311 lines
11 KiB
Python
311 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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import torch
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from parameterized import parameterized
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from diffusers import AutoencoderKL
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from diffusers.models import ModelMixin
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from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
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from ..test_modeling_common import ModelTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
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model_class = AutoencoderKL
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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return {"sample": image}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": [32, 64],
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"in_channels": 3,
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"out_channels": 3,
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"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
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"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
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"latent_channels": 4,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_forward_signature(self):
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pass
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def test_training(self):
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pass
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def test_from_pretrained_hub(self):
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model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
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model = model.to(torch_device)
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model.eval()
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# One-time warmup pass (see #372)
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if torch_device == "mps" and isinstance(model, ModelMixin):
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image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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image = image.to(torch_device)
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with torch.no_grad():
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_ = model(image, sample_posterior=True).sample
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generator = torch.manual_seed(0)
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else:
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = torch.randn(
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1,
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model.config.in_channels,
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model.config.sample_size,
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model.config.sample_size,
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generator=torch.manual_seed(0),
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)
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image = image.to(torch_device)
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with torch.no_grad():
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output = model(image, sample_posterior=True, generator=generator).sample
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output_slice = output[0, -1, -3:, -3:].flatten().cpu()
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# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
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# the expected output slices are not the same for CPU and GPU.
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if torch_device == "mps":
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expected_output_slice = torch.tensor(
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[
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-4.0078e-01,
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-3.8323e-04,
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-1.2681e-01,
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-1.1462e-01,
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2.0095e-01,
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1.0893e-01,
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-8.8247e-02,
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-3.0361e-01,
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-9.8644e-03,
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]
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)
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elif torch_device == "cpu":
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expected_output_slice = torch.tensor(
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[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
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)
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else:
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expected_output_slice = torch.tensor(
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[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
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)
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self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
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@slow
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class AutoencoderKLIntegrationTests(unittest.TestCase):
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def get_file_format(self, seed, shape):
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return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
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dtype = torch.float16 if fp16 else torch.float32
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image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
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return image
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def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):
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revision = "fp16" if fp16 else None
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torch_dtype = torch.float16 if fp16 else torch.float32
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model = AutoencoderKL.from_pretrained(
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model_id,
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subfolder="vae",
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torch_dtype=torch_dtype,
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revision=revision,
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)
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model.to(torch_device).eval()
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return model
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def get_generator(self, seed=0):
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if torch_device == "mps":
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return torch.manual_seed(seed)
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return torch.Generator(device=torch_device).manual_seed(seed)
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@parameterized.expand(
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[
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# fmt: off
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[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
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[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
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# fmt: on
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]
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)
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def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
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model = self.get_sd_vae_model()
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image = self.get_sd_image(seed)
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generator = self.get_generator(seed)
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with torch.no_grad():
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sample = model(image, generator=generator, sample_posterior=True).sample
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assert sample.shape == image.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
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assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
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@parameterized.expand(
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[
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# fmt: off
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[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
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[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_stable_diffusion_fp16(self, seed, expected_slice):
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model = self.get_sd_vae_model(fp16=True)
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image = self.get_sd_image(seed, fp16=True)
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generator = self.get_generator(seed)
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with torch.no_grad():
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sample = model(image, generator=generator, sample_posterior=True).sample
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assert sample.shape == image.shape
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output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch_all_close(output_slice, expected_output_slice, atol=1e-2)
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@parameterized.expand(
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[
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# fmt: off
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[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
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[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
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# fmt: on
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]
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)
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def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
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model = self.get_sd_vae_model()
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image = self.get_sd_image(seed)
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with torch.no_grad():
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sample = model(image).sample
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assert sample.shape == image.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
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assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
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@parameterized.expand(
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[
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# fmt: off
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[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
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[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_stable_diffusion_decode(self, seed, expected_slice):
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model = self.get_sd_vae_model()
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encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
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with torch.no_grad():
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sample = model.decode(encoding).sample
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assert list(sample.shape) == [3, 3, 512, 512]
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output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
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@parameterized.expand(
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[
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# fmt: off
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[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
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[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
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model = self.get_sd_vae_model(fp16=True)
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encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
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with torch.no_grad():
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sample = model.decode(encoding).sample
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assert list(sample.shape) == [3, 3, 512, 512]
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output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
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@parameterized.expand(
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[
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# fmt: off
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[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
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[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
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# fmt: on
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]
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)
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def test_stable_diffusion_encode_sample(self, seed, expected_slice):
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model = self.get_sd_vae_model()
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image = self.get_sd_image(seed)
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generator = self.get_generator(seed)
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with torch.no_grad():
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dist = model.encode(image).latent_dist
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sample = dist.sample(generator=generator)
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assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
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output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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tolerance = 1e-3 if torch_device != "mps" else 1e-2
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assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)
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