# coding=utf-8

# Copyright 2022 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 unittest

import numpy as np

import torch

from diffusers.models.embeddings import get_timestep_embedding

from diffusers.models.resnet import Downsample2D, Upsample2D

torch.backends.cuda.matmul.allow_tf32 = False

class EmbeddingsTests(unittest.TestCase):

def test_timestep_embeddings(self):

embedding_dim = 256

timesteps = torch.arange(16)

t1 = get_timestep_embedding(timesteps, embedding_dim)

# first vector should always be composed only of 0's and 1's

assert (t1[0, : embedding_dim // 2] - 0).abs().sum() < 1e-5

assert (t1[0, embedding_dim // 2 :] - 1).abs().sum() < 1e-5

# last element of each vector should be one

assert (t1[:, -1] - 1).abs().sum() < 1e-5

# For large embeddings (e.g. 128) the frequency of every vector is higher

# than the previous one which means that the gradients of later vectors are

# ALWAYS higher than the previous ones

grad_mean = np.abs(np.gradient(t1, axis=-1)).mean(axis=1)

prev_grad = 0.0

for grad in grad_mean:

assert grad > prev_grad

prev_grad = grad

def test_timestep_defaults(self):

embedding_dim = 16

timesteps = torch.arange(10)

t1 = get_timestep_embedding(timesteps, embedding_dim)

t2 = get_timestep_embedding(

timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1, max_period=10_000

)

assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)

def test_timestep_flip_sin_cos(self):

embedding_dim = 16

timesteps = torch.arange(10)

t1 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=True)

t1 = torch.cat([t1[:, embedding_dim // 2 :], t1[:, : embedding_dim // 2]], dim=-1)

t2 = get_timestep_embedding(timesteps, embedding_dim, flip_sin_to_cos=False)

assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)

def test_timestep_downscale_freq_shift(self):

embedding_dim = 16

timesteps = torch.arange(10)

t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0)

t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1)

# get cosine half (vectors that are wrapped into cosine)

cosine_half = (t1 - t2)[:, embedding_dim // 2 :]

# cosine needs to be negative

assert (np.abs((cosine_half <= 0).numpy()) - 1).sum() < 1e-5

def test_sinoid_embeddings_hardcoded(self):

embedding_dim = 64

timesteps = torch.arange(128)

# standard unet, score_vde

t1 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=1, flip_sin_to_cos=False)

# glide, ldm

t2 = get_timestep_embedding(timesteps, embedding_dim, downscale_freq_shift=0, flip_sin_to_cos=True)

# grad-tts

t3 = get_timestep_embedding(timesteps, embedding_dim, scale=1000)

assert torch.allclose(

t1[23:26, 47:50].flatten().cpu(),

torch.tensor([0.9646, 0.9804, 0.9892, 0.9615, 0.9787, 0.9882, 0.9582, 0.9769, 0.9872]),

1e-3,

)

assert torch.allclose(

t2[23:26, 47:50].flatten().cpu(),

torch.tensor([0.3019, 0.2280, 0.1716, 0.3146, 0.2377, 0.1790, 0.3272, 0.2474, 0.1864]),

1e-3,

)

assert torch.allclose(

t3[23:26, 47:50].flatten().cpu(),

torch.tensor([-0.9801, -0.9464, -0.9349, -0.3952, 0.8887, -0.9709, 0.5299, -0.2853, -0.9927]),

1e-3,

)

class Upsample2DBlockTests(unittest.TestCase):

def test_upsample_default(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 32, 32)

upsample = Upsample2D(channels=32, use_conv=False)

with torch.no_grad():

upsampled = upsample(sample)

assert upsampled.shape == (1, 32, 64, 64)

output_slice = upsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([-0.2173, -1.2079, -1.2079, 0.2952, 1.1254, 1.1254, 0.2952, 1.1254, 1.1254])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

def test_upsample_with_conv(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 32, 32)

upsample = Upsample2D(channels=32, use_conv=True)

with torch.no_grad():

upsampled = upsample(sample)

assert upsampled.shape == (1, 32, 64, 64)

output_slice = upsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([0.7145, 1.3773, 0.3492, 0.8448, 1.0839, -0.3341, 0.5956, 0.1250, -0.4841])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

def test_upsample_with_conv_out_dim(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 32, 32)

upsample = Upsample2D(channels=32, use_conv=True, out_channels=64)

with torch.no_grad():

upsampled = upsample(sample)

assert upsampled.shape == (1, 64, 64, 64)

output_slice = upsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([0.2703, 0.1656, -0.2538, -0.0553, -0.2984, 0.1044, 0.1155, 0.2579, 0.7755])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

def test_upsample_with_transpose(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 32, 32)

upsample = Upsample2D(channels=32, use_conv=False, use_conv_transpose=True)

with torch.no_grad():

upsampled = upsample(sample)

assert upsampled.shape == (1, 32, 64, 64)

output_slice = upsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([-0.3028, -0.1582, 0.0071, 0.0350, -0.4799, -0.1139, 0.1056, -0.1153, -0.1046])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

class Downsample2DBlockTests(unittest.TestCase):

def test_downsample_default(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 64, 64)

downsample = Downsample2D(channels=32, use_conv=False)

with torch.no_grad():

downsampled = downsample(sample)

assert downsampled.shape == (1, 32, 32, 32)

output_slice = downsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([-0.0513, -0.3889, 0.0640, 0.0836, -0.5460, -0.0341, -0.0169, -0.6967, 0.1179])

max_diff = (output_slice.flatten() - expected_slice).abs().sum().item()

assert max_diff <= 1e-3

# assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-1)

def test_downsample_with_conv(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 64, 64)

downsample = Downsample2D(channels=32, use_conv=True)

with torch.no_grad():

downsampled = downsample(sample)

assert downsampled.shape == (1, 32, 32, 32)

output_slice = downsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor(

[0.9267, 0.5878, 0.3337, 1.2321, -0.1191, -0.3984, -0.7532, -0.0715, -0.3913],

)

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

def test_downsample_with_conv_pad1(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 64, 64)

downsample = Downsample2D(channels=32, use_conv=True, padding=1)

with torch.no_grad():

downsampled = downsample(sample)

assert downsampled.shape == (1, 32, 32, 32)

output_slice = downsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([0.9267, 0.5878, 0.3337, 1.2321, -0.1191, -0.3984, -0.7532, -0.0715, -0.3913])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)

def test_downsample_with_conv_out_dim(self):

torch.manual_seed(0)

sample = torch.randn(1, 32, 64, 64)

downsample = Downsample2D(channels=32, use_conv=True, out_channels=16)

with torch.no_grad():

downsampled = downsample(sample)

assert downsampled.shape == (1, 16, 32, 32)

output_slice = downsampled[0, -1, -3:, -3:]

expected_slice = torch.tensor([-0.6586, 0.5985, 0.0721, 0.1256, -0.1492, 0.4436, -0.2544, 0.5021, 1.1522])

assert torch.allclose(output_slice.flatten(), expected_slice, atol=1e-3)