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# coding=utf-8
# Copyright 2024 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 torch
from diffusers import (
ControlNetModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
slow,
)
enable_full_determinism()
@slow
@require_torch_gpu
class ControlNetModelSingleFileTests(unittest.TestCase):
model_class = ControlNetModel
ckpt_path = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
repo_id = "lllyasviel/control_v11p_sd15_canny"
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_single_file_components(self):
model = self.model_class.from_pretrained(self.repo_id)
model_single_file = self.model_class.from_single_file(self.ckpt_path)
PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
for param_name, param_value in model_single_file.config.items():
if param_name in PARAMS_TO_IGNORE:
continue
assert (
model.config[param_name] == param_value
), f"{param_name} differs between single file loading and pretrained loading"
def test_single_file_arguments(self):
model_default = self.model_class.from_single_file(self.ckpt_path)
assert model_default.config.upcast_attention is False
assert model_default.dtype == torch.float32
torch_dtype = torch.float16
upcast_attention = True
model = self.model_class.from_single_file(
self.ckpt_path,
upcast_attention=upcast_attention,
torch_dtype=torch_dtype,
)
assert model.config.upcast_attention == upcast_attention
assert model.dtype == torch_dtype
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