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/community/stable_diffusion_comparison.py

 from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -13,7 +14,6 @@ from diffusers import (
 )
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
 pipe1_model_id = "CompVis/stable-diffusion-v1-1"

examples/community/stable_diffusion_mega.py

 from typing import Any, Callable, Dict, List, Optional, Union
 
+import PIL.Image
 import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
-import PIL.Image
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
@@ -17,7 +18,6 @@ from diffusers import (
 from diffusers.configuration_utils import FrozenDict
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.utils import deprecate, logging
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/stable_unclip.py

@@ -2,13 +2,13 @@ import types
 from typing import List, Optional, Tuple, Union
 
 import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from transformers.models.clip.modeling_clip import CLIPTextModelOutput
 
 from diffusers.models import PriorTransformer
 from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
 from diffusers.schedulers import UnCLIPScheduler
 from diffusers.utils import logging, randn_tensor
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
-from transformers.models.clip.modeling_clip import CLIPTextModelOutput
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/text_inpainting.py

 from typing import Callable, List, Optional, Union
 
+import PIL
 import torch
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPSegForImageSegmentation,
+    CLIPSegProcessor,
+    CLIPTextModel,
+    CLIPTokenizer,
+)
 
-import PIL
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -10,13 +17,6 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from diffusers.utils import deprecate, is_accelerate_available, logging
-from transformers import (
-    CLIPFeatureExtractor,
-    CLIPSegForImageSegmentation,
-    CLIPSegProcessor,
-    CLIPTextModel,
-    CLIPTokenizer,
-)
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/tiled_upscaling.py

@@ -16,14 +16,14 @@ import math
 from typing import Callable, List, Optional, Union
 
 import numpy as np
+import PIL
 import torch
+from PIL import Image
+from transformers import CLIPTextModel, CLIPTokenizer
 
-import PIL
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
 from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
-from PIL import Image
-from transformers import CLIPTextModel, CLIPTokenizer
 
 
 def make_transparency_mask(size, overlap_pixels, remove_borders=[]):

examples/community/wildcard_stable_diffusion.py

@@ -6,6 +6,7 @@ from dataclasses import dataclass
 from typing import Callable, Dict, List, Optional, Union
 
 import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
@@ -14,7 +15,6 @@ from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import Stabl
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from diffusers.utils import deprecate, logging
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/dreambooth/train_dreambooth.py

@@ -23,28 +23,28 @@ import warnings
 from pathlib import Path
 from typing import Optional
 
+import accelerate
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import accelerate
-import diffusers
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 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 AutoTokenizer, PretrainedConfig
 
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.13.0.dev0")

examples/dreambooth/train_dreambooth_flax.py

@@ -6,15 +6,24 @@ import os
 from pathlib import Path
 from typing import Optional
 
+import jax
+import jax.numpy as jnp
 import numpy as np
+import optax
 import torch
 import torch.utils.checkpoint
+import transformers
+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 jax.experimental.compilation_cache import compilation_cache as cc
+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
 
-import jax
-import jax.numpy as jnp
-import optax
-import transformers
 from diffusers import (
     FlaxAutoencoderKL,
     FlaxDDPMScheduler,
@@ -24,15 +33,6 @@ 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 jax.experimental.compilation_cache import compilation_cache as cc
-from PIL import Image
-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/dreambooth/train_dreambooth_lora.py

@@ -26,13 +26,18 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import diffusers
 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
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+
+import diffusers
 from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
@@ -45,11 +50,6 @@ 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 PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.

examples/research_projects/colossalai/train_dreambooth_colossalai.py

@@ -5,12 +5,10 @@ import os
 from pathlib import Path
 from typing import Optional
 
+import colossalai
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import colossalai
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
@@ -18,14 +16,16 @@ from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
 from colossalai.nn.parallel.utils import get_static_torch_model
 from colossalai.utils import get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from PIL import Image
+from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
 from transformers import AutoTokenizer, PretrainedConfig
 
