Replace flake8 with ruff and update black (#2279)

* before running make style

* remove left overs from flake8

* finish

* make fix-copies

* final fix

* more fixes

examples/text_to_image/train_text_to_image_flax.py

@@ -6,15 +6,22 @@ import random
 from pathlib import Path
 from typing import Optional
 
-import numpy as np
-import torch
-import torch.utils.checkpoint
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
+import torch
+import torch.utils.checkpoint
 import transformers
 from datasets import load_dataset
+from flax import jax_utils
+from flax.training import train_state
+from flax.training.common_utils import shard
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+
 from diffusers import (
     FlaxAutoencoderKL,
     FlaxDDPMScheduler,
@@ -24,13 +31,6 @@ from diffusers import (
 )
 from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
 from diffusers.utils import check_min_version
-from flax import jax_utils
-from flax.training import train_state
-from flax.training.common_utils import shard
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.

examples/text_to_image/train_text_to_image_lora.py

@@ -22,28 +22,28 @@ import random
 from pathlib import Path
 from typing import Optional
 
+import datasets
 import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-
-import datasets
-import diffusers
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from datasets import load_dataset
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+import diffusers
 from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
 from diffusers.loaders import AttnProcsLayers
 from diffusers.models.cross_attention import LoRACrossAttnProcessor
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.

examples/textual_inversion/textual_inversion.py

@@ -22,17 +22,25 @@ from pathlib import Path
 from typing import Optional
 
 import numpy as np
+import PIL
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import diffusers
-import PIL
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+import diffusers
 from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
@@ -44,14 +52,6 @@ from diffusers import (
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version, is_wandb_available
 from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
 
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):

examples/textual_inversion/textual_inversion_flax.py

@@ -6,25 +6,14 @@ import random
 from pathlib import Path
 from typing import Optional
 
-import numpy as np
-import torch
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
 import PIL
+import torch
+import torch.utils.checkpoint
 import transformers
-from diffusers import (
-    FlaxAutoencoderKL,
-    FlaxDDPMScheduler,
-    FlaxPNDMScheduler,
-    FlaxStableDiffusionPipeline,
-    FlaxUNet2DConditionModel,
-)
-from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
-from diffusers.utils import check_min_version
 from flax import jax_utils
 from flax.training import train_state
 from flax.training.common_utils import shard
@@ -33,10 +22,21 @@ from huggingface_hub import HfFolder, Repository, create_repo, whoami
 # TODO: remove and import from diffusers.utils when the new version of diffusers is released
 from packaging import version
 from PIL import Image
+from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
 from transformers import CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
+from diffusers import (
+    FlaxAutoencoderKL,
+    FlaxDDPMScheduler,
+    FlaxPNDMScheduler,
+    FlaxStableDiffusionPipeline,
+    FlaxUNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
+from diffusers.utils import check_min_version
+
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {

examples/unconditional_image_generation/train_unconditional.py

@@ -6,24 +6,24 @@ import os
 from pathlib import Path
 from typing import Optional
 
-import torch
-import torch.nn.functional as F
-
 import accelerate
 import datasets
-import diffusers
+import torch
+import torch.nn.functional as F
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from datasets import load_dataset
-from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from packaging import version
 from torchvision import transforms
 from tqdm.auto import tqdm
 
+import diffusers
+from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from diffusers.utils import check_min_version
+
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.13.0.dev0")

pyproject.toml

 [tool.black]
 line-length = 119
-target-version = ['py36']
+target-version = ['py37']
+
+[tool.ruff]
+# Never enforce `E501` (line length violations).
+ignore = ["E501", "E741", "W605"]
+select = ["E", "F", "I", "W"]
+line-length = 119
+
+# Ignore import violations in all `__init__.py` files.
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["E402", "F401", "F403", "F811"]
+"src/diffusers/utils/dummy_*.py" = ["F401"]
+
+[tool.ruff.isort]
+lines-after-imports = 2
+known-first-party = ["diffusers"]

scripts/change_naming_configs_and_checkpoints.py

@@ -19,9 +19,9 @@ import json
 import os
 
 import torch
+from transformers.file_utils import has_file
 
 from diffusers import UNet2DConditionModel, UNet2DModel
-from transformers.file_utils import has_file
 
 
 do_only_config = False

scripts/conversion_ldm_uncond.py

 import argparse
 
+import OmegaConf
 import torch
 
-import OmegaConf
 from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
 
 

scripts/convert_dance_diffusion_to_diffusers.py

@@ -5,11 +5,11 @@ import os
 from copy import deepcopy
 
 import torch
+from audio_diffusion.models import DiffusionAttnUnet1D
+from diffusion import sampling
 from torch import nn
 
