update

src/diffusers/models/transformers/transformer_wan.py

@@ -42,7 +42,7 @@ def _get_qkv_projections(attn: "WanAttention", hidden_states: torch.Tensor, enco
         encoder_hidden_states = hidden_states
 
     if attn.fused_projections:
-        if not attn.is_cross_attention:
+        if attn.cross_attention_dim_head is None:
             # In self-attention layers, we can fuse the entire QKV projection into a single linear
             query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
         else:
@@ -214,10 +214,7 @@ class WanAttention(torch.nn.Module, AttentionModuleMixin):
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=True)
             self.norm_added_k = torch.nn.RMSNorm(dim_head * heads, eps=eps)
 
-        if is_cross_attention is not None:
-            self.is_cross_attention = is_cross_attention
-        else:
-            self.is_cross_attention = cross_attention_dim_head is not None
+        self.is_cross_attention = cross_attention_dim_head is not None
 
         self.set_processor(processor)
 
@@ -225,7 +222,7 @@ class WanAttention(torch.nn.Module, AttentionModuleMixin):
         if getattr(self, "fused_projections", False):
             return
 
-        if not self.is_cross_attention:
+        if self.cross_attention_dim_head is None:
             concatenated_weights = torch.cat([self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data])
             concatenated_bias = torch.cat([self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data])
             out_features, in_features = concatenated_weights.shape

src/diffusers/models/transformers/transformer_wan_animate.py

@@ -502,16 +502,13 @@ class WanAnimateFaceBlockCrossAttention(nn.Module, AttentionModuleMixin):
         dim_head: int = 64,
         eps: float = 1e-6,
         cross_attention_dim_head: Optional[int] = None,
-        bias: bool = True,
         processor=None,
     ):
         super().__init__()
         self.inner_dim = dim_head * heads
         self.heads = heads
-        self.cross_attention_dim_head = cross_attention_dim_head
+        self.cross_attention_head_dim = cross_attention_dim_head
         self.kv_inner_dim = self.inner_dim if cross_attention_dim_head is None else cross_attention_dim_head * heads
-        self.use_bias = bias
-        self.is_cross_attention = cross_attention_dim_head is not None
 
         # 1. Pre-Attention Norms for the hidden_states (video latents) and encoder_hidden_states (motion vector).
         # NOTE: this is not used in "vanilla" WanAttention
@@ -519,10 +516,10 @@ class WanAnimateFaceBlockCrossAttention(nn.Module, AttentionModuleMixin):
         self.pre_norm_kv = nn.LayerNorm(dim, eps, elementwise_affine=False)
 
         # 2. QKV and Output Projections
-        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=bias)
-        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=bias)
-        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=bias)
-        self.to_out = torch.nn.Linear(self.inner_dim, dim, bias=bias)
+        self.to_q = torch.nn.Linear(dim, self.inner_dim, bias=True)
+        self.to_k = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_v = torch.nn.Linear(dim, self.kv_inner_dim, bias=True)
+        self.to_out = torch.nn.Linear(self.inner_dim, dim, bias=True)
 
         # 3. QK Norm
         # NOTE: this is applied after the reshape, so only over dim_head rather than dim_head * heads

src/diffusers/models/transformers/transformer_wan_vace.py

@@ -76,7 +76,6 @@ class WanVACETransformerBlock(nn.Module):
             eps=eps,
             added_kv_proj_dim=added_kv_proj_dim,
             processor=WanAttnProcessor(),
-            is_cross_attention=True,
         )
         self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
 
@@ -179,7 +178,6 @@ class WanVACETransformer3DModel(
     _no_split_modules = ["WanTransformerBlock", "WanVACETransformerBlock"]
     _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
     _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
-    _repeated_blocks = ["WanTransformerBlock", "WanVACETransformerBlock"]
 
     @register_to_config
     def __init__(

src/diffusers/quantizers/gguf/gguf_quantizer.py

@@ -41,7 +41,7 @@ class GGUFQuantizer(DiffusersQuantizer):
 
         self.compute_dtype = quantization_config.compute_dtype
         self.pre_quantized = quantization_config.pre_quantized
-        self.modules_to_not_convert = quantization_config.modules_to_not_convert or []
+        self.modules_to_not_convert = quantization_config.modules_to_not_convert
 
         if not isinstance(self.modules_to_not_convert, list):
             self.modules_to_not_convert = [self.modules_to_not_convert]

tests/models/testing_utils/common.py

@@ -446,17 +446,16 @@ class ModelTesterMixin:
         torch_device not in ["cuda", "xpu"],
         reason="float16 and bfloat16 can only be used with an accelerator",
     )
-    def test_keep_in_fp32_modules(self, tmp_path):
+    def test_keep_in_fp32_modules(self):
         model = self.model_class(**self.get_init_dict())
         fp32_modules = model._keep_in_fp32_modules
 
         if fp32_modules is None or len(fp32_modules) == 0:
             pytest.skip("Model does not have _keep_in_fp32_modules defined.")
 
