run lint rules using latest isort settings

scripts/conversion/convert_diffusers_autoencoder_kl.py

@@ -1,10 +1,12 @@
 import argparse
 from pathlib import Path
+
 import torch
-from torch import nn
 from diffusers import AutoencoderKL  # type: ignore
-from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
+from torch import nn
+
 from refiners.fluxion.model_converter import ModelConverter
+from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 
 
 class Args(argparse.Namespace):

scripts/conversion/convert_diffusers_controlnet.py

@@ -1,15 +1,17 @@
 # pyright: reportPrivateUsage=false
 import argparse
 from pathlib import Path
+
 import torch
-from torch import nn
 from diffusers import ControlNetModel  # type: ignore
-from refiners.fluxion.utils import save_to_safetensors
+from torch import nn
+
 from refiners.fluxion.model_converter import ModelConverter
+from refiners.fluxion.utils import save_to_safetensors
 from refiners.foundationals.latent_diffusion import (
-    SD1UNet,
-    SD1ControlnetAdapter,
     DPMSolver,
+    SD1ControlnetAdapter,
+    SD1UNet,
 )
 
 

scripts/conversion/convert_diffusers_ip_adapter.py

@@ -1,11 +1,11 @@
+import argparse
 from pathlib import Path
 from typing import Any
-import argparse
 
 import torch
 
-from refiners.foundationals.latent_diffusion import SD1UNet, SD1IPAdapter, SDXLUNet, SDXLIPAdapter
 from refiners.fluxion.utils import save_to_safetensors
+from refiners.foundationals.latent_diffusion import SD1IPAdapter, SD1UNet, SDXLIPAdapter, SDXLUNet
 
 # Running:
 #

scripts/conversion/convert_diffusers_lora.py

@@ -3,16 +3,15 @@ from pathlib import Path
 from typing import cast
 
 import torch
-from torch import Tensor
-from torch.nn.init import zeros_
-from torch.nn import Parameter as TorchParameter
-
 from diffusers import DiffusionPipeline  # type: ignore
+from torch import Tensor
+from torch.nn import Parameter as TorchParameter
+from torch.nn.init import zeros_
 
 import refiners.fluxion.layers as fl
+from refiners.fluxion.adapters.lora import Lora, LoraAdapter
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import save_to_safetensors
-from refiners.fluxion.adapters.lora import Lora, LoraAdapter
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget, lora_targets
 

scripts/conversion/convert_diffusers_t2i_adapter.py

@@ -1,10 +1,12 @@
 import argparse
 from pathlib import Path
+
 import torch
-from torch import nn
 from diffusers import T2IAdapter  # type: ignore
-from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, ConditionEncoderXL
+from torch import nn
+
 from refiners.fluxion.model_converter import ModelConverter
+from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, ConditionEncoderXL
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Convert a pretrained diffusers T2I-Adapter model to refiners")

scripts/conversion/convert_diffusers_unet.py

@@ -1,9 +1,11 @@
 import argparse
 from pathlib import Path
+
 import torch
-from torch import nn
-from refiners.fluxion.model_converter import ModelConverter
 from diffusers import UNet2DConditionModel  # type: ignore
+from torch import nn
+
+from refiners.fluxion.model_converter import ModelConverter
 from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
 
 

scripts/conversion/convert_informative_drawings.py

@@ -1,7 +1,9 @@
 import argparse
 from typing import TYPE_CHECKING, cast
+
 import torch
 from torch import nn
+
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings import InformativeDrawings
 

scripts/conversion/convert_refiners_lora_to_sdwebui.py

@@ -1,20 +1,22 @@
 import argparse
 from functools import partial
+
+from convert_diffusers_unet import Args as UnetConversionArgs, setup_converter as convert_unet
+from convert_transformers_clip_text_model import (
+    Args as TextEncoderConversionArgs,
+    setup_converter as convert_text_encoder,
+)
 from torch import Tensor
+
+import refiners.fluxion.layers as fl
 from refiners.fluxion.utils import (
     load_from_safetensors,
     load_metadata_from_safetensors,
     save_to_safetensors,
 )
-from convert_diffusers_unet import setup_converter as convert_unet, Args as UnetConversionArgs
-from convert_transformers_clip_text_model import (
-    setup_converter as convert_text_encoder,
-    Args as TextEncoderConversionArgs,
-)
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget
-import refiners.fluxion.layers as fl
 
 
 def get_unet_mapping(source_path: str) -> dict[str, str]:

scripts/conversion/convert_segment_anything.py

@@ -1,20 +1,19 @@
 import argparse
 import types
 from typing import Any, Callable, cast
+
 import torch
 import torch.nn as nn
+from segment_anything import build_sam_vit_h  # type: ignore
+from segment_anything.modeling.common import LayerNorm2d  # type: ignore
 from torch import Tensor
 
 import refiners.fluxion.layers as fl
 from refiners.fluxion.model_converter import ModelConverter
 from refiners.fluxion.utils import manual_seed, save_to_safetensors
 from refiners.foundationals.segment_anything.image_encoder import SAMViTH
-from refiners.foundationals.segment_anything.prompt_encoder import PointEncoder, MaskEncoder
-
-from segment_anything import build_sam_vit_h  # type: ignore
-from segment_anything.modeling.common import LayerNorm2d  # type: ignore
-
 from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
+from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder
 
 
 class FacebookSAM(nn.Module):
@@ -134,9 +133,10 @@ def convert_mask_decoder(mask_decoder: nn.Module) -> dict[str, Tensor]:
     point_embedding = torch.randn(1, 3, 256)
     mask_embedding = torch.randn(1, 256, 64, 64)
 
-    import refiners.fluxion.layers as fl
     from segment_anything.modeling.common import LayerNorm2d  # type: ignore
 
+    import refiners.fluxion.layers as fl
+
     assert issubclass(LayerNorm2d, nn.Module)
     custom_layers = {LayerNorm2d: fl.LayerNorm2d}
 

scripts/conversion/convert_transformers_clip_image_model.py

@@ -1,12 +1,14 @@
 import argparse
 from pathlib import Path
-from torch import nn
+
-from refiners.fluxion.model_converter import ModelConverter
-from transformers import CLIPVisionModelWithProjection  # type: ignore
-from refiners.foundationals.clip.image_encoder import CLIPImageEncoder
-from refiners.fluxion.utils import save_to_safetensors
 import torch
+from torch import nn
+from transformers import CLIPVisionModelWithProjection  # type: ignore
+
 import refiners.fluxion.layers as fl
+from refiners.fluxion.model_converter import ModelConverter
+from refiners.fluxion.utils import save_to_safetensors
+from refiners.foundationals.clip.image_encoder import CLIPImageEncoder
 
 
 class Args(argparse.Namespace):

scripts/conversion/convert_transformers_clip_text_model.py

@@ -1,14 +1,16 @@
 import argparse
 from pathlib import Path
 from typing import cast
+
 from torch import nn
-from refiners.fluxion.model_converter import ModelConverter
 from transformers import CLIPTextModelWithProjection  # type: ignore
-from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, CLIPTextEncoderL, CLIPTextEncoderG
+
+import refiners.fluxion.layers as fl
+from refiners.fluxion.model_converter import ModelConverter
+from refiners.fluxion.utils import save_to_safetensors
+from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, CLIPTextEncoderG, CLIPTextEncoderL
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
-from refiners.fluxion.utils import save_to_safetensors
-import refiners.fluxion.layers as fl
 
 
 class Args(argparse.Namespace):

scripts/training/finetune-ldm-lora.py

@@ -1,20 +1,21 @@
 import random
 from typing import Any
-from pydantic import BaseModel
+
 from loguru import logger
-from refiners.fluxion.utils import save_to_safetensors
+from pydantic import BaseModel
-from refiners.foundationals.latent_diffusion.lora import LoraTarget, LoraAdapter, MODELS, lora_targets
-import refiners.fluxion.layers as fl
 from torch import Tensor
 from torch.utils.data import Dataset
 
