121 lines
4.3 KiB
Python
121 lines
4.3 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 gc
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import unittest
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import numpy as np
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import torch
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from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
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from diffusers.utils import slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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torch.backends.cuda.matmul.allow_tf32 = False
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class PipelineFastTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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@property
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def dummy_unet(self):
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torch.manual_seed(0)
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model = UNet1DModel(
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block_out_channels=(32, 32, 64),
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extra_in_channels=16,
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sample_size=512,
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sample_rate=16_000,
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in_channels=2,
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out_channels=2,
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down_block_types=["DownBlock1DNoSkip"] + ["DownBlock1D"] + ["AttnDownBlock1D"],
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up_block_types=["AttnUpBlock1D"] + ["UpBlock1D"] + ["UpBlock1DNoSkip"],
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)
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return model
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def test_dance_diffusion(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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scheduler = IPNDMScheduler()
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pipe = DanceDiffusionPipeline(unet=self.dummy_unet, scheduler=scheduler)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe(generator=generator, num_inference_steps=4)
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audio = output.audios
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe(generator=generator, num_inference_steps=4, return_dict=False)
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audio_from_tuple = output[0]
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audio_slice = audio[0, -3:, -3:]
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audio_from_tuple_slice = audio_from_tuple[0, -3:, -3:]
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assert audio.shape == (1, 2, self.dummy_unet.sample_size)
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expected_slice = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000])
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assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(audio_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch_gpu
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class PipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_dance_diffusion(self):
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device = torch_device
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pipe = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k", device_map="auto")
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
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audio = output.audios
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audio_slice = audio[0, -3:, -3:]
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assert audio.shape == (1, 2, pipe.unet.sample_size)
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expected_slice = np.array([-0.1576, -0.1526, -0.127, -0.2699, -0.2762, -0.2487])
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assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
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def test_dance_diffusion_fp16(self):
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device = torch_device
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pipe = DanceDiffusionPipeline.from_pretrained(
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"harmonai/maestro-150k", torch_dtype=torch.float16, device_map="auto"
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)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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generator = torch.Generator(device=device).manual_seed(0)
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output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
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audio = output.audios
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audio_slice = audio[0, -3:, -3:]
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assert audio.shape == (1, 2, pipe.unet.sample_size)
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expected_slice = np.array([-0.1693, -0.1698, -0.1447, -0.3044, -0.3203, -0.2937])
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assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
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