diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..b76501a
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,15 @@
+[tool:pytest]
+testpaths = tests
+python_files = test_*.py
+python_classes = Test*
+python_functions = test_*
+addopts = 
+    -v
+    --strict-markers
+    --disable-warnings
+    --tb=short
+markers =
+    unit: Unit tests for individual components
+    integration: Integration tests for complete workflows
+    slow: Tests that take a long time to run
+    gpu: Tests that require GPU hardware 
\ No newline at end of file
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..2be1293
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,2 @@
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2025 Apple Inc. and EPFL. All Rights Reserved. 
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..6ca6159
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,162 @@
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2025 Apple Inc. and EPFL. All Rights Reserved.
+
+import pytest
+import torch
+import numpy as np
+from typing import Dict, Any, List
+from unittest.mock import MagicMock, patch
+
+
+@pytest.fixture
+def device():
+    """Fixture providing the device to use for testing."""
+    return torch.device("cpu")  # Use CPU for testing to avoid GPU dependencies
+
+
+@pytest.fixture
+def sample_image_tensor():
+    """Fixture providing a sample image tensor for testing."""
+    # Create a small RGB image tensor [B=1, C=3, H=64, W=64]
+    return torch.randn(1, 3, 64, 64)
+
+
+@pytest.fixture
+def sample_image_list():
+    """Fixture providing a list of sample image tensors."""
+    # Create list of small RGB image tensors [1, 3, 64, 64] each
+    return [torch.randn(1, 3, 64, 64) for _ in range(2)]
+
+
+@pytest.fixture
+def sample_vae_latents():
+    """Fixture providing sample VAE latents."""
+    # VAE typically downsamples by factor of 8, so 64x64 -> 8x8
+    return torch.randn(1, 16, 8, 8)
+
+
+@pytest.fixture
+def sample_token_ids():
+    """Fixture providing sample token ID sequences."""
+    # Sample token sequences of varying lengths
+    return [
+        torch.randint(0, 1000, (1, 50)),  # 50 tokens
+        torch.randint(0, 1000, (1, 75)),  # 75 tokens
+    ]
+
+
+@pytest.fixture
+def sample_data_dict(sample_image_list):
+    """Fixture providing a sample data dictionary."""
+    return {
+        "rgb": sample_image_list,
+    }
+
+
+@pytest.fixture
+def mock_vae():
+    """Fixture providing a mock VAE module."""
+    vae = MagicMock()
+    vae.images_read_key = "rgb"
+    vae.images_reconst_write_key = "images_reconst"
+    vae.downsample_factor = 8
+    vae.latent_dim = 16
+    vae.encode.return_value = {"vae_latents": torch.randn(1, 16, 8, 8)}
+    vae.decode.return_value = {"images_reconst": [torch.randn(1, 3, 64, 64)]}
+    return vae
+
+
+@pytest.fixture
+def mock_encoder():
+    """Fixture providing a mock encoder module."""
+    encoder = MagicMock()
+    encoder.return_value = {"encoder_output": torch.randn(1, 128, 64)}
+    encoder.init_weights_muP = MagicMock()
+    # Mock the register module for max tokens
+    encoder.module_dict = {
+        "enc_register_module": MagicMock(n_max=100)
+    }
+    return encoder
+
+
+@pytest.fixture
+def mock_decoder():
+    """Fixture providing a mock decoder module."""
+    decoder = MagicMock()
+    decoder.return_value = {"decoder_output": torch.randn(1, 16, 8, 8)}
+    decoder.init_weights_muP = MagicMock()
+    # Mock the nested dropout module
+    decoder.module_dict = {
+        "dec_nested_dropout": MagicMock(eval_keep_k_read_key="eval_keep_k")
+    }
+    return decoder
+
+
+@pytest.fixture
+def mock_regularizer():
+    """Fixture providing a mock regularizer module."""
+    regularizer = MagicMock()
+    regularizer.tokens_write_key = "token_ids"
+    regularizer.quants_write_key = "quantized_tokens"
+    regularizer.return_value = {
+        "token_ids": [torch.randint(0, 1000, (1, 50))],
+        "quantized_tokens": [torch.randn(1, 50, 512)]
+    }
+    regularizer.indices_to_embedding.return_value = torch.randn(1, 50, 512)
+    return regularizer
+
+
+@pytest.fixture
+def mock_flow_matching():
+    """Fixture providing a mock flow matching noise module."""
+    flow_matching = MagicMock()
+    flow_matching.return_value = {
+        "noised_latents": torch.randn(1, 16, 8, 8),
+        "timesteps": torch.tensor([0.5]),
+        "sigmas": torch.tensor([1.0]),
+    }
+    return flow_matching
+
+
+@pytest.fixture
+def mock_pipeline():
+    """Fixture providing a mock pipeline module."""
+
+    def pipeline_init(model):
+        pipeline = MagicMock()
+        pipeline.return_value = {"denoised_latents": torch.randn(1, 16, 8, 8)}
+        return pipeline
+
+    return pipeline_init
+
+
+@pytest.fixture
+def generator():
+    """Fixture providing a torch.Generator for reproducible testing."""
+    gen = torch.Generator()
+    gen.manual_seed(42)
+    return gen
+
+
+@pytest.fixture(autouse=True)
+def set_random_seed():
+    """Automatically set random seeds for reproducible tests."""
+    torch.manual_seed(42)
+    np.random.seed(42)
+
+
+# Utility functions for testing
+def assert_tensor_shape(tensor: torch.Tensor, expected_shape: tuple, name: str = "tensor"):
+    """Helper function to assert tensor shapes."""
+    assert tensor.shape == expected_shape, f"{name} shape {tensor.shape} != expected {expected_shape}"
+
+
+def assert_tensor_dtype(tensor: torch.Tensor, expected_dtype: torch.dtype, name: str = "tensor"):
+    """Helper function to assert tensor dtypes."""
+    assert tensor.dtype == expected_dtype, f"{name} dtype {tensor.dtype} != expected {expected_dtype}"
+
+
+def assert_dict_keys(data_dict: Dict[str, Any], expected_keys: List[str], name: str = "dict"):
+    """Helper function to assert dictionary contains expected keys."""
+    missing_keys = set(expected_keys) - set(data_dict.keys())
+    assert not missing_keys, f"{name} missing keys: {missing_keys}"
diff --git a/tests/test_flextok_core.py b/tests/test_flextok_core.py
new file mode 100644
index 0000000..cda9e7d
--- /dev/null
+++ b/tests/test_flextok_core.py
@@ -0,0 +1,325 @@
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2025 Apple Inc. and EPFL. All Rights Reserved.
+
+import pytest
+import torch
+from unittest.mock import MagicMock, patch
+from typing import Dict, Any
+
+from flextok.flextok_wrapper import FlexTok, FlexTokFromHub
+from tests.conftest import assert_tensor_shape, assert_dict_keys
+
+
+class TestFlexTok:
+    """Test suite for the main FlexTok class."""
