update

tests/models/transformers/test_models_transformer_hunyuan_1_5.py

@@ -12,71 +12,53 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import HunyuanVideo15Transformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..test_modeling_common import ModelTesterMixin
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class HunyuanVideo15Transformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideo15Transformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-    model_split_percents = [0.99, 0.99, 0.99]
-
+class HunyuanVideo15TransformerTesterConfig(BaseModelTesterConfig):
     text_embed_dim = 16
     text_embed_2_dim = 8
     image_embed_dim = 12
 
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 4
-        num_frames = 1
-        height = 8
-        width = 8
-        sequence_length = 6
-        sequence_length_2 = 4
-        image_sequence_length = 3
+    def model_class(self):
+        return HunyuanVideo15Transformer3DModel
 
-        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
-        timestep = torch.tensor([1.0]).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, self.text_embed_dim), device=torch_device)
-        encoder_hidden_states_2 = torch.randn(
-            (batch_size, sequence_length_2, self.text_embed_2_dim), device=torch_device
-        )
-        encoder_attention_mask = torch.ones((batch_size, sequence_length), device=torch_device)
-        encoder_attention_mask_2 = torch.ones((batch_size, sequence_length_2), device=torch_device)
-        # All zeros for inducing T2V path in the model.
-        image_embeds = torch.zeros((batch_size, image_sequence_length, self.image_embed_dim), device=torch_device)
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
+    @property
+    def model_split_percents(self) -> list:
+        return [0.99, 0.99, 0.99]
+
+    @property
+    def output_shape(self) -> tuple:
+        return (4, 1, 8, 8)
+
+    @property
+    def input_shape(self) -> tuple:
+        return (4, 1, 8, 8)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "encoder_attention_mask": encoder_attention_mask,
-            "encoder_hidden_states_2": encoder_hidden_states_2,
-            "encoder_attention_mask_2": encoder_attention_mask_2,
-            "image_embeds": image_embeds,
-        }
-
-    @property
-    def input_shape(self):
-        return (4, 1, 8, 8)
-
-    @property
-    def output_shape(self):
-        return (4, 1, 8, 8)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
             "in_channels": 4,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -93,9 +75,40 @@ class HunyuanVideo15Transformer3DTests(ModelTesterMixin, unittest.TestCase):
             "target_size": 16,
             "task_type": "t2v",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        num_frames = 1
+        height = 8
+        width = 8
+        sequence_length = 6
+        sequence_length_2 = 4
+        image_sequence_length = 3
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_channels, num_frames, height, width), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.tensor([1.0]).to(torch_device).expand(batch_size),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, self.text_embed_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_hidden_states_2": randn_tensor(
+                (batch_size, sequence_length_2, self.text_embed_2_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length), device=torch_device),
+            "encoder_attention_mask_2": torch.ones((batch_size, sequence_length_2), device=torch_device),
+            "image_embeds": torch.zeros(
+                (batch_size, image_sequence_length, self.image_embed_dim), device=torch_device
+            ),
+        }
+
+
+class TestHunyuanVideo15Transformer(HunyuanVideo15TransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestHunyuanVideo15TransformerTraining(HunyuanVideo15TransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"HunyuanVideo15Transformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)

tests/models/transformers/test_models_transformer_hunyuan_dit.py

@@ -13,75 +13,56 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import HunyuanDiT2DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    BitsAndBytesTesterMixin,
+    ModelTesterMixin,
+    TorchAoTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
 )
-from ..test_modeling_common import ModelTesterMixin
 
 
 enable_full_determinism()
 