+import refiners.fluxion.layers as fl
+from refiners.fluxion.utils import save_to_safetensors
+from refiners.foundationals.latent_diffusion.lora import MODELS, LoraAdapter, LoraTarget, lora_targets
 from refiners.training_utils.callback import Callback
 from refiners.training_utils.latent_diffusion import (
     FinetuneLatentDiffusionConfig,
+    LatentDiffusionConfig,
+    LatentDiffusionTrainer,
     TextEmbeddingLatentsBatch,
     TextEmbeddingLatentsDataset,
-    LatentDiffusionTrainer,
-    LatentDiffusionConfig,
 )
 
 

scripts/training/finetune-ldm-textual-inversion.py

@@ -1,24 +1,24 @@
-from typing import Any
-from pydantic import BaseModel
-from loguru import logger
-from torch.utils.data import Dataset
-from torch import randn, Tensor
 import random
+from typing import Any
 
+from loguru import logger
+from pydantic import BaseModel
+from torch import Tensor, randn
+from torch.utils.data import Dataset
+
+from refiners.fluxion.utils import save_to_safetensors
 from refiners.foundationals.clip.concepts import ConceptExtender, EmbeddingExtender
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
-from refiners.fluxion.utils import save_to_safetensors
 from refiners.training_utils.callback import Callback
 from refiners.training_utils.latent_diffusion import (
     FinetuneLatentDiffusionConfig,
-    TextEmbeddingLatentsBatch,
-    LatentDiffusionTrainer,
     LatentDiffusionConfig,
+    LatentDiffusionTrainer,
+    TextEmbeddingLatentsBatch,
     TextEmbeddingLatentsDataset,
 )
 
-
 IMAGENET_TEMPLATES_SMALL = [
     "a photo of a {}",
     "a rendering of a {}",

src/refiners/fluxion/__init__.py

@@ -1,3 +1,3 @@
-from refiners.fluxion.utils import save_to_safetensors, load_from_safetensors, norm, manual_seed, pad
+from refiners.fluxion.utils import load_from_safetensors, manual_seed, norm, pad, save_to_safetensors
 
 __all__ = ["norm", "manual_seed", "save_to_safetensors", "load_from_safetensors", "pad"]

src/refiners/fluxion/adapters/adapter.py

@@ -1,7 +1,7 @@
 import contextlib
-import refiners.fluxion.layers as fl
+from typing import Any, Generic, Iterator, TypeVar
-from typing import Any, Generic, TypeVar, Iterator
 
+import refiners.fluxion.layers as fl
 
 T = TypeVar("T", bound=fl.Module)
 TAdapter = TypeVar("TAdapter", bound="Adapter[Any]")  # Self (see PEP 673)

src/refiners/fluxion/adapters/lora.py

@@ -1,11 +1,11 @@
-from typing import Iterable, Generic, TypeVar, Any
+from typing import Any, Generic, Iterable, TypeVar
-
-import refiners.fluxion.layers as fl
-from refiners.fluxion.adapters.adapter import Adapter
 
 from torch import Tensor, device as Device, dtype as DType
 from torch.nn import Parameter as TorchParameter
-from torch.nn.init import zeros_, normal_
+from torch.nn.init import normal_, zeros_
+
+import refiners.fluxion.layers as fl
+from refiners.fluxion.adapters.adapter import Adapter
 
 T = TypeVar("T", bound=fl.Chain)
 TLoraAdapter = TypeVar("TLoraAdapter", bound="LoraAdapter[Any]")  # Self (see PEP 673)

src/refiners/fluxion/context.py

@@ -1,4 +1,5 @@
 from typing import Any
+
 from torch import Tensor
 
 Context = dict[str, Any]

src/refiners/fluxion/layers/__init__.py

@@ -1,50 +1,50 @@
-from refiners.fluxion.layers.activations import GLU, SiLU, ReLU, ApproximateGeLU, GeLU, Sigmoid
+from refiners.fluxion.layers.activations import GLU, ApproximateGeLU, GeLU, ReLU, Sigmoid, SiLU
-from refiners.fluxion.layers.norm import LayerNorm, GroupNorm, LayerNorm2d, InstanceNorm2d
 from refiners.fluxion.layers.attentions import Attention, SelfAttention, SelfAttention2d
 from refiners.fluxion.layers.basics import (
-    Identity,
+    Buffer,
-    View,
+    Chunk,
+    Cos,
     Flatten,
-    Unflatten,
-    Transpose,
     GetArg,
+    Identity,
+    Multiply,
+    Parameter,
     Permute,
     Reshape,
-    Squeeze,
-    Unsqueeze,
-    Slicing,
     Sin,
-    Cos,
+    Slicing,
-    Chunk,
+    Squeeze,
-    Multiply,
+    Transpose,
     Unbind,
-    Parameter,
+    Unflatten,
-    Buffer,
+    Unsqueeze,
+    View,
 )
 from refiners.fluxion.layers.chain import (
+    Breakpoint,
+    Chain,
+    Concatenate,
+    Distribute,
     Lambda,
-    Sum,
+    Matmul,
+    Parallel,
+    Passthrough,
     Residual,
     Return,
-    Chain,
-    UseContext,
     SetContext,
-    Parallel,
+    Sum,
-    Distribute,
+    UseContext,
-    Passthrough,
-    Breakpoint,
-    Concatenate,
-    Matmul,
 )
 from refiners.fluxion.layers.conv import Conv2d, ConvTranspose2d
+from refiners.fluxion.layers.converter import Converter
+from refiners.fluxion.layers.embedding import Embedding
 from refiners.fluxion.layers.linear import Linear, MultiLinear
-from refiners.fluxion.layers.module import Module, WeightedModule, ContextModule
+from refiners.fluxion.layers.maxpool import MaxPool1d, MaxPool2d
+from refiners.fluxion.layers.module import ContextModule, Module, WeightedModule
+from refiners.fluxion.layers.norm import GroupNorm, InstanceNorm2d, LayerNorm, LayerNorm2d
 from refiners.fluxion.layers.padding import ReflectionPad2d
 from refiners.fluxion.layers.pixelshuffle import PixelUnshuffle
-from refiners.fluxion.layers.sampling import Downsample, Upsample, Interpolate
+from refiners.fluxion.layers.sampling import Downsample, Interpolate, Upsample
-from refiners.fluxion.layers.embedding import Embedding
-from refiners.fluxion.layers.converter import Converter
-from refiners.fluxion.layers.maxpool import MaxPool1d, MaxPool2d
 
