[Tests] Improve transformers model test suite coverage - Hunyuan DiT (#8916)

* add hunyuan model test

* apply suggestions

* reduce dims further

* reduce dims further

* run make style

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: rootonchair

tests/models/transformers/test_models_transformer_hunyuan_dit.py

@@ -0,0 +1,113 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from diffusers import HunyuanDiT2DModel
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    torch_device,
+)
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+enable_full_determinism()
+
+
+class HunyuanDiTTests(ModelTesterMixin, unittest.TestCase):
+    model_class = HunyuanDiT2DModel
+    main_input_name = "hidden_states"
+
+    @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,
+        }
+
+    @property
+    def input_shape(self):
+        return (4, 8, 8)
+
+    @property
+    def output_shape(self):
+        return (8, 8, 8)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "sample_size": 8,
+            "patch_size": 2,
+            "in_channels": 4,
+            "num_layers": 1,
+            "attention_head_dim": 8,
+            "num_attention_heads": 2,
+            "cross_attention_dim": 8,
+            "cross_attention_dim_t5": 8,
+            "pooled_projection_dim": 4,
+            "hidden_size": 16,
+            "text_len": 4,
+            "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
+        )
+
+    @unittest.skip("HunyuanDIT use a custom processor HunyuanAttnProcessor2_0")
+    def test_set_xformers_attn_processor_for_determinism(self):
+        pass
+
+    @unittest.skip("HunyuanDIT use a custom processor HunyuanAttnProcessor2_0")
+    def test_set_attn_processor_for_determinism(self):
+        pass