Merge branch 'main' into refactor-caching-tests

tests/hooks/test_hooks.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import gc
+import unittest
 
-import pytest
 import torch
 
 from diffusers.hooks import HookRegistry, ModelHook
@@ -134,18 +134,20 @@ class SkipLayerHook(ModelHook):
         return output
 
 
-class TestHooks:
+class HookTests(unittest.TestCase):
     in_features = 4
     hidden_features = 8
     out_features = 4
     num_layers = 2
 
-    def setup_method(self):
+    def setUp(self):
         params = self.get_module_parameters()
         self.model = DummyModel(**params)
         self.model.to(torch_device)
 
-    def teardown_method(self):
+    def tearDown(self):
+        super().tearDown()
+
         del self.model
         gc.collect()
         free_memory()
@@ -169,20 +171,20 @@ class TestHooks:
         registry_repr = repr(registry)
         expected_repr = "HookRegistry(\n  (0) add_hook - AddHook\n  (1) multiply_hook - MultiplyHook(value=2)\n)"
 
-        assert len(registry.hooks) == 2
-        assert registry._hook_order == ["add_hook", "multiply_hook"]
-        assert registry_repr == expected_repr
+        self.assertEqual(len(registry.hooks), 2)
+        self.assertEqual(registry._hook_order, ["add_hook", "multiply_hook"])
+        self.assertEqual(registry_repr, expected_repr)
 
         registry.remove_hook("add_hook")
 
-        assert len(registry.hooks) == 1
-        assert registry._hook_order == ["multiply_hook"]
+        self.assertEqual(len(registry.hooks), 1)
+        self.assertEqual(registry._hook_order, ["multiply_hook"])
 
     def test_stateful_hook(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
         registry.register_hook(StatefulAddHook(1), "stateful_add_hook")
 
-        assert registry.hooks["stateful_add_hook"].increment == 0
+        self.assertEqual(registry.hooks["stateful_add_hook"].increment, 0)
 
         input = torch.randn(1, 4, device=torch_device, generator=self.get_generator())
         num_repeats = 3
@@ -192,13 +194,13 @@ class TestHooks:
             if i == 0:
                 output1 = result
 
-        assert registry.get_hook("stateful_add_hook").increment == num_repeats
+        self.assertEqual(registry.get_hook("stateful_add_hook").increment, num_repeats)
 
         registry.reset_stateful_hooks()
         output2 = self.model(input)
 
-        assert registry.get_hook("stateful_add_hook").increment == 1
-        assert torch.allclose(output1, output2)
+        self.assertEqual(registry.get_hook("stateful_add_hook").increment, 1)
+        self.assertTrue(torch.allclose(output1, output2))
 
     def test_inference(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -216,9 +218,9 @@ class TestHooks:
         new_input = input * 2 + 1
         output3 = self.model(new_input).mean().detach().cpu().item()
 
-        assert output1 == pytest.approx(output2, abs=5e-6)
-        assert output1 == pytest.approx(output3, abs=5e-6)
-        assert output2 == pytest.approx(output3, abs=5e-6)
+        self.assertAlmostEqual(output1, output2, places=5)
+        self.assertAlmostEqual(output1, output3, places=5)
+        self.assertAlmostEqual(output2, output3, places=5)
 
     def test_skip_layer_hook(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -226,29 +228,30 @@ class TestHooks:
 
         input = torch.zeros(1, 4, device=torch_device)
         output = self.model(input).mean().detach().cpu().item()
-        assert output == 0.0
+        self.assertEqual(output, 0.0)
 
         registry.remove_hook("skip_layer_hook")
         registry.register_hook(SkipLayerHook(skip_layer=False), "skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        assert output != 0.0
+        self.assertNotEqual(output, 0.0)
 
     def test_skip_layer_internal_block(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model.linear_1)
         input = torch.zeros(1, 4, device=torch_device)
 
         registry.register_hook(SkipLayerHook(skip_layer=True), "skip_layer_hook")
-        with pytest.raises(RuntimeError, match="mat1 and mat2 shapes cannot be multiplied"):
+        with self.assertRaises(RuntimeError) as cm:
             self.model(input).mean().detach().cpu().item()
+        self.assertIn("mat1 and mat2 shapes cannot be multiplied", str(cm.exception))
 