-from audio_diffusion.models import DiffusionAttnUnet1D
 from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
-from diffusion import sampling
 
 
 MODELS_MAP = {

scripts/convert_diffusers_to_original_stable_diffusion.py

@@ -7,7 +7,6 @@ import os.path as osp
 import re
 
 import torch
-
 from safetensors.torch import load_file, save_file
 
 

scripts/convert_dit_to_diffusers.py

@@ -2,9 +2,9 @@ import argparse
 import os
 
 import torch
+from torchvision.datasets.utils import download_url
 
 from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel
-from torchvision.datasets.utils import download_url
 
 
 pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}

scripts/convert_k_upscaler_to_diffusers.py

 import argparse
 
-import torch
-
 import huggingface_hub
 import k_diffusion as K
+import torch
+
 from diffusers import UNet2DConditionModel
 
 

scripts/convert_kakao_brain_unclip_to_diffusers.py

@@ -2,13 +2,13 @@ import argparse
 import tempfile
 
 import torch
-
 from accelerate import load_checkpoint_and_dispatch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+
 from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
 from diffusers.models.prior_transformer import PriorTransformer
 from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
 from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
 
 
 """
@@ -249,7 +249,6 @@ DECODER_CONFIG = {
     "class_embed_type": "identity",
     "attention_head_dim": 64,
     "resnet_time_scale_shift": "scale_shift",
-    "class_embed_type": "identity",
 }
 
 

scripts/convert_ldm_original_checkpoint_to_diffusers.py

@@ -355,5 +355,5 @@ if __name__ == "__main__":
 
         pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
         pipe.save_pretrained(args.dump_path)
-    except:
+    except:  # noqa: E722
         model.save_pretrained(args.dump_path)

scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py

@@ -181,5 +181,5 @@ if __name__ == "__main__":
 
         pipe = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
         pipe.save_pretrained(args.dump_path)
-    except:
+    except:  # noqa: E722
         model.save_pretrained(args.dump_path)

scripts/convert_stable_diffusion_checkpoint_to_onnx.py

@@ -17,12 +17,12 @@ import os
 import shutil
 from pathlib import Path
 
+import onnx
 import torch
+from packaging import version
 from torch.onnx import export
 
-import onnx
 from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
-from packaging import version
 
 
 is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

scripts/convert_unclip_txt2img_to_image_variation.py

 import argparse
 
-from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
+from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
+
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()

scripts/convert_vae_pt_to_diffusers.py

 import argparse
 import io
 
+import requests
 import torch
+from omegaconf import OmegaConf
 
-import requests
 from diffusers import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
     assign_to_checkpoint,
@@ -12,7 +13,6 @@ from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
     renew_vae_attention_paths,
     renew_vae_resnet_paths,
 )
-from omegaconf import OmegaConf
 
 
 def custom_convert_ldm_vae_checkpoint(checkpoint, config):

scripts/convert_versatile_diffusion_to_diffusers.py

@@ -18,6 +18,12 @@ import argparse
 from argparse import Namespace
 
 import torch
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
 
 from diffusers import (
     AutoencoderKL,
@@ -31,12 +37,6 @@ from diffusers import (
     VersatileDiffusionPipeline,
 )
 from diffusers.pipelines.versatile_diffusion.modeling_text_unet import UNetFlatConditionModel
-from transformers import (
-    CLIPFeatureExtractor,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
-    CLIPVisionModelWithProjection,
-)
 
 
 SCHEDULER_CONFIG = Namespace(

scripts/convert_vq_diffusion_to_diffusers.py

@@ -36,14 +36,14 @@ import argparse
 import tempfile
 
 import torch
-
 import yaml
 from accelerate import init_empty_weights, load_checkpoint_and_dispatch
-from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
-from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
 from transformers import CLIPTextModel, CLIPTokenizer
 from yaml.loader import FullLoader
 
+from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
+from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
+
 
 try:
     from omegaconf import OmegaConf