mirror of
https://github.com/huggingface/diffusers.git
synced 2026-10-24 20:59:27 +03:00
7aa57aafff
* download kernels when users request for it.
* migrate tests/others to pytest
* Revert "download kernels when users request for it."
This reverts commit 6fe34ade6b.
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Co-authored-by: dg845 <58458699+dg845@users.noreply.github.com>
71 lines
2.9 KiB
Python
71 lines
2.9 KiB
Python
# coding=utf-8
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# Copyright 2026 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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from diffusers import AutoPipelineForText2Image
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from diffusers.models.auto_model import AutoModel
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from ..testing_utils import is_torch_available, require_flashpack, require_torch_gpu
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if is_torch_available():
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import torch
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class TestFlashPack:
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model_id: str = "hf-internal-testing/tiny-flux-pipe"
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# `AutoModel.from_pretrained` builds `_diffusers_load_id` by string-joining the path it is given,
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# so the `tmp_path` fixture has to be passed to it as a `str`.
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@require_flashpack
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def test_save_load_model(self, tmp_path):
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model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
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model.save_pretrained(tmp_path, use_flashpack=True)
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assert (tmp_path / "model.flashpack").exists()
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model = AutoModel.from_pretrained(str(tmp_path), use_flashpack=True)
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@require_flashpack
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def test_save_load_pipeline(self, tmp_path):
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pipeline = AutoPipelineForText2Image.from_pretrained(self.model_id)
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pipeline.save_pretrained(tmp_path, use_flashpack=True)
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assert (tmp_path / "transformer" / "model.flashpack").exists()
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assert (tmp_path / "vae" / "model.flashpack").exists()
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pipeline = AutoPipelineForText2Image.from_pretrained(tmp_path, use_flashpack=True)
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@require_torch_gpu
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@require_flashpack
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def test_load_model_device_str(self, tmp_path):
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model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
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model.save_pretrained(tmp_path, use_flashpack=True)
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model = AutoModel.from_pretrained(str(tmp_path), use_flashpack=True, device_map={"": "cuda"})
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assert model.device.type == "cuda"
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@require_torch_gpu
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@require_flashpack
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def test_load_model_device(self, tmp_path):
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model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
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model.save_pretrained(tmp_path, use_flashpack=True)
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model = AutoModel.from_pretrained(str(tmp_path), use_flashpack=True, device_map={"": torch.device("cuda")})
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assert model.device.type == "cuda"
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@require_flashpack
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def test_load_model_device_auto(self, tmp_path):
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model = AutoModel.from_pretrained(self.model_id, subfolder="transformer")
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model.save_pretrained(tmp_path, use_flashpack=True)
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with pytest.raises(ValueError):
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model = AutoModel.from_pretrained(str(tmp_path), use_flashpack=True, device_map={"": "auto"})
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