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

.github/workflows/pr_quality.yml

@@ -27,9 +27,8 @@ jobs:
           pip install .[quality]
       - name: Check quality
         run: |
-          black  --check --preview examples tests src utils scripts
+          black --check examples tests src utils scripts
-          isort --check-only examples tests src utils scripts
+          ruff examples tests src utils scripts
-          flake8 examples tests src utils scripts
           doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
 
   check_repository_consistency:

.gitignore

@@ -169,3 +169,6 @@ tags
 
 # dependencies
 /transformers
+
+# ruff
+.ruff_cache

CONTRIBUTING.md

@@ -177,7 +177,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
    $ make style
    ```
 
-   🧨 Diffusers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality
    control runs in CI, however you can also run the same checks with:
 
    ```bash

Makefile

@@ -9,9 +9,8 @@ modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))
 	@if test -n "$(modified_py_files)"; then \
 		echo "Checking/fixing $(modified_py_files)"; \
-		black --preview $(modified_py_files); \
+		black $(modified_py_files); \
-		isort $(modified_py_files); \
+		ruff $(modified_py_files); \
-		flake8 $(modified_py_files); \
 	else \
 		echo "No library .py files were modified"; \
 	fi
@@ -41,9 +40,8 @@ repo-consistency:
 # this target runs checks on all files
 
 quality:
-	black --check --preview $(check_dirs)
+	black --check $(check_dirs)
-	isort --check-only $(check_dirs)
+	ruff $(check_dirs)
-	flake8 $(check_dirs)
 	doc-builder style src/diffusers docs/source --max_len 119 --check_only --path_to_docs docs/source
 	python utils/check_doc_toc.py
 
@@ -57,8 +55,8 @@ extra_style_checks:
 # this target runs checks on all files and potentially modifies some of them
 
 style:
-	black --preview $(check_dirs)
+	black $(check_dirs)
-	isort $(check_dirs)
+	ruff $(check_dirs) --fix
 	${MAKE} autogenerate_code
 	${MAKE} extra_style_checks
 

docs/source/en/conceptual/contribution.mdx

@@ -177,7 +177,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
    $ make style
    ```
 
-   🧨 Diffusers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality
+   🧨 Diffusers also uses `ruff` and a few custom scripts to check for coding mistakes. Quality
    control runs in CI, however you can also run the same checks with:
 
    ```bash

docs/source/en/optimization/fp16.mdx

@@ -210,6 +210,7 @@ torch.set_grad_enabled(False)
 n_experiments = 2
 unet_runs_per_experiment = 50
 
+
 # load inputs
 def generate_inputs():
     sample = torch.randn(2, 4, 64, 64).half().cuda()
@@ -288,6 +289,8 @@ pipe = StableDiffusionPipeline.from_pretrained(
 
 # use jitted unet
 unet_traced = torch.jit.load("unet_traced.pt")
+
+
 # del pipe.unet
 class TracedUNet(torch.nn.Module):
     def __init__(self):

examples/community/bit_diffusion.py

@@ -1,11 +1,11 @@
 from typing import Optional, Tuple, Union
 
 import torch
+from einops import rearrange, reduce
 
 from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
 from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
 from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
-from einops import rearrange, reduce
 
 
 BITS = 8

examples/community/checkpoint_merger.py

@@ -10,10 +10,11 @@ from diffusers.utils import is_safetensors_available
 if is_safetensors_available():
     import safetensors.torch
 