-        # Save the model and reload with float16 dtype
-        # _keep_in_fp32_modules is only enforced during from_pretrained loading
-        model.save_pretrained(tmp_path)
-        model = self.model_class.from_pretrained(tmp_path, torch_dtype=torch.float16).to(torch_device)
+        # Test with float16
+        model.to(torch_device)
+        model.to(torch.float16)
 
         for name, param in model.named_parameters():
             if any(module_to_keep_in_fp32 in name.split(".") for module_to_keep_in_fp32 in fp32_modules):
@@ -471,7 +470,7 @@ class ModelTesterMixin:
     )
     @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
     @torch.no_grad()
-    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype, atol=1e-4, rtol=0):
+    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
         model = self.model_class(**self.get_init_dict())
         model.to(torch_device)
         fp32_modules = model._keep_in_fp32_modules or []
@@ -491,6 +490,10 @@ class ModelTesterMixin:
         output = model(**inputs, return_dict=False)[0]
         output_loaded = model_loaded(**inputs, return_dict=False)[0]
 
+        self._check_dtype_inference_output(output, output_loaded, dtype)
+
+    def _check_dtype_inference_output(self, output, output_loaded, dtype, atol=1e-4, rtol=0):
+        """Check dtype inference output with configurable tolerance."""
         assert_tensors_close(
             output, output_loaded, atol=atol, rtol=rtol, msg=f"Loaded model output differs for {dtype}"
         )

tests/models/testing_utils/quantization.py

@@ -176,7 +176,15 @@ class QuantizationTesterMixin:
         model_quantized = self._create_quantized_model(config_kwargs)
         model_quantized.to(torch_device)
 
+        # Get model dtype from first parameter
+        model_dtype = next(model_quantized.parameters()).dtype
+
         inputs = self.get_dummy_inputs()
+        # Cast inputs to model dtype
+        inputs = {
+            k: v.to(model_dtype) if isinstance(v, torch.Tensor) and v.is_floating_point() else v
+            for k, v in inputs.items()
+        }
         output = model_quantized(**inputs, return_dict=False)[0]
 
         assert output is not None, "Model output is None"
@@ -221,8 +229,6 @@ class QuantizationTesterMixin:
             init_lora_weights=False,
         )
         model.add_adapter(lora_config)
-        # Move LoRA adapter weights to device (they default to CPU)
-        model.to(torch_device)
 
         inputs = self.get_dummy_inputs()
         output = model(**inputs, return_dict=False)[0]
@@ -1015,6 +1021,9 @@ class GGUFTesterMixin(GGUFConfigMixin, QuantizationTesterMixin):
         """Test that dequantize() works correctly."""
         self._test_dequantize({"compute_dtype": torch.bfloat16})
 
+    def test_gguf_quantized_layers(self):
+        self._test_quantized_layers({"compute_dtype": torch.bfloat16})
+
 
 @is_quantization
 @is_modelopt

tests/models/transformers/test_models_transformer_flux.py

@@ -219,6 +219,10 @@ class TestFluxTransformerMemory(FluxTransformerTesterConfig, MemoryTesterMixin):
 class TestFluxTransformerTraining(FluxTransformerTesterConfig, TrainingTesterMixin):
     """Training tests for Flux Transformer."""
 
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"FluxTransformer2DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
 
 class TestFluxTransformerAttention(FluxTransformerTesterConfig, AttentionTesterMixin):
     """Attention processor tests for Flux Transformer."""

tests/models/transformers/test_models_transformer_flux2.py

@@ -13,48 +13,88 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
-from diffusers import Flux2Transformer2DModel, attention_backend
+from diffusers import Flux2Transformer2DModel
+from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..test_modeling_common import LoraHotSwappingForModelTesterMixin, ModelTesterMixin, TorchCompileTesterMixin
+from ..testing_utils import (
+    AttentionTesterMixin,
+    BaseModelTesterConfig,
+    BitsAndBytesTesterMixin,
+    ContextParallelTesterMixin,
+    GGUFCompileTesterMixin,
+    GGUFTesterMixin,
+    LoraHotSwappingForModelTesterMixin,
+    LoraTesterMixin,
+    MemoryTesterMixin,
+    ModelTesterMixin,
+    TorchAoTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class Flux2TransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = Flux2Transformer2DModel
-    main_input_name = "hidden_states"
-    # We override the items here because the transformer under consideration is small.
-    model_split_percents = [0.7, 0.6, 0.6]
-
-    # Skip setting testing with default: AttnProcessor
-    uses_custom_attn_processor = True
+class Flux2TransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return Flux2Transformer2DModel
 
     @property
-    def dummy_input(self):
-        return self.prepare_dummy_input()
-
-    @property
-    def input_shape(self):
+    def output_shape(self) -> tuple[int, int]:
         return (16, 4)
 
     @property
-    def output_shape(self):
+    def input_shape(self) -> tuple[int, int]:
         return (16, 4)
 
-    def prepare_dummy_input(self, height=4, width=4):
+    @property
+    def model_split_percents(self) -> list:
+        # We override the items here because the transformer under consideration is small.
+        return [0.7, 0.6, 0.6]
+
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def uses_custom_attn_processor(self) -> bool:
+        # Skip setting testing with default: AttnProcessor
+        return True
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict[str, int | list[int]]:
+        return {
+            "patch_size": 1,
+            "in_channels": 4,
+            "num_layers": 1,
+            "num_single_layers": 1,
+            "attention_head_dim": 16,
+            "num_attention_heads": 2,
+            "joint_attention_dim": 32,
+            "timestep_guidance_channels": 256,  # Hardcoded in original code
+            "axes_dims_rope": [4, 4, 4, 4],
+        }
+
+    def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:
         batch_size = 1
         num_latent_channels = 4
         sequence_length = 48
         embedding_dim = 32
 
