,10 @@ def encode_prompt(
                 hidden_states_layers=text_encoder_out_layers,
             )
 
+        # Normalize prompt_embeds dtype and device to match the pipeline's precision.
+        target_dtype = self.transformer.dtype if self.transformer is not None else self.text_encoder.dtype
+        prompt_embeds = prompt_embeds.to(device=device, dtype=target_dtype)
+
         batch_size, seq_len, _ = prompt_embeds.shape
         prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
         prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)