+from huggingface_hub import snapshot_download
+
 from diffusers import DiffusionPipeline, __version__
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
-from huggingface_hub import snapshot_download
 
 
 class CheckpointMergerPipeline(DiffusionPipeline):

examples/community/clip_guided_stable_diffusion.py

@@ -4,6 +4,8 @@ from typing import List, Optional, Union
 import torch
 from torch import nn
 from torch.nn import functional as F
+from torchvision import transforms
+from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -14,8 +16,6 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
-from torchvision import transforms
-from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
 
 
 class MakeCutouts(nn.Module):

examples/community/composable_stable_diffusion.py

@@ -16,6 +16,8 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import torch
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
@@ -29,8 +31,6 @@ from diffusers.schedulers import (
     PNDMScheduler,
 )
 from diffusers.utils import is_accelerate_available
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput

examples/community/imagic_stable_diffusion.py

@@ -7,11 +7,16 @@ import warnings
 from typing import List, Optional, Union
 
 import numpy as np
+import PIL
 import torch
 import torch.nn.functional as F
-
-import PIL
 from accelerate import Accelerator
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
 from diffusers import DiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
@@ -19,11 +24,6 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from diffusers.utils import deprecate, logging
 
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from tqdm.auto import tqdm
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
     PIL_INTERPOLATION = {

examples/community/img2img_inpainting.py

@@ -2,9 +2,10 @@ import inspect
 from typing import Callable, List, Optional, Tuple, Union
 
 import numpy as np
-import torch
-
 import PIL
+import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -12,7 +13,6 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 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/community/interpolate_stable_diffusion.py

@@ -5,6 +5,7 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import DiffusionPipeline
 from diffusers.configuration_utils import FrozenDict
@@ -13,7 +14,6 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 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/community/lpw_stable_diffusion.py

@@ -3,16 +3,16 @@ import re
 from typing import Callable, List, Optional, Union
 
 import numpy as np
+import PIL
 import torch
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 import diffusers
-import PIL
 from diffusers import SchedulerMixin, StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker
 from diffusers.utils import deprecate, logging
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
 try:

examples/community/lpw_stable_diffusion_onnx.py

@@ -3,15 +3,15 @@ import re
 from typing import Callable, List, Optional, Union
 
 import numpy as np
+import PIL
 import torch
+from packaging import version
+from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 import diffusers
-import PIL
 from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.utils import deprecate, logging
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 
 try:

examples/community/magic_mix.py

@@ -1,6 +1,10 @@
 from typing import Union
 
 import torch
+from PIL import Image
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
@@ -10,10 +14,6 @@ from diffusers import (
     PNDMScheduler,
     UNet2DConditionModel,
 )
-from PIL import Image
-from torchvision import transforms as tfms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
 
 
 class MagicMixPipeline(DiffusionPipeline):

examples/community/multilingual_stable_diffusion.py

@@ -2,14 +2,6 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import torch
-
-from diffusers import DiffusionPipeline
-from diffusers.configuration_utils import FrozenDict
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-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,
@@ -19,6 +11,14 @@ from transformers import (
     pipeline,
 )
 
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils import deprecate, logging
+
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 

examples/community/sd_text2img_k_diffusion.py

@@ -17,11 +17,11 @@ import warnings
 from typing import Callable, List, Optional, Union
 
 import torch
+from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
 
 from diffusers import DiffusionPipeline, LMSDiscreteScheduler
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.utils import is_accelerate_available, logging
-from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/seed_resize_stable_diffusion.py

@@ -5,6 +5,7 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import DiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -12,7 +13,6 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from diffusers.utils import logging
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/speech_to_image_diffusion.py

@@ -2,6 +2,13 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import torch
+from transformers import (
+    CLIPFeatureExtractor,
+    CLIPTextModel,
+    CLIPTokenizer,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
+)
 
 from diffusers import (
     AutoencoderKL,
@@ -14,13 +21,6 @@ from diffusers import (
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.utils import logging
-from transformers import (
-    CLIPFeatureExtractor,
-    CLIPTextModel,
-    CLIPTokenizer,
-    WhisperForConditionalGeneration,
-    WhisperProcessor,
-)
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

examples/community/stable_diffusion_comparison.py

@@ -1,6 +1,7 @@
 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

@@ -1,8 +1,9 @@
 from typing import Any, Callable, Dict, List, Optional, Union
 
-import torch
-
 import PIL.Image
+import torch
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
 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