-        hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
+        hidden_states = randn_tensor(
+            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
+        )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+        )
 
         t_coords = torch.arange(1)
         h_coords = torch.arange(height)
@@ -82,81 +122,244 @@ class Flux2TransformerTests(ModelTesterMixin, unittest.TestCase):
             "guidance": guidance,
         }
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "patch_size": 1,
-            "in_channels": 4,
-            "num_layers": 1,
-            "num_single_layers": 1,
-            "attention_head_dim": 16,
-            "num_attention_heads": 2,
-            "joint_attention_dim": 32,
-            "timestep_guidance_channels": 256,  # Hardcoded in original code
-            "axes_dims_rope": [4, 4, 4, 4],
-        }
 
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+class TestFlux2Transformer(Flux2TransformerTesterConfig, ModelTesterMixin):
+    pass
 
-    # TODO (Daniel, Sayak): We can remove this test.
-    def test_flux2_consistency(self, seed=0):
-        torch.manual_seed(seed)
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 
-        torch.manual_seed(seed)
-        model = self.model_class(**init_dict)
-        # state_dict = model.state_dict()
-        # for key, param in state_dict.items():
-        #     print(f"{key} | {param.shape}")
-        # torch.save(state_dict, "/raid/daniel_gu/test_flux2_params/diffusers.pt")
-        model.to(torch_device)
-        model.eval()
+class TestFlux2TransformerMemory(Flux2TransformerTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for Flux2 Transformer."""
 
-        with attention_backend("native"):
-            with torch.no_grad():
-                output = model(**inputs_dict)
 
-                if isinstance(output, dict):
-                    output = output.to_tuple()[0]
-
-        self.assertIsNotNone(output)
-
-        # input & output have to have the same shape
-        input_tensor = inputs_dict[self.main_input_name]
-        expected_shape = input_tensor.shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-        # Check against expected slice
-        # fmt: off
-        expected_slice = torch.tensor([-0.3662, 0.4844, 0.6334, -0.3497, 0.2162, 0.0188, 0.0521, -0.2061, -0.2041, -0.0342, -0.7107, 0.4797, -0.3280, 0.7059, -0.0849, 0.4416])
-        # fmt: on
-
-        flat_output = output.cpu().flatten()
-        generated_slice = torch.cat([flat_output[:8], flat_output[-8:]])
-        self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-4))
+class TestFlux2TransformerTraining(Flux2TransformerTesterConfig, TrainingTesterMixin):
+    """Training tests for Flux2 Transformer."""
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"Flux2Transformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
 
-class Flux2TransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = Flux2Transformer2DModel
-    different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return Flux2TransformerTests().prepare_init_args_and_inputs_for_common()
-
-    def prepare_dummy_input(self, height, width):
-        return Flux2TransformerTests().prepare_dummy_input(height=height, width=width)
+class TestFlux2TransformerAttention(Flux2TransformerTesterConfig, AttentionTesterMixin):
+    """Attention processor tests for Flux2 Transformer."""
 
 
-class Flux2TransformerLoRAHotSwapTests(LoraHotSwappingForModelTesterMixin, unittest.TestCase):
-    model_class = Flux2Transformer2DModel
-    different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
+class TestFlux2TransformerContextParallel(Flux2TransformerTesterConfig, ContextParallelTesterMixin):
+    """Context Parallel inference tests for Flux2 Transformer."""
 
-    def prepare_init_args_and_inputs_for_common(self):
-        return Flux2TransformerTests().prepare_init_args_and_inputs_for_common()
 
