Add initial Wan Animate transformer tests

Author: dg845

tests/models/transformers/test_models_transformer_wan_animate.py

@@ -0,0 +1,121 @@
+# 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 unittest
+
+import torch
+
+from diffusers import WanAnimateTransformer3DModel
+
+from ...testing_utils import (
+    enable_full_determinism,
+    torch_device,
+)
+from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
+
+
+enable_full_determinism()
+
+
+class WanAnimateTransformer3DTests(ModelTesterMixin, unittest.TestCase):
+    model_class = WanAnimateTransformer3DModel
+    main_input_name = "hidden_states"
+    uses_custom_attn_processor = True
+
+    @property
+    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 = 8
+        face_width = 8
+
+        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 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,
+            "in_channels": 12,  # 2 * C + 4 = 2 * 4 + 4 = 12
+            "latent_channels": 4,
+            "out_channels": 4,
+            "text_dim": 16,
+            "freq_dim": 256,
+            "ffn_dim": 32,
+            "num_layers": 2,
+            "cross_attn_norm": True,
+            "qk_norm": "rms_norm_across_heads",
+            "image_dim": 16,
+            "rope_max_seq_len": 32,
+            "motion_encoder_size": 8,  # Start of Wan Animate-specific config
+            "motion_style_dim": 8,
+            "motion_dim": 4,
+            "motion_encoder_dim": 16,
+            "face_encoder_hidden_dim": 16,
+            "face_encoder_num_heads": 2,
+            "inject_face_latents_blocks": 2,
+        }
+        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)
+
+    # 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 WanAnimateTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
+    model_class = WanAnimateTransformer3DModel
+
+    def prepare_init_args_and_inputs_for_common(self):
+        return WanAnimateTransformer3DTests().prepare_init_args_and_inputs_for_common()