@@ -1,15 +1,7 @@
 from typing import Callable, List, Optional, Union
 
-import torch
-
 import PIL
-from diffusers import DiffusionPipeline
+import torch
-from diffusers.configuration_utils import FrozenDict
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-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,
@@ -18,6 +10,14 @@ from transformers import (
     CLIPTokenizer,
 )
 
+from diffusers import DiffusionPipeline
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+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
+
 
 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 torch
-
 import PIL
+import torch
+from PIL import Image
+from transformers import CLIPTextModel, CLIPTokenizer
+
 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

@@ -23,27 +23,27 @@ 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 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
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from packaging import version
-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/dreambooth/train_dreambooth_flax.py

@@ -6,15 +6,24 @@ import os
 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 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
+
 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

@@ -1,9 +1,9 @@
-import torch
-
 import intel_extension_for_pytorch as ipex
-from diffusers import StableDiffusionPipeline
+import torch
 from PIL import Image
 
+from diffusers import StableDiffusionPipeline
+
 
 def image_grid(imgs, rows, cols):
     assert len(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

@@ -8,26 +8,26 @@ 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 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
-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/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")

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

@@ -6,16 +6,27 @@ 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 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
+
+# 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,
@@ -25,17 +36,6 @@ 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
-
-# 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 CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
 
 
 if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):

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

@@ -1,3 +1,18 @@
 [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

@@ -1,8 +1,8 @@
 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

@@ -1,9 +1,9 @@
 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

@@ -1,8 +1,9 @@
 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

@@ -1,9 +1,10 @@
 import argparse
 import io
 
-import torch
-
 import requests
+import torch
+from omegaconf import OmegaConf
+
 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

scripts/generate_logits.py

@@ -1,9 +1,9 @@
 import random
 
 import torch
+from huggingface_hub import HfApi
 
 from diffusers import UNet2DModel
-from huggingface_hub import HfApi
 
 
 api = HfApi()

setup.py

@@ -80,10 +80,9 @@ from setuptools import find_packages, setup
 _deps = [
     "Pillow",  # keep the PIL.Image.Resampling deprecation away
     "accelerate>=0.11.0",
-    "black==22.12",
+    "black~=23.1",
     "datasets",
     "filelock",
-    "flake8>=3.8.3",
     "flax>=0.4.1",
     "hf-doc-builder>=0.3.0",
     "huggingface-hub>=0.10.0",
@@ -99,6 +98,7 @@ _deps = [
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
+    "ruff>=0.0.241",
     "safetensors",
     "sentencepiece>=0.1.91,!=0.1.92",
     "scipy",
@@ -178,7 +178,7 @@ extras = {}
 
 
 extras = {}
-extras["quality"] = deps_list("black", "isort", "flake8", "hf-doc-builder")
+extras["quality"] = deps_list("black", "isort", "ruff", "hf-doc-builder")
 extras["docs"] = deps_list("hf-doc-builder")
 extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "Jinja2")
 extras["test"] = deps_list(

src/diffusers/configuration_utils.py

@@ -26,7 +26,6 @@ from pathlib import PosixPath
 from typing import Any, Dict, Tuple, Union
 
 import numpy as np
-
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError

src/diffusers/dependency_versions_table.py

@@ -4,10 +4,9 @@
 deps = {
     "Pillow": "Pillow",
     "accelerate": "accelerate>=0.11.0",
-    "black": "black==22.12",
+    "black": "black~=23.1",
     "datasets": "datasets",
     "filelock": "filelock",
-    "flake8": "flake8>=3.8.3",
     "flax": "flax>=0.4.1",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
     "huggingface-hub": "huggingface-hub>=0.10.0",
@@ -23,6 +22,7 @@ deps = {
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",
+    "ruff": "ruff>=0.0.241",
     "safetensors": "safetensors",
     "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
     "scipy": "scipy",