+
+    @pytest.fixture
+    def flextok_model(self, mock_vae, mock_encoder, mock_decoder, mock_regularizer,
+                      mock_flow_matching, mock_pipeline):
+        """Fixture providing a FlexTok model with mocked components."""
+        return FlexTok(
+            vae=mock_vae,
+            encoder=mock_encoder,
+            decoder=mock_decoder,
+            regularizer=mock_regularizer,
+            flow_matching_noise_module=mock_flow_matching,
+            pipeline=mock_pipeline,
+        )
+
+    def test_init(self, flextok_model, mock_vae, mock_encoder, mock_decoder,
+                  mock_regularizer, mock_flow_matching, mock_pipeline):
+        """Test FlexTok initialization."""
+        assert flextok_model.vae == mock_vae
+        assert flextok_model.encoder == mock_encoder
+        assert flextok_model.decoder == mock_decoder
+        assert flextok_model.regularizer == mock_regularizer
+        assert flextok_model.flow_matching_noise_module == mock_flow_matching
+
+        # Test that keys are properly set from regularizer and VAE
+        assert flextok_model.token_write_key == mock_regularizer.tokens_write_key
+        assert flextok_model.quants_write_key == mock_regularizer.quants_write_key
+        assert flextok_model.image_write_key == mock_vae.images_reconst_write_key
+
+    def test_properties(self, flextok_model, mock_vae):
+        """Test FlexTok properties."""
+        assert flextok_model.downsample_factor == mock_vae.downsample_factor
+
+        # Test device property (should return device of first parameter)
+        with patch.object(flextok_model, 'parameters') as mock_params:
+            mock_param = MagicMock()
+            mock_param.device = torch.device('cpu')
+            mock_params.return_value = [mock_param]
+            assert flextok_model.device == torch.device('cpu')
+
+    def test_init_weights_muP(self, flextok_model, mock_encoder, mock_decoder):
+        """Test muP initialization."""
+        flextok_model.init_weights_muP()
+        mock_encoder.init_weights_muP.assert_called_once()
+        mock_decoder.init_weights_muP.assert_called_once()
+
+    @pytest.mark.unit
+    def test_encode(self, flextok_model, sample_data_dict, mock_vae, mock_encoder, mock_regularizer):
+        """Test the encode method."""
+        # Mock the components to return expected data
+        mock_vae.encode.return_value = {"vae_latents": torch.randn(1, 16, 8, 8)}
+        mock_encoder.return_value = {"encoder_output": torch.randn(1, 128, 64)}
+        mock_regularizer.return_value = {
+            "token_ids": [torch.randint(0, 1000, (1, 50))],
+            "quantized_tokens": [torch.randn(1, 50, 512)]
+        }
+
+        result = flextok_model.encode(sample_data_dict)
+
+        # Verify the pipeline was called correctly
+        mock_vae.encode.assert_called_once_with(sample_data_dict)
+        mock_encoder.assert_called_once()
+        mock_regularizer.assert_called_once()
+
+        # Check that result contains expected keys
+        assert_dict_keys(result, ["token_ids", "quantized_tokens"])
+
+    @pytest.mark.unit
+    def test_decode(self, flextok_model, mock_pipeline, mock_vae):
+        """Test the decode method."""
+        # Prepare test data
+        data_dict = {
+            "quantized_tokens": [torch.randn(1, 50, 512)],
+        }
+
+        # Mock pipeline and VAE returns
+        mock_pipeline_instance = MagicMock()
+        mock_pipeline_instance.return_value = {"denoised_latents": torch.randn(1, 16, 8, 8)}
+        flextok_model.pipeline = mock_pipeline_instance
+
+        mock_vae.decode.return_value = {"images_reconst": [torch.randn(1, 3, 64, 64)]}
+
+        result = flextok_model.decode(
+            data_dict,
+            timesteps=10,
+            generator=None,
+            vae_image_sizes=32,
+            verbose=False,
+            guidance_scale=1.0,
+            perform_norm_guidance=False
+        )
+
+        # Verify pipeline was called with correct arguments
+        mock_pipeline_instance.assert_called_once_with(
+            data_dict,
+            generator=None,
+            timesteps=10,
+            vae_image_sizes=32,
+            verbose=False,
+            guidance_scale=1.0,
+            perform_norm_guidance=False
+        )
+
+        # Verify VAE decode was called
+        mock_vae.decode.assert_called_once()
+
+    @pytest.mark.unit
+    def test_autoencode(self, flextok_model, sample_data_dict):
+        """Test the autoencode method (encode + decode)."""
+        # Mock encode and decode methods
+        encoded_result = {
+            "token_ids": [torch.randint(0, 1000, (1, 50))],
+            "quantized_tokens": [torch.randn(1, 50, 512)]
+        }
+        decoded_result = {"images_reconst": [torch.randn(1, 3, 64, 64)]}
+
+        with patch.object(flextok_model, 'encode', return_value=encoded_result) as mock_encode, \
+                patch.object(flextok_model, 'decode', return_value=decoded_result) as mock_decode:
+            result = flextok_model.autoencode(
+                sample_data_dict,
+                timesteps=10,
+                guidance_scale=1.0
+            )
+
+            # Verify encode was called with original data
+            mock_encode.assert_called_once_with(sample_data_dict)
+
+            # Verify decode was called with encoded result and parameters
+            mock_decode.assert_called_once_with(
+                encoded_result,
+                timesteps=10,
+                generator=None,
+                vae_image_sizes=None,
+                verbose=True,
+                guidance_scale=1.0,
+                perform_norm_guidance=False
+            )
+
+    @pytest.mark.unit
+    def test_tokenize(self, flextok_model, sample_image_tensor, mock_vae):
+        """Test the tokenize method."""
+        # Mock the encode method to return token IDs
+        token_ids = [torch.randint(0, 1000, (1, 50)), torch.randint(0, 1000, (1, 75))]
+
+        with patch.object(flextok_model, 'encode') as mock_encode:
+            mock_encode.return_value = {flextok_model.token_write_key: token_ids}
+
+            result = flextok_model.tokenize(sample_image_tensor)
+
+            # Verify encode was called with properly formatted data dict
+            expected_data_dict = {mock_vae.images_read_key: [sample_image_tensor]}
+            mock_encode.assert_called_once_with(expected_data_dict)
+
+            # Verify result is the token IDs list
+            assert result == token_ids
+
+    @pytest.mark.unit
+    def test_get_padded_token_seq(self, flextok_model):
+        """Test the _get_padded_token_seq method."""
+        # Test padding a shorter sequence
+        token_ids = torch.randint(0, 1000, (1, 30))
+        max_seq_len = 50
+
+        padded = flextok_model._get_padded_token_seq(token_ids, max_seq_len)
+
+        assert_tensor_shape(padded, (1, 50), "padded tokens")
+
+        # Verify original tokens are preserved
+        assert torch.equal(padded[:, :30], token_ids)
+
+        # Verify padding is zeros
+        assert torch.all(padded[:, 30:] == 0)
+
+    @pytest.mark.unit
+    def test_prepare_data_dict_for_detokenization(self, flextok_model, sample_token_ids,
+                                                  mock_encoder, mock_regularizer):
+        """Test the _prepare_data_dict_for_detokenization method."""