-    def prepare_dummy_input(self, height, width):
-        return Flux2TransformerTests().prepare_dummy_input(height=height, width=width)
+class TestFlux2TransformerLoRA(Flux2TransformerTesterConfig, LoraTesterMixin):
+    """LoRA adapter tests for Flux2 Transformer."""
+
+
+class TestFlux2TransformerLoRAHotSwap(Flux2TransformerTesterConfig, LoraHotSwappingForModelTesterMixin):
+    """LoRA hot-swapping tests for Flux2 Transformer."""
+
+    @property
+    def different_shapes_for_compilation(self):
+        return [(4, 4), (4, 8), (8, 8)]
+
+    def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:
+        """Override to support dynamic height/width for LoRA hotswap tests."""
+        batch_size = 1
+        num_latent_channels = 4
+        sequence_length = 48
+        embedding_dim = 32
+
+        hidden_states = randn_tensor(
+            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
+        )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+        )
+
+        t_coords = torch.arange(1)
+        h_coords = torch.arange(height)
+        w_coords = torch.arange(width)
+        l_coords = torch.arange(1)
+        image_ids = torch.cartesian_prod(t_coords, h_coords, w_coords, l_coords)
+        image_ids = image_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        text_t_coords = torch.arange(1)
+        text_h_coords = torch.arange(1)
+        text_w_coords = torch.arange(1)
+        text_l_coords = torch.arange(sequence_length)
+        text_ids = torch.cartesian_prod(text_t_coords, text_h_coords, text_w_coords, text_l_coords)
+        text_ids = text_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
+        guidance = torch.tensor([1.0]).to(torch_device).expand(batch_size)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "img_ids": image_ids,
+            "txt_ids": text_ids,
+            "timestep": timestep,
+            "guidance": guidance,
+        }
+
+
+class TestFlux2TransformerCompile(Flux2TransformerTesterConfig, TorchCompileTesterMixin):
+    @property
+    def different_shapes_for_compilation(self):
+        return [(4, 4), (4, 8), (8, 8)]
+
+    def get_dummy_inputs(self, height: int = 4, width: int = 4) -> dict[str, torch.Tensor]:
+        """Override to support dynamic height/width for compilation tests."""
+        batch_size = 1
+        num_latent_channels = 4
+        sequence_length = 48
+        embedding_dim = 32
+
+        hidden_states = randn_tensor(
+            (batch_size, height * width, num_latent_channels), generator=self.generator, device=torch_device
+        )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+        )
+
+        t_coords = torch.arange(1)
+        h_coords = torch.arange(height)
+        w_coords = torch.arange(width)
+        l_coords = torch.arange(1)
+        image_ids = torch.cartesian_prod(t_coords, h_coords, w_coords, l_coords)
+        image_ids = image_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        text_t_coords = torch.arange(1)
+        text_h_coords = torch.arange(1)
+        text_w_coords = torch.arange(1)
+        text_l_coords = torch.arange(sequence_length)
+        text_ids = torch.cartesian_prod(text_t_coords, text_h_coords, text_w_coords, text_l_coords)
+        text_ids = text_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
+        guidance = torch.tensor([1.0]).to(torch_device).expand(batch_size)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "img_ids": image_ids,
+            "txt_ids": text_ids,
+            "timestep": timestep,
+            "guidance": guidance,
+        }
+
+
+class TestFlux2TransformerBitsAndBytes(Flux2TransformerTesterConfig, BitsAndBytesTesterMixin):
+    """BitsAndBytes quantization tests for Flux2 Transformer."""
+
+
+class TestFlux2TransformerTorchAo(Flux2TransformerTesterConfig, TorchAoTesterMixin):
+    """TorchAO quantization tests for Flux2 Transformer."""
+
+
+class TestFlux2TransformerGGUF(Flux2TransformerTesterConfig, GGUFTesterMixin):
+    """GGUF quantization tests for Flux2 Transformer."""
+
+    @property
+    def gguf_filename(self):
+        return "https://huggingface.co/unsloth/FLUX.2-dev-GGUF/blob/main/flux2-dev-Q2_K.gguf"
+
+    @property
+    def torch_dtype(self):
+        return torch.bfloat16
+
+    def get_dummy_inputs(self):
+        """Override to provide inputs matching the real FLUX2 model dimensions.
+
+        Flux2 defaults: in_channels=128, joint_attention_dim=15360
+        """
+        batch_size = 1
+        height = 64
+        width = 64
+        sequence_length = 512
+
+        hidden_states = randn_tensor(
+            (batch_size, height * width, 128), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+        )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, 15360), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+        )
+
+        # Flux2 uses 4D image/text IDs (t, h, w, l)
+        t_coords = torch.arange(1)
+        h_coords = torch.arange(height)
+        w_coords = torch.arange(width)
+        l_coords = torch.arange(1)
+        image_ids = torch.cartesian_prod(t_coords, h_coords, w_coords, l_coords)
+        image_ids = image_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        text_t_coords = torch.arange(1)
+        text_h_coords = torch.arange(1)
+        text_w_coords = torch.arange(1)
+        text_l_coords = torch.arange(sequence_length)
+        text_ids = torch.cartesian_prod(text_t_coords, text_h_coords, text_w_coords, text_l_coords)
+        text_ids = text_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        timestep = torch.tensor([1.0]).to(torch_device, self.torch_dtype)
+        guidance = torch.tensor([3.5]).to(torch_device, self.torch_dtype)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "img_ids": image_ids,
+            "txt_ids": text_ids,
+            "timestep": timestep,
+            "guidance": guidance,
+        }
+
+
+class TestFlux2TransformerGGUFCompile(Flux2TransformerTesterConfig, GGUFCompileTesterMixin):
+    """GGUF + compile tests for Flux2 Transformer."""
+
+    @property
+    def gguf_filename(self):
+        return "https://huggingface.co/unsloth/FLUX.2-dev-GGUF/blob/main/flux2-dev-Q2_K.gguf"
+
+    @property
+    def torch_dtype(self):
+        return torch.bfloat16
+
+    def get_dummy_inputs(self):
+        """Override to provide inputs matching the real FLUX2 model dimensions.
+
+        Flux2 defaults: in_channels=128, joint_attention_dim=15360
+        """
+        batch_size = 1
+        height = 64
+        width = 64
+        sequence_length = 512
+
+        hidden_states = randn_tensor(
+            (batch_size, height * width, 128), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+        )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, 15360), generator=self.generator, device=torch_device, dtype=self.torch_dtype
+        )
+
+        # Flux2 uses 4D image/text IDs (t, h, w, l)
+        t_coords = torch.arange(1)
+        h_coords = torch.arange(height)
+        w_coords = torch.arange(width)
+        l_coords = torch.arange(1)
+        image_ids = torch.cartesian_prod(t_coords, h_coords, w_coords, l_coords)
+        image_ids = image_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        text_t_coords = torch.arange(1)
+        text_h_coords = torch.arange(1)
+        text_w_coords = torch.arange(1)
+        text_l_coords = torch.arange(sequence_length)
+        text_ids = torch.cartesian_prod(text_t_coords, text_h_coords, text_w_coords, text_l_coords)
+        text_ids = text_ids.unsqueeze(0).expand(batch_size, -1, -1).to(torch_device)
+
+        timestep = torch.tensor([1.0]).to(torch_device, self.torch_dtype)
+        guidance = torch.tensor([3.5]).to(torch_device, self.torch_dtype)
+
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "img_ids": image_ids,
+            "txt_ids": text_ids,
+            "timestep": timestep,
+            "guidance": guidance,
+        }

tests/models/transformers/test_models_transformer_wan.py

@@ -12,57 +12,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pytest
+import unittest
+
 import torch
 
 from diffusers import WanTransformer3DModel
-from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    AttentionTesterMixin,
-    BaseModelTesterConfig,
-    BitsAndBytesTesterMixin,
-    GGUFCompileTesterMixin,
-    GGUFTesterMixin,
-    MemoryTesterMixin,
-    ModelTesterMixin,
-    TorchAoTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
+from ...testing_utils import (
+    enable_full_determinism,
+    torch_device,
 )
+from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
 
 
 enable_full_determinism()
 