         registry.remove_hook("skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        assert output != 0.0
+        self.assertNotEqual(output, 0.0)
 
         registry = HookRegistry.check_if_exists_or_initialize(self.model.blocks[1])
         registry.register_hook(SkipLayerHook(skip_layer=True), "skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        assert output != 0.0
+        self.assertNotEqual(output, 0.0)
 
     def test_invocation_order_stateful_first(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -275,7 +278,7 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -286,7 +289,7 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
     def test_invocation_order_stateful_middle(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -313,7 +316,7 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -324,7 +327,7 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
         registry.remove_hook("add_hook_2")
         with CaptureLogger(logger) as cap_logger:
@@ -333,7 +336,7 @@ class TestHooks:
         expected_invocation_order_log = (
             ("MultiplyHook pre_forward\nMultiplyHook post_forward\n").replace(" ", "").replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
     def test_invocation_order_stateful_last(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -360,7 +363,7 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -371,4 +374,4 @@ class TestHooks:
             .replace(" ", "")
             .replace("\n", "")
         )
-        assert output == expected_invocation_order_log
+        self.assertEqual(output, expected_invocation_order_log)

tests/hooks/test_mag_cache.py

@@ -1,244 +0,0 @@
-# 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 unittest
-
-import numpy as np
-import torch
-
-from diffusers import MagCacheConfig, apply_mag_cache
-from diffusers.hooks._helpers import TransformerBlockMetadata, TransformerBlockRegistry
-from diffusers.models import ModelMixin
-from diffusers.utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-
-class DummyBlock(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, hidden_states, encoder_hidden_states=None, **kwargs):
-        # Output is double input
-        # This ensures Residual = 2*Input - Input = Input
-        return hidden_states * 2.0
-
-
-class DummyTransformer(ModelMixin):
-    def __init__(self):
-        super().__init__()
-        self.transformer_blocks = torch.nn.ModuleList([DummyBlock(), DummyBlock()])
-
-    def forward(self, hidden_states, encoder_hidden_states=None):
-        for block in self.transformer_blocks:
-            hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states)
-        return hidden_states
-
-
-class TupleOutputBlock(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, hidden_states, encoder_hidden_states=None, **kwargs):
-        # Returns a tuple
-        return hidden_states * 2.0, encoder_hidden_states
-
-
-class TupleTransformer(ModelMixin):
-    def __init__(self):
-        super().__init__()
-        self.transformer_blocks = torch.nn.ModuleList([TupleOutputBlock()])
-
-    def forward(self, hidden_states, encoder_hidden_states=None):
-        for block in self.transformer_blocks:
-            # Emulate Flux-like behavior
-            output = block(hidden_states, encoder_hidden_states=encoder_hidden_states)
-            hidden_states = output[0]
-            encoder_hidden_states = output[1]
-        return hidden_states, encoder_hidden_states
-
-
-class MagCacheTests(unittest.TestCase):
-    def setUp(self):
-        # Register standard dummy block
-        TransformerBlockRegistry.register(
-            DummyBlock,
-            TransformerBlockMetadata(return_hidden_states_index=None, return_encoder_hidden_states_index=None),
-        )
-        # Register tuple block (Flux style)
-        TransformerBlockRegistry.register(
-            TupleOutputBlock,
-            TransformerBlockMetadata(return_hidden_states_index=0, return_encoder_hidden_states_index=1),
-        )
-
-    def _set_context(self, model, context_name):
-        """Helper to set context on all hooks in the model."""
-        for module in model.modules():
-            if hasattr(module, "_diffusers_hook"):
-                module._diffusers_hook._set_context(context_name)
-
-    def _get_calibration_data(self, model):
-        for module in model.modules():
-            if hasattr(module, "_diffusers_hook"):
-                hook = module._diffusers_hook.get_hook("mag_cache_block_hook")
-                if hook:
-                    return hook.state_manager.get_state().calibration_ratios
-        return []
-
-    def test_mag_cache_validation(self):
-        """Test that missing mag_ratios raises ValueError."""
-        with self.assertRaises(ValueError):
-            MagCacheConfig(num_inference_steps=10, calibrate=False)
-
-    def test_mag_cache_skipping_logic(self):
-        """
-        Tests that MagCache correctly calculates residuals and skips blocks when conditions are met.
-        """
-        model = DummyTransformer()
-
-        # Dummy ratios: [1.0, 1.0] implies 0 accumulated error if we skip