 __all__ = [
     "Embedding",

src/refiners/fluxion/layers/activations.py

@@ -1,7 +1,10 @@
-from refiners.fluxion.layers.module import Module
-from torch.nn.functional import silu
 from torch import Tensor, sigmoid
-from torch.nn.functional import gelu  # type: ignore
+from torch.nn.functional import (
+    gelu,  # type: ignore
+    silu,
+)
+
+from refiners.fluxion.layers.module import Module
 
 
 class Activation(Module):

src/refiners/fluxion/layers/attentions.py

@@ -2,14 +2,14 @@ import math
 
 import torch
 from jaxtyping import Float
-from torch.nn.functional import scaled_dot_product_attention as _scaled_dot_product_attention  # type: ignore
 from torch import Tensor, device as Device, dtype as DType
+from torch.nn.functional import scaled_dot_product_attention as _scaled_dot_product_attention  # type: ignore
 
+from refiners.fluxion.context import Contexts
+from refiners.fluxion.layers.basics import Identity
+from refiners.fluxion.layers.chain import Chain, Distribute, Lambda, Parallel
 from refiners.fluxion.layers.linear import Linear
 from refiners.fluxion.layers.module import Module
-from refiners.fluxion.layers.chain import Chain, Distribute, Parallel, Lambda
-from refiners.fluxion.layers.basics import Identity
-from refiners.fluxion.context import Contexts
 
 
 def scaled_dot_product_attention(

src/refiners/fluxion/layers/basics.py

@@ -1,8 +1,9 @@
-from refiners.fluxion.layers.module import Module, WeightedModule
 import torch
-from torch import randn, Tensor, Size, device as Device, dtype as DType
+from torch import Size, Tensor, device as Device, dtype as DType, randn
 from torch.nn import Parameter as TorchParameter
 
+from refiners.fluxion.layers.module import Module, WeightedModule
+
 
 class Identity(Module):
     def __init__(self) -> None:

src/refiners/fluxion/layers/chain.py

@@ -1,15 +1,16 @@
-from collections import defaultdict
 import inspect
 import re
 import sys
 import traceback
+from collections import defaultdict
 from typing import Any, Callable, Iterable, Iterator, TypeVar, cast, overload
+
 import torch
 from torch import Tensor, cat, device as Device, dtype as DType
-from refiners.fluxion.layers.module import Module, ContextModule, ModuleTree, WeightedModule
-from refiners.fluxion.context import Contexts, ContextProvider
-from refiners.fluxion.utils import summarize_tensor
 
+from refiners.fluxion.context import ContextProvider, Contexts
+from refiners.fluxion.layers.module import ContextModule, Module, ModuleTree, WeightedModule
+from refiners.fluxion.utils import summarize_tensor
 
 T = TypeVar("T", bound=Module)
 TChain = TypeVar("TChain", bound="Chain")  # because Self (PEP 673) is not in 3.10

src/refiners/fluxion/layers/conv.py

@@ -1,4 +1,5 @@
-from torch import nn, device as Device, dtype as DType
+from torch import device as Device, dtype as DType, nn
+
 from refiners.fluxion.layers.module import WeightedModule
 
 

src/refiners/fluxion/layers/converter.py

@@ -1,6 +1,7 @@
-from refiners.fluxion.layers.module import ContextModule
 from torch import Tensor
 
+from refiners.fluxion.layers.module import ContextModule
+
 
 class Converter(ContextModule):
     """

src/refiners/fluxion/layers/embedding.py

@@ -1,8 +1,8 @@
-from refiners.fluxion.layers.module import WeightedModule
-from torch.nn import Embedding as _Embedding
-from torch import Tensor, device as Device, dtype as DType
-
 from jaxtyping import Float, Int
+from torch import Tensor, device as Device, dtype as DType
+from torch.nn import Embedding as _Embedding
+
+from refiners.fluxion.layers.module import WeightedModule
 
 
 class Embedding(_Embedding, WeightedModule):  # type: ignore

src/refiners/fluxion/layers/linear.py

@@ -1,11 +1,10 @@
-from torch import device as Device, dtype as DType
+from jaxtyping import Float
+from torch import Tensor, device as Device, dtype as DType
 from torch.nn import Linear as _Linear
-from torch import Tensor
+
-from refiners.fluxion.layers.module import Module, WeightedModule
 from refiners.fluxion.layers.activations import ReLU
 from refiners.fluxion.layers.chain import Chain
-
+from refiners.fluxion.layers.module import Module, WeightedModule
-from jaxtyping import Float
 
 
 class Linear(_Linear, WeightedModule):

src/refiners/fluxion/layers/maxpool.py

@@ -1,4 +1,5 @@
 from torch import nn
+
 from refiners.fluxion.layers.module import Module
 
 

src/refiners/fluxion/layers/module.py

@@ -1,17 +1,15 @@
-from collections import defaultdict
-from inspect import signature, Parameter
 import sys
+from collections import defaultdict
+from inspect import Parameter, signature
 from pathlib import Path
 from types import ModuleType
-from typing import Any, DefaultDict, Generator, TypeVar, TypedDict, cast
+from typing import TYPE_CHECKING, Any, DefaultDict, Generator, Sequence, TypedDict, TypeVar, cast
 
 from torch import device as Device, dtype as DType
 from torch.nn.modules.module import Module as TorchModule
 
-from refiners.fluxion.utils import load_from_safetensors
 from refiners.fluxion.context import Context, ContextProvider
-
+from refiners.fluxion.utils import load_from_safetensors
-from typing import TYPE_CHECKING, Sequence
 
 if TYPE_CHECKING:
     from refiners.fluxion.layers.chain import Chain

src/refiners/fluxion/layers/norm.py

@@ -1,5 +1,6 @@
-from torch import nn, ones, zeros, Tensor, sqrt, device as Device, dtype as DType
 from jaxtyping import Float
+from torch import Tensor, device as Device, dtype as DType, nn, ones, sqrt, zeros
+
 from refiners.fluxion.layers.module import Module, WeightedModule
 
 

src/refiners/fluxion/layers/padding.py

@@ -1,4 +1,5 @@
 from torch import nn
+
 from refiners.fluxion.layers.module import Module
 
 

src/refiners/fluxion/layers/pixelshuffle.py

@@ -1,6 +1,7 @@
-from refiners.fluxion.layers.module import Module
 from torch.nn import PixelUnshuffle as _PixelUnshuffle
 
+from refiners.fluxion.layers.module import Module
+
 
 class PixelUnshuffle(_PixelUnshuffle, Module):
     def __init__(self, downscale_factor: int):

src/refiners/fluxion/layers/sampling.py

@@ -1,11 +1,11 @@
-from refiners.fluxion.layers.chain import Chain, UseContext, SetContext
+from torch import Size, Tensor, device as Device, dtype as DType
-from refiners.fluxion.layers.conv import Conv2d
+from torch.nn.functional import pad
+
 from refiners.fluxion.layers.basics import Identity
-from refiners.fluxion.layers.chain import Parallel, Lambda
+from refiners.fluxion.layers.chain import Chain, Lambda, Parallel, SetContext, UseContext
+from refiners.fluxion.layers.conv import Conv2d
 from refiners.fluxion.layers.module import Module
 from refiners.fluxion.utils import interpolate
-from torch.nn.functional import pad
-from torch import Tensor, Size, device as Device, dtype as DType
 
 
 class Downsample(Chain):