 
-class WanTransformer3DTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return WanTransformer3DModel
+class WanTransformer3DTests(ModelTesterMixin, unittest.TestCase):
+    model_class = WanTransformer3DModel
+    main_input_name = "hidden_states"
+    uses_custom_attn_processor = True
 
     @property
-    def pretrained_model_name_or_path(self):
-        return "hf-internal-testing/tiny-wan22-transformer"
+    def dummy_input(self):
+        batch_size = 1
+        num_channels = 4
+        num_frames = 2
+        height = 16
+        width = 16
+        text_encoder_embedding_dim = 16
+        sequence_length = 12
 
-    @property
-    def output_shape(self) -> tuple[int, ...]:
-        return (4, 2, 16, 16)
+        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, text_encoder_embedding_dim)).to(torch_device)
 
-    @property
-    def input_shape(self) -> tuple[int, ...]:
-        return (4, 2, 16, 16)
-
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict[str, int | list[int] | tuple | str | bool]:
         return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "timestep": timestep,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 1, 16, 16)
+
+    @property
+    def output_shape(self):
+        return (4, 1, 16, 16)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
             "patch_size": (1, 2, 2),
             "num_attention_heads": 2,
             "attention_head_dim": 12,
@@ -76,128 +76,16 @@ class WanTransformer3DTesterConfig(BaseModelTesterConfig):
             "qk_norm": "rms_norm_across_heads",
             "rope_max_seq_len": 32,
         }
-
-    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
-        batch_size = 1
-        num_channels = 4
-        num_frames = 2
-        height = 16
-        width = 16
-        text_encoder_embedding_dim = 16
-        sequence_length = 12
-
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, num_channels, num_frames, height, width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, text_encoder_embedding_dim),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-        }
-
-
-class TestWanTransformer3D(WanTransformer3DTesterConfig, ModelTesterMixin):
-    """Core model tests for Wan Transformer 3D."""
-
-    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
-    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
-        # Skip: fp16/bf16 require very high atol to pass, providing little signal.
-        # Dtype preservation is already tested by test_from_save_pretrained_dtype and test_keep_in_fp32_modules.
-        pytest.skip("Tolerance requirements too high for meaningful test")
-
-
-class TestWanTransformer3DMemory(WanTransformer3DTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for Wan Transformer 3D."""
-
-
-class TestWanTransformer3DTraining(WanTransformer3DTesterConfig, TrainingTesterMixin):
-    """Training tests for Wan Transformer 3D."""
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"WanTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
 
-class TestWanTransformer3DAttention(WanTransformer3DTesterConfig, AttentionTesterMixin):
-    """Attention processor tests for Wan Transformer 3D."""
+class WanTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
+    model_class = WanTransformer3DModel
 
-
-class TestWanTransformer3DCompile(WanTransformer3DTesterConfig, TorchCompileTesterMixin):
-    """Torch compile tests for Wan Transformer 3D."""
-
-
-class TestWanTransformer3DBitsAndBytes(WanTransformer3DTesterConfig, BitsAndBytesTesterMixin):
-    """BitsAndBytes quantization tests for Wan Transformer 3D."""
-
-
-class TestWanTransformer3DTorchAo(WanTransformer3DTesterConfig, TorchAoTesterMixin):
-    """TorchAO quantization tests for Wan Transformer 3D."""
-
-
-class TestWanTransformer3DGGUF(WanTransformer3DTesterConfig, GGUFTesterMixin):
-    """GGUF quantization tests for Wan Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.2-I2V-A14B-GGUF/blob/main/LowNoise/Wan2.2-I2V-A14B-LowNoise-Q2_K.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def _create_quantized_model(self, config_kwargs=None, **extra_kwargs):
-        return super()._create_quantized_model(
-            config_kwargs, config="Wan-AI/Wan2.2-I2V-A14B-Diffusers", subfolder="transformer", **extra_kwargs
-        )
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan I2V model dimensions.
-
-        Wan 2.2 I2V: in_channels=36, text_dim=4096
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 36, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }
-
-
-class TestWanTransformer3DGGUFCompile(WanTransformer3DTesterConfig, GGUFCompileTesterMixin):
-    """GGUF + compile tests for Wan Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.2-I2V-A14B-GGUF/blob/main/LowNoise/Wan2.2-I2V-A14B-LowNoise-Q2_K.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def _create_quantized_model(self, config_kwargs=None, **extra_kwargs):
-        return super()._create_quantized_model(
-            config_kwargs, config="Wan-AI/Wan2.2-I2V-A14B-Diffusers", subfolder="transformer", **extra_kwargs
-        )
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan I2V model dimensions.
-
-        Wan 2.2 I2V: in_channels=36, text_dim=4096
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 36, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }
+    def prepare_init_args_and_inputs_for_common(self):
+        return WanTransformer3DTests().prepare_init_args_and_inputs_for_common()

tests/models/transformers/test_models_transformer_wan_animate.py

@@ -12,62 +12,76 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import pytest
+import unittest
+
 import torch
 
 from diffusers import WanAnimateTransformer3DModel
-from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    AttentionTesterMixin,
-    BaseModelTesterConfig,
-    BitsAndBytesTesterMixin,
-    GGUFCompileTesterMixin,
-    GGUFTesterMixin,
-    MemoryTesterMixin,
-    ModelTesterMixin,
-    TorchAoTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
+from ...testing_utils import (
+    enable_full_determinism,
+    torch_device,
 )
+from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
 
 
 enable_full_determinism()
 