-        ratios = np.array([1.0, 1.0])
-
-        config = MagCacheConfig(
-            threshold=100.0,
-            num_inference_steps=2,
-            retention_ratio=0.0,  # Enable immediate skipping
-            max_skip_steps=5,
-            mag_ratios=ratios,
-        )
-
-        apply_mag_cache(model, config)
-        self._set_context(model, "test_context")
-
-        # Step 0: Input 10.0 -> Output 40.0 (2 blocks * 2x each)
-        # HeadInput=10. Output=40. Residual=30.
-        input_t0 = torch.tensor([[[10.0]]])
-        output_t0 = model(input_t0)
-        self.assertTrue(torch.allclose(output_t0, torch.tensor([[[40.0]]])), "Step 0 failed")
-
-        # Step 1: Input 11.0.
-        # If Skipped: Output = Input(11) + Residual(30) = 41.0
-        # If Computed: Output = 11 * 4 = 44.0
-        input_t1 = torch.tensor([[[11.0]]])
-        output_t1 = model(input_t1)
-
-        self.assertTrue(
-            torch.allclose(output_t1, torch.tensor([[[41.0]]])), f"Expected Skip (41.0), got {output_t1.item()}"
-        )
-
-    def test_mag_cache_retention(self):
-        """Test that retention_ratio prevents skipping even if error is low."""
-        model = DummyTransformer()
-        # Ratios that imply 0 error, so it *would* skip if retention allowed it
-        ratios = np.array([1.0, 1.0])
-
-        config = MagCacheConfig(
-            threshold=100.0,
-            num_inference_steps=2,
-            retention_ratio=1.0,  # Force retention for ALL steps
-            mag_ratios=ratios,
-        )
-
-        apply_mag_cache(model, config)
-        self._set_context(model, "test_context")
-
-        # Step 0
-        model(torch.tensor([[[10.0]]]))
-
-        # Step 1: Should COMPUTE (44.0) not SKIP (41.0) because of retention
-        input_t1 = torch.tensor([[[11.0]]])
-        output_t1 = model(input_t1)
-
-        self.assertTrue(
-            torch.allclose(output_t1, torch.tensor([[[44.0]]])),
-            f"Expected Compute (44.0) due to retention, got {output_t1.item()}",
-        )
-
-    def test_mag_cache_tuple_outputs(self):
-        """Test compatibility with models returning (hidden, encoder_hidden) like Flux."""
-        model = TupleTransformer()
-        ratios = np.array([1.0, 1.0])
-
-        config = MagCacheConfig(threshold=100.0, num_inference_steps=2, retention_ratio=0.0, mag_ratios=ratios)
-
-        apply_mag_cache(model, config)
-        self._set_context(model, "test_context")
-
-        # Step 0: Compute. Input 10.0 -> Output 20.0 (1 block * 2x)
-        # Residual = 10.0
-        input_t0 = torch.tensor([[[10.0]]])
-        enc_t0 = torch.tensor([[[1.0]]])
-        out_0, _ = model(input_t0, encoder_hidden_states=enc_t0)
-        self.assertTrue(torch.allclose(out_0, torch.tensor([[[20.0]]])))
-
-        # Step 1: Skip. Input 11.0.
-        # Skipped Output = 11 + 10 = 21.0
-        input_t1 = torch.tensor([[[11.0]]])
-        out_1, _ = model(input_t1, encoder_hidden_states=enc_t0)
-
-        self.assertTrue(
-            torch.allclose(out_1, torch.tensor([[[21.0]]])), f"Tuple skip failed. Expected 21.0, got {out_1.item()}"
-        )
-
-    def test_mag_cache_reset(self):
-        """Test that state resets correctly after num_inference_steps."""
-        model = DummyTransformer()
-        config = MagCacheConfig(
-            threshold=100.0, num_inference_steps=2, retention_ratio=0.0, mag_ratios=np.array([1.0, 1.0])
-        )
-        apply_mag_cache(model, config)
-        self._set_context(model, "test_context")
-
-        input_t = torch.ones(1, 1, 1)
-
-        model(input_t)  # Step 0
-        model(input_t)  # Step 1 (Skipped)
-
-        # Step 2 (Reset -> Step 0) -> Should Compute
-        # Input 2.0 -> Output 8.0
-        input_t2 = torch.tensor([[[2.0]]])
-        output_t2 = model(input_t2)
-
-        self.assertTrue(torch.allclose(output_t2, torch.tensor([[[8.0]]])), "State did not reset correctly")
-
-    def test_mag_cache_calibration(self):
-        """Test that calibration mode records ratios."""
-        model = DummyTransformer()
-        config = MagCacheConfig(num_inference_steps=2, calibrate=True)
-        apply_mag_cache(model, config)
-        self._set_context(model, "test_context")
-
-        # Step 0
-        # HeadInput = 10. Output = 40. Residual = 30.
-        # Ratio 0 is placeholder 1.0
-        model(torch.tensor([[[10.0]]]))
-
-        # Check intermediate state
-        ratios = self._get_calibration_data(model)
-        self.assertEqual(len(ratios), 1)
-        self.assertEqual(ratios[0], 1.0)
-
-        # Step 1
-        # HeadInput = 10. Output = 40. Residual = 30.
-        # PrevResidual = 30. CurrResidual = 30.
-        # Ratio = 30/30 = 1.0
-        model(torch.tensor([[[10.0]]]))
-
-        # Verify it computes fully (no skip)
-        # If it skipped, output would be 41.0. It should be 40.0
-        # Actually in test setup, input is same (10.0) so output 40.0.
-        # Let's ensure list is empty after reset (end of step 1)
-        ratios_after = self._get_calibration_data(model)
-        self.assertEqual(ratios_after, [])

