[tests] refactor vae tests

src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py

@@ -433,7 +433,7 @@ def create_forward(*inputs):
                     hidden_states,
                     temb,
                     zq,
-                    conv_cache=conv_cache.get(conv_cache_key),
+                    conv_cache.get(conv_cache_key),
                 )
             else:
                 hidden_states, new_conv_cache[conv_cache_key] = resnet(
@@ -531,7 +531,7 @@ def create_forward(*inputs):
                     return create_forward
 
                 hidden_states, new_conv_cache[conv_cache_key] = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(resnet), hidden_states, temb, zq, conv_cache=conv_cache.get(conv_cache_key)
+                    create_custom_forward(resnet), hidden_states, temb, zq, conv_cache.get(conv_cache_key)
                 )
             else:
                 hidden_states, new_conv_cache[conv_cache_key] = resnet(
@@ -649,7 +649,7 @@ def create_forward(*inputs):
                     hidden_states,
                     temb,
                     zq,
-                    conv_cache=conv_cache.get(conv_cache_key),
+                    conv_cache.get(conv_cache_key),
                 )
             else:
                 hidden_states, new_conv_cache[conv_cache_key] = resnet(
@@ -789,7 +789,7 @@ def custom_forward(*inputs):
                     hidden_states,
                     temb,
                     None,
-                    conv_cache=conv_cache.get(conv_cache_key),
+                    conv_cache.get(conv_cache_key),
                 )
 
             # 2. Mid
@@ -798,14 +798,14 @@ def custom_forward(*inputs):
                 hidden_states,
                 temb,
                 None,
-                conv_cache=conv_cache.get("mid_block"),
+                conv_cache.get("mid_block"),
             )
         else:
             # 1. Down
             for i, down_block in enumerate(self.down_blocks):
                 conv_cache_key = f"down_block_{i}"
                 hidden_states, new_conv_cache[conv_cache_key] = down_block(
-                    hidden_states, temb, None, conv_cache=conv_cache.get(conv_cache_key)
+                    hidden_states, temb, None, conv_cache.get(conv_cache_key)
                 )
 
             # 2. Mid
@@ -953,7 +953,7 @@ def custom_forward(*inputs):
                 hidden_states,
                 temb,
                 sample,
-                conv_cache=conv_cache.get("mid_block"),
+                conv_cache.get("mid_block"),
             )
 
             # 2. Up
@@ -964,7 +964,7 @@ def custom_forward(*inputs):
                     hidden_states,
                     temb,
                     sample,
-                    conv_cache=conv_cache.get(conv_cache_key),
+                    conv_cache.get(conv_cache_key),
                 )
         else:
             # 1. Mid
@@ -1475,7 +1475,7 @@ def forward(
             z = posterior.sample(generator=generator)
         else:
             z = posterior.mode()
-        dec = self.decode(z)
+        dec = self.decode(z).sample
         if not return_dict:
             return (dec,)
-        return dec
+        return DecoderOutput(sample=dec)

src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py

@@ -228,14 +228,6 @@ def __init__(
 
         self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
 
-        sample_size = (
-            self.config.sample_size[0]
-            if isinstance(self.config.sample_size, (list, tuple))
-            else self.config.sample_size
-        )
-        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
-        self.tile_overlap_factor = 0.25
-
     def _set_gradient_checkpointing(self, module, value=False):
         if isinstance(module, (Encoder, TemporalDecoder)):
             module.gradient_checkpointing = value

src/diffusers/models/autoencoders/autoencoder_tiny.py

@@ -310,7 +310,9 @@ def decode(
         self, x: torch.Tensor, generator: Optional[torch.Generator] = None, return_dict: bool = True
     ) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
         if self.use_slicing and x.shape[0] > 1:
-            output = [self._tiled_decode(x_slice) if self.use_tiling else self.decoder(x) for x_slice in x.split(1)]
+            output = [
+                self._tiled_decode(x_slice) if self.use_tiling else self.decoder(x_slice) for x_slice in x.split(1)
+            ]
             output = torch.cat(output)
         else:
             output = self._tiled_decode(x) if self.use_tiling else self.decoder(x)
@@ -341,7 +343,7 @@ def forward(
         # as if we were loading the latents from an RGBA uint8 image.
         unscaled_enc = self.unscale_latents(scaled_enc / 255.0)
 