 
-class WanAnimateTransformer3DTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return WanAnimateTransformer3DModel
+class WanAnimateTransformer3DTests(ModelTesterMixin, unittest.TestCase):
+    model_class = WanAnimateTransformer3DModel
+    main_input_name = "hidden_states"
+    uses_custom_attn_processor = True
 
     @property
-    def pretrained_model_name_or_path(self):
-        return "hf-internal-testing/tiny-wan-animate-transformer"
+    def dummy_input(self):
+        batch_size = 1
+        num_channels = 4
+        num_frames = 20  # To make the shapes work out; for complicated reasons we want 21 to divide num_frames + 1
+        height = 16
+        width = 16
+        text_encoder_embedding_dim = 16
+        sequence_length = 12
+
+        clip_seq_len = 12
+        clip_dim = 16
+
+        inference_segment_length = 77  # The inference segment length in the full Wan2.2-Animate-14B model
+        face_height = 16  # Should be square and match `motion_encoder_size` below
+        face_width = 16
+
+        hidden_states = torch.randn((batch_size, 2 * num_channels + 4, num_frames + 1, height, width)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+        encoder_hidden_states = torch.randn((batch_size, sequence_length, text_encoder_embedding_dim)).to(torch_device)
+        clip_ref_features = torch.randn((batch_size, clip_seq_len, clip_dim)).to(torch_device)
+        pose_latents = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
+        face_pixel_values = torch.randn((batch_size, 3, inference_segment_length, face_height, face_width)).to(
+            torch_device
+        )
+
+        return {
+            "hidden_states": hidden_states,
+            "timestep": timestep,
+            "encoder_hidden_states": encoder_hidden_states,
+            "encoder_hidden_states_image": clip_ref_features,
+            "pose_hidden_states": pose_latents,
+            "face_pixel_values": face_pixel_values,
+        }
 
     @property
-    def output_shape(self) -> tuple[int, ...]:
-        # Output has fewer channels than input (4 vs 12)
-        return (4, 21, 16, 16)
+    def input_shape(self):
+        return (12, 1, 16, 16)
 
     @property
-    def input_shape(self) -> tuple[int, ...]:
-        return (12, 21, 16, 16)
+    def output_shape(self):
+        return (4, 1, 16, 16)
 
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict[str, int | list[int] | tuple | str | bool | float | dict]:
+    def prepare_init_args_and_inputs_for_common(self):
         # Use custom channel sizes since the default Wan Animate channel sizes will cause the motion encoder to
         # contain the vast majority of the parameters in the test model
         channel_sizes = {"4": 16, "8": 16, "16": 16}
 
-        return {
+        init_dict = {
             "patch_size": (1, 2, 2),
             "num_attention_heads": 2,
             "attention_head_dim": 12,
@@ -91,169 +105,22 @@ class WanAnimateTransformer3DTesterConfig(BaseModelTesterConfig):
             "face_encoder_num_heads": 2,
             "inject_face_latents_blocks": 2,
         }
-
-    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
-        batch_size = 1
-        num_channels = 4
-        num_frames = 20  # To make the shapes work out; for complicated reasons we want 21 to divide num_frames + 1
-        height = 16
-        width = 16
-        text_encoder_embedding_dim = 16
-        sequence_length = 12
-
-        clip_seq_len = 12
-        clip_dim = 16
-
-        inference_segment_length = 77  # The inference segment length in the full Wan2.2-Animate-14B model
-        face_height = 16  # Should be square and match `motion_encoder_size`
-        face_width = 16
-
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, 2 * num_channels + 4, num_frames + 1, height, width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, text_encoder_embedding_dim),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "encoder_hidden_states_image": randn_tensor(
-                (batch_size, clip_seq_len, clip_dim),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "pose_hidden_states": randn_tensor(
-                (batch_size, num_channels, num_frames, height, width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "face_pixel_values": randn_tensor(
-                (batch_size, 3, inference_segment_length, face_height, face_width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-        }
-
-
-class TestWanAnimateTransformer3D(WanAnimateTransformer3DTesterConfig, ModelTesterMixin):
-    """Core model tests for Wan Animate Transformer 3D."""
-
-    def test_output(self):
-        # Override test_output because the transformer output is expected to have less channels
-        # than the main transformer input.
-        expected_output_shape = (1, 4, 21, 16, 16)
-        super().test_output(expected_output_shape=expected_output_shape)
-
-    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
-    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
-        # Skip: fp16/bf16 require very high atol (~1e-2) to pass, providing little signal.
-        # Dtype preservation is already tested by test_from_save_pretrained_dtype and test_keep_in_fp32_modules.
-        pytest.skip("Tolerance requirements too high for meaningful test")
-
-
-class TestWanAnimateTransformer3DMemory(WanAnimateTransformer3DTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for Wan Animate Transformer 3D."""
-
-
-class TestWanAnimateTransformer3DTraining(WanAnimateTransformer3DTesterConfig, TrainingTesterMixin):
-    """Training tests for Wan Animate Transformer 3D."""
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
 