src/diffusers/experimental/rl/value_guided_sampling.py

@@ -14,7 +14,6 @@
 
 import numpy as np
 import torch
-
 import tqdm
 
 from ...models.unet_1d import UNet1DModel
@@ -57,13 +56,13 @@ class ValueGuidedRLPipeline(DiffusionPipeline):
         for key in self.data.keys():
             try:
                 self.means[key] = self.data[key].mean()
-            except:
+            except:  # noqa: E722
                 pass
         self.stds = dict()
         for key in self.data.keys():
             try:
                 self.stds[key] = self.data[key].std()
-            except:
+            except:  # noqa: E722
                 pass
         self.state_dim = env.observation_space.shape[0]
         self.action_dim = env.action_space.shape[0]

src/diffusers/models/modeling_pytorch_flax_utils.py

@@ -16,10 +16,9 @@
 
 from pickle import UnpicklingError
 
-import numpy as np
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 from flax.serialization import from_bytes
 from flax.traverse_util import flatten_dict
 

src/diffusers/models/modeling_utils.py

@@ -20,11 +20,10 @@ from functools import partial
 from typing import Callable, List, Optional, Tuple, Union
 
 import torch
-from torch import Tensor, device
-
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
+from torch import Tensor, device
 
 from .. import __version__
 from ..utils import (
@@ -500,7 +499,7 @@ class ModelMixin(torch.nn.Module):
                         subfolder=subfolder,
                         user_agent=user_agent,
                     )
-                except:
+                except:  # noqa: E722
                     pass
             if model_file is None:
                 model_file = _get_model_file(

src/diffusers/pipelines/alt_diffusion/__init__.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import numpy as np
-
 import PIL
 from PIL import Image
 

src/diffusers/pipelines/alt_diffusion/modeling_roberta_series.py

@@ -3,7 +3,6 @@ from typing import Optional, Tuple
 
 import torch
 from torch import nn
-
 from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
 from transformers.utils import ModelOutput
 

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py

@@ -16,11 +16,11 @@ import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
-
-from diffusers.utils import is_accelerate_available
 from packaging import version
 from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
 
+from diffusers.utils import is_accelerate_available
+
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -16,13 +16,13 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
-from diffusers.utils import is_accelerate_available
+import torch
 from packaging import version
 from transformers import CLIPFeatureExtractor, XLMRobertaTokenizer
 
+from diffusers.utils import is_accelerate_available
+
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers

src/diffusers/pipelines/audio_diffusion/__init__.py

@@ -1,3 +1,2 @@
-# flake8: noqa
 from .mel import Mel
 from .pipeline_audio_diffusion import AudioDiffusionPipeline

src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py

@@ -18,7 +18,6 @@ from typing import List, Tuple, Union
 
 import numpy as np
 import torch
-
 from PIL import Image
 
 from ...models import AutoencoderKL, UNet2DConditionModel

src/diffusers/pipelines/dance_diffusion/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_dance_diffusion import DanceDiffusionPipeline

src/diffusers/pipelines/ddim/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_ddim import DDIMPipeline

src/diffusers/pipelines/ddpm/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_ddpm import DDPMPipeline

src/diffusers/pipelines/latent_diffusion/__init__.py

@@ -1,4 +1,3 @@
-# flake8: noqa
 from ...utils import is_transformers_available
 from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
 

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -18,7 +18,6 @@ from typing import List, Optional, Tuple, Union
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint
-
 from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
 from transformers.activations import ACT2FN
 from transformers.modeling_outputs import BaseModelOutput

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py

@@ -2,11 +2,10 @@ import inspect
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
+import PIL
 import torch
 import torch.utils.checkpoint
 
