46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
# 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 torch
|
|
|
|
from diffusers import UNet1DModel
|
|
from diffusers.utils import slow, torch_device
|
|
|
|
|
|
torch.backends.cuda.matmul.allow_tf32 = False
|
|
|
|
|
|
class UnetModel1DTests(unittest.TestCase):
|
|
@slow
|
|
def test_unet_1d_maestro(self):
|
|
model_id = "harmonai/maestro-150k"
|
|
model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
|
|
model.to(torch_device)
|
|
|
|
sample_size = 65536
|
|
noise = torch.sin(torch.arange(sample_size)[None, None, :].repeat(1, 2, 1)).to(torch_device)
|
|
timestep = torch.tensor([1]).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
output = model(noise, timestep).sample
|
|
|
|
output_sum = output.abs().sum()
|
|
output_max = output.abs().max()
|
|
|
|
assert (output_sum - 224.0896).abs() < 4e-2
|
|
assert (output_max - 0.0607).abs() < 4e-4
|