151 lines
4.9 KiB
Python
151 lines
4.9 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, skip_mps
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class DanceDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = DanceDiffusionPipeline
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test_attention_slicing = False
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test_cpu_offload = False
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = 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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flip_sin_to_cos=True,
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use_timestep_embedding=False,
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time_embedding_type="fourier",
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mid_block_type="UNetMidBlock1D",
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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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scheduler = IPNDMScheduler()
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components = {
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"unet": unet,
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"scheduler": scheduler,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"batch_size": 1,
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"generator": generator,
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"num_inference_steps": 4,
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}
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return inputs
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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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components = self.get_dummy_components()
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pipe = DanceDiffusionPipeline(**components)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = pipe(**inputs)
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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, components["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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@skip_mps
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def test_save_load_local(self):
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return super().test_save_load_local()
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@skip_mps
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def test_dict_tuple_outputs_equivalent(self):
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return super().test_dict_tuple_outputs_equivalent()
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@skip_mps
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def test_save_load_optional_components(self):
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return super().test_save_load_optional_components()
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@skip_mps
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def test_attention_slicing_forward_pass(self):
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return super().test_attention_slicing_forward_pass()
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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")
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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.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([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020])
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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("harmonai/maestro-150k", torch_dtype=torch.float16)
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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.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([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341])
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assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
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