src/refiners/fluxion/model_converter.py

@@ -1,10 +1,11 @@
 from collections import defaultdict
 from enum import Enum, auto
 from pathlib import Path
+from typing import Any, DefaultDict, TypedDict
+
+import torch
 from torch import Tensor, nn
 from torch.utils.hooks import RemovableHandle
-import torch
-from typing import Any, DefaultDict, TypedDict
 
 from refiners.fluxion.utils import norm, save_to_safetensors
 

src/refiners/fluxion/utils.py

@@ -1,15 +1,14 @@
-from typing import Iterable, Literal, TypeVar
-from PIL import Image
-from numpy import array, float32
 from pathlib import Path
+from typing import Iterable, Literal, TypeVar
+
+import torch
+from jaxtyping import Float
+from numpy import array, float32
+from PIL import Image
 from safetensors import safe_open as _safe_open  # type: ignore
 from safetensors.torch import save_file as _save_file  # type: ignore
-from torch import norm as _norm, manual_seed as _manual_seed  # type: ignore
+from torch import Tensor, device as Device, dtype as DType, manual_seed as _manual_seed, norm as _norm  # type: ignore
-import torch
+from torch.nn.functional import conv2d, interpolate as _interpolate, pad as _pad  # type: ignore
-from torch.nn.functional import pad as _pad, interpolate as _interpolate, conv2d  # type: ignore
-from torch import Tensor, device as Device, dtype as DType
-from jaxtyping import Float
-
 
 T = TypeVar("T")
 E = TypeVar("E")

src/refiners/foundationals/clip/common.py

@@ -1,4 +1,5 @@
 from torch import Tensor, arange, device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
 
 

src/refiners/foundationals/clip/concepts.py

@@ -1,13 +1,14 @@
+import re
+from typing import cast
+
+import torch.nn.functional as F
+from torch import Tensor, cat, zeros
+from torch.nn import Parameter
+
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, TokenEncoder
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
-import refiners.fluxion.layers as fl
-from typing import cast
-
-from torch import Tensor, cat, zeros
-import torch.nn.functional as F
-from torch.nn import Parameter
-import re
 
 
 class EmbeddingExtender(fl.Chain, Adapter[TokenEncoder]):

src/refiners/foundationals/clip/image_encoder.py

@@ -1,6 +1,7 @@
 from torch import device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
-from refiners.foundationals.clip.common import PositionalEncoder, FeedForward
+from refiners.foundationals.clip.common import FeedForward, PositionalEncoder
 
 
 class ClassToken(fl.Chain):

src/refiners/foundationals/clip/text_encoder.py

@@ -1,6 +1,7 @@
 from torch import device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
-from refiners.foundationals.clip.common import PositionalEncoder, FeedForward
+from refiners.foundationals.clip.common import FeedForward, PositionalEncoder
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 

src/refiners/foundationals/clip/tokenizer.py

@@ -1,11 +1,13 @@
 import gzip
-from pathlib import Path
+import re
 from functools import lru_cache
 from itertools import islice
-import re
+from pathlib import Path
+
 from torch import Tensor, tensor
-from refiners.fluxion import pad
+
 import refiners.fluxion.layers as fl
+from refiners.fluxion import pad
 
 
 class CLIPTokenizer(fl.Module):

src/refiners/foundationals/latent_diffusion/__init__.py

@@ -1,27 +1,26 @@
-from refiners.foundationals.latent_diffusion.auto_encoder import (
-    LatentDiffusionAutoencoder,
-)
 from refiners.foundationals.clip.text_encoder import (
     CLIPTextEncoderL,
 )
+from refiners.foundationals.latent_diffusion.auto_encoder import (
+    LatentDiffusionAutoencoder,
+)
 from refiners.foundationals.latent_diffusion.freeu import SDFreeUAdapter
-from refiners.foundationals.latent_diffusion.schedulers import Scheduler, DPMSolver
+from refiners.foundationals.latent_diffusion.schedulers import DPMSolver, Scheduler
 from refiners.foundationals.latent_diffusion.stable_diffusion_1 import (
-    StableDiffusion_1,
-    StableDiffusion_1_Inpainting,
-    SD1UNet,
     SD1ControlnetAdapter,
     SD1IPAdapter,
     SD1T2IAdapter,
+    SD1UNet,
+    StableDiffusion_1,
+    StableDiffusion_1_Inpainting,
 )
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl import (
-    SDXLUNet,
     DoubleTextEncoder,
     SDXLIPAdapter,
     SDXLT2IAdapter,
+    SDXLUNet,
 )
 
-
 __all__ = [
     "StableDiffusion_1",
     "StableDiffusion_1_Inpainting",

src/refiners/foundationals/latent_diffusion/auto_encoder.py

@@ -1,20 +1,21 @@
+from PIL import Image
+from torch import Tensor, device as Device, dtype as DType
+
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.layers import (
     Chain,
     Conv2d,
+    Downsample,
     GroupNorm,
     Identity,
-    SiLU,
-    Downsample,
-    Upsample,
-    Sum,
-    SelfAttention2d,
-    Slicing,
     Residual,
+    SelfAttention2d,
+    SiLU,
+    Slicing,
+    Sum,
+    Upsample,
 )
 from refiners.fluxion.utils import image_to_tensor, tensor_to_image
-from torch import Tensor, device as Device, dtype as DType
-from PIL import Image
 
 
 class Resnet(Sum):

src/refiners/foundationals/latent_diffusion/cross_attention.py

@@ -1,24 +1,24 @@
-from torch import Tensor, Size, device as Device, dtype as DType
+from torch import Size, Tensor, device as Device, dtype as DType
 
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.layers import (
-    Identity,
-    Flatten,
-    Unflatten,
-    Transpose,
-    Chain,
-    Parallel,
-    LayerNorm,
-    Attention,
-    UseContext,
-    Linear,
     GLU,
+    Attention,
+    Chain,
+    Conv2d,
+    Flatten,
     GeLU,
     GroupNorm,
-    Conv2d,
+    Identity,
+    LayerNorm,
+    Linear,
+    Parallel,
+    Residual,
     SelfAttention,
     SetContext,
-    Residual,
+    Transpose,
+    Unflatten,
+    UseContext,
 )
 
 

src/refiners/foundationals/latent_diffusion/freeu.py

@@ -1,13 +1,14 @@
 import math
 from typing import Any, Generic, TypeVar
 
-import refiners.fluxion.layers as fl
 import torch
+from torch import Tensor
+from torch.fft import fftn, fftshift, ifftn, ifftshift  # type: ignore
+
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualConcatenator, SD1UNet
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
-from torch import Tensor
-from torch.fft import fftn, fftshift, ifftn, ifftshift  # type: ignore
 
 T = TypeVar("T", bound="SD1UNet | SDXLUNet")
 TSDFreeUAdapter = TypeVar("TSDFreeUAdapter", bound="SDFreeUAdapter[Any]")  # Self (see PEP 673)

src/refiners/foundationals/latent_diffusion/image_prompt.py

@@ -1,19 +1,19 @@
+import math
 from enum import IntEnum
 from functools import partial
-from typing import Generic, TypeVar, Any, Callable, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Callable, Generic, TypeVar
-import math
 
 from jaxtyping import Float
-from torch import Tensor, cat, softmax, zeros_like, device as Device, dtype as DType
 from PIL import Image
+from torch import Tensor, cat, device as Device, dtype as DType, softmax, zeros_like
 