-import PIL
-
 from ...models import UNet2DModel, VQModel
 from ...schedulers import (
     DDIMScheduler,

src/diffusers/pipelines/latent_diffusion_uncond/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_latent_diffusion_uncond import LDMPipeline

src/diffusers/pipelines/onnx_utils.py

@@ -21,7 +21,6 @@ from pathlib import Path
 from typing import Optional, Union
 
 import numpy as np
-
 from huggingface_hub import hf_hub_download
 
 from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging

src/diffusers/pipelines/paint_by_example/__init__.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import numpy as np
-
 import PIL
 from PIL import Image
 

src/diffusers/pipelines/paint_by_example/image_encoder.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 import torch
 from torch import nn
-
 from transformers import CLIPPreTrainedModel, CLIPVisionModel
 
 from ...models.attention import BasicTransformerBlock

src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py

@@ -16,12 +16,12 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
-from diffusers.utils import is_accelerate_available
+import torch
 from transformers import CLIPFeatureExtractor
 
+from diffusers.utils import is_accelerate_available
+
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from ...utils import logging, randn_tensor

src/diffusers/pipelines/pipeline_flax_utils.py

@@ -19,9 +19,8 @@ import inspect
 import os
 from typing import Any, Dict, List, Optional, Union
 
-import numpy as np
-
 import flax
+import numpy as np
 import PIL
 from flax.core.frozen_dict import FrozenDict
 from huggingface_hub import snapshot_download

src/diffusers/pipelines/pipeline_utils.py

@@ -22,15 +22,15 @@ from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
-import torch
-
-import diffusers
 import PIL
+import torch
 from huggingface_hub import model_info, snapshot_download
 from packaging import version
 from PIL import Image
 from tqdm.auto import tqdm
 
+import diffusers
+
 from ..configuration_utils import ConfigMixin
 from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
 from ..schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME

src/diffusers/pipelines/pndm/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_pndm import PNDMPipeline

src/diffusers/pipelines/repaint/pipeline_repaint.py

@@ -16,9 +16,8 @@
 from typing import List, Optional, Tuple, Union
 
 import numpy as np
-import torch
-
 import PIL
+import torch
 
 from ...models import UNet2DModel
 from ...schedulers import RePaintScheduler

src/diffusers/pipelines/score_sde_ve/__init__.py

@@ -1,2 +1 @@
-# flake8: noqa
 from .pipeline_score_sde_ve import ScoreSdeVePipeline

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import numpy as np
-
 import PIL
 from PIL import Image
 

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -18,9 +18,10 @@ import os
 import re
 import tempfile
 
-import torch
-
 import requests
+import torch
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
+
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
@@ -37,7 +38,6 @@ from diffusers import (
 from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
 
 from ...utils import is_omegaconf_available, is_safetensors_available
 from ...utils.import_utils import BACKENDS_MAPPING

src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py

@@ -16,13 +16,13 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
-from diffusers.utils import is_accelerate_available
+import torch
 from packaging import version
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
+from diffusers.utils import is_accelerate_available
+
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import DDIMScheduler

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py

@@ -16,10 +16,9 @@ import warnings
 from functools import partial
 from typing import Dict, List, Optional, Union
 
-import numpy as np
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 from flax.core.frozen_dict import FrozenDict
 from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py

@@ -16,10 +16,9 @@ import warnings
 from functools import partial
 from typing import Dict, List, Optional, Union
 
-import numpy as np
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 from flax.core.frozen_dict import FrozenDict
 from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard

src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py

@@ -16,10 +16,9 @@ import warnings
 from functools import partial
 from typing import Dict, List, Optional, Union
 
-import numpy as np
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 from flax.core.frozen_dict import FrozenDict
 from flax.jax_utils import unreplicate
 from flax.training.common_utils import shard

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py

@@ -17,7 +17,6 @@ from typing import Callable, List, Optional, Union
 
 import numpy as np
 import torch
-
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py

@@ -16,9 +16,8 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
+import torch
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py

@@ -16,9 +16,8 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
+import torch
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict

src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py

@@ -2,9 +2,8 @@ import inspect
 from typing import Callable, List, Optional, Union
 
 import numpy as np
-import torch
-
 import PIL
+import torch
 from transformers import CLIPFeatureExtractor, CLIPTokenizer
 
 from ...configuration_utils import FrozenDict

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -16,7 +16,6 @@ import inspect
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
-
 from packaging import version
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer