[Styling] stylify using ruff (#5841)

* ruff format

* not need to use doc-builder's black styling as the doc is styled in ruff

* make fix-copies

* comment

* use run_ruff

pyproject.toml

-[tool.black]
-line-length = 119
-target-version = ['py37']
-
 [tool.ruff]
 # Never enforce `E501` (line length violations).
-ignore = ["C901", "E501", "E741", "W605"]
+ignore = ["C901", "E501", "E741", "F402", "F823"]
 select = ["C", "E", "F", "I", "W"]
 line-length = 119
 
@@ -16,3 +12,16 @@ line-length = 119
 [tool.ruff.isort]
 lines-after-imports = 2
 known-first-party = ["diffusers"]
+
+[tool.ruff.format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"

scripts/convert_kakao_brain_unclip_to_diffusers.py

@@ -11,7 +11,7 @@ from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
 from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
 
 
-"""
+r"""
 Example - From the diffusers root directory:
 
 Download weights:

setup.cfg

-[isort]
-default_section = FIRSTPARTY
-ensure_newline_before_comments = True
-force_grid_wrap = 0
-include_trailing_comma = True
-known_first_party = accelerate
-known_third_party =
-    numpy
-    torch
-    torch_xla
-
-line_length = 119
-lines_after_imports = 2
-multi_line_output = 3
-use_parentheses = True
-
-[flake8]
-ignore = E203, E722, E501, E741, W503, W605
-max-line-length = 119
-per-file-ignores = __init__.py:F401

setup.py

@@ -78,9 +78,9 @@ To create the package for PyPI.
     you need to go back to main before executing this.
 """
 
-import sys
 import os
 import re
+import sys
 from distutils.core import Command
 
 from setuptools import find_packages, setup
@@ -93,7 +93,6 @@ _deps = [
     "Pillow",  # keep the PIL.Image.Resampling deprecation away
     "accelerate>=0.11.0",
     "compel==0.1.8",
-    "black~=23.1",
     "datasets",
     "filelock",
     "flax>=0.4.1",
@@ -119,7 +118,7 @@ _deps = [
     "pytest-timeout",
     "pytest-xdist",
     "python>=3.8.0",
-    "ruff==0.0.280",
+    "ruff>=0.1.5,<=0.2",
     "safetensors>=0.3.1",
     "sentencepiece>=0.1.91,!=0.1.92",
     "scipy",
@@ -171,7 +170,11 @@ class DepsTableUpdateCommand(Command):
     description = "build runtime dependency table"
     user_options = [
         # format: (long option, short option, description).
-        ("dep-table-update", None, "updates src/diffusers/dependency_versions_table.py"),
+        (
+            "dep-table-update",
+            None,
+            "updates src/diffusers/dependency_versions_table.py",
+        ),
     ]
 
     def initialize_options(self):
@@ -197,10 +200,8 @@ class DepsTableUpdateCommand(Command):
             f.write("\n".join(content))
 
 
-
-
 extras = {}
-extras["quality"] = deps_list("urllib3", "black", "isort", "ruff", "hf-doc-builder")
+extras["quality"] = deps_list("urllib3", "isort", "ruff", "hf-doc-builder")
 extras["docs"] = deps_list("hf-doc-builder")
 extras["training"] = deps_list("accelerate", "datasets", "protobuf", "tensorboard", "Jinja2")
 extras["test"] = deps_list(
@@ -275,10 +276,7 @@ setup(
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Programming Language :: Python :: 3",
     ]
-    + [
-        f"Programming Language :: Python :: 3.{i}"
-        for i in range(8, version_range_max)
-    ],
+    + [f"Programming Language :: Python :: 3.{i}" for i in range(8, version_range_max)],
     cmdclass={"deps_table_update": DepsTableUpdateCommand},
 )
 

src/diffusers/configuration_utils.py

@@ -95,6 +95,7 @@ class ConfigMixin:
           should only have a `kwargs` argument if at least one argument is deprecated (should be overridden by
           subclass).
     """
+
     config_name = None
     ignore_for_config = []
     has_compatibles = False

src/diffusers/dependency_versions_table.py

@@ -5,7 +5,6 @@ deps = {
     "Pillow": "Pillow",
     "accelerate": "accelerate>=0.11.0",
     "compel": "compel==0.1.8",
-    "black": "black~=23.1",
     "datasets": "datasets",
     "filelock": "filelock",
     "flax": "flax>=0.4.1",
@@ -31,7 +30,7 @@ deps = {
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",
     "python": "python>=3.8.0",
-    "ruff": "ruff==0.0.280",
+    "ruff": "ruff>=0.1.5,<=0.2",
     "safetensors": "safetensors>=0.3.1",
     "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
     "scipy": "scipy",

src/diffusers/loaders/lora.py

@@ -71,6 +71,7 @@ class LoraLoaderMixin:
     Load LoRA layers into [`UNet2DConditionModel`] and
     [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
     """
+
     text_encoder_name = TEXT_ENCODER_NAME
     unet_name = UNET_NAME
     num_fused_loras = 0

src/diffusers/models/attention_flax.py

@@ -110,7 +110,10 @@ def jax_memory_efficient_attention(
         )
 
     _, res = jax.lax.scan(
-        f=chunk_scanner, init=0, xs=None, length=math.ceil(num_q / query_chunk_size)  # start counter  # stop counter
+        f=chunk_scanner,
+        init=0,
+        xs=None,
+        length=math.ceil(num_q / query_chunk_size),  # start counter  # stop counter
     )
 
     return jnp.concatenate(res, axis=-3)  # fuse the chunked result back
@@ -138,6 +141,7 @@ class FlaxAttention(nn.Module):
             Parameters `dtype`
 
     """
+
     query_dim: int
     heads: int = 8
     dim_head: int = 64
@@ -262,6 +266,7 @@ class FlaxBasicTransformerBlock(nn.Module):
             Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
             enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
     """
+
     dim: int
     n_heads: int
     d_head: int
@@ -347,6 +352,7 @@ class FlaxTransformer2DModel(nn.Module):
             Whether to split the head dimension into a new axis for the self-attention computation. In most cases,
             enabling this flag should speed up the computation for Stable Diffusion 2.x and Stable Diffusion XL.
     """
+
     in_channels: int
     n_heads: int
     d_head: int
@@ -442,6 +448,7 @@ class FlaxFeedForward(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     dim: int
     dropout: float = 0.0
     dtype: jnp.dtype = jnp.float32
@@ -471,6 +478,7 @@ class FlaxGEGLU(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     dim: int
     dropout: float = 0.0
     dtype: jnp.dtype = jnp.float32

src/diffusers/models/autoencoder_tiny.py

@@ -91,6 +91,7 @@ class AutoencoderTiny(ModelMixin, ConfigMixin):
             `force_upcast` can be set to `False` (see this fp16-friendly
             [AutoEncoder](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix)).
     """
+
     _supports_gradient_checkpointing = True
 
     @register_to_config

src/diffusers/models/controlnet_flax.py

@@ -146,6 +146,7 @@ class FlaxControlNetModel(nn.Module, FlaxModelMixin, ConfigMixin):
         conditioning_embedding_out_channels (`tuple`, *optional*, defaults to `(16, 32, 96, 256)`):
             The tuple of output channel for each block in the `conditioning_embedding` layer.
     """
+
     sample_size: int = 32
     in_channels: int = 4
     down_block_types: Tuple[str, ...] = (

src/diffusers/models/embeddings_flax.py

@@ -65,6 +65,7 @@ class FlaxTimestepEmbedding(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
                 Parameters `dtype`
     """
+
     time_embed_dim: int = 32
     dtype: jnp.dtype = jnp.float32
 
@@ -84,6 +85,7 @@ class FlaxTimesteps(nn.Module):
         dim (`int`, *optional*, defaults to `32`):
                 Time step embedding dimension
     """
+
     dim: int = 32
     flip_sin_to_cos: bool = False
     freq_shift: float = 1

src/diffusers/models/modeling_flax_utils.py

@@ -52,6 +52,7 @@ class FlaxModelMixin(PushToHubMixin):
 
         - **config_name** ([`str`]) -- Filename to save a model to when calling [`~FlaxModelMixin.save_pretrained`].
     """
+
     config_name = CONFIG_NAME
     _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
     _flax_internal_args = ["name", "parent", "dtype"]

src/diffusers/models/modeling_utils.py

@@ -193,6 +193,7 @@ class ModelMixin(torch.nn.Module, PushToHubMixin):
 
         - **config_name** ([`str`]) -- Filename to save a model to when calling [`~models.ModelMixin.save_pretrained`].
     """
+
     config_name = CONFIG_NAME
     _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
     _supports_gradient_checkpointing = False

src/diffusers/models/unet_2d_blocks_flax.py

@@ -45,6 +45,7 @@ class FlaxCrossAttnDownBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -125,6 +126,7 @@ class FlaxDownBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -190,6 +192,7 @@ class FlaxCrossAttnUpBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     prev_output_channel: int
@@ -275,6 +278,7 @@ class FlaxUpBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     prev_output_channel: int
@@ -339,6 +343,7 @@ class FlaxUNetMidBlock2DCrossAttn(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     dropout: float = 0.0
     num_layers: int = 1

src/diffusers/models/unet_motion_model.py

@@ -174,6 +174,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
     This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
     for all models (such as downloading or saving).
     """
+
     _supports_gradient_checkpointing = True
 