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"WanAnimateTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
-
-class TestWanAnimateTransformer3DAttention(WanAnimateTransformer3DTesterConfig, AttentionTesterMixin):
-    """Attention processor tests for Wan Animate Transformer 3D."""
+    # Override test_output because the transformer output is expected to have less channels than the main transformer
+    # input.
+    def test_output(self):
+        expected_output_shape = (1, 4, 21, 16, 16)
+        super().test_output(expected_output_shape=expected_output_shape)
 
 
-class TestWanAnimateTransformer3DCompile(WanAnimateTransformer3DTesterConfig, TorchCompileTesterMixin):
-    """Torch compile tests for Wan Animate Transformer 3D."""
+class WanAnimateTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
+    model_class = WanAnimateTransformer3DModel
 
-    def test_torch_compile_recompilation_and_graph_break(self):
-        # Skip: F.pad with mode="replicate" in WanAnimateFaceEncoder triggers importlib.import_module
-        # internally, which dynamo doesn't support tracing through.
-        pytest.skip("F.pad with replicate mode triggers unsupported import in torch.compile")
-
-
-class TestWanAnimateTransformer3DBitsAndBytes(WanAnimateTransformer3DTesterConfig, BitsAndBytesTesterMixin):
-    """BitsAndBytes quantization tests for Wan Animate Transformer 3D."""
-
-
-class TestWanAnimateTransformer3DTorchAo(WanAnimateTransformer3DTesterConfig, TorchAoTesterMixin):
-    """TorchAO quantization tests for Wan Animate Transformer 3D."""
-
-
-class TestWanAnimateTransformer3DGGUF(WanAnimateTransformer3DTesterConfig, GGUFTesterMixin):
-    """GGUF quantization tests for Wan Animate Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.2-Animate-14B-GGUF/blob/main/Wan2.2-Animate-14B-Q2_K.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan Animate model dimensions.
-
-        Wan 2.2 Animate: in_channels=36 (2*16+4), text_dim=4096, image_dim=1280
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 36, 21, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states_image": randn_tensor(
-                (1, 257, 1280), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "pose_hidden_states": randn_tensor(
-                (1, 16, 20, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "face_pixel_values": randn_tensor(
-                (1, 3, 77, 512, 512), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }
-
-
-class TestWanAnimateTransformer3DGGUFCompile(WanAnimateTransformer3DTesterConfig, GGUFCompileTesterMixin):
-    """GGUF + compile tests for Wan Animate Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.2-Animate-14B-GGUF/blob/main/Wan2.2-Animate-14B-Q2_K.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan Animate model dimensions.
-
-        Wan 2.2 Animate: in_channels=36 (2*16+4), text_dim=4096, image_dim=1280
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 36, 21, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states_image": randn_tensor(
-                (1, 257, 1280), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "pose_hidden_states": randn_tensor(
-                (1, 16, 20, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "face_pixel_values": randn_tensor(
-                (1, 3, 77, 512, 512), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }
+    def prepare_init_args_and_inputs_for_common(self):
+        return WanAnimateTransformer3DTests().prepare_init_args_and_inputs_for_common()

