autoencodertiny.

Author: sayakpaul

tests/models/autoencoders/test_models_autoencoder_tiny.py

@@ -0,0 +1,197 @@
+# 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 AutoencoderTiny
+from diffusers.utils.testing_utils import (
+    backend_empty_cache,
+    enable_full_determinism,
+    floats_tensor,
+    load_hf_numpy,
+    slow,
+    torch_all_close,
+    torch_device,
+)
+
+from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
+
+
+enable_full_determinism()
+
+
+class AutoencoderTinyTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
+    model_class = AutoencoderTiny
+    main_input_name = "sample"
+    base_precision = 1e-2
+
+    def get_autoencoder_tiny_config(self, block_out_channels=None):
+        block_out_channels = (len(block_out_channels) * [32]) if block_out_channels is not None else [32, 32]
+        init_dict = {
+            "in_channels": 3,
+            "out_channels": 3,
+            "encoder_block_out_channels": block_out_channels,
+            "decoder_block_out_channels": block_out_channels,
+            "num_encoder_blocks": [b // min(block_out_channels) for b in block_out_channels],
+            "num_decoder_blocks": [b // min(block_out_channels) for b in reversed(block_out_channels)],
+        }
+        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)
+
+        return {"sample": image}
+
+    @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_autoencoder_tiny_config()
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    @unittest.skip("Model doesn't yet support smaller resolution.")
+    def test_enable_disable_tiling(self):
+        pass
+
+    def test_enable_disable_slicing(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        torch.manual_seed(0)
+        model = self.model_class(**init_dict).to(torch_device)
+
+        inputs_dict.update({"return_dict": False})
+
+        torch.manual_seed(0)
+        output_without_slicing = model(**inputs_dict)[0]
+
+        torch.manual_seed(0)
+        model.enable_slicing()
+        output_with_slicing = model(**inputs_dict)[0]
+
+        self.assertLess(
+            (output_without_slicing.detach().cpu().numpy() - output_with_slicing.detach().cpu().numpy()).max(),
+            0.5,
+            "VAE slicing should not affect the inference results",
+        )
+
+        torch.manual_seed(0)
+        model.disable_slicing()
+        output_without_slicing_2 = model(**inputs_dict)[0]
+
+        self.assertEqual(
+            output_without_slicing.detach().cpu().numpy().all(),
+            output_without_slicing_2.detach().cpu().numpy().all(),
+            "Without slicing outputs should match with the outputs when slicing is manually disabled.",
+        )
+
+    @unittest.skip("Test not supported.")
+    def test_outputs_equivalence(self):
+        pass
+
+    @unittest.skip("Test not supported.")
+    def test_forward_with_norm_groups(self):
+        pass
+
+
+@slow
+class AutoencoderTinyIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def get_file_format(self, seed, shape):
+        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
+    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="hf-internal-testing/taesd-diffusers", fp16=False):
+        torch_dtype = torch.float16 if fp16 else torch.float32
+
+        model = AutoencoderTiny.from_pretrained(model_id, torch_dtype=torch_dtype)
+        model.to(torch_device).eval()
+        return model
+
+    @parameterized.expand(
+        [
+            [(1, 4, 73, 97), (1, 3, 584, 776)],
+            [(1, 4, 97, 73), (1, 3, 776, 584)],
+            [(1, 4, 49, 65), (1, 3, 392, 520)],
+            [(1, 4, 65, 49), (1, 3, 520, 392)],
+            [(1, 4, 49, 49), (1, 3, 392, 392)],
+        ]
+    )
+    def test_tae_tiling(self, in_shape, out_shape):
+        model = self.get_sd_vae_model()
+        model.enable_tiling()
+        with torch.no_grad():
+            zeros = torch.zeros(in_shape).to(torch_device)
+            dec = model.decode(zeros).sample
+            assert dec.shape == out_shape
+
+    def test_stable_diffusion(self):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed=33)
+
+        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([0.0093, 0.6385, -0.1274, 0.1631, -0.1762, 0.5232, -0.3108, -0.0382])
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
+
+    @parameterized.expand([(True,), (False,)])
+    def test_tae_roundtrip(self, enable_tiling):
+        # load the autoencoder
+        model = self.get_sd_vae_model()
+        if enable_tiling:
+            model.enable_tiling()
+
+        # make a black image with a white square in the middle,
+        # which is large enough to split across multiple tiles
+        image = -torch.ones(1, 3, 1024, 1024, device=torch_device)
+        image[..., 256:768, 256:768] = 1.0
+
+        # round-trip the image through the autoencoder
+        with torch.no_grad():
+            sample = model(image).sample
+
+        # the autoencoder reconstruction should match original image, sorta
+        def downscale(x):
+            return torch.nn.functional.avg_pool2d(x, model.spatial_scale_factor)
+
+        assert torch_all_close(downscale(sample), downscale(image), atol=0.125)