 
-class HunyuanDiTTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanDiT2DModel
-    main_input_name = "hidden_states"
+class HunyuanDiTTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return HunyuanDiT2DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 2
-        num_channels = 4
-        height = width = 8
-        embedding_dim = 8
-        sequence_length = 4
-        sequence_length_t5 = 4
-
-        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        text_embedding_mask = torch.ones(size=(batch_size, sequence_length)).to(torch_device)
-        encoder_hidden_states_t5 = torch.randn((batch_size, sequence_length_t5, embedding_dim)).to(torch_device)
-        text_embedding_mask_t5 = torch.ones(size=(batch_size, sequence_length_t5)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,), dtype=encoder_hidden_states.dtype).to(torch_device)
-
-        original_size = [1024, 1024]
-        target_size = [16, 16]
-        crops_coords_top_left = [0, 0]
-        add_time_ids = list(original_size + target_size + crops_coords_top_left)
-        add_time_ids = torch.tensor([add_time_ids, add_time_ids], dtype=encoder_hidden_states.dtype).to(torch_device)
-        style = torch.zeros(size=(batch_size,), dtype=int).to(torch_device)
-        image_rotary_emb = [
-            torch.ones(size=(1, 8), dtype=encoder_hidden_states.dtype),
-            torch.zeros(size=(1, 8), dtype=encoder_hidden_states.dtype),
-        ]
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "text_embedding_mask": text_embedding_mask,
-            "encoder_hidden_states_t5": encoder_hidden_states_t5,
-            "text_embedding_mask_t5": text_embedding_mask_t5,
-            "timestep": timestep,
-            "image_meta_size": add_time_ids,
-            "style": style,
-            "image_rotary_emb": image_rotary_emb,
-        }
+    def pretrained_model_name_or_path(self):
+        return "hf-internal-testing/tiny-hunyuan-dit-pipe"
 
     @property
-    def input_shape(self):
+    def pretrained_model_kwargs(self):
+        return {"subfolder": "transformer"}
+
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def output_shape(self) -> tuple:
+        return (8, 8, 8)
+
+    @property
+    def input_shape(self) -> tuple:
         return (4, 8, 8)
 
     @property
-    def output_shape(self):
-        return (8, 8, 8)
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "sample_size": 8,
             "patch_size": 2,
             "in_channels": 4,
@@ -96,18 +77,70 @@ class HunyuanDiTTests(ModelTesterMixin, unittest.TestCase):
             "text_len_t5": 4,
             "activation_fn": "gelu-approximate",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_output(self):
-        super().test_output(
-            expected_output_shape=(self.dummy_input[self.main_input_name].shape[0],) + self.output_shape
+    def get_dummy_inputs(self, batch_size: int = 2) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        height = width = 8
+        embedding_dim = 8
+        sequence_length = 4
+        sequence_length_t5 = 4
+
+        hidden_states = randn_tensor(
+            (batch_size, num_channels, height, width), generator=self.generator, device=torch_device
         )
+        encoder_hidden_states = randn_tensor(
+            (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+        )
+        text_embedding_mask = torch.ones(size=(batch_size, sequence_length)).to(torch_device)
+        encoder_hidden_states_t5 = randn_tensor(
+            (batch_size, sequence_length_t5, embedding_dim), generator=self.generator, device=torch_device
+        )
+        text_embedding_mask_t5 = torch.ones(size=(batch_size, sequence_length_t5)).to(torch_device)
+        timestep = torch.randint(0, 1000, size=(batch_size,), generator=self.generator).float().to(torch_device)
 
-    @unittest.skip("HunyuanDIT use a custom processor HunyuanAttnProcessor2_0")
-    def test_set_xformers_attn_processor_for_determinism(self):
-        pass
+        original_size = [1024, 1024]
+        target_size = [16, 16]
+        crops_coords_top_left = [0, 0]
+        add_time_ids = list(original_size + target_size + crops_coords_top_left)
+        add_time_ids = torch.tensor([add_time_ids] * batch_size, dtype=torch.float32).to(torch_device)
+        style = torch.zeros(size=(batch_size,), dtype=int).to(torch_device)
+        image_rotary_emb = [
+            torch.ones(size=(1, 8), dtype=torch.float32),
+            torch.zeros(size=(1, 8), dtype=torch.float32),
+        ]
 