tests/models/testing_utils/__init__.py

@@ -5,8 +5,12 @@ from .cache import (
     FasterCacheTesterMixin,
     FirstBlockCacheConfigMixin,
     FirstBlockCacheTesterMixin,
+    MagCacheConfigMixin,
+    MagCacheTesterMixin,
     PyramidAttentionBroadcastConfigMixin,
     PyramidAttentionBroadcastTesterMixin,
+    TaylorSeerCacheConfigMixin,
+    TaylorSeerCacheTesterMixin,
 )
 from .common import BaseModelTesterConfig, ModelTesterMixin
 from .compile import TorchCompileTesterMixin
@@ -50,6 +54,8 @@ __all__ = [
     "FasterCacheTesterMixin",
     "FirstBlockCacheConfigMixin",
     "FirstBlockCacheTesterMixin",
+    "MagCacheConfigMixin",
+    "MagCacheTesterMixin",
     "GGUFCompileTesterMixin",
     "GGUFConfigMixin",
     "GGUFTesterMixin",
@@ -65,6 +71,8 @@ __all__ = [
     "ModelTesterMixin",
     "PyramidAttentionBroadcastConfigMixin",
     "PyramidAttentionBroadcastTesterMixin",
+    "TaylorSeerCacheConfigMixin",
+    "TaylorSeerCacheTesterMixin",
     "QuantizationCompileTesterMixin",
     "QuantizationTesterMixin",
     "QuantoCompileTesterMixin",

tests/models/testing_utils/cache.py

@@ -18,10 +18,18 @@ import gc
 import pytest
 import torch
 
-from diffusers.hooks import FasterCacheConfig, FirstBlockCacheConfig, PyramidAttentionBroadcastConfig
+from diffusers.hooks import (
+    FasterCacheConfig,
+    FirstBlockCacheConfig,
+    MagCacheConfig,
+    PyramidAttentionBroadcastConfig,
+    TaylorSeerCacheConfig,
+)
 from diffusers.hooks.faster_cache import _FASTER_CACHE_BLOCK_HOOK, _FASTER_CACHE_DENOISER_HOOK
 from diffusers.hooks.first_block_cache import _FBC_BLOCK_HOOK, _FBC_LEADER_BLOCK_HOOK
+from diffusers.hooks.mag_cache import _MAG_CACHE_BLOCK_HOOK, _MAG_CACHE_LEADER_BLOCK_HOOK
 from diffusers.hooks.pyramid_attention_broadcast import _PYRAMID_ATTENTION_BROADCAST_HOOK
+from diffusers.hooks.taylorseer_cache import _TAYLORSEER_CACHE_HOOK
 from diffusers.models.cache_utils import CacheMixin
 
