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# Copyright 2025 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 pathlib
import tempfile
import unittest
from diffusers import AutoPipelineForText2Image
from diffusers.models.auto_model import AutoModel
from ..testing_utils import is_torch_available, require_flashpack, require_torch_gpu
if is_torch_available():
import torch
class FlashPackTests(unittest.TestCase):
model_id: str = "hf-internal-testing/tiny-flux-pipe"
@require_flashpack
def test_save_load_model(self):
model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
with tempfile.TemporaryDirectory() as temp_dir:
model.save_pretrained(temp_dir, use_flashpack=True)
self.assertTrue((pathlib.Path(temp_dir) / "model.flashpack").exists())
model = AutoModel.from_pretrained(temp_dir, use_flashpack=True)
@require_flashpack
def test_save_load_pipeline(self):
pipeline = AutoPipelineForText2Image.from_pretrained(self.model_id)
with tempfile.TemporaryDirectory() as temp_dir:
pipeline.save_pretrained(temp_dir, use_flashpack=True)
self.assertTrue((pathlib.Path(temp_dir) / "transformer" / "model.flashpack").exists())
self.assertTrue((pathlib.Path(temp_dir) / "vae" / "model.flashpack").exists())
pipeline = AutoPipelineForText2Image.from_pretrained(temp_dir, use_flashpack=True)
@require_torch_gpu
@require_flashpack
def test_load_model_device_str(self):
model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
with tempfile.TemporaryDirectory() as temp_dir:
model.save_pretrained(temp_dir, use_flashpack=True)
model = AutoModel.from_pretrained(temp_dir, use_flashpack=True, device_map={"": "cuda"})
self.assertTrue(model.device.type == "cuda")
@require_torch_gpu
@require_flashpack
def test_load_model_device(self):
model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
with tempfile.TemporaryDirectory() as temp_dir:
model.save_pretrained(temp_dir, use_flashpack=True)
model = AutoModel.from_pretrained(temp_dir, use_flashpack=True, device_map={"": torch.device("cuda")})
self.assertTrue(model.device.type == "cuda")
@require_flashpack
def test_load_model_device_auto(self):
model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
with tempfile.TemporaryDirectory() as temp_dir:
model.save_pretrained(temp_dir, use_flashpack=True)
with self.assertRaises(ValueError):
model = AutoModel.from_pretrained(temp_dir, use_flashpack=True, device_map={"": "auto"})
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