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.adapters.lora import Lora
-from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.layers.attentions import ScaledDotProductAttention
 from refiners.fluxion.utils import image_to_tensor, normalize
-import refiners.fluxion.layers as fl
+from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 
 if TYPE_CHECKING:
     from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet

src/refiners/foundationals/latent_diffusion/lora.py

@@ -1,23 +1,21 @@
 from enum import Enum
 from pathlib import Path
-from typing import Iterator, Callable
+from typing import Callable, Iterator
 
 from torch import Tensor
 
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import load_from_safetensors, load_metadata_from_safetensors
-
 from refiners.fluxion.adapters.adapter import Adapter
-from refiners.fluxion.adapters.lora import LoraAdapter, Lora
+from refiners.fluxion.adapters.lora import Lora, LoraAdapter
-
+from refiners.fluxion.utils import load_from_safetensors, load_metadata_from_safetensors
 from refiners.foundationals.clip.text_encoder import FeedForward, TransformerLayer
-from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
 from refiners.foundationals.latent_diffusion import (
-    StableDiffusion_1,
-    SD1UNet,
     CLIPTextEncoderL,
     LatentDiffusionAutoencoder,
+    SD1UNet,
+    StableDiffusion_1,
 )
+from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import Controlnet
 
 MODELS = ["unet", "text_encoder", "lda"]

src/refiners/foundationals/latent_diffusion/model.py

@@ -1,8 +1,10 @@
 from abc import ABC, abstractmethod
 from typing import TypeVar
-from torch import Tensor, device as Device, dtype as DType
+
-from PIL import Image
 import torch
+from PIL import Image
+from torch import Tensor, device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler

src/refiners/foundationals/latent_diffusion/multi_diffusion.py

@@ -8,7 +8,6 @@ from torch import Tensor, device as Device, dtype as DType
 
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
 
-
 MAX_STEPS = 1000
 
 

src/refiners/foundationals/latent_diffusion/preprocessors/informative_drawings.py

@@ -1,6 +1,7 @@
 # Adapted from https://github.com/carolineec/informative-drawings, MIT License
 
 from torch import device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
 
 

src/refiners/foundationals/latent_diffusion/range_adapter.py

@@ -1,9 +1,10 @@
 import math
-from torch import Tensor, arange, float32, exp, sin, cat, cos, device as Device, dtype as DType
-from jaxtyping import Float, Int
 
-from refiners.fluxion.adapters.adapter import Adapter
+from jaxtyping import Float, Int
+from torch import Tensor, arange, cat, cos, device as Device, dtype as DType, exp, float32, sin
+
 import refiners.fluxion.layers as fl
+from refiners.fluxion.adapters.adapter import Adapter
 
 
 def compute_sinusoidal_embedding(

src/refiners/foundationals/latent_diffusion/reference_only_control.py

@@ -1,18 +1,19 @@
+from torch import Tensor
+
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.layers import (
-    Passthrough,
-    Lambda,
     Chain,
     Concatenate,
-    UseContext,
+    Identity,
+    Lambda,
+    Parallel,
+    Passthrough,
     SelfAttention,
     SetContext,
-    Identity,
+    UseContext,
-    Parallel,
 )
-from refiners.fluxion.adapters.adapter import Adapter
 from refiners.foundationals.latent_diffusion import SD1UNet
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock
-from torch import Tensor
 
 
 class SaveLayerNormAdapter(Chain, Adapter[SelfAttention]):

src/refiners/foundationals/latent_diffusion/schedulers/__init__.py

@@ -1,7 +1,7 @@
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
-from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
-from refiners.foundationals.latent_diffusion.schedulers.ddpm import DDPM
 from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
+from refiners.foundationals.latent_diffusion.schedulers.ddpm import DDPM
+from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
+from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
 
 __all__ = [
     "Scheduler",

src/refiners/foundationals/latent_diffusion/schedulers/ddim.py

@@ -1,4 +1,5 @@
-from torch import Tensor, device as Device, dtype as Dtype, arange, sqrt, float32, tensor
+from torch import Tensor, arange, device as Device, dtype as Dtype, float32, sqrt, tensor
+
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
 
 

src/refiners/foundationals/latent_diffusion/schedulers/ddpm.py

@@ -1,4 +1,5 @@
-from torch import Tensor, device as Device, randn, arange, Generator, tensor
+from torch import Generator, Tensor, arange, device as Device, randn, tensor
+
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
 
 

src/refiners/foundationals/latent_diffusion/schedulers/dpm_solver.py

@@ -1,8 +1,10 @@
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
-import numpy as np
-from torch import Tensor, device as Device, tensor, exp, float32, dtype as Dtype
 from collections import deque
 
+import numpy as np
+from torch import Tensor, device as Device, dtype as Dtype, exp, float32, tensor
+
+from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+
 
 class DPMSolver(Scheduler):
     """Implements DPM-Solver++ from https://arxiv.org/abs/2211.01095

src/refiners/foundationals/latent_diffusion/schedulers/scheduler.py

@@ -1,8 +1,9 @@
 from abc import ABC, abstractmethod
 from enum import Enum
-from torch import Tensor, device as Device, dtype as DType, linspace, float32, sqrt, log
 from typing import TypeVar
 
+from torch import Tensor, device as Device, dtype as DType, float32, linspace, log, sqrt
+
 T = TypeVar("T", bound="Scheduler")
 
 

src/refiners/foundationals/latent_diffusion/self_attention_guidance.py

@@ -1,15 +1,15 @@
-from typing import Any, Generic, TypeVar, TYPE_CHECKING
 import math
+from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
-from torch import Tensor, Size
-from jaxtyping import Float
 import torch
+from jaxtyping import Float
+from torch import Size, Tensor
 
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
-from refiners.fluxion.utils import interpolate, gaussian_blur
+from refiners.fluxion.utils import gaussian_blur, interpolate
-import refiners.fluxion.layers as fl
+from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
 
 if TYPE_CHECKING:
     from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/__init__.py

@@ -1,11 +1,11 @@
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
     StableDiffusion_1,
     StableDiffusion_1_Inpainting,
 )
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.t2i_adapter import SD1T2IAdapter
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
 
 __all__ = [
     "StableDiffusion_1",

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/controlnet.py

@@ -1,16 +1,18 @@
+from typing import Iterable, cast
+
+from torch import Tensor, device as Device, dtype as DType
+
+from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
-from refiners.fluxion.layers import Chain, Conv2d, SiLU, Lambda, Passthrough, UseContext, Slicing, Residual
+from refiners.fluxion.layers import Chain, Conv2d, Lambda, Passthrough, Residual, SiLU, Slicing, UseContext
+from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
-    SD1UNet,
     DownBlocks,
     MiddleBlock,
     ResidualBlock,
+    SD1UNet,
     TimestepEncoder,
 )
-from refiners.fluxion.adapters.adapter import Adapter
-from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d
-from typing import cast, Iterable
-from torch import Tensor, device as Device, dtype as DType
 
 
 class ConditionEncoder(Chain):

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/image_prompt.py

@@ -2,7 +2,7 @@ from torch import Tensor
 
 from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
-from refiners.foundationals.latent_diffusion.image_prompt import IPAdapter, ImageProjection, PerceiverResampler
+from refiners.foundationals.latent_diffusion.image_prompt import ImageProjection, IPAdapter, PerceiverResampler
 from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1UNet
 
