# 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)