add tests

Author: a-r-r-o-w

tests/models/transformers/test_models_transformer_hunyuan_video.py

@@ -80,6 +80,7 @@ def prepare_init_args_and_inputs_for_common(self):
             "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
@@ -144,6 +145,7 @@ def prepare_init_args_and_inputs_for_common(self):
             "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
@@ -209,6 +211,75 @@ def prepare_init_args_and_inputs_for_common(self):
             "text_embed_dim": 16,
             "pooled_projection_dim": 8,
             "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 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
+        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)
+        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,
+        }
+
+    @property
+    def input_shape(self):
+        return (8, 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": 2,
+            "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": "token_replace",
         }
         inputs_dict = self.dummy_input
         return init_dict, inputs_dict