Add initial LTX 2.0 video VAE tests (part 2)

tests/models/autoencoders/test_models_autoencoder_ltx2_video.py

@@ -0,0 +1,102 @@
+# coding=utf-8
+# 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 AutoencoderKLLTX2Video
+
+from ...testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    torch_device,
+)
+from ..test_modeling_common import ModelTesterMixin
+from .testing_utils import AutoencoderTesterMixin
+
+
+enable_full_determinism()
+
+
+class AutoencoderKLLTX2VideoTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
+    model_class = AutoencoderKLLTX2Video
+    main_input_name = "sample"
+    base_precision = 1e-2
+
+    def get_autoencoder_kl_ltx_video_config(self):
+        return {
+            "in_channels": 3,
+            "out_channels": 3,
+            "latent_channels": 8,
+            "block_out_channels": (8, 8, 8, 8),
+            "decoder_block_out_channels": (16, 32, 64),
+            "layers_per_block": (1, 1, 1, 1, 1),
+            "decoder_layers_per_block": (1, 1, 1, 1),
+            "spatio_temporal_scaling": (True, True, True, True),
+            "decoder_spatio_temporal_scaling": (True, True, True),
+            "decoder_inject_noise": (False, False, False, False),
+            "downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
+            "upsample_residual": (True, True, True),
+            "upsample_factor": (2, 2, 2),
+            "timestep_conditioning": False,
+            "patch_size": 1,
+            "patch_size_t": 1,
+            "encoder_causal": True,
+            "decoder_causal": True,
+        }
+
+    @property
+    def dummy_input(self):
+        batch_size = 2
+        num_frames = 9
+        num_channels = 3
+        sizes = (16, 16)
+
+        image = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
+
+        input_dict = {"sample": image}
+        return input_dict
+
+    @property
+    def input_shape(self):
+        return (3, 9, 16, 16)
+
+    @property
+    def output_shape(self):
+        return (3, 9, 16, 16)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = self.get_autoencoder_kl_ltx_video_config()
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {
+            "LTX2VideoEncoder3d",
+            "LTX2VideoDecoder3d",
+            "LTX2VideoDownBlock3D",
+            "LTX2VideoMidBlock3d",
+            "LTX2VideoUpBlock3d",
+        }
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+
+    @unittest.skip("Unsupported test.")
+    def test_outputs_equivalence(self):
+        pass
+
+    @unittest.skip("AutoencoderKLLTXVideo does not support `norm_num_groups` because it does not use GroupNorm.")
+    def test_forward_with_norm_groups(self):
+        pass