 from ...testing_utils import assert_tensors_close, backend_empty_cache, is_cache, torch_device
@@ -554,3 +562,192 @@ class FasterCacheTesterMixin(FasterCacheConfigMixin, CacheTesterMixin):
     @require_cache_mixin
     def test_faster_cache_reset_stateful_cache(self):
         self._test_reset_stateful_cache()
+
+
+@is_cache
+class MagCacheConfigMixin:
+    """
+    Base mixin providing MagCache config.
+
+    Expected class attributes:
+        - model_class: The model class to test (must use CacheMixin)
+    """
+
+    # Default MagCache config - can be overridden by subclasses.
+    # Uses neutral ratios [1.0, 1.0] and a high threshold so the second
+    # inference step is always skipped, which is required by _test_cache_inference.
+    MAG_CACHE_CONFIG = {
+        "num_inference_steps": 2,
+        "retention_ratio": 0.0,
+        "threshold": 100.0,
+        "mag_ratios": [1.0, 1.0],
+    }
+
+    def _get_cache_config(self):
+        return MagCacheConfig(**self.MAG_CACHE_CONFIG)
+
+    def _get_hook_names(self):
+        return [_MAG_CACHE_LEADER_BLOCK_HOOK, _MAG_CACHE_BLOCK_HOOK]
+
+
+@is_cache
+class MagCacheTesterMixin(MagCacheConfigMixin, CacheTesterMixin):
+    """
+    Mixin class for testing MagCache on models.
+
+    Expected class attributes:
+        - model_class: The model class to test (must use CacheMixin)
+
+    Expected methods to be implemented by subclasses:
+        - get_init_dict(): Returns dict of arguments to initialize the model
+        - get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
+
+    Pytest mark: cache
+        Use `pytest -m "not cache"` to skip these tests
+    """
+
+    @require_cache_mixin
+    def test_mag_cache_enable_disable_state(self):
+        self._test_cache_enable_disable_state()
+
+    @require_cache_mixin
+    def test_mag_cache_double_enable_raises_error(self):
+        self._test_cache_double_enable_raises_error()
+
+    @require_cache_mixin
+    def test_mag_cache_hooks_registered(self):
+        self._test_cache_hooks_registered()
+
+    @require_cache_mixin
+    def test_mag_cache_inference(self):
+        self._test_cache_inference()
+
+    @require_cache_mixin
+    def test_mag_cache_context_manager(self):
+        self._test_cache_context_manager()
+
+    @require_cache_mixin
+    def test_mag_cache_reset_stateful_cache(self):
+        self._test_reset_stateful_cache()
+
+
+@is_cache
+class TaylorSeerCacheConfigMixin:
+    """
+    Base mixin providing TaylorSeerCache config.
+
+    Expected class attributes:
+        - model_class: The model class to test (must use CacheMixin)
+    """
+
+    # Default TaylorSeerCache config - can be overridden by subclasses.
+    # Uses a low cache_interval and disable_cache_before_step=0 so the second
+    # inference step is always predicted, which is required by _test_cache_inference.
+    TAYLORSEER_CACHE_CONFIG = {
+        "cache_interval": 3,
+        "disable_cache_before_step": 1,
+        "max_order": 1,
+    }
+
+    def _get_cache_config(self):
+        return TaylorSeerCacheConfig(**self.TAYLORSEER_CACHE_CONFIG)
+
+    def _get_hook_names(self):
+        return [_TAYLORSEER_CACHE_HOOK]
+
+
+@is_cache
+class TaylorSeerCacheTesterMixin(TaylorSeerCacheConfigMixin, CacheTesterMixin):
+    """
+    Mixin class for testing TaylorSeerCache on models.
+
+    Expected class attributes:
+        - model_class: The model class to test (must use CacheMixin)
+
+    Expected methods to be implemented by subclasses:
+        - get_init_dict(): Returns dict of arguments to initialize the model
+        - get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
+
+    Pytest mark: cache
+        Use `pytest -m "not cache"` to skip these tests
+    """
+
+    @torch.no_grad()
+    def _test_cache_inference(self):
+        """Test that model can run inference with TaylorSeer cache enabled (requires cache_context)."""
+        init_dict = self.get_init_dict()
+        inputs_dict = self.get_dummy_inputs()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
+
+        config = self._get_cache_config()
+        model.enable_cache(config)
+
+        # TaylorSeer requires cache_context to be set for inference
+        with model.cache_context("taylorseer_test"):
+            # First pass populates the cache
+            _ = model(**inputs_dict, return_dict=False)[0]
+
+            # Create modified inputs for second pass
+            inputs_dict_step2 = inputs_dict.copy()
+            if self.cache_input_key in inputs_dict_step2:
+                inputs_dict_step2[self.cache_input_key] = inputs_dict_step2[self.cache_input_key] + torch.randn_like(
+                    inputs_dict_step2[self.cache_input_key]
+                )
+
+            # Second pass - TaylorSeer should use cached Taylor series predictions
+            output_with_cache = model(**inputs_dict_step2, return_dict=False)[0]
+
+        assert output_with_cache is not None, "Model output should not be None with cache enabled."
+        assert not torch.isnan(output_with_cache).any(), "Model output contains NaN with cache enabled."
+
+        # Run same inputs without cache to compare
+        model.disable_cache()
+        output_without_cache = model(**inputs_dict_step2, return_dict=False)[0]
+
+        # Cached output should be different from non-cached output (due to approximation)
+        assert not torch.allclose(output_without_cache, output_with_cache, atol=1e-5), (
+            "Cached output should be different from non-cached output due to cache approximation."
+        )
+
+    @torch.no_grad()
+    def _test_reset_stateful_cache(self):
+        """Test that _reset_stateful_cache resets the TaylorSeer cache state (requires cache_context)."""
+        init_dict = self.get_init_dict()
+        inputs_dict = self.get_dummy_inputs()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.eval()
+
+        config = self._get_cache_config()
+        model.enable_cache(config)
+
+        with model.cache_context("taylorseer_test"):
+            _ = model(**inputs_dict, return_dict=False)[0]
+
+        model._reset_stateful_cache()
+
+        model.disable_cache()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_enable_disable_state(self):
+        self._test_cache_enable_disable_state()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_double_enable_raises_error(self):
+        self._test_cache_double_enable_raises_error()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_hooks_registered(self):
+        self._test_cache_hooks_registered()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_inference(self):
+        self._test_cache_inference()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_context_manager(self):
+        self._test_cache_context_manager()
+
+    @require_cache_mixin
+    def test_taylorseer_cache_reset_stateful_cache(self):
+        self._test_reset_stateful_cache()