-        dec = self.decode(unscaled_enc)
+        dec = self.decode(unscaled_enc).sample
 
         if not return_dict:
             return (dec,)

tests/models/autoencoders/test_models_asymmetric_autoencoder_kl.py

@@ -0,0 +1,261 @@
+# 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 gc
+import unittest
+
+import torch
+from parameterized import parameterized
+
+from diffusers import AsymmetricAutoencoderKL
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    floats_tensor,
+    load_hf_numpy,
+    require_torch_accelerator,
+    require_torch_gpu,
+    skip_mps,
+    slow,
+    torch_all_close,
+    torch_device,
+)
+
+from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
+
+
+enable_full_determinism()
+
+
+class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
+    model_class = AsymmetricAutoencoderKL
+    main_input_name = "sample"
+    base_precision = 1e-2
+
+    def get_asym_autoencoder_kl_config(self, block_out_channels=None, norm_num_groups=None):
+        block_out_channels = block_out_channels or [2, 4]
+        norm_num_groups = norm_num_groups or 2
+        init_dict = {
+            "in_channels": 3,
+            "out_channels": 3,
+            "down_block_types": ["DownEncoderBlock2D"] * len(block_out_channels),
+            "down_block_out_channels": block_out_channels,
+            "layers_per_down_block": 1,
+            "up_block_types": ["UpDecoderBlock2D"] * len(block_out_channels),
+            "up_block_out_channels": block_out_channels,
+            "layers_per_up_block": 1,
+            "act_fn": "silu",
+            "latent_channels": 4,
+            "norm_num_groups": norm_num_groups,
+            "sample_size": 32,
+            "scaling_factor": 0.18215,
+        }
+        return init_dict
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_channels = 3
+        sizes = (32, 32)
+
+        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
+        mask = torch.ones((batch_size, 1) + sizes).to(torch_device)
+
+        return {"sample": image, "mask": mask}
+
+    @property
+    def input_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (3, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = self.get_asym_autoencoder_kl_config()
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    @unittest.skip("Unsupported test.")
+    def test_forward_with_norm_groups(self):
+        pass
+
+
+@slow
+class AsymmetricAutoencoderKLIntegrationTests(unittest.TestCase):
+    def get_file_format(self, seed, shape):
+        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
+        dtype = torch.float16 if fp16 else torch.float32
+        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
+        return image
+
+    def get_sd_vae_model(self, model_id="cross-attention/asymmetric-autoencoder-kl-x-1-5", fp16=False):
+        revision = "main"
+        torch_dtype = torch.float32
+
+        model = AsymmetricAutoencoderKL.from_pretrained(
+            model_id,
+            torch_dtype=torch_dtype,
+            revision=revision,
+        )
+        model.to(torch_device).eval()
+
+        return model
+
+    def get_generator(self, seed=0):
+        generator_device = "cpu" if not torch_device.startswith("cuda") else "cuda"
+        if torch_device != "mps":
+            return torch.Generator(device=generator_device).manual_seed(seed)
+        return torch.manual_seed(seed)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [
+                33,
+                [-0.0336, 0.3011, 0.1764, 0.0087, -0.3401, 0.3645, -0.1247, 0.1205],
+                [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],
+            ],
+            [
+                47,
+                [0.4400, 0.0543, 0.2873, 0.2946, 0.0553, 0.0839, -0.1585, 0.2529],
+                [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089],
+            ],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+        generator = self.get_generator(seed)
+
+        with torch.no_grad():
+            sample = model(image, generator=generator, sample_posterior=True).sample
+
+        assert sample.shape == image.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [
+                33,
+                [-0.0340, 0.2870, 0.1698, -0.0105, -0.3448, 0.3529, -0.1321, 0.1097],
+                [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078],
+            ],
+            [
+                47,
+                [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],
+                [0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],
+            ],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+
+        with torch.no_grad():
+            sample = model(image).sample
+
+        assert sample.shape == image.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [13, [-0.0521, -0.2939, 0.1540, -0.1855, -0.5936, -0.3138, -0.4579, -0.2275]],
+            [37, [-0.1820, -0.4345, -0.0455, -0.2923, -0.8035, -0.5089, -0.4795, -0.3106]],
+            # fmt: on
+        ]
+    )
+    @require_torch_accelerator
+    @skip_mps
+    def test_stable_diffusion_decode(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
+
+        with torch.no_grad():
+            sample = model.decode(encoding).sample
+
+        assert list(sample.shape) == [3, 3, 512, 512]
+
+        output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=2e-3)
+
+    @parameterized.expand([(13,), (16,), (37,)])
+    @require_torch_gpu
+    @unittest.skipIf(
+        not is_xformers_available(),
+        reason="xformers is not required when using PyTorch 2.0.",
+    )
+    def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):
+        model = self.get_sd_vae_model()
+        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
+
+        with torch.no_grad():
+            sample = model.decode(encoding).sample
+
+        model.enable_xformers_memory_efficient_attention()
+        with torch.no_grad():
+            sample_2 = model.decode(encoding).sample
+
+        assert list(sample.shape) == [3, 3, 512, 512]
+
+        assert torch_all_close(sample, sample_2, atol=5e-2)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
+            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion_encode_sample(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+        generator = self.get_generator(seed)
+
+        with torch.no_grad():
+            dist = model.encode(image).latent_dist
+            sample = dist.sample(generator=generator)
+
+        assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
+
+        output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        tolerance = 3e-3 if torch_device != "mps" else 1e-2
+        assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)