116 lines
4.1 KiB
Python
Executable File
116 lines
4.1 KiB
Python
Executable File
# 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 inspect
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import tempfile
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import unittest
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import numpy as np
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import torch
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from diffusers.models.embeddings import get_timestep_embedding
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from diffusers.testing_utils import floats_tensor, slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class EmbeddingsTests(unittest.TestCase):
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def test_timestep_embeddings(self):
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embedding_dim = 256
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timesteps = torch.arange(16)
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t1 = get_timestep_embedding(timesteps, embedding_dim)
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# first vector should always be composed only of 0's and 1's
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assert (t1[0, : embedding_dim // 2] - 0).abs().sum() < 1e-5
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assert (t1[0, embedding_dim // 2 :] - 1).abs().sum() < 1e-5
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# last element of each vector should be one
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assert (t1[:, -1] - 1).abs().sum() < 1e-5
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# For large embeddings (e.g. 128) the frequency of every vector is higher
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# than the previous one which means that the gradients of later vectors are
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# ALWAYS higher than the previous ones
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grad_mean = np.abs(np.gradient(t1, axis=-1)).mean(axis=1)
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prev_grad = 0.0
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for grad in grad_mean:
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assert grad > prev_grad
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prev_grad = grad
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def test_timestep_defaults(self):
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embedding_dim = 16
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timesteps = torch.arange(10)
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t1 = get_timestep_embedding(timesteps, embedding_dim)
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t2 = get_timestep_embedding(
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timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1, max_period=10_000
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)
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assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)
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def test_timestep_flip_sin_cos(self):
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embedding_dim = 16
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timesteps = torch.arange(10)
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t1 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=True)
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t1 = torch.cat([t1[:, embedding_dim // 2 :], t1[:, : embedding_dim // 2]], dim=-1)
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t2 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False)
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assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)
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def test_timestep_downscale_freq_shift(self):
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embedding_dim = 16
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timesteps = torch.arange(10)
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t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0)
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t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1)
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# get cosine half (vectors that are wrapped into cosine)
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cosine_half = (t1 - t2)[:, embedding_dim // 2 :]
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# cosine needs to be negative
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assert (np.abs((cosine_half <= 0).numpy()) - 1).sum() < 1e-5
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def test_sinoid_embeddings_hardcoded(self):
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embedding_dim = 64
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timesteps = torch.arange(128)
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# standard unet, score_vde
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t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1, flip_sin_to_cos=False)
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# glide, ldm
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t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0, flip_sin_to_cos=True)
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# grad-tts
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t3 = get_timestep_embedding(timesteps, embedding_dim, scale=1000)
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assert torch.allclose(
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t1[23:26, 47:50].flatten().cpu(),
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torch.tensor([0.9646, 0.9804, 0.9892, 0.9615, 0.9787, 0.9882, 0.9582, 0.9769, 0.9872]),
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1e-3,
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)
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assert torch.allclose(
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t2[23:26, 47:50].flatten().cpu(),
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torch.tensor([0.3019, 0.2280, 0.1716, 0.3146, 0.2377, 0.1790, 0.3272, 0.2474, 0.1864]),
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1e-3,
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)
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assert torch.allclose(
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t3[23:26, 47:50].flatten().cpu(),
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torch.tensor([-0.9801, -0.9464, -0.9349, -0.3952, 0.8887, -0.9709, 0.5299, -0.2853, -0.9927]),
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1e-3,
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)
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