diff --git a/src/diffusers/modular_pipelines/flux2/encoders.py b/src/diffusers/modular_pipelines/flux2/encoders.py
index b4b6a4b533..265fb38736 100644
--- a/src/diffusers/modular_pipelines/flux2/encoders.py
+++ b/src/diffusers/modular_pipelines/flux2/encoders.py
@@ -514,7 +514,7 @@ class Flux2KleinBaseTextEncoderStep(ModularPipelineBlocks):
         )
 
         if components.requires_unconditional_embeds:
-            negative_prompt = ""
+            negative_prompt = [""] * len(prompt)
             block_state.negative_prompt_embeds = self._get_qwen3_prompt_embeds(
                 text_encoder=components.text_encoder,
                 tokenizer=components.tokenizer,
diff --git a/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein.py b/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein.py
new file mode 100644
index 0000000000..26653b20f8
--- /dev/null
+++ b/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein.py
@@ -0,0 +1,91 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+
+import numpy as np
+import PIL
+import pytest
+
+from diffusers.modular_pipelines import (
+    Flux2KleinAutoBlocks,
+    Flux2KleinModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+
+    params = frozenset(["prompt", "height", "width"])
+    batch_params = frozenset(["prompt"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+
+class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-modular"
+
+    params = frozenset(["prompt", "height", "width", "image"])
+    batch_params = frozenset(["prompt", "image"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(torch_device)
+        image = image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = PIL.Image.fromarray(np.uint8(image * 255)).convert("RGB")
+        inputs["image"] = init_image
+
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+    @pytest.mark.skip(reason="batched inference is currently not supported")
+    def test_inference_batch_single_identical(self, batch_size=2, expected_max_diff=0.0001):
+        return
diff --git a/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein_base.py b/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein_base.py
new file mode 100644
index 0000000000..701dd0fed8
--- /dev/null
+++ b/tests/modular_pipelines/flux2/test_modular_pipeline_flux2_klein_base.py
@@ -0,0 +1,91 @@
+# coding=utf-8
+# Copyright 2025 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+
+import numpy as np
+import PIL
+import pytest
+
+from diffusers.modular_pipelines import (
+    Flux2KleinBaseAutoBlocks,
+    Flux2KleinModularPipeline,
+)
+
+from ...testing_utils import floats_tensor, torch_device
+from ..test_modular_pipelines_common import ModularPipelineTesterMixin
+
+
+class TestFlux2ModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinBaseAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
+
+    params = frozenset(["prompt", "height", "width"])
+    batch_params = frozenset(["prompt"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+
+class TestFlux2ImageConditionedModularPipelineFast(ModularPipelineTesterMixin):
+    pipeline_class = Flux2KleinModularPipeline
+    pipeline_blocks_class = Flux2KleinBaseAutoBlocks
+    pretrained_model_name_or_path = "hf-internal-testing/tiny-flux2-klein-base-modular"
+
+    params = frozenset(["prompt", "height", "width", "image"])
+    batch_params = frozenset(["prompt", "image"])
+
+    def get_dummy_inputs(self, seed=0):
+        generator = self.get_generator(seed)
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            # TODO (Dhruv): Update text encoder config so that vocab_size matches tokenizer
+            "max_sequence_length": 8,  # bit of a hack to workaround vocab size mismatch
+            "text_encoder_out_layers": (1,),
+            "generator": generator,
+            "num_inference_steps": 2,
+            "height": 32,
+            "width": 32,
+            "output_type": "pt",
+        }
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(seed)).to(torch_device)
+        image = image.cpu().permute(0, 2, 3, 1)[0]
+        init_image = PIL.Image.fromarray(np.uint8(image * 255)).convert("RGB")
+        inputs["image"] = init_image
+
+        return inputs
+
+    def test_float16_inference(self):
+        super().test_float16_inference(9e-2)
+
+    @pytest.mark.skip(reason="batched inference is currently not supported")
+    def test_inference_batch_single_identical(self, batch_size=2, expected_max_diff=0.0001):
+        return