Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,985 Bytes
ffead1e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
# coding=utf-8
# Copyright 2023 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 numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNet2DConditionModel,
UNet2DModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def dummy_unet(self):
torch.manual_seed(0)
model = UNet2DModel(
sample_size=(32, 64),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("AttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "AttnUpBlock2D"),
)
return model
@property
def dummy_unet_condition(self):
torch.manual_seed(0)
model = UNet2DConditionModel(
sample_size=(64, 32),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
cross_attention_dim=10,
)
return model
@property
def dummy_vqvae_and_unet(self):
torch.manual_seed(0)
vqvae = AutoencoderKL(
sample_size=(128, 64),
in_channels=1,
out_channels=1,
latent_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),
up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),
)
unet = UNet2DModel(
sample_size=(64, 32),
in_channels=1,
out_channels=1,
layers_per_block=2,
block_out_channels=(128, 128),
down_block_types=("AttnDownBlock2D", "DownBlock2D"),
up_block_types=("UpBlock2D", "AttnUpBlock2D"),
)
return vqvae, unet
@slow
def test_audio_diffusion(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
mel = Mel()
scheduler = DDPMScheduler()
pipe = AudioDiffusionPipeline(vqvae=None, unet=self.dummy_unet, mel=mel, scheduler=scheduler)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator, steps=4)
audio = output.audios[0]
image = output.images[0]
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator, steps=4, return_dict=False)
image_from_tuple = output[0][0]
assert audio.shape == (1, (self.dummy_unet.sample_size[1] - 1) * mel.hop_length)
assert image.height == self.dummy_unet.sample_size[0] and image.width == self.dummy_unet.sample_size[1]
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
image_from_tuple_slice = np.frombuffer(image_from_tuple.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() == 0
scheduler = DDIMScheduler()
dummy_vqvae_and_unet = self.dummy_vqvae_and_unet
pipe = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_vqvae_and_unet[1], mel=mel, scheduler=scheduler
)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
np.random.seed(0)
raw_audio = np.random.uniform(-1, 1, ((dummy_vqvae_and_unet[0].sample_size[1] - 1) * mel.hop_length,))
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(raw_audio=raw_audio, generator=generator, start_step=5, steps=10)
image = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].sample_size[1]
)
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
dummy_unet_condition = self.dummy_unet_condition
pipe = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_unet_condition, mel=mel, scheduler=scheduler
)
np.random.seed(0)
encoding = torch.rand((1, 1, 10))
output = pipe(generator=generator, encoding=encoding)
image = output.images[0]
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([120, 139, 147, 123, 124, 96, 115, 121, 126, 144])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
@slow
@require_torch_gpu
class PipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_audio_diffusion(self):
device = torch_device
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256")
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(42)
output = pipe(generator=generator)
audio = output.audios[0]
image = output.images[0]
assert audio.shape == (1, (pipe.unet.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.sample_size[0] and image.width == pipe.unet.sample_size[1]
image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
expected_slice = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26])
assert np.abs(image_slice.flatten() - expected_slice).max() == 0
|