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -1,15 +1,16 @@
+import numpy as np
 import torch
+from PIL import Image
+from torch import Tensor, device as Device, dtype as DType
+
 from refiners.fluxion.utils import image_to_tensor, interpolate
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
 from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.self_attention_guidance import SD1SAGAdapter
-from PIL import Image
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
-import numpy as np
-from torch import device as Device, dtype as DType, Tensor
 
 
 class SD1Autoencoder(LatentDiffusionAutoencoder):

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/multi_diffusion.py

@@ -1,6 +1,7 @@
-from dataclasses import field, dataclass
+from dataclasses import dataclass, field
-from torch import Tensor
+
 from PIL import Image
+from torch import Tensor
 
 from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget, MultiDiffusion
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/self_attention_guidance.py

@@ -1,11 +1,11 @@
+import refiners.fluxion.layers as fl
+from refiners.fluxion.layers.attentions import ScaledDotProductAttention
 from refiners.foundationals.latent_diffusion.self_attention_guidance import (
     SAGAdapter,
-    SelfAttentionShape,
     SelfAttentionMap,
+    SelfAttentionShape,
 )
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet, MiddleBlock, ResidualBlock
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import MiddleBlock, ResidualBlock, SD1UNet
-from refiners.fluxion.layers.attentions import ScaledDotProductAttention
-import refiners.fluxion.layers as fl
 
 
 class SD1SAGAdapter(SAGAdapter[SD1UNet]):

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/t2i_adapter.py

@@ -1,8 +1,8 @@
 from torch import Tensor
 
-from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoder
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet, ResidualAccumulator
 import refiners.fluxion.layers as fl
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator, SD1UNet
+from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, T2IAdapter, T2IFeatures
 
 
 class SD1T2IAdapter(T2IAdapter[SD1UNet]):

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/unet.py

@@ -1,12 +1,11 @@
-from typing import cast, Iterable
+from typing import Iterable, cast
 
 from torch import Tensor, device as Device, dtype as DType
 
-from refiners.fluxion.context import Contexts
 import refiners.fluxion.layers as fl
-
+from refiners.fluxion.context import Contexts
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
-from refiners.foundationals.latent_diffusion.range_adapter import RangeEncoder, RangeAdapter2d
+from refiners.foundationals.latent_diffusion.range_adapter import RangeAdapter2d, RangeEncoder
 
 
 class TimestepEncoder(fl.Passthrough):

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/__init__.py

@@ -1,9 +1,8 @@
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt import SDXLIPAdapter
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.t2i_adapter import SDXLT2IAdapter
-
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
 
 __all__ = [
     "SDXLUNet",

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/image_prompt.py

@@ -2,7 +2,7 @@ from torch import Tensor
 
 from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
-from refiners.foundationals.latent_diffusion.image_prompt import IPAdapter, ImageProjection, PerceiverResampler
+from refiners.foundationals.latent_diffusion.image_prompt import ImageProjection, IPAdapter, PerceiverResampler
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
 
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py

@@ -1,12 +1,13 @@
 import torch
+from torch import Tensor, device as Device, dtype as DType
+
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
 from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.self_attention_guidance import SDXLSAGAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
-from torch import device as Device, dtype as DType, Tensor
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
 
 
 class SDXLAutoencoder(LatentDiffusionAutoencoder):

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/self_attention_guidance.py

@@ -1,11 +1,11 @@
+import refiners.fluxion.layers as fl
+from refiners.fluxion.layers.attentions import ScaledDotProductAttention
 from refiners.foundationals.latent_diffusion.self_attention_guidance import (
     SAGAdapter,
-    SelfAttentionShape,
     SelfAttentionMap,
+    SelfAttentionShape,
 )
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet, MiddleBlock, ResidualBlock
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import MiddleBlock, ResidualBlock, SDXLUNet
-from refiners.fluxion.layers.attentions import ScaledDotProductAttention
-import refiners.fluxion.layers as fl
 
 
 class SDXLSAGAdapter(SAGAdapter[SDXLUNet]):

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/t2i_adapter.py

@@ -1,9 +1,9 @@
 from torch import Tensor
 
-from refiners.foundationals.latent_diffusion.t2i_adapter import T2IAdapter, T2IFeatures, ConditionEncoderXL
-from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator
 import refiners.fluxion.layers as fl
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import ResidualAccumulator
+from refiners.foundationals.latent_diffusion.stable_diffusion_xl import SDXLUNet
+from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoderXL, T2IAdapter, T2IFeatures
 
 
 class SDXLT2IAdapter(T2IAdapter[SDXLUNet]):

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/text_encoder.py

@@ -1,11 +1,12 @@
 from typing import cast
-from torch import device as Device, dtype as DType, Tensor, cat
+
+from jaxtyping import Float
+from torch import Tensor, cat, device as Device, dtype as DType
+
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
-import refiners.fluxion.layers as fl
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderG, CLIPTextEncoderL
-from jaxtyping import Float
-
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/unet.py

@@ -1,18 +1,20 @@
 from typing import cast
+
 from torch import Tensor, device as Device, dtype as DType
-from refiners.fluxion.context import Contexts
+
 import refiners.fluxion.layers as fl
+from refiners.fluxion.context import Contexts
 from refiners.foundationals.latent_diffusion.cross_attention import CrossAttentionBlock2d
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
-    ResidualAccumulator,
-    ResidualBlock,
-    ResidualConcatenator,
-)
 from refiners.foundationals.latent_diffusion.range_adapter import (
     RangeAdapter2d,
     RangeEncoder,
     compute_sinusoidal_embedding,
 )
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import (
+    ResidualAccumulator,
+    ResidualBlock,
+    ResidualConcatenator,
+)
 
 
 class TextTimeEmbedding(fl.Chain):

src/refiners/foundationals/latent_diffusion/t2i_adapter.py

@@ -1,12 +1,12 @@
-from typing import Generic, TypeVar, Any, TYPE_CHECKING
+from typing import TYPE_CHECKING, Any, Generic, TypeVar
 
 from torch import Tensor, device as Device, dtype as DType
 from torch.nn import AvgPool2d as _AvgPool2d
 
+import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.layers.module import Module
-import refiners.fluxion.layers as fl
 
 if TYPE_CHECKING:
     from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet

src/refiners/foundationals/segment_anything/image_encoder.py

@@ -1,9 +1,9 @@
-from torch import device as Device, dtype as DType, Tensor
-from refiners.fluxion.context import Contexts
-import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import pad
-from torch import nn
 import torch
+from torch import Tensor, device as Device, dtype as DType, nn
+
+import refiners.fluxion.layers as fl
+from refiners.fluxion.context import Contexts
+from refiners.fluxion.utils import pad
 
 
 class PatchEncoder(fl.Chain):

src/refiners/foundationals/segment_anything/mask_decoder.py

@@ -1,12 +1,12 @@
-import refiners.fluxion.layers as fl
-from torch import device as Device, dtype as DType, Tensor, nn
 import torch
+from torch import Tensor, device as Device, dtype as DType, nn
 