tests/models/transformers/test_models_transformer_wan_vace.py

@@ -1,233 +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 pytest
-import torch
-
-from diffusers import WanVACETransformer3DModel
-from diffusers.utils.torch_utils import randn_tensor
-
-from ...testing_utils import enable_full_determinism, torch_device
-from ..testing_utils import (
-    AttentionTesterMixin,
-    BaseModelTesterConfig,
-    BitsAndBytesTesterMixin,
-    GGUFCompileTesterMixin,
-    GGUFTesterMixin,
-    MemoryTesterMixin,
-    ModelTesterMixin,
-    TorchAoTesterMixin,
-    TorchCompileTesterMixin,
-    TrainingTesterMixin,
-)
-
-
-enable_full_determinism()
-
-
-class WanVACETransformer3DTesterConfig(BaseModelTesterConfig):
-    @property
-    def model_class(self):
-        return WanVACETransformer3DModel
-
-    @property
-    def pretrained_model_name_or_path(self):
-        return "hf-internal-testing/tiny-wan-vace-transformer"
-
-    @property
-    def output_shape(self) -> tuple[int, ...]:
-        return (16, 2, 16, 16)
-
-    @property
-    def input_shape(self) -> tuple[int, ...]:
-        return (16, 2, 16, 16)
-
-    @property
-    def main_input_name(self) -> str:
-        return "hidden_states"
-
-    @property
-    def generator(self):
-        return torch.Generator("cpu").manual_seed(0)
-
-    def get_init_dict(self) -> dict[str, int | list[int] | tuple | str | bool | None]:
-        return {
-            "patch_size": (1, 2, 2),
-            "num_attention_heads": 2,
-            "attention_head_dim": 12,
-            "in_channels": 16,
-            "out_channels": 16,
-            "text_dim": 32,
-            "freq_dim": 256,
-            "ffn_dim": 32,
-            "num_layers": 4,
-            "cross_attn_norm": True,
-            "qk_norm": "rms_norm_across_heads",
-            "rope_max_seq_len": 32,
-            "vace_layers": [0, 2],
-            "vace_in_channels": 48,  # 3 * in_channels = 3 * 16 = 48
-        }
-
-    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
-        batch_size = 1
-        num_channels = 16
-        num_frames = 2
-        height = 16
-        width = 16
-        text_encoder_embedding_dim = 32
-        sequence_length = 12
-
-        # VACE requires control_hidden_states with vace_in_channels (3 * in_channels)
-        vace_in_channels = 48
-
-        return {
-            "hidden_states": randn_tensor(
-                (batch_size, num_channels, num_frames, height, width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (batch_size, sequence_length, text_encoder_embedding_dim),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "control_hidden_states": randn_tensor(
-                (batch_size, vace_in_channels, num_frames, height, width),
-                generator=self.generator,
-                device=torch_device,
-            ),
-            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
-        }
-
-
-class TestWanVACETransformer3D(WanVACETransformer3DTesterConfig, ModelTesterMixin):
-    """Core model tests for Wan VACE Transformer 3D."""
-
-    @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
-    def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
-        # Skip: fp16/bf16 require very high atol to pass, providing little signal.
-        # Dtype preservation is already tested by test_from_save_pretrained_dtype and test_keep_in_fp32_modules.
-        pytest.skip("Tolerance requirements too high for meaningful test")
-
-    def test_model_parallelism(self, tmp_path):
-        # Skip: Device mismatch between cuda:0 and cuda:1 in VACE control flow
-        pytest.skip("Model parallelism not yet supported for WanVACE")
-
-
-class TestWanVACETransformer3DMemory(WanVACETransformer3DTesterConfig, MemoryTesterMixin):
-    """Memory optimization tests for Wan VACE Transformer 3D."""
-
-
-class TestWanVACETransformer3DTraining(WanVACETransformer3DTesterConfig, TrainingTesterMixin):
-    """Training tests for Wan VACE Transformer 3D."""
-
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"WanVACETransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class TestWanVACETransformer3DAttention(WanVACETransformer3DTesterConfig, AttentionTesterMixin):
-    """Attention processor tests for Wan VACE Transformer 3D."""
-
-
-class TestWanVACETransformer3DCompile(WanVACETransformer3DTesterConfig, TorchCompileTesterMixin):
-    """Torch compile tests for Wan VACE Transformer 3D."""
-
-    def test_torch_compile_repeated_blocks(self):
-        # WanVACE has two block types (WanTransformerBlock and WanVACETransformerBlock),
-        # so we need recompile_limit=2 instead of the default 1.
-        import torch._dynamo
-        import torch._inductor.utils
-
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-
-        model = self.model_class(**init_dict).to(torch_device)
-        model.eval()
-        model.compile_repeated_blocks(fullgraph=True)
-
-        with (
-            torch._inductor.utils.fresh_inductor_cache(),
-            torch._dynamo.config.patch(recompile_limit=2),
-        ):
-            _ = model(**inputs_dict)
-            _ = model(**inputs_dict)
-
-
-class TestWanVACETransformer3DBitsAndBytes(WanVACETransformer3DTesterConfig, BitsAndBytesTesterMixin):
-    """BitsAndBytes quantization tests for Wan VACE Transformer 3D."""
-
-
-class TestWanVACETransformer3DTorchAo(WanVACETransformer3DTesterConfig, TorchAoTesterMixin):
-    """TorchAO quantization tests for Wan VACE Transformer 3D."""
-
-
-class TestWanVACETransformer3DGGUF(WanVACETransformer3DTesterConfig, GGUFTesterMixin):
-    """GGUF quantization tests for Wan VACE Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/blob/main/Wan2.1_14B_VACE-Q3_K_S.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan VACE model dimensions.
-
-        Wan 2.1 VACE: in_channels=16, text_dim=4096, vace_in_channels=96
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 16, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "control_hidden_states": randn_tensor(
-                (1, 96, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }
-
-
-class TestWanVACETransformer3DGGUFCompile(WanVACETransformer3DTesterConfig, GGUFCompileTesterMixin):
-    """GGUF + compile tests for Wan VACE Transformer 3D."""
-
-    @property
-    def gguf_filename(self):
-        return "https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/blob/main/Wan2.1_14B_VACE-Q3_K_S.gguf"
-
-    @property
-    def torch_dtype(self):
-        return torch.bfloat16
-
-    def get_dummy_inputs(self):
-        """Override to provide inputs matching the real Wan VACE model dimensions.
-
-        Wan 2.1 VACE: in_channels=16, text_dim=4096, vace_in_channels=96
-        """
-        return {
-            "hidden_states": randn_tensor(
-                (1, 16, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "encoder_hidden_states": randn_tensor(
-                (1, 512, 4096), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "control_hidden_states": randn_tensor(
-                (1, 96, 2, 64, 64), generator=self.generator, device=torch_device, dtype=self.torch_dtype
-            ),
-            "timestep": torch.tensor([1.0]).to(torch_device, self.torch_dtype),
-        }

tests/testing_utils.py

@@ -168,7 +168,7 @@ def assert_tensors_close(
         max_diff = abs_diff.max().item()
 
         flat_idx = abs_diff.argmax().item()
-        max_idx = tuple(idx.item() for idx in torch.unravel_index(torch.tensor(flat_idx), actual.shape))
+        max_idx = tuple(torch.unravel_index(torch.tensor(flat_idx), actual.shape).tolist())
 
         threshold = atol + rtol * expected.abs()
         mismatched = (abs_diff > threshold).sum().item()