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fix: Fixed few docstrings according to the Google Style Guide (#7717)

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Fixed few docstrings according to the Google Style Guide.
This commit is contained in:
Sai-Suraj-27
2024-05-20 22:56:05 +05:30
committed by GitHub
parent a7bf77fc28
commit df2bc5ef28
21 changed files with 21 additions and 21 deletions
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2
examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py
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@@ -981,7 +981,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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@@ -1136,7 +1136,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/community/stable_diffusion_reference.py
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@@ -78,7 +78,7 @@ def torch_dfs(model: torch.nn.Module):
class StableDiffusionReferencePipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
r""" "
r"""
Pipeline for Stable Diffusion Reference.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods

2
examples/custom_diffusion/train_custom_diffusion.py
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@@ -152,7 +152,7 @@ def collate_fn(examples, with_prior_preservation):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/dreambooth/train_dreambooth.py
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@@ -742,7 +742,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/dreambooth/train_dreambooth_flax.py
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@@ -301,7 +301,7 @@ class DreamBoothDataset(Dataset):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/dreambooth/train_dreambooth_lora.py
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@@ -680,7 +680,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/dreambooth/train_dreambooth_lora_sdxl.py
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@@ -903,7 +903,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/colossalai/train_dreambooth_colossalai.py
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@@ -327,7 +327,7 @@ class DreamBoothDataset(Dataset):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py
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@@ -385,7 +385,7 @@ class DreamBoothDataset(Dataset):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py
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@@ -384,7 +384,7 @@ class DreamBoothDataset(Dataset):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth.py
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@@ -762,7 +762,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora.py
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@@ -700,7 +700,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora_sdxl.py
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@@ -922,7 +922,7 @@ def collate_fn(examples, with_prior_preservation=False):
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
"""A simple dataset to prepare the prompts to generate class images on multiple GPUs."""
def __init__(self, prompt, num_samples):
self.prompt = prompt

2
src/diffusers/pipelines/animatediff/pipeline_output.py
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@@ -13,7 +13,7 @@ class AnimateDiffPipelineOutput(BaseOutput):
r"""
Output class for AnimateDiff pipelines.
Args:
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised

2
src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py
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@@ -76,7 +76,7 @@ class I2VGenXLPipelineOutput(BaseOutput):
r"""
Output class for image-to-video pipeline.
Args:
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised

2
src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py
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@@ -1216,7 +1216,7 @@ class LEditsPPPipelineStableDiffusion(
Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.
Args:
Args:
image (`PipelineImageInput`):
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect
ratio.

2
src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py
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@@ -1449,7 +1449,7 @@ class LEditsPPPipelineStableDiffusionXL(
Paper](https://arxiv.org/abs/2301.12247). If the scheduler is set to [`~schedulers.DDIMScheduler`] the
inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) will be performed instead.
Args:
Args:
image (`PipelineImageInput`):
Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect
ratio.

2
src/diffusers/pipelines/shap_e/renderer.py
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@@ -844,7 +844,7 @@ class ShapERenderer(ModelMixin, ConfigMixin):
transmittance(t[i + 1]) := transmittance(t[i]). 4) The last term is integration to infinity (e.g. [t[-1],
math.inf]) that is evaluated by the void_model (i.e. we consider this space to be empty).
args:
Args:
rays: [batch_size x ... x 2 x 3] origin and direction. sampler: disjoint volume integrals. n_samples:
number of ts to sample. prev_model_outputs: model outputs from the previous rendering step, including

2
src/diffusers/pipelines/text_to_video_synthesis/pipeline_output.py
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@@ -15,7 +15,7 @@ class TextToVideoSDPipelineOutput(BaseOutput):
"""
Output class for text-to-video pipelines.
Args:
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised

2
utils/check_table.py
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@@ -73,7 +73,7 @@ diffusers_module = spec.loader.load_module()
# Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
def camel_case_split(identifier):
"Split a camelcased `identifier` into words."
"""Split a camelcased `identifier` into words."""
matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier)
return [m.group(0) for m in matches]