+        # Set up mocks
+        mock_encoder.module_dict["enc_register_module"].n_max = 100
+
+        # Mock regularizer's indices_to_embedding
+        def mock_indices_to_embedding(token_ids):
+            return torch.randn(1, token_ids.shape[1], 512)
+
+        mock_regularizer.indices_to_embedding = mock_indices_to_embedding
+
+        result = flextok_model._prepare_data_dict_for_detokenization(sample_token_ids)
+
+        # Check that result contains expected keys
+        expected_keys = [
+            flextok_model.quants_write_key,
+            mock_encoder.module_dict["dec_nested_dropout"].eval_keep_k_read_key
+        ]
+        assert_dict_keys(result, expected_keys)
+
+        # Check that token lengths are preserved
+        token_lens = result[mock_encoder.module_dict["dec_nested_dropout"].eval_keep_k_read_key]
+        assert token_lens == [50, 75]  # Original lengths from sample_token_ids
+
+    @pytest.mark.unit
+    def test_detokenize(self, flextok_model, sample_token_ids, mock_vae):
+        """Test the detokenize method."""
+        # Mock the helper methods and decode
+        mock_data_dict = {"prepared": "data"}
+        decoded_images = [torch.randn(1, 3, 64, 64), torch.randn(1, 3, 64, 64)]
+
+        with patch.object(flextok_model, '_prepare_data_dict_for_detokenization',
+                          return_value=mock_data_dict) as mock_prepare, \
+                patch.object(flextok_model, 'decode') as mock_decode:
+            mock_decode.return_value = {mock_vae.images_reconst_write_key: decoded_images}
+
+            result = flextok_model.detokenize(
+                sample_token_ids,
+                vae_image_sizes=32,
+                timesteps=10
+            )
+
+            # Verify preparation was called with token IDs
+            mock_prepare.assert_called_once_with(token_ids_list=sample_token_ids)
+
+            # Verify decode was called with prepared data and parameters
+            mock_decode.assert_called_once_with(
+                data_dict=mock_data_dict,
+                vae_image_sizes=32,
+                timesteps=10
+            )
+
+            # Verify result is concatenated images
+            expected_result = torch.cat(decoded_images, dim=0)
+            assert torch.equal(result, expected_result)
+
+    @pytest.mark.unit
+    def test_forward(self, flextok_model, sample_data_dict, mock_flow_matching, mock_decoder):
+        """Test the forward method (training pipeline)."""
+        # Mock encode method
+        encoded_data = {
+            "token_ids": [torch.randint(0, 1000, (1, 50))],
+            "quantized_tokens": [torch.randn(1, 50, 512)]
+        }
+
+        # Mock flow matching to add noise-related data
+        noised_data = encoded_data.copy()
+        noised_data.update({
+            "noised_latents": torch.randn(1, 16, 8, 8),
+            "timesteps": torch.tensor([0.5]),
+            "sigmas": torch.tensor([1.0]),
+        })
+
+        # Mock decoder output
+        decoder_output = {"decoder_output": torch.randn(1, 16, 8, 8)}
+
+        with patch.object(flextok_model, 'encode', return_value=encoded_data) as mock_encode:
+            mock_flow_matching.return_value = noised_data
+            mock_decoder.return_value = decoder_output
+
+            result = flextok_model.forward(sample_data_dict)
+
+            # Verify the full pipeline was executed
+            mock_encode.assert_called_once_with(sample_data_dict)
+            mock_flow_matching.assert_called_once_with(encoded_data)
+            mock_decoder.assert_called_once_with(noised_data)
+
+            assert result == decoder_output
+
+
+class TestFlexTokFromHub:
+    """Test suite for the FlexTokFromHub class."""
+
+    @pytest.mark.unit
+    def test_init_with_config(self):
+        """Test FlexTokFromHub initialization with config."""
+        # Mock config
+        mock_config = {
+            "vae": {"_target_": "flextok.vae_wrapper.StableDiffusionVAE"},
+            "encoder": {"_target_": "mock.encoder"},
+            "decoder": {"_target_": "mock.decoder"},
+            "regularizer": {"_target_": "mock.regularizer"},
+            "flow_matching_noise_module": {"_target_": "mock.flow_matching"},
+            "pipeline": {"_target_": "mock.pipeline"},
+        }
+
+        # Mock the instantiate function and sanitize config
+        with patch('flextok.flextok_wrapper.instantiate') as mock_instantiate, \
+                patch('flextok.flextok_wrapper._sanitize_hydra_config') as mock_sanitize:
+            # Mock instantiate to return mock objects
+            mock_components = {
+                "vae": MagicMock(),
+                "encoder": MagicMock(),
+                "decoder": MagicMock(),
+                "regularizer": MagicMock(),
+                "flow_matching_noise_module": MagicMock(),
+                "pipeline": MagicMock(),
+            }
+            mock_instantiate.side_effect = lambda config: mock_components[config["_target_"].split(".")[-1]]
+
+            # Create FlexTokFromHub instance
+            model = FlexTokFromHub(mock_config)
+
+            # Verify config was sanitized
+            mock_sanitize.assert_called_once()
+
+            # Verify all components were instantiated
+            assert mock_instantiate.call_count == 6
+
+    @pytest.mark.unit
+    def test_inheritance(self):
+        """Test that FlexTokFromHub properly inherits from both FlexTok and PyTorchModelHubMixin."""
+        from flextok.flextok_wrapper import FlexTok
+        from huggingface_hub import PyTorchModelHubMixin
+
+        assert issubclass(FlexTokFromHub, FlexTok)
+        assert issubclass(FlexTokFromHub, PyTorchModelHubMixin)
diff --git a/tests/test_flow_matching.py b/tests/test_flow_matching.py
new file mode 100644
index 0000000..bd8215e
--- /dev/null
+++ b/tests/test_flow_matching.py
@@ -0,0 +1,556 @@
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2025 Apple Inc. and EPFL. All Rights Reserved.
+
+import pytest
+import torch
+import math
+from unittest.mock import MagicMock, patch
+from typing import Dict, Any, List
+
+from flextok.flow_matching.noise_modules import MinRFNoiseModule
+from flextok.flow_matching.pipelines import MinRFPipeline
+from flextok.flow_matching.cfg_utils import (
+    MomentumBuffer,
+    normalized_guidance,
+    classifier_free_guidance,
+    project
+)
+from tests.conftest import assert_tensor_shape, assert_dict_keys
+
+
+class TestMinRFNoiseModule:
+    """Test suite for the MinRFNoiseModule class."""
+
+    @pytest.fixture
+    def noise_config(self):
+        """Fixture providing basic noise module configuration."""
+        return {
+            "clean_images_read_key": "clean_images",
+            "noised_images_write_key": "noised_images",
+            "timesteps_write_key": "timesteps",
+            "sigmas_write_key": "sigmas",
+            "ln": True,
+            "stratisfied": False,
+            "mode_scale": 0.0,
+            "noise_write_key": "flow_noise",
+            "noise_read_key": None,
+        }
+
+    @pytest.fixture
+    def noise_module(self, noise_config):
+        """Fixture providing a MinRFNoiseModule instance."""