+from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+
 
 disable_existing_loggers()
 logger = get_dist_logger()

examples/research_projects/dreambooth_inpaint/train_dreambooth_inpaint.py

@@ -11,11 +11,16 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
 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
+from PIL import Image, ImageDraw
+from torch.utils.data import Dataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
 from diffusers import (
     AutoencoderKL,
     DDPMScheduler,
@@ -25,11 +30,6 @@ from diffusers import (
 )
 from diffusers.optimization import get_scheduler
 from diffusers.utils import check_min_version
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from PIL import Image, ImageDraw
-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/research_projects/dreambooth_inpaint/train_dreambooth_inpaint_lora.py

@@ -10,22 +10,22 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
 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
+from PIL import Image, ImageDraw
+from torch.utils.data import Dataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
 from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionInpaintPipeline, 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
 from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from PIL import Image, ImageDraw
-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/research_projects/intel_opts/inference_bf16.py

+import intel_extension_for_pytorch as ipex
 import torch
+from PIL import Image
 
-import intel_extension_for_pytorch as ipex
 from diffusers import StableDiffusionPipeline
-from PIL import Image
 
 
 def image_grid(imgs, rows, cols):

examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py

@@ -6,30 +6,30 @@ import random
 from pathlib import Path
 from typing import Optional
 
+import intel_extension_for_pytorch as ipex
 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 intel_extension_for_pytorch as ipex
-import PIL
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from diffusers.utils import check_min_version
 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, CLIPTextModel, CLIPTokenizer
 
+from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from diffusers.utils import check_min_version
+
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {

examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py

@@ -8,27 +8,27 @@ import warnings
 from pathlib import Path
 from typing import Optional
 
+import datasets
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import datasets
-import diffusers
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from PIL import Image
+from torch.utils.data import Dataset
 from torchvision import transforms
 from tqdm.auto import tqdm
 from transformers import AutoTokenizer, PretrainedConfig
 
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.13.0.dev0")

examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py

@@ -21,29 +21,29 @@ 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 diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from onnxruntime.training.ortmodule import ORTModule
 from torchvision import transforms
 from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.13.0.dev0")

examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py

@@ -21,19 +21,28 @@ import random
 from pathlib import Path
 from typing import Optional
 
+import datasets
 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 datasets
-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
+from onnxruntime.training.ortmodule import ORTModule
+
+# 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,
@@ -45,15 +54,6 @@ 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
-from onnxruntime.training.ortmodule import ORTModule
-
-# 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/research_projects/onnxruntime/unconditional_image_generation/train_unconditional.py

@@ -6,23 +6,23 @@ import os
 from pathlib import Path
 from typing import Optional
 
+import datasets
 import torch
 import torch.nn.functional as F
-
-import datasets
-import diffusers
 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 onnxruntime.training.ortmodule import ORTModule
 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")

examples/test_examples.py

@@ -24,6 +24,7 @@ import unittest
 from typing import List
 
 from accelerate.utils import write_basic_config
+
 from diffusers.utils import slow
 
 

examples/text_to_image/train_text_to_image.py

@@ -21,30 +21,30 @@ import random
 from pathlib import Path
 from typing import Optional
 
+import accelerate
+import datasets
 import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
-
-import accelerate
-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 diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.training_utils import EMAModel
-from diffusers.utils import check_min_version, deprecate
-from diffusers.utils.import_utils import is_xformers_available
 from huggingface_hub import HfFolder, Repository, create_repo, whoami
 from packaging import version
 from torchvision import transforms
 from tqdm.auto import tqdm
 from transformers import CLIPTextModel, CLIPTokenizer
 
+import diffusers
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from diffusers.utils import check_min_version, deprecate
+from diffusers.utils.import_utils import is_xformers_available
+
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
 check_min_version("0.13.0.dev0")