tests/models/transformers/test_models_transformer_flux.py

@@ -37,6 +37,7 @@ from ..testing_utils import (
     IPAdapterTesterMixin,
     LoraHotSwappingForModelTesterMixin,
     LoraTesterMixin,
+    MagCacheTesterMixin,
     MemoryTesterMixin,
     ModelOptCompileTesterMixin,
     ModelOptTesterMixin,
@@ -45,6 +46,7 @@ from ..testing_utils import (
     QuantoCompileTesterMixin,
     QuantoTesterMixin,
     SingleFileTesterMixin,
+    TaylorSeerCacheTesterMixin,
     TorchAoCompileTesterMixin,
     TorchAoTesterMixin,
     TorchCompileTesterMixin,
@@ -430,3 +432,11 @@ class TestFluxTransformerFasterCache(FluxTransformerTesterConfig, FasterCacheTes
         "tensor_format": "BCHW",
         "is_guidance_distilled": True,
     }
+
+
+class TestFluxTransformerMagCache(FluxTransformerTesterConfig, MagCacheTesterMixin):
+    """MagCache tests for Flux Transformer."""
+
+
+class TestFluxTransformerTaylorSeerCache(FluxTransformerTesterConfig, TaylorSeerCacheTesterMixin):
+    """TaylorSeerCache tests for Flux Transformer."""