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diffusers/tests/test_modeling_utils.py
2022-05-31 14:27:59 +02:00

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# 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 random
import tempfile
import unittest
import torch
from diffusers import UNetConfig, UNetModel
global_rng = random.Random()
def floats_tensor(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
class ModelTesterMixin(unittest.TestCase):
def test_from_pretrained_save_pretrained(self):
config = UNetConfig(dim=8, dim_mults=(1, 2), resnet_block_groups=2)
model = UNetModel(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
new_model = UNetModel.from_pretrained(tmpdirname)
batch_size = 1
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes)
time_step = torch.tensor([10])
image = model(noise, time_step)
new_image = new_model(noise, time_step)
assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"