     @register_to_config

src/diffusers/models/vae_flax.py

@@ -214,6 +214,7 @@ class FlaxAttentionBlock(nn.Module):
             Parameters `dtype`
 
     """
+
     channels: int
     num_head_channels: int = None
     num_groups: int = 32
@@ -291,6 +292,7 @@ class FlaxDownEncoderBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -347,6 +349,7 @@ class FlaxUpDecoderBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     out_channels: int
     dropout: float = 0.0
@@ -401,6 +404,7 @@ class FlaxUNetMidBlock2D(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int
     dropout: float = 0.0
     num_layers: int = 1
@@ -488,6 +492,7 @@ class FlaxEncoder(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             Parameters `dtype`
     """
+
     in_channels: int = 3
     out_channels: int = 3
     down_block_types: Tuple[str] = ("DownEncoderBlock2D",)
@@ -600,6 +605,7 @@ class FlaxDecoder(nn.Module):
         dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
             parameters `dtype`
     """
+
     in_channels: int = 3
     out_channels: int = 3
     up_block_types: Tuple[str] = ("UpDecoderBlock2D",)
@@ -767,6 +773,7 @@ class FlaxAutoencoderKL(nn.Module, FlaxModelMixin, ConfigMixin):
         dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
             The `dtype` of the parameters.
     """
+
     in_channels: int = 3
     out_channels: int = 3
     down_block_types: Tuple[str] = ("DownEncoderBlock2D",)

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -243,10 +243,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
         lora_scale: Optional[float] = None,
         **kwargs,
     ):
-        deprecation_message = (
-            "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()`"
-            " instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
-        )
+        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
         deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
 
         prompt_embeds_tuple = self.encode_prompt(
@@ -462,10 +459,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
         return image, has_nsfw_concept
 
     def decode_latents(self, latents):
-        deprecation_message = (
-            "The decode_latents method is deprecated and will be removed in 1.0.0. Please use"
-            " VaeImageProcessor.postprocess(...) instead"
-        )
+        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
         deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
 
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -515,8 +509,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
             k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
         ):
             raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found"
-                f" {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
             )
 
         if prompt is not None and prompt_embeds is not None:
@@ -747,15 +740,13 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
             deprecate(
                 "callback",
                 "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider using"
-                " `callback_on_step_end`",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
             )
         if callback_steps is not None:
             deprecate(
                 "callback_steps",
                 "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using"
-                " `callback_on_step_end`",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
             )
 
         # 0. Default height and width to unet

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -252,10 +252,7 @@ class AltDiffusionImg2ImgPipeline(
         lora_scale: Optional[float] = None,
         **kwargs,
     ):
-        deprecation_message = (
-            "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()`"
-            " instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
-        )
+        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
         deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
 
         prompt_embeds_tuple = self.encode_prompt(
@@ -471,10 +468,7 @@ class AltDiffusionImg2ImgPipeline(
         return image, has_nsfw_concept
 
     def decode_latents(self, latents):
-        deprecation_message = (
-            "The decode_latents method is deprecated and will be removed in 1.0.0. Please use"
-            " VaeImageProcessor.postprocess(...) instead"
-        )
+        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
         deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
 
         latents = 1 / self.vae.config.scaling_factor * latents
@@ -524,8 +518,7 @@ class AltDiffusionImg2ImgPipeline(
             k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
         ):
             raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found"
-                f" {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
             )
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
@@ -578,8 +571,8 @@ class AltDiffusionImg2ImgPipeline(
         else:
             if isinstance(generator, list) and len(generator) != batch_size:
                 raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective"
-                    f" batch size of {batch_size}. Make sure the batch size matches the length of the generators."
+                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
                 )
 
             elif isinstance(generator, list):
@@ -798,15 +791,13 @@ class AltDiffusionImg2ImgPipeline(
             deprecate(
                 "callback",
                 "1.0.0",
-                "Passing `callback` as an input argument to `__call__` is deprecated, consider use"
-                " `callback_on_step_end`",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
             )
         if callback_steps is not None:
             deprecate(
                 "callback_steps",
                 "1.0.0",
-                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use"
-                " `callback_on_step_end`",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
             )
 
         # 1. Check inputs. Raise error if not correct

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -99,6 +99,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
 
     def __init__(

src/diffusers/pipelines/audioldm/pipeline_audioldm.py

@@ -72,6 +72,7 @@ class AudioLDMPipeline(DiffusionPipeline):
         vocoder ([`~transformers.SpeechT5HifiGan`]):
             Vocoder of class `SpeechT5HifiGan`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
 
     def __init__(