-    @unittest.skip("HunyuanDIT use a custom processor HunyuanAttnProcessor2_0")
-    def test_set_attn_processor_for_determinism(self):
-        pass
+        return {
+            "hidden_states": hidden_states,
+            "encoder_hidden_states": encoder_hidden_states,
+            "text_embedding_mask": text_embedding_mask,
+            "encoder_hidden_states_t5": encoder_hidden_states_t5,
+            "text_embedding_mask_t5": text_embedding_mask_t5,
+            "timestep": timestep,
+            "image_meta_size": add_time_ids,
+            "style": style,
+            "image_rotary_emb": image_rotary_emb,
+        }
+
+
+class TestHunyuanDiT(HunyuanDiTTesterConfig, ModelTesterMixin):
+    def test_output(self):
+        batch_size = self.get_dummy_inputs()[self.main_input_name].shape[0]
+        super().test_output(expected_output_shape=(batch_size,) + self.output_shape)
+
+
+class TestHunyuanDiTTraining(HunyuanDiTTesterConfig, TrainingTesterMixin):
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"HunyuanDiT2DModel"}
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+
+class TestHunyuanDiTCompile(HunyuanDiTTesterConfig, TorchCompileTesterMixin):
+    pass
+
+
+class TestHunyuanDiTBitsAndBytes(HunyuanDiTTesterConfig, BitsAndBytesTesterMixin):
+    """BitsAndBytes quantization tests for HunyuanDiT."""
+
+
+class TestHunyuanDiTTorchAo(HunyuanDiTTesterConfig, TorchAoTesterMixin):
+    """TorchAO quantization tests for HunyuanDiT."""

tests/models/transformers/test_models_transformer_hunyuan_video.py

@@ -12,64 +12,59 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import HunyuanVideoTransformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    BitsAndBytesTesterMixin,
+    ModelTesterMixin,
+    TorchAoTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
 )
-from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
 
 
 enable_full_determinism()
 
 
-class HunyuanVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
+# ======================== HunyuanVideo Text-to-Video ========================
+
+
+class HunyuanVideoTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return HunyuanVideoTransformer3DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 4
-        num_frames = 1
-        height = 16
-        width = 16
-        text_encoder_embedding_dim = 16
-        pooled_projection_dim = 8
-        sequence_length = 12
+    def pretrained_model_name_or_path(self):
+        return "hf-internal-testing/tiny-random-hunyuanvideo"
 
-        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)
-        pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        guidance = torch.randint(0, 1000, size=(batch_size,)).to(torch_device, dtype=torch.float32)
+    @property
+    def pretrained_model_kwargs(self):
+        return {"subfolder": "transformer"}
 
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def output_shape(self) -> tuple:
+        return (4, 1, 16, 16)
+
+    @property
+    def input_shape(self) -> tuple:
+        return (4, 1, 16, 16)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "pooled_projections": pooled_projections,
-            "encoder_attention_mask": encoder_attention_mask,
-            "guidance": guidance,
-        }
-
-    @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 = {
             "in_channels": 4,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -85,30 +80,9 @@ class HunyuanVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
             "rope_axes_dim": (2, 4, 4),
             "image_condition_type": None,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"HunyuanVideoTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class HunyuanTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return HunyuanVideoTransformer3DTests().prepare_init_args_and_inputs_for_common()
-
-
-class HunyuanSkyreelsImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-
-    @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 8
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 4
         num_frames = 1
         height = 16
         width = 16
@@ -116,105 +90,74 @@ class HunyuanSkyreelsImageToVideoTransformer3DTests(ModelTesterMixin, unittest.T
         pooled_projection_dim = 8
         sequence_length = 12
 
-        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)
-        pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        guidance = torch.randint(0, 1000, size=(batch_size,)).to(torch_device, dtype=torch.float32)
-
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "pooled_projections": pooled_projections,
-            "encoder_attention_mask": encoder_attention_mask,
-            "guidance": guidance,
+            "hidden_states": randn_tensor(
+                (batch_size, num_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),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_encoder_embedding_dim),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "pooled_projections": randn_tensor(
+                (batch_size, pooled_projection_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
+            "guidance": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(
+                torch_device, dtype=torch.float32
+            ),
         }
 