+        return MinRFNoiseModule(**noise_config)
+
+    @pytest.fixture
+    def sample_clean_images(self):
+        """Fixture providing sample clean images."""
+        return [torch.randn(1, 16, 8, 8), torch.randn(1, 16, 8, 8)]
+
+    @pytest.fixture
+    def data_dict_with_clean_images(self, sample_clean_images):
+        """Fixture providing data dict with clean images."""
+        return {"clean_images": sample_clean_images}
+
+    @pytest.mark.unit
+    def test_init(self, noise_config):
+        """Test MinRFNoiseModule initialization."""
+        module = MinRFNoiseModule(**noise_config)
+
+        assert module.clean_images_read_key == "clean_images"
+        assert module.noised_images_write_key == "noised_images"
+        assert module.timesteps_write_key == "timesteps"
+        assert module.sigmas_write_key == "sigmas"
+        assert module.ln is True
+        assert module.stratisfied is False
+        assert module.mode_scale == 0.0
+        assert module.noise_write_key == "flow_noise"
+        assert module.noise_read_key is None
+
+    @pytest.mark.unit
+    def test_forward_logit_normal(self, noise_module, data_dict_with_clean_images):
+        """Test forward pass with logit-normal noise (ln=True)."""
+        noise_module.ln = True
+        noise_module.stratisfied = False
+
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        # Check that all expected keys are written
+        expected_keys = ["noised_images", "flow_noise", "sigmas", "timesteps"]
+        assert_dict_keys(result, expected_keys)
+
+        # Check shapes and types
+        noised_images = result["noised_images"]
+        flow_noise = result["flow_noise"]
+        sigmas = result["sigmas"]
+        timesteps = result["timesteps"]
+
+        assert len(noised_images) == 2
+        assert len(flow_noise) == 2
+        assert_tensor_shape(sigmas, (2,), "sigmas")
+        assert_tensor_shape(timesteps, (2,), "timesteps")
+
+        # Verify sigmas are in valid range [0, 1] for sigmoid
+        assert torch.all(sigmas >= 0) and torch.all(sigmas <= 1)
+
+        # Verify timesteps equal sigmas in this case
+        assert torch.equal(timesteps, sigmas)
+
+    @pytest.mark.unit
+    def test_forward_logit_normal_stratified(self, noise_module, data_dict_with_clean_images):
+        """Test forward pass with stratified logit-normal noise."""
+        noise_module.ln = True
+        noise_module.stratisfied = True
+
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        # Check basic structure
+        assert_dict_keys(result, ["noised_images", "flow_noise", "sigmas", "timesteps"])
+
+        sigmas = result["sigmas"]
+        assert_tensor_shape(sigmas, (2,), "sigmas")
+        assert torch.all(sigmas >= 0) and torch.all(sigmas <= 1)
+
+    @pytest.mark.unit
+    def test_forward_uniform_noise(self, noise_module, data_dict_with_clean_images):
+        """Test forward pass with uniform noise (ln=False)."""
+        noise_module.ln = False
+        noise_module.mode_scale = 0.0
+
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        # Check basic structure
+        assert_dict_keys(result, ["noised_images", "flow_noise", "sigmas", "timesteps"])
+
+        sigmas = result["sigmas"]
+        assert_tensor_shape(sigmas, (2,), "sigmas")
+        # Uniform noise should be in [0, 1]
+        assert torch.all(sigmas >= 0) and torch.all(sigmas <= 1)
+
+    @pytest.mark.unit
+    def test_forward_uniform_with_mode_scale(self, noise_module, data_dict_with_clean_images):
+        """Test forward pass with uniform noise and mode scaling (SD3 style)."""
+        noise_module.ln = False
+        noise_module.mode_scale = 1.5
+
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        sigmas = result["sigmas"]
+        assert_tensor_shape(sigmas, (2,), "sigmas")
+        # With mode scaling, sigmas might be outside [0, 1] range
+        assert sigmas is not None
+
+    @pytest.mark.unit
+    def test_forward_with_provided_noise(self, noise_module, data_dict_with_clean_images):
+        """Test forward pass when noise is provided in data_dict."""
+        noise_module.noise_read_key = "custom_noise"
+
+        # Add custom noise to data dict
+        custom_noise = [torch.randn(1, 16, 8, 8), torch.randn(1, 16, 8, 8)]
+        data_dict_with_clean_images["custom_noise"] = custom_noise
+
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        # Verify the provided noise was used
+        flow_noise = result["flow_noise"]
+        assert len(flow_noise) == len(custom_noise)
+
+        # The noise should be the same as what we provided
+        for provided, returned in zip(custom_noise, flow_noise):
+            assert torch.equal(provided, returned)
+
+    @pytest.mark.unit
+    def test_noising_formula(self, noise_module, data_dict_with_clean_images):
+        """Test that the noising formula is correctly applied."""
+        result = noise_module.forward(data_dict_with_clean_images)
+
+        clean_images = data_dict_with_clean_images["clean_images"]
+        noised_images = result["noised_images"]
+        flow_noise = result["flow_noise"]
+        sigmas = result["sigmas"]
+
+        # Verify noising formula: noised = sigma * noise + (1 - sigma) * clean
+        for i, (clean, noised, noise, sigma) in enumerate(zip(clean_images, noised_images, flow_noise, sigmas)):
+            expected_noised = sigma * noise + (1.0 - sigma) * clean
+            assert torch.allclose(noised, expected_noised, rtol=1e-5), f"Noising formula failed for image {i}"
+
+    @pytest.mark.unit
+    def test_device_consistency(self, noise_module):
+        """Test that tensors are created on the correct device."""
+        device = torch.device("cpu")
+        clean_images = [torch.randn(1, 16, 8, 8, device=device) for _ in range(2)]
+        data_dict = {"clean_images": clean_images}
+
+        result = noise_module.forward(data_dict)
+
+        # Check that all outputs are on the same device
+        for tensor in result["sigmas"], result["timesteps"]:
+            assert tensor.device == device
+
+
+class TestMinRFPipeline:
+    """Test suite for the MinRFPipeline class."""
+
+    @pytest.fixture
+    def mock_model(self):
+        """Fixture providing a mock flow model."""
+        model = MagicMock()
+        model.device = torch.device("cpu")
+
+        def mock_forward(data_dict):
+            # Mock model output
+            noised_images = data_dict["noised_images"]
+            output_dict = data_dict.copy()
+            output_dict["reconst"] = [torch.randn_like(img) for img in noised_images]
+            return output_dict
+
+        model.side_effect = mock_forward
+        return model
+
+    @pytest.fixture
+    def pipeline_config(self):
+        """Fixture providing basic pipeline configuration."""