+import refiners.fluxion.layers as fl
+from refiners.fluxion.context import Contexts
 from refiners.foundationals.segment_anything.transformer import (
     SparseCrossDenseAttention,
     TwoWayTranformerLayer,
 )
-from refiners.fluxion.context import Contexts
 
 
 class EmbeddingsAggregator(fl.ContextModule):

src/refiners/foundationals/segment_anything/model.py

@@ -1,11 +1,13 @@
 from dataclasses import dataclass
 from typing import Sequence
-from PIL import Image
+
-from torch import device as Device, dtype as DType, Tensor
 import numpy as np
 import torch
+from PIL import Image
+from torch import Tensor, device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
-from refiners.fluxion.utils import image_to_tensor, normalize, pad, interpolate
+from refiners.fluxion.utils import image_to_tensor, interpolate, normalize, pad
 from refiners.foundationals.segment_anything.image_encoder import SAMViT, SAMViTH
 from refiners.foundationals.segment_anything.mask_decoder import MaskDecoder
 from refiners.foundationals.segment_anything.prompt_encoder import MaskEncoder, PointEncoder

src/refiners/foundationals/segment_anything/prompt_encoder.py

@@ -1,8 +1,10 @@
-from enum import Enum, auto
 from collections.abc import Sequence
-from torch import device as Device, dtype as DType, Tensor, nn
+from enum import Enum, auto
+
 import torch
 from jaxtyping import Float, Int
+from torch import Tensor, device as Device, dtype as DType, nn
+
 import refiners.fluxion.layers as fl
 from refiners.fluxion.context import Contexts
 

src/refiners/foundationals/segment_anything/transformer.py

@@ -1,4 +1,5 @@
-from torch import dtype as DType, device as Device
+from torch import device as Device, dtype as DType
+
 import refiners.fluxion.layers as fl
 
 

src/refiners/training_utils/__init__.py

@@ -1,7 +1,8 @@
+import sys
 from importlib import import_module
 from importlib.metadata import requires
+
 from packaging.requirements import Requirement
-import sys
 
 refiners_requires = requires("refiners")
 assert refiners_requires is not None

src/refiners/training_utils/callback.py

@@ -1,7 +1,8 @@
-from typing import TYPE_CHECKING, Generic, Iterable, Any, TypeVar
+from typing import TYPE_CHECKING, Any, Generic, Iterable, TypeVar
+
+from loguru import logger
 from torch import tensor
 from torch.nn import Parameter
-from loguru import logger
 
 if TYPE_CHECKING:
     from refiners.training_utils.config import BaseConfig

src/refiners/training_utils/config.py

@@ -1,17 +1,18 @@
+from enum import Enum
 from logging import warn
 from pathlib import Path
 from typing import Any, Callable, Iterable, Literal, Type, TypeVar
-from typing_extensions import TypedDict  # https://errors.pydantic.dev/2.0b3/u/typed-dict-version
-from torch.optim import AdamW, SGD, Optimizer, Adam
-from torch.nn import Parameter
-from enum import Enum
-from bitsandbytes.optim import AdamW8bit, Lion8bit  # type: ignore
-from pydantic import BaseModel, validator
-import tomli
-import refiners.fluxion.layers as fl
-from prodigyopt import Prodigy  # type: ignore
-from refiners.training_utils.dropout import apply_dropout, apply_gyro_dropout
 
+import tomli
+from bitsandbytes.optim import AdamW8bit, Lion8bit  # type: ignore
+from prodigyopt import Prodigy  # type: ignore
+from pydantic import BaseModel, validator
+from torch.nn import Parameter
+from torch.optim import SGD, Adam, AdamW, Optimizer
+from typing_extensions import TypedDict  # https://errors.pydantic.dev/2.0b3/u/typed-dict-version
+
+import refiners.fluxion.layers as fl
+from refiners.training_utils.dropout import apply_dropout, apply_gyro_dropout
 
 __all__ = [
     "parse_number_unit_field",

src/refiners/training_utils/dropout.py

@@ -1,11 +1,11 @@
 from typing import TYPE_CHECKING, Any, TypeVar
 
-from torch import Tensor, randint, cat, rand
+from torch import Tensor, cat, rand, randint
 from torch.nn import Dropout as TorchDropout
 
 import refiners.fluxion.layers as fl
-from refiners.training_utils.callback import Callback
 from refiners.fluxion.adapters.adapter import Adapter
+from refiners.training_utils.callback import Callback
 
 if TYPE_CHECKING:
     from refiners.training_utils.config import BaseConfig

src/refiners/training_utils/huggingface_datasets.py

@@ -1,6 +1,7 @@
-from datasets import load_dataset as _load_dataset, VerificationMode  # type: ignore
 from typing import Any, Generic, Protocol, TypeVar, cast
 
+from datasets import VerificationMode, load_dataset as _load_dataset  # type: ignore
+
 __all__ = ["load_hf_dataset", "HuggingfaceDataset"]
 
 

src/refiners/training_utils/latent_diffusion.py

@@ -1,29 +1,31 @@
+import random
 from dataclasses import dataclass
-from typing import Any, TypeVar, TypedDict, Callable
-from pydantic import BaseModel
-from torch import device as Device, Tensor, randn, dtype as DType, Generator, cat
-from loguru import logger
-from torch.utils.data import Dataset
-from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
-from torchvision.transforms import Compose, RandomCrop, RandomHorizontalFlip  # type: ignore
-import refiners.fluxion.layers as fl
-from PIL import Image
 from functools import cached_property
-from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
+from typing import Any, Callable, TypedDict, TypeVar
-from refiners.training_utils.config import BaseConfig
+
+from loguru import logger
+from PIL import Image
+from pydantic import BaseModel
+from torch import Generator, Tensor, cat, device as Device, dtype as DType, randn
+from torch.nn import Module
+from torch.nn.functional import mse_loss
+from torch.utils.data import Dataset
+from torchvision.transforms import Compose, RandomCrop, RandomHorizontalFlip  # type: ignore
+
+import refiners.fluxion.layers as fl
+from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.latent_diffusion import (
-    StableDiffusion_1,
     DPMSolver,
     SD1UNet,
+    StableDiffusion_1,
 )
 from refiners.foundationals.latent_diffusion.schedulers import DDPM
-from torch.nn.functional import mse_loss
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
-import random
-from refiners.training_utils.wandb import WandbLoggable
-from refiners.training_utils.trainer import Trainer
 from refiners.training_utils.callback import Callback
-from refiners.training_utils.huggingface_datasets import load_hf_dataset, HuggingfaceDataset
+from refiners.training_utils.config import BaseConfig
-from torch.nn import Module
+from refiners.training_utils.huggingface_datasets import HuggingfaceDataset, load_hf_dataset
+from refiners.training_utils.trainer import Trainer
+from refiners.training_utils.wandb import WandbLoggable
 
 
 class LatentDiffusionConfig(BaseModel):