-    @property
-    def input_shape(self):
-        return (8, 1, 16, 16)
 
-    @property
-    def output_shape(self):
-        return (4, 1, 16, 16)
+class TestHunyuanVideoTransformer(HunyuanVideoTransformerTesterConfig, ModelTesterMixin):
+    pass
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "in_channels": 8,
-            "out_channels": 4,
-            "num_attention_heads": 2,
-            "attention_head_dim": 10,
-            "num_layers": 1,
-            "num_single_layers": 1,
-            "num_refiner_layers": 1,
-            "patch_size": 1,
-            "patch_size_t": 1,
-            "guidance_embeds": True,
-            "text_embed_dim": 16,
-            "pooled_projection_dim": 8,
-            "rope_axes_dim": (2, 4, 4),
-            "image_condition_type": None,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def test_output(self):
-        super().test_output(expected_output_shape=(1, *self.output_shape))
 
+class TestHunyuanVideoTransformerTraining(HunyuanVideoTransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"HunyuanVideoTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
 
-class HunyuanSkyreelsImageToVideoCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return HunyuanSkyreelsImageToVideoTransformer3DTests().prepare_init_args_and_inputs_for_common()
+class TestHunyuanVideoTransformerCompile(HunyuanVideoTransformerTesterConfig, TorchCompileTesterMixin):
+    pass
 
 
-class HunyuanVideoImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
+class TestHunyuanVideoTransformerBitsAndBytes(HunyuanVideoTransformerTesterConfig, BitsAndBytesTesterMixin):
+    """BitsAndBytes quantization tests for HunyuanVideo Transformer."""
+
+
+class TestHunyuanVideoTransformerTorchAo(HunyuanVideoTransformerTesterConfig, TorchAoTesterMixin):
+    """TorchAO quantization tests for HunyuanVideo Transformer."""
+
+
+# ======================== HunyuanVideo Image-to-Video (Latent Concat) ========================
+
+
+class HunyuanVideoI2VTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return HunyuanVideoTransformer3DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 2 * 4 + 1
-        num_frames = 1
-        height = 16
-        width = 16
-        text_encoder_embedding_dim = 16
-        pooled_projection_dim = 8
-        sequence_length = 12
-
-        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)
-        pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-
-        return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "pooled_projections": pooled_projections,
-            "encoder_attention_mask": encoder_attention_mask,
-        }
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
     @property
-    def input_shape(self):
+    def output_shape(self) -> tuple:
+        return (4, 1, 16, 16)
+
+    @property
+    def input_shape(self) -> tuple:
         return (8, 1, 16, 16)
 
     @property
-    def output_shape(self):
-        return (4, 1, 16, 16)
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "in_channels": 2 * 4 + 1,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -230,33 +173,9 @@ class HunyuanVideoImageToVideoTransformer3DTests(ModelTesterMixin, unittest.Test
             "rope_axes_dim": (2, 4, 4),
             "image_condition_type": "latent_concat",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
-    def test_output(self):
-        super().test_output(expected_output_shape=(1, *self.output_shape))
-
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"HunyuanVideoTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
-
-
-class HunyuanImageToVideoCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return HunyuanVideoImageToVideoTransformer3DTests().prepare_init_args_and_inputs_for_common()
-
-
-class HunyuanVideoTokenReplaceImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-
-    @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 2
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 2 * 4 + 1
         num_frames = 1
         height = 16
         width = 16
@@ -264,32 +183,58 @@ class HunyuanVideoTokenReplaceImageToVideoTransformer3DTests(ModelTesterMixin, u
         pooled_projection_dim = 8
         sequence_length = 12
 
-        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)
-        pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        guidance = torch.randint(0, 1000, size=(batch_size,)).to(torch_device, dtype=torch.float32)
-
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "pooled_projections": pooled_projections,
-            "encoder_attention_mask": encoder_attention_mask,
-            "guidance": guidance,
+            "hidden_states": randn_tensor(
+                (batch_size, num_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),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_encoder_embedding_dim),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "pooled_projections": randn_tensor(
+                (batch_size, pooled_projection_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
         }
 