+        return {
+            "noise_read_key": "noise",
+            "target_sizes_read_key": "target_sizes",
+            "latents_read_key": "latents",
+            "timesteps_read_key": "timesteps",
+            "noised_images_read_key": "noised_images",
+            "reconst_write_key": "reconst",
+            "out_channels": 16,
+        }
+
+    @pytest.fixture
+    def pipeline(self, mock_model, pipeline_config):
+        """Fixture providing a MinRFPipeline instance."""
+        return MinRFPipeline(model=mock_model, **pipeline_config)
+
+    @pytest.fixture
+    def sample_data_dict(self):
+        """Fixture providing sample data dict for pipeline."""
+        return {
+            "latents": [torch.randn(1, 64, 8), torch.randn(1, 64, 8)],
+            "target_sizes": [(8, 8), (8, 8)],
+        }
+
+    @pytest.mark.unit
+    def test_init(self, mock_model, pipeline_config):
+        """Test MinRFPipeline initialization."""
+        pipeline = MinRFPipeline(model=mock_model, **pipeline_config)
+
+        assert pipeline.model == mock_model
+        assert pipeline.noise_read_key == "noise"
+        assert pipeline.target_sizes_read_key == "target_sizes"
+        assert pipeline.out_channels == 16
+
+    @pytest.mark.unit
+    def test_call_basic(self, pipeline, sample_data_dict, mock_model):
+        """Test basic pipeline call without CFG."""
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            guidance_scale=1.0,
+            verbose=False
+        )
+
+        # Check that model was called
+        assert mock_model.call_count > 0
+
+        # Check that output contains reconstructions
+        assert "reconst" in result
+        assert len(result["reconst"]) == 2
+
+    @pytest.mark.unit
+    def test_call_with_vae_image_sizes_int(self, pipeline, sample_data_dict):
+        """Test pipeline call with integer vae_image_sizes."""
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            vae_image_sizes=8,
+            guidance_scale=1.0,
+            verbose=False
+        )
+
+        assert "reconst" in result
+        assert len(result["reconst"]) == 2
+
+    @pytest.mark.unit
+    def test_call_with_cfg(self, pipeline, sample_data_dict, mock_model):
+        """Test pipeline call with classifier-free guidance."""
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            guidance_scale=2.0,
+            perform_norm_guidance=False,
+            verbose=False
+        )
+
+        # With CFG, model should be called twice per timestep (conditional + unconditional)
+        # With 2 timesteps, that's 4 calls minimum
+        assert mock_model.call_count >= 4
+
+        assert "reconst" in result
+
+    @pytest.mark.unit
+    def test_call_with_norm_guidance(self, pipeline, sample_data_dict, mock_model):
+        """Test pipeline call with normalized guidance (APG)."""
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            guidance_scale=2.0,
+            perform_norm_guidance=True,
+            verbose=False
+        )
+
+        # With normalized guidance, model should still be called multiple times
+        assert mock_model.call_count >= 4
+
+        assert "reconst" in result
+
+    @pytest.mark.unit
+    def test_call_with_callable_guidance_scale(self, pipeline, sample_data_dict):
+        """Test pipeline call with callable guidance scale."""
+
+        def dynamic_scale(t):
+            return 1.0 + t  # Scale increases with time
+
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            guidance_scale=dynamic_scale,
+            verbose=False
+        )
+
+        assert "reconst" in result
+
+    @pytest.mark.unit
+    def test_call_with_provided_noise(self, pipeline, sample_data_dict):
+        """Test pipeline call when noise is provided."""
+        noise = [torch.randn(1, 16, 8, 8), torch.randn(1, 16, 8, 8)]
+        sample_data_dict["noise"] = noise
+
+        result = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            guidance_scale=1.0,
+            verbose=False
+        )
+
+        assert "reconst" in result
+
+    @pytest.mark.unit
+    def test_call_with_generator(self, pipeline, sample_data_dict):
+        """Test pipeline call with specified generator for reproducibility."""
+        generator = torch.Generator()
+        generator.manual_seed(42)
+
+        result1 = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            generator=generator,
+            guidance_scale=1.0,
+            verbose=False
+        )
+
+        # Reset generator and run again
+        generator.manual_seed(42)
+        result2 = pipeline(
+            sample_data_dict,
+            timesteps=2,
+            generator=generator,
+            guidance_scale=1.0,
+            verbose=False
+        )
+
+        # Results should be deterministic
+        assert "reconst" in result1
+        assert "reconst" in result2
+
+
+class TestCFGUtils:
+    """Test suite for CFG utility functions."""
+
+    @pytest.mark.unit
+    def test_momentum_buffer_init(self):
+        """Test MomentumBuffer initialization."""
+        buffer = MomentumBuffer(momentum=0.9)
+        assert buffer.momentum == 0.9
+        assert buffer.running_average == 0
+
+    @pytest.mark.unit
+    def test_momentum_buffer_update(self):
+        """Test MomentumBuffer update mechanism."""
+        buffer = MomentumBuffer(momentum=0.9)
+
+        # First update
+        update1 = torch.tensor(2.0)
+        buffer.update(update1)
+        expected1 = update1 + 0.9 * 0  # 0.9 * initial value (0)
+        assert torch.allclose(buffer.running_average, expected1)
+
+        # Second update
+        update2 = torch.tensor(1.0)
+        buffer.update(update2)
+        expected2 = update2 + 0.9 * buffer.running_average
+        assert torch.allclose(buffer.running_average, expected2)
+
+    @pytest.mark.unit
+    def test_project_function(self):
+        """Test the project function for vector projection."""
