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88fa6b7d68
* start * add more logic * Update src/diffusers/models/unet_2d_condition_flax.py * match weights * up * make model work * making class more general, fixing missed file rename * small fix * make new conversion work * up * finalize conversion * up * first batch of variable renamings * remove c and c_prev var names * add mid and out block structure * add pipeline * up * finish conversion * finish * upload * more fixes * Apply suggestions from code review * add attr * up * uP * up * finish tests * finish * uP * finish * fix test * up * naming consistency in tests * Apply suggestions from code review Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Nathan Lambert <nathan@huggingface.co> Co-authored-by: Anton Lozhkov <anton@huggingface.co> * remove hardcoded 16 * Remove bogus * fix some stuff * finish * improve logging * docs * upload Co-authored-by: Nathan Lambert <nol@berkeley.edu> Co-authored-by: Suraj Patil <surajp815@gmail.com> Co-authored-by: Pedro Cuenca <pedro@huggingface.co> Co-authored-by: Nathan Lambert <nathan@huggingface.co> 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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