+
+class TestHunyuanVideoI2VTransformer(HunyuanVideoI2VTransformerTesterConfig, ModelTesterMixin):
+    def test_output(self):
+        super().test_output(expected_output_shape=(1, *self.output_shape))
+
+
+class TestHunyuanVideoI2VTransformerCompile(HunyuanVideoI2VTransformerTesterConfig, TorchCompileTesterMixin):
+    pass
+
+
+# ======================== HunyuanVideo Token Replace Image-to-Video ========================
+
+
+class HunyuanVideoTokenReplaceTransformerTesterConfig(BaseModelTesterConfig):
     @property
-    def input_shape(self):
+    def model_class(self):
+        return HunyuanVideoTransformer3DModel
+
+    @property
+    def main_input_name(self) -> str:
+        return "hidden_states"
+
+    @property
+    def output_shape(self) -> tuple:
+        return (4, 1, 16, 16)
+
+    @property
+    def input_shape(self) -> tuple:
         return (8, 1, 16, 16)
 
     @property
-    def output_shape(self):
-        return (4, 1, 16, 16)
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "in_channels": 2,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -305,19 +250,42 @@ class HunyuanVideoTokenReplaceImageToVideoTransformer3DTests(ModelTesterMixin, u
             "rope_axes_dim": (2, 4, 4),
             "image_condition_type": "token_replace",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 2
+        num_frames = 1
+        height = 16
+        width = 16
+        text_encoder_embedding_dim = 16
+        pooled_projection_dim = 8
+        sequence_length = 12
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_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),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_encoder_embedding_dim),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "pooled_projections": randn_tensor(
+                (batch_size, pooled_projection_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
+            "guidance": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(
+                torch_device, dtype=torch.float32
+            ),
+        }
+
+
+class TestHunyuanVideoTokenReplaceTransformer(HunyuanVideoTokenReplaceTransformerTesterConfig, ModelTesterMixin):
     def test_output(self):
         super().test_output(expected_output_shape=(1, *self.output_shape))
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"HunyuanVideoTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
-
-class HunyuanVideoTokenReplaceCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoTransformer3DModel
-
-    def prepare_init_args_and_inputs_for_common(self):
-        return HunyuanVideoTokenReplaceImageToVideoTransformer3DTests().prepare_init_args_and_inputs_for_common()
+class TestHunyuanVideoTokenReplaceTransformerCompile(
+    HunyuanVideoTokenReplaceTransformerTesterConfig, TorchCompileTesterMixin
+):
+    pass

tests/models/transformers/test_models_transformer_hunyuan_video_framepack.py

@@ -12,84 +12,49 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import HunyuanVideoFramepackTransformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    enable_full_determinism,
-    torch_device,
+from ...testing_utils import enable_full_determinism, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    ModelTesterMixin,
+    TrainingTesterMixin,
 )
-from ..test_modeling_common import ModelTesterMixin
 
 
 enable_full_determinism()
 
 
-class HunyuanVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
-    model_class = HunyuanVideoFramepackTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
-    model_split_percents = [0.5, 0.7, 0.9]
+class HunyuanVideoFramepackTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return HunyuanVideoFramepackTransformer3DModel
 
     @property
-    def dummy_input(self):
-        batch_size = 1
-        num_channels = 4
-        num_frames = 3
-        height = 4
-        width = 4
-        text_encoder_embedding_dim = 16
-        image_encoder_embedding_dim = 16
-        pooled_projection_dim = 8
-        sequence_length = 12
+    def main_input_name(self) -> str:
+        return "hidden_states"
 