+        # Create test vectors
+        v0 = torch.randn(1, 4, 8, 8)
+        v1 = torch.randn(1, 4, 8, 8)
+
+        v0_parallel, v0_orthogonal = project(v0, v1)
+
+        # Check shapes
+        assert_tensor_shape(v0_parallel, v0.shape, "parallel component")
+        assert_tensor_shape(v0_orthogonal, v0.shape, "orthogonal component")
+
+        # Check that parallel + orthogonal = original
+        reconstructed = v0_parallel + v0_orthogonal
+        assert torch.allclose(reconstructed, v0, rtol=1e-4)
+
+        # Check that parallel is indeed parallel to normalized v1
+        v1_normalized = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+        dot_product = (v0_parallel * v1_normalized).sum(dim=[-1, -2, -3])
+        parallel_magnitude = v0_parallel.norm(dim=[-1, -2, -3])
+
+        # The ratio should be constant (indicating parallel vectors)
+        if parallel_magnitude.item() > 1e-6:  # Avoid division by zero
+            assert torch.allclose(
+                torch.abs(dot_product), parallel_magnitude, rtol=1e-4
+            )
+
+    @pytest.mark.unit
+    def test_classifier_free_guidance(self):
+        """Test standard classifier-free guidance."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 2.0
+
+        result = classifier_free_guidance(pred_cond, pred_uncond, guidance_scale)
+
+        # Check formula: uncond + scale * (cond - uncond)
+        expected = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
+        assert torch.allclose(result, expected)
+
+        # Check shape preservation
+        assert_tensor_shape(result, pred_cond.shape, "CFG result")
+
+    @pytest.mark.unit
+    def test_classifier_free_guidance_no_guidance(self):
+        """Test CFG with guidance scale = 1.0 (no guidance)."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 1.0
+
+        result = classifier_free_guidance(pred_cond, pred_uncond, guidance_scale)
+
+        # With scale=1.0, result should equal pred_cond
+        assert torch.allclose(result, pred_cond)
+
+    @pytest.mark.unit
+    def test_normalized_guidance_basic(self):
+        """Test normalized guidance without momentum buffer."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 2.0
+
+        result = normalized_guidance(
+            pred_cond=pred_cond,
+            pred_uncond=pred_uncond,
+            guidance_scale=guidance_scale,
+            momentum_buffer=None,
+            eta=0.0,
+            norm_threshold=0.0  # No thresholding
+        )
+
+        # Check shape preservation
+        assert_tensor_shape(result, pred_cond.shape, "normalized guidance result")
+
+    @pytest.mark.unit
+    def test_normalized_guidance_with_momentum(self):
+        """Test normalized guidance with momentum buffer."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 2.0
+        momentum_buffer = MomentumBuffer(momentum=0.9)
+
+        result = normalized_guidance(
+            pred_cond=pred_cond,
+            pred_uncond=pred_uncond,
+            guidance_scale=guidance_scale,
+            momentum_buffer=momentum_buffer,
+            eta=0.0,
+            norm_threshold=0.0
+        )
+
+        # Check that momentum buffer was updated
+        assert momentum_buffer.running_average is not None
+        assert_tensor_shape(result, pred_cond.shape, "normalized guidance with momentum")
+
+    @pytest.mark.unit
+    def test_normalized_guidance_with_thresholding(self):
+        """Test normalized guidance with norm thresholding."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 2.0
+        norm_threshold = 1.0
+
+        result = normalized_guidance(
+            pred_cond=pred_cond,
+            pred_uncond=pred_uncond,
+            guidance_scale=guidance_scale,
+            momentum_buffer=None,
+            eta=0.0,
+            norm_threshold=norm_threshold
+        )
+
+        assert_tensor_shape(result, pred_cond.shape, "normalized guidance with thresholding")
+
+    @pytest.mark.unit
+    def test_normalized_guidance_with_eta(self):
+        """Test normalized guidance with parallel component (eta > 0)."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 2.0
+        eta = 0.5
+
+        result = normalized_guidance(
+            pred_cond=pred_cond,
+            pred_uncond=pred_uncond,
+            guidance_scale=guidance_scale,
+            momentum_buffer=None,
+            eta=eta,
+            norm_threshold=0.0
+        )
+
+        assert_tensor_shape(result, pred_cond.shape, "normalized guidance with eta")
+
+    @pytest.mark.unit
+    def test_guidance_consistency(self):
+        """Test that normalized guidance reduces to CFG under certain conditions."""
+        pred_cond = torch.randn(1, 4, 8, 8)
+        pred_uncond = torch.randn(1, 4, 8, 8)
+        guidance_scale = 1.5
+
+        # Standard CFG
+        cfg_result = classifier_free_guidance(pred_cond, pred_uncond, guidance_scale)
+
+        # Normalized guidance with specific settings should approximate CFG
+        # Note: They won't be exactly equal due to the normalization and projection operations
+        norm_result = normalized_guidance(
+            pred_cond=pred_cond,
+            pred_uncond=pred_uncond,
+            guidance_scale=guidance_scale,
+            momentum_buffer=None,
+            eta=1.0,  # Full parallel component
+            norm_threshold=0.0  # No thresholding
+        )
+
+        # They should be similar in magnitude
+        cfg_norm = cfg_result.norm()
+        norm_norm = norm_result.norm()
+
+        # Allow for some difference due to the normalization process
+        assert torch.abs(cfg_norm - norm_norm) / cfg_norm < 0.5  # Within 50%
diff --git a/tests/test_vae_wrapper.py b/tests/test_vae_wrapper.py
new file mode 100644
index 0000000..4bc23ad
--- /dev/null
+++ b/tests/test_vae_wrapper.py
@@ -0,0 +1,450 @@
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2025 Apple Inc. and EPFL. All Rights Reserved.
+
+import pytest
+import torch
+from unittest.mock import MagicMock, patch, PropertyMock
+from typing import Dict, Any
+
+from flextok.vae_wrapper import StableDiffusionVAE, VAE_BASE_CFG
+from tests.conftest import assert_tensor_shape, assert_dict_keys
+
+
+class TestStableDiffusionVAE:
+    """Test suite for the StableDiffusionVAE wrapper class."""
+
+    @pytest.fixture
+    def vae_config(self):
+        """Fixture providing basic VAE configuration."""
+        return {
+            "images_read_key": "rgb",
+            "vae_latents_read_key": "vae_latents",
+            "vae_latents_write_key": "vae_latents_out",
+            "images_reconst_write_key": "images_reconst",
+            "vae_kl_loss_write_key": "kl_loss",
+            "latent_channels": 16,
+            "scaling_factor": 0.13025,
+            "dtype_override": "fp32",
+            "sample_posterior": True,
+            "learnable_logvar": False,
+            "logvar_init": 0.0,
+            "compile_encode_fn": False,
+            "force_vae_encode": True,
+            "frozen": False,
+        }
+
+    @pytest.fixture
+    def mock_autoencoder_kl(self):
+        """Fixture providing a mock AutoencoderKL."""
+        mock_vae = MagicMock()
+        mock_vae.config = {
+            "down_block_types": ["DownEncoderBlock2D"] * 4,
+            "latent_channels": 16,
+            "scaling_factor": 0.13025
+        }
+
+        # Mock the latent distribution
+        mock_latent_dist = MagicMock()
+        mock_latent_dist.sample.return_value = torch.randn(1, 16, 8, 8)
+        mock_latent_dist.mode.return_value = torch.randn(1, 16, 8, 8)
+        mock_latent_dist.kl.return_value = torch.randn(1)
+
+        # Mock encode/decode methods
+        mock_encode_result = MagicMock()
+        mock_encode_result.latent_dist = mock_latent_dist
+        mock_vae.encode.return_value = mock_encode_result
+
+        mock_decode_result = MagicMock()
+        mock_decode_result.sample = torch.randn(1, 3, 64, 64)
+        mock_vae.decode.return_value = mock_decode_result
+
+        return mock_vae
+
+    @pytest.fixture
+    def vae_wrapper(self, vae_config, mock_autoencoder_kl):
+        """Fixture providing a StableDiffusionVAE wrapper."""
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+            return StableDiffusionVAE(**vae_config)
+
+    @pytest.mark.unit
+    def test_init_from_scratch(self, vae_config, mock_autoencoder_kl):
+        """Test VAE initialization from scratch (no HuggingFace path)."""
