[CI] Refactor LTX Transformer Tests (#13254)

* update

* update

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: DN6

tests/models/transformers/test_models_transformer_ltx.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2025 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,26 +12,58 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
 import torch
 
 from diffusers import LTXVideoTransformer3DModel
+from diffusers.utils.torch_utils import randn_tensor
 
 from ...testing_utils import enable_full_determinism, torch_device
-from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    MemoryTesterMixin,
+    ModelTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 enable_full_determinism()
 
 
-class LTXTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = LTXVideoTransformer3DModel
-    main_input_name = "hidden_states"
-    uses_custom_attn_processor = True
+class LTXTransformerTesterConfig(BaseModelTesterConfig):
+    @property
+    def model_class(self):
+        return LTXVideoTransformer3DModel
+
+    @property
+    def output_shape(self) -> tuple[int, int]:
+        return (512, 4)
 
     @property
-    def dummy_input(self):
+    def input_shape(self) -> tuple[int, int]:
+        return (512, 4)
+
+    @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):
+        return {
+            "in_channels": 4,
+            "out_channels": 4,
+            "num_attention_heads": 2,
+            "attention_head_dim": 8,
+            "cross_attention_dim": 16,
+            "num_layers": 1,
+            "qk_norm": "rms_norm_across_heads",
+            "caption_channels": 16,
+        }
+
+    def get_dummy_inputs(self) -> dict[str, torch.Tensor]:
         batch_size = 2
         num_channels = 4
         num_frames = 2
@@ -41,50 +72,47 @@ def dummy_input(self):
         embedding_dim = 16
         sequence_length = 16
 
-        hidden_states = torch.randn((batch_size, num_frames * height * width, num_channels)).to(torch_device)
-        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
-        encoder_attention_mask = torch.ones((batch_size, sequence_length)).bool().to(torch_device)
-        timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
-
         return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
-            "encoder_attention_mask": encoder_attention_mask,
+            "hidden_states": randn_tensor(
+                (batch_size, num_frames * height * width, num_channels),
+                generator=self.generator,
+                device=torch_device,
+            ),
+            "encoder_hidden_states": randn_tensor(
+                (batch_size, sequence_length, embedding_dim), generator=self.generator, device=torch_device
+            ),
+            "timestep": torch.randint(0, 1000, size=(batch_size,), generator=self.generator).to(torch_device),
+            "encoder_attention_mask": torch.ones((batch_size, sequence_length)).bool().to(torch_device),
             "num_frames": num_frames,
             "height": height,
             "width": width,
         }
 
-    @property
-    def input_shape(self):
-        return (512, 4)
 
-    @property
-    def output_shape(self):
-        return (512, 4)
+class TestLTXTransformer(LTXTransformerTesterConfig, ModelTesterMixin):
+    """Core model tests for LTX Video Transformer."""
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "in_channels": 4,
-            "out_channels": 4,
-            "num_attention_heads": 2,
-            "attention_head_dim": 8,
-            "cross_attention_dim": 16,
-            "num_layers": 1,
-            "qk_norm": "rms_norm_across_heads",
-            "caption_channels": 16,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+
+class TestLTXTransformerMemory(LTXTransformerTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for LTX Video Transformer."""
+
+
+class TestLTXTransformerTraining(LTXTransformerTesterConfig, TrainingTesterMixin):
+    """Training tests for LTX Video Transformer."""
 
     def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"LTXVideoTransformer3DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+        super().test_gradient_checkpointing_is_applied(expected_set={"LTXVideoTransformer3DModel"})
+
+
+class TestLTXTransformerCompile(LTXTransformerTesterConfig, TorchCompileTesterMixin):
+    """Torch compile tests for LTX Video Transformer."""
+
 
+# TODO: Add pretrained_model_name_or_path once a tiny LTX model is available on the Hub
+# class TestLTXTransformerBitsAndBytes(LTXTransformerTesterConfig, BitsAndBytesTesterMixin):
+#     """BitsAndBytes quantization tests for LTX Video Transformer."""
 
-class LTXTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = LTXVideoTransformer3DModel
 
-    def prepare_init_args_and_inputs_for_common(self):
-        return LTXTransformerTests().prepare_init_args_and_inputs_for_common()
+# TODO: Add pretrained_model_name_or_path once a tiny LTX model is available on the Hub
+# class TestLTXTransformerTorchAo(LTXTransformerTesterConfig, TorchAoTesterMixin):
+#     """TorchAo quantization tests for LTX Video Transformer."""