-        hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, text_encoder_embedding_dim)).to(torch_device)
-        pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
-        image_embeds = torch.randn((batch_size, sequence_length, image_encoder_embedding_dim)).to(torch_device)
-        indices_latents = torch.ones((3,)).to(torch_device)
-        latents_clean = torch.randn((batch_size, num_channels, num_frames - 1, height, width)).to(torch_device)
-        indices_latents_clean = torch.ones((num_frames - 1,)).to(torch_device)
-        latents_history_2x = torch.randn((batch_size, num_channels, num_frames - 1, height, width)).to(torch_device)
-        indices_latents_history_2x = torch.ones((num_frames - 1,)).to(torch_device)
-        latents_history_4x = torch.randn((batch_size, num_channels, (num_frames - 1) * 4, height, width)).to(
-            torch_device
-        )
-        indices_latents_history_4x = torch.ones(((num_frames - 1) * 4,)).to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-        guidance = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
+    @property
+    def model_split_percents(self) -> list:
+        return [0.5, 0.7, 0.9]
 
+    @property
+    def output_shape(self) -> tuple:
+        return (4, 3, 4, 4)
+
+    @property
+    def input_shape(self) -> tuple:
+        return (4, 3, 4, 4)
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self) -> dict:
         return {
-            "hidden_states": hidden_states,
-            "timestep": timestep,
-            "encoder_hidden_states": encoder_hidden_states,
-            "pooled_projections": pooled_projections,
-            "encoder_attention_mask": encoder_attention_mask,
-            "guidance": guidance,
-            "image_embeds": image_embeds,
-            "indices_latents": indices_latents,
-            "latents_clean": latents_clean,
-            "indices_latents_clean": indices_latents_clean,
-            "latents_history_2x": latents_history_2x,
-            "indices_latents_history_2x": indices_latents_history_2x,
-            "latents_history_4x": latents_history_4x,
-            "indices_latents_history_4x": indices_latents_history_4x,
-        }
-
-    @property
-    def input_shape(self):
-        return (4, 3, 4, 4)
-
-    @property
-    def output_shape(self):
-        return (4, 3, 4, 4)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
             "in_channels": 4,
             "out_channels": 4,
             "num_attention_heads": 2,
@@ -108,9 +73,64 @@ class HunyuanVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
             "image_proj_dim": 16,
             "has_clean_x_embedder": True,
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self, batch_size: int = 1) -> dict[str, torch.Tensor]:
+        num_channels = 4
+        num_frames = 3
+        height = 4
+        width = 4
+        text_encoder_embedding_dim = 16
+        image_encoder_embedding_dim = 16
+        pooled_projection_dim = 8
+        sequence_length = 12
+
+        return {
+            "hidden_states": randn_tensor(
+                (batch_size, num_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),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, text_encoder_embedding_dim),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "pooled_projections": randn_tensor(
+                (batch_size, pooled_projection_dim), generator=self.generator, device=torch_device
+            ),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length)).to(torch_device),
+            "guidance": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+            "image_embeds": randn_tensor(
+                (batch_size, sequence_length, image_encoder_embedding_dim),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "indices_latents": torch.ones((num_frames,)).to(torch_device),
+            "latents_clean": randn_tensor(
+                (batch_size, num_channels, num_frames - 1, height, width),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "indices_latents_clean": torch.ones((num_frames - 1,)).to(torch_device),
+            "latents_history_2x": randn_tensor(
+                (batch_size, num_channels, num_frames - 1, height, width),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "indices_latents_history_2x": torch.ones((num_frames - 1,)).to(torch_device),
+            "latents_history_4x": randn_tensor(
+                (batch_size, num_channels, (num_frames - 1) * 4, height, width),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "indices_latents_history_4x": torch.ones(((num_frames - 1) * 4,)).to(torch_device),
+        }
+
+
+class TestHunyuanVideoFramepackTransformer(HunyuanVideoFramepackTransformerTesterConfig, ModelTesterMixin):
+    pass
+
+
+class TestHunyuanVideoFramepackTransformerTraining(HunyuanVideoFramepackTransformerTesterConfig, TrainingTesterMixin):
     def test_gradient_checkpointing_is_applied(self):
         expected_set = {"HunyuanVideoFramepackTransformer3DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)