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            # Verify AutoencoderKL was created from config
+            mock_ae_class.from_config.assert_called_once()
+            config_used = mock_ae_class.from_config.call_args[0][0]
+
+            # Check that base config was used with latent_channels override
+            assert config_used["latent_channels"] == 16
+            assert "act_fn" in config_used  # From VAE_BASE_CFG
+
+            # Verify properties are set correctly
+            assert vae.images_read_key == "rgb"
+            assert vae.vae_latents_write_key == "vae_latents_out"
+            assert vae.scaling_factor == 0.13025
+            assert vae.sample_posterior is True
+            assert vae.force_vae_encode is True
+
+    @pytest.mark.unit
+    def test_init_from_hub(self, vae_config, mock_autoencoder_kl):
+        """Test VAE initialization from HuggingFace Hub."""
+        vae_config["hf_hub_path"] = "stabilityai/sdxl-vae"
+
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_pretrained.return_value = mock_autoencoder_kl
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            # Verify AutoencoderKL was loaded from Hub
+            mock_ae_class.from_pretrained.assert_called_once_with(
+                "stabilityai/sdxl-vae",
+                low_cpu_mem_usage=False
+            )
+
+    @pytest.mark.unit
+    def test_init_with_learnable_logvar(self, vae_config, mock_autoencoder_kl):
+        """Test VAE initialization with learnable logvar."""
+        vae_config["learnable_logvar"] = True
+        vae_config["logvar_init"] = -2.0
+
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            # Verify logvar parameter was created
+            assert vae.logvar is not None
+            assert isinstance(vae.logvar, torch.nn.Parameter)
+            assert vae.logvar.item() == -2.0
+
+    @pytest.mark.unit
+    def test_init_frozen(self, vae_config, mock_autoencoder_kl):
+        """Test VAE initialization in frozen mode."""
+        vae_config["frozen"] = True
+
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            # Verify freeze was called
+            with patch.object(vae, 'freeze') as mock_freeze:
+                vae.__init__(**vae_config)
+                # Note: This would be called during actual init, but our fixture doesn't capture it
+
+    @pytest.mark.unit
+    def test_properties(self, vae_wrapper, mock_autoencoder_kl):
+        """Test VAE properties."""
+        # Test downsample_factor
+        assert vae_wrapper.downsample_factor == 8  # 2^(4-1) based on mock config
+
+        # Test latent_dim
+        assert vae_wrapper.latent_dim == 16
+
+        # Test device and device_type
+        with patch.object(vae_wrapper, 'parameters') as mock_params:
+            mock_param = MagicMock()
+            mock_param.device = torch.device('cpu')
+            mock_params.return_value = [mock_param]
+
+            assert vae_wrapper.device == torch.device('cpu')
+            assert vae_wrapper.device_type == 'cpu'
+
+    @pytest.mark.unit
+    def test_freeze(self, vae_wrapper):
+        """Test the freeze method."""
+        # Create some dummy parameters
+        with patch.object(vae_wrapper, 'parameters') as mock_params:
+            mock_param1 = MagicMock()
+            mock_param2 = MagicMock()
+            mock_params.return_value = [mock_param1, mock_param2]
+
+            with patch.object(vae_wrapper, 'eval') as mock_eval:
+                result = vae_wrapper.freeze()
+
+                # Verify all parameters had requires_grad set to False
+                mock_param1.__setattr__.assert_called_with('requires_grad', False)
+                mock_param2.__setattr__.assert_called_with('requires_grad', False)
+
+                # Verify eval was called
+                mock_eval.assert_called_once()
+
+                # Verify method returns self
+                assert result == vae_wrapper
+
+    @pytest.mark.unit
+    def test_train_frozen(self, vae_wrapper):
+        """Test train method when frozen."""
+        vae_wrapper.frozen = True
+
+        # When frozen, train should return self without calling super().train()
+        with patch('super') as mock_super:
+            result = vae_wrapper.train(mode=True)
+            assert result == vae_wrapper
+            mock_super.assert_not_called()
+
+    @pytest.mark.unit
+    def test_train_not_frozen(self, vae_wrapper):
+        """Test train method when not frozen."""
+        vae_wrapper.frozen = False
+
+        # When not frozen, should call normal train method
+        with patch('torch.nn.Module.train', return_value=vae_wrapper) as mock_train:
+            result = vae_wrapper.train(mode=False)
+            assert result == vae_wrapper
+            mock_train.assert_called_once_with(mode=False)
+
+    @pytest.mark.unit
+    def test_encode_tensor_input(self, vae_wrapper, sample_image_tensor, mock_autoencoder_kl):
+        """Test encode method with tensor input."""
+        data_dict = {"rgb": sample_image_tensor}
+
+        result = vae_wrapper.encode(data_dict)
+
+        # Verify VAE encode was called
+        mock_autoencoder_kl.encode.assert_called_once()
+
+        # Check output keys
+        expected_keys = ["vae_latents_out", "kl_loss"]
+        assert_dict_keys(result, expected_keys)
+
+        # Check output types and shapes
+        latents = result["vae_latents_out"]
+        kl_loss = result["kl_loss"]
+
+        assert isinstance(latents, torch.Tensor)
+        assert isinstance(kl_loss, torch.Tensor)
+
+    @pytest.mark.unit
+    def test_encode_list_input(self, vae_wrapper, sample_image_list, mock_autoencoder_kl):
+        """Test encode method with list input."""
+        data_dict = {"rgb": sample_image_list}
+
+        result = vae_wrapper.encode(data_dict)
+
+        # Verify VAE encode was called with concatenated tensor
+        mock_autoencoder_kl.encode.assert_called_once()
+
+        # Check that outputs are lists
+        latents = result["vae_latents_out"]
+        kl_loss = result["kl_loss"]
+
+        assert isinstance(latents, list)
+        assert isinstance(kl_loss, list)
+        assert len(latents) == len(sample_image_list)
+        assert len(kl_loss) == len(sample_image_list)
+
+    @pytest.mark.unit
+    def test_encode_skip_when_exists(self, vae_wrapper, sample_image_tensor, mock_autoencoder_kl):
+        """Test encode method skips when latents already exist and force_vae_encode is False."""
+        vae_wrapper.force_vae_encode = False
+
+        # Data dict already contains VAE latents
+        data_dict = {
+            "rgb": sample_image_tensor,
+            "vae_latents_out": torch.randn(1, 16, 8, 8)
+        }
+
+        result = vae_wrapper.encode(data_dict)
+
+        # Verify VAE encode was NOT called
+        mock_autoencoder_kl.encode.assert_not_called()
+
+        # Original data should be returned unchanged
+        assert result == data_dict
+
+    @pytest.mark.unit
+    def test_encode_sample_posterior(self, vae_wrapper, sample_image_tensor, mock_autoencoder_kl):
+        """Test encode method with sample_posterior=True."""
+        vae_wrapper.sample_posterior = True
+        data_dict = {"rgb": sample_image_tensor}
+
+        vae_wrapper.encode(data_dict)
+
+        # Verify sample() was called on latent distribution
+        latent_dist = mock_autoencoder_kl.encode.return_value.latent_dist
+        latent_dist.sample.assert_called_once()
+        latent_dist.mode.assert_not_called()
+
+    @pytest.mark.unit
+    def test_encode_mode_posterior(self, vae_wrapper, sample_image_tensor, mock_autoencoder_kl):
+        """Test encode method with sample_posterior=False."""
