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[Dance Diffusion] Better naming (#981)

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This commit is contained in:
Patrick von Platen
2022-10-25 19:52:41 +02:00
committed by GitHub
parent 365ff8f76d
commit 59f0ce82eb
2 changed files with 12 additions and 9 deletions
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17
src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py
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@@ -47,7 +47,7 @@ class DanceDiffusionPipeline(DiffusionPipeline):
batch_size: int = 1,
num_inference_steps: int = 100,
generator: Optional[torch.Generator] = None,
sample_length_in_s: Optional[float] = None,
audio_length_in_s: Optional[float] = None,
return_dict: bool = True,
) -> Union[AudioPipelineOutput, Tuple]:
r"""
@@ -60,6 +60,9 @@ class DanceDiffusionPipeline(DiffusionPipeline):
generator (`torch.Generator`, *optional*):
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
deterministic.
audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
The length of the generated audio sample in seconds. Note that the output of the pipeline, *i.e.*
`sample_size`, will be `audio_length_in_s` * `self.unet.sample_rate`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipeline_utils.AudioPipelineOutput`] instead of a plain tuple.
@@ -69,23 +72,23 @@ class DanceDiffusionPipeline(DiffusionPipeline):
generated images.
"""
if sample_length_in_s is None:
sample_length_in_s = self.unet.sample_size / self.unet.sample_rate
if audio_length_in_s is None:
audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
sample_size = sample_length_in_s * self.unet.sample_rate
sample_size = audio_length_in_s * self.unet.sample_rate
down_scale_factor = 2 ** len(self.unet.up_blocks)
if sample_size < 3 * down_scale_factor:
raise ValueError(
f"{sample_length_in_s} is too small. Make sure it's bigger or equal to"
f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
f" {3 * down_scale_factor / self.unet.sample_rate}."
)
original_sample_size = int(sample_size)
if sample_size % down_scale_factor != 0:
sample_size = ((sample_length_in_s * self.unet.sample_rate) // down_scale_factor + 1) * down_scale_factor
sample_size = ((audio_length_in_s * self.unet.sample_rate) // down_scale_factor + 1) * down_scale_factor
logger.info(
f"{sample_length_in_s} is increased to {sample_size / self.unet.sample_rate} so that it can be handled"
f"{audio_length_in_s} is increased to {sample_size / self.unet.sample_rate} so that it can be handled"
f" by the model. It will be cut to {original_sample_size / self.unet.sample_rate} after the denoising"
" process."
)

4
tests/pipelines/dance_diffusion/test_dance_diffusion.py
View File

@@ -91,7 +91,7 @@ class PipelineIntegrationTests(unittest.TestCase):
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(0)
output = pipe(generator=generator, num_inference_steps=100, sample_length_in_s=4.096)
output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
audio = output.audios
audio_slice = audio[0, -3:, -3:]
@@ -108,7 +108,7 @@ class PipelineIntegrationTests(unittest.TestCase):
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device=device).manual_seed(0)
output = pipe(generator=generator, num_inference_steps=100, sample_length_in_s=4.096)
output = pipe(generator=generator, num_inference_steps=100, audio_length_in_s=4.096)
audio = output.audios
audio_slice = audio[0, -3:, -3:]