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* make accelerate hard dep * default fast init * move params to cpu when device map is None * handle device_map=None * handle torch < 1.9 * remove device_map="auto" * style * add accelerate in torch extra * remove accelerate from extras["test"] * raise an error if torch is available but not accelerate * update installation docs * Apply suggestions from code review Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * improve defautl loading speed even further, allow disabling fats loading * address review comments * adapt the tests * fix test_stable_diffusion_fast_load * fix test_read_init * temp fix for dummy checks * Trigger Build * Apply suggestions from code review Co-authored-by: Anton Lozhkov <anton@huggingface.co> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Anton Lozhkov <anton@huggingface.co>
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
# coding=utf-8
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# Copyright 2022 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 unittest
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import torch
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from diffusers import UNet1DModel
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from diffusers.utils import slow, torch_device
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torch.backends.cuda.matmul.allow_tf32 = False
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class UnetModel1DTests(unittest.TestCase):
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@slow
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def test_unet_1d_maestro(self):
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model_id = "harmonai/maestro-150k"
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model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
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model.to(torch_device)
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sample_size = 65536
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noise = torch.sin(torch.arange(sample_size)[None, None, :].repeat(1, 2, 1)).to(torch_device)
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timestep = torch.tensor([1]).to(torch_device)
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with torch.no_grad():
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output = model(noise, timestep).sample
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output_sum = output.abs().sum()
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output_max = output.abs().max()
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assert (output_sum - 224.0896).abs() < 4e-2
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assert (output_max - 0.0607).abs() < 4e-4
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