+        vae_wrapper.sample_posterior = False
+        data_dict = {"rgb": sample_image_tensor}
+
+        vae_wrapper.encode(data_dict)
+
+        # Verify mode() was called on latent distribution
+        latent_dist = mock_autoencoder_kl.encode.return_value.latent_dist
+        latent_dist.mode.assert_called_once()
+        latent_dist.sample.assert_not_called()
+
+    @pytest.mark.unit
+    def test_decode_tensor_input(self, vae_wrapper, sample_vae_latents, mock_autoencoder_kl):
+        """Test decode method with tensor input."""
+        data_dict = {"vae_latents": sample_vae_latents}
+
+        result = vae_wrapper.decode(data_dict)
+
+        # Verify VAE decode was called with scaled latents
+        mock_autoencoder_kl.decode.assert_called_once()
+
+        # Check output
+        assert "images_reconst" in result
+        images = result["images_reconst"]
+        assert isinstance(images, torch.Tensor)
+
+    @pytest.mark.unit
+    def test_decode_list_input(self, vae_wrapper, mock_autoencoder_kl):
+        """Test decode method with list input."""
+        latents_list = [torch.randn(1, 16, 8, 8), torch.randn(1, 16, 8, 8)]
+        data_dict = {"vae_latents": latents_list}
+
+        result = vae_wrapper.decode(data_dict)
+
+        # Verify VAE decode was called
+        mock_autoencoder_kl.decode.assert_called_once()
+
+        # Check that output is a list
+        images = result["images_reconst"]
+        assert isinstance(images, list)
+        assert len(images) == len(latents_list)
+
+    @pytest.mark.unit
+    def test_autoencode(self, vae_wrapper, sample_image_tensor):
+        """Test autoencode method (encode + decode)."""
+        data_dict = {"rgb": sample_image_tensor}
+
+        # Mock encode and decode methods
+        encode_result = {
+            "vae_latents_out": torch.randn(1, 16, 8, 8),
+            "kl_loss": torch.randn(1)
+        }
+        decode_result = {"images_reconst": torch.randn(1, 3, 64, 64)}
+
+        with patch.object(vae_wrapper, 'encode', return_value=encode_result) as mock_encode, \
+                patch.object(vae_wrapper, 'decode', return_value=decode_result) as mock_decode:
+            result = vae_wrapper.autoencode(data_dict)
+
+            # Verify encode was called
+            mock_encode.assert_called_once_with(data_dict)
+
+            # Verify decode was called with latents added to read key
+            decode_call_args = mock_decode.call_args[0][0]
+            assert "vae_latents" in decode_call_args  # Read key for decode
+
+            assert result == decode_result
+
+    @pytest.mark.unit
+    def test_forward(self, vae_wrapper, sample_image_tensor):
+        """Test forward method (should call autoencode)."""
+        data_dict = {"rgb": sample_image_tensor}
+        expected_result = {"images_reconst": torch.randn(1, 3, 64, 64)}
+
+        with patch.object(vae_wrapper, 'autoencode', return_value=expected_result) as mock_autoencode:
+            result = vae_wrapper.forward(data_dict)
+
+            mock_autoencode.assert_called_once_with(data_dict)
+            assert result == expected_result
+
+    @pytest.mark.unit
+    def test_get_last_layer(self, vae_wrapper, mock_autoencoder_kl):
+        """Test get_last_layer method."""
+        # Mock the decoder conv_out layer
+        mock_conv_out = MagicMock()
+        mock_conv_out.weight = torch.randn(3, 16, 3, 3)
+        mock_autoencoder_kl.decoder.conv_out = mock_conv_out
+
+        result = vae_wrapper.get_last_layer()
+        assert result is mock_conv_out.weight
+
+    @pytest.mark.unit
+    def test_get_logvar_with_learnable(self, vae_config, mock_autoencoder_kl):
+        """Test get_logvar method when learnable_logvar is True."""
+        vae_config["learnable_logvar"] = True
+        vae_config["logvar_init"] = -1.5
+
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class:
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            logvar = vae.get_logvar()
+            assert logvar is not None
+            assert isinstance(logvar, torch.nn.Parameter)
+
+    @pytest.mark.unit
+    def test_get_logvar_without_learnable(self, vae_wrapper):
+        """Test get_logvar method when learnable_logvar is False."""
+        logvar = vae_wrapper.get_logvar()
+        assert logvar is None
+
+    @pytest.mark.unit
+    def test_compile_encode_fn(self, vae_config, mock_autoencoder_kl):
+        """Test VAE initialization with compile_encode_fn=True."""
+        vae_config["compile_encode_fn"] = True
+
+        with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class, \
+                patch('torch.compile') as mock_compile, \
+                patch('torch._inductor.config'):
+            mock_ae_class.from_config.return_value = mock_autoencoder_kl
+            mock_compile.return_value = MagicMock()
+
+            vae = StableDiffusionVAE(**vae_config)
+
+            # Verify torch.compile was called on encode
+            mock_compile.assert_called_once()
+
+            # Verify memory format and fuse_qkv_projections were called
+            mock_autoencoder_kl.to.assert_called_once_with(memory_format=torch.channels_last)
+            mock_autoencoder_kl.fuse_qkv_projections.assert_called_once()
+
+    @pytest.mark.unit
+    def test_dtype_override_handling(self, vae_config, mock_autoencoder_kl):
+        """Test different dtype override values."""
+        test_cases = [
+            ("fp32", torch.float32),
+            ("fp16", torch.float16),
+            ("bf16", torch.bfloat16),
+            (None, torch.float32),  # Default fallback
+        ]
+
+        for dtype_str, expected_dtype in test_cases:
+            vae_config["dtype_override"] = dtype_str
+
+            with patch('flextok.vae_wrapper.AutoencoderKL') as mock_ae_class, \
+                    patch('flextok.utils.misc.str_to_dtype') as mock_str_to_dtype:
+
+                if dtype_str is not None:
+                    mock_str_to_dtype.return_value = expected_dtype
+
+                mock_ae_class.from_config.return_value = mock_autoencoder_kl
+
+                vae = StableDiffusionVAE(**vae_config)
+
+                assert vae.dtype_override == expected_dtype
+
+                if dtype_str is not None:
+                    mock_str_to_dtype.assert_called_once_with(dtype_str)
+
+    @pytest.mark.unit
+    def test_vae_base_cfg_structure(self):
+        """Test that VAE_BASE_CFG has required structure."""
+        required_keys = [
+            "_class_name", "act_fn", "block_out_channels",
+            "down_block_types", "in_channels", "layers_per_block",
+            "norm_num_groups", "out_channels", "sample_size",
+            "up_block_types"
+        ]
+
+        for key in required_keys:
+            assert key in VAE_BASE_CFG, f"Missing required key: {key}"
+
+        # Check that override fields are None
+        assert VAE_BASE_CFG["latent_channels"] is None
+        assert VAE_BASE_CFG["scaling_factor"] is None
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new file mode 100644
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