src/refiners/training_utils/trainer.py

@@ -1,41 +1,43 @@
-from functools import cached_property, wraps
-from pathlib import Path
 import random
 import time
+from functools import cached_property, wraps
+from pathlib import Path
+from typing import Any, Callable, Generic, Iterable, TypeVar, cast
+
 import numpy as np
-from torch import device as Device, Tensor, get_rng_state, no_grad, set_rng_state, cuda, stack
+from loguru import logger
+from torch import Tensor, cuda, device as Device, get_rng_state, no_grad, set_rng_state, stack
+from torch.autograd import backward
 from torch.nn import Parameter
 from torch.optim import Optimizer
+from torch.optim.lr_scheduler import (
+    CosineAnnealingLR,
+    CosineAnnealingWarmRestarts,
+    CyclicLR,
+    ExponentialLR,
+    LambdaLR,
+    LRScheduler,
+    MultiplicativeLR,
+    MultiStepLR,
+    OneCycleLR,
+    ReduceLROnPlateau,
+    StepLR,
+)
 from torch.utils.data import DataLoader, Dataset
-from torch.autograd import backward
+
-from typing import Any, Callable, Generic, Iterable, TypeVar, cast
-from loguru import logger
 from refiners.fluxion import layers as fl
 from refiners.fluxion.utils import manual_seed
-from refiners.training_utils.wandb import WandbLogger, WandbLoggable
-from refiners.training_utils.config import BaseConfig, TimeUnit, TimeValue, SchedulerType
-from refiners.training_utils.dropout import DropoutCallback
 from refiners.training_utils.callback import (
     Callback,
     ClockCallback,
     GradientNormClipping,
-    GradientValueClipping,
     GradientNormLogging,
+    GradientValueClipping,
     MonitorLoss,
 )
-from torch.optim.lr_scheduler import (
+from refiners.training_utils.config import BaseConfig, SchedulerType, TimeUnit, TimeValue
-    StepLR,
+from refiners.training_utils.dropout import DropoutCallback
-    ExponentialLR,
+from refiners.training_utils.wandb import WandbLoggable, WandbLogger
-    ReduceLROnPlateau,
-    CosineAnnealingLR,
-    LambdaLR,
-    OneCycleLR,
-    LRScheduler,
-    MultiplicativeLR,
-    CosineAnnealingWarmRestarts,
-    CyclicLR,
-    MultiStepLR,
-)
 
 __all__ = ["seed_everything", "scoped_seed", "Trainer"]
 

src/refiners/training_utils/wandb.py

@@ -1,4 +1,5 @@
 from typing import Any
+
 import wandb
 from PIL import Image
 

tests/adapters/test_adapter.py

@@ -1,4 +1,5 @@
 import pytest
+
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.layers import Chain, Linear
 

tests/adapters/test_lora.py

@@ -1,6 +1,7 @@
-from refiners.fluxion.adapters.lora import Lora, SingleLoraAdapter, LoraAdapter
+from torch import allclose, randn
-from torch import randn, allclose
+
 import refiners.fluxion.layers as fl
+from refiners.fluxion.adapters.lora import Lora, LoraAdapter, SingleLoraAdapter
 
 
 def test_single_lora_adapter() -> None:

tests/adapters/test_range_adapter.py

@@ -1,7 +1,8 @@
 import torch
+
 from refiners.fluxion.adapters.adapter import Adapter
-from refiners.foundationals.latent_diffusion.range_adapter import RangeEncoder
 from refiners.fluxion.layers import Chain, Linear
+from refiners.foundationals.latent_diffusion.range_adapter import RangeEncoder
 
 
 class DummyLinearAdapter(Chain, Adapter[Linear]):

tests/conftest.py

@@ -1,6 +1,7 @@
 import os
-import torch
 from pathlib import Path
+
+import torch
 from pytest import fixture
 
 PARENT_PATH = Path(__file__).parent

tests/e2e/test_diffusion.py

@@ -1,34 +1,32 @@
-import torch
-import pytest
-
-from typing import Iterator
-
-from warnings import warn
-from PIL import Image
 from pathlib import Path
+from typing import Iterator
+from warnings import warn
 
-from refiners.fluxion.utils import load_from_safetensors, image_to_tensor, manual_seed
+import pytest
+import torch
+from PIL import Image
+
+from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, manual_seed
+from refiners.foundationals.clip.concepts import ConceptExtender
 from refiners.foundationals.latent_diffusion import (
-    StableDiffusion_1,
-    StableDiffusion_1_Inpainting,
-    SD1UNet,
     SD1ControlnetAdapter,
     SD1IPAdapter,
     SD1T2IAdapter,
+    SD1UNet,
+    SDFreeUAdapter,
     SDXLIPAdapter,
     SDXLT2IAdapter,
-    SDFreeUAdapter,
+    StableDiffusion_1,
+    StableDiffusion_1_Inpainting,
 )
 from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
 from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget
+from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter
 from refiners.foundationals.latent_diffusion.restart import Restart
 from refiners.foundationals.latent_diffusion.schedulers import DDIM
-from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter
-from refiners.foundationals.clip.concepts import ConceptExtender
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
-
 from tests.utils import ensure_similar_images
 
 

tests/e2e/test_preprocessors.py

@@ -1,13 +1,12 @@
-import torch
-import pytest
-
-from warnings import warn
-from PIL import Image
 from pathlib import Path
+from warnings import warn
+
+import pytest
+import torch
+from PIL import Image
 
 from refiners.fluxion.utils import image_to_tensor, tensor_to_image
 from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings import InformativeDrawings
-
 from tests.utils import ensure_similar_images
 
 

tests/fluxion/layers/test_chain.py

@@ -1,5 +1,6 @@
 import pytest
 import torch
+
 import refiners.fluxion.layers as fl
 from refiners.fluxion.context import Contexts
 

tests/fluxion/layers/test_converter.py

@@ -1,7 +1,8 @@
-import torch
-import pytest
 from warnings import warn
 
+import pytest
+import torch
+
 import refiners.fluxion.layers as fl
 from refiners.fluxion.layers.chain import ChainError, Distribute
 

tests/fluxion/test_model_converter.py

@@ -1,10 +1,11 @@
 # pyright: reportPrivateUsage=false
 import pytest
 import torch
-from torch import nn, Tensor
+from torch import Tensor, nn
-from refiners.fluxion.utils import manual_seed
+
-from refiners.fluxion.model_converter import ModelConverter, ConversionStage
 import refiners.fluxion.layers as fl
+from refiners.fluxion.model_converter import ConversionStage, ModelConverter
+from refiners.fluxion.utils import manual_seed
 
 
 class CustomBasicLayer1(fl.Module):

tests/fluxion/test_utils.py

@@ -1,11 +1,11 @@
 from dataclasses import dataclass
 from warnings import warn
 
-from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur  # type: ignore
-from torch import device as Device, dtype as DType
-from PIL import Image
 import pytest
 import torch
+from PIL import Image
+from torch import device as Device, dtype as DType
+from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur  # type: ignore
 
 from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, tensor_to_image
 

tests/foundationals/clip/test_concepts.py

@@ -1,18 +1,16 @@
-import torch
-import pytest
-
-from warnings import warn
 from pathlib import Path
+from warnings import warn
 
+import pytest
+import torch
+import transformers  # type: ignore
+from diffusers import StableDiffusionPipeline  # type: ignore
+
+import refiners.fluxion.layers as fl
+from refiners.fluxion.utils import load_from_safetensors
 from refiners.foundationals.clip.concepts import ConceptExtender, TokenExtender
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.clip.tokenizer import CLIPTokenizer
-from refiners.fluxion.utils import load_from_safetensors
-import refiners.fluxion.layers as fl
-
-from diffusers import StableDiffusionPipeline  # type: ignore
-import transformers  # type: ignore
-
 
 PROMPTS = [
     "a cute cat",  # a simple prompt

tests/foundationals/clip/test_image_encoder.py

@@ -1,13 +1,12 @@
-import torch
-import pytest
-
-from warnings import warn
 from pathlib import Path
+from warnings import warn
 
+import pytest
+import torch
 from transformers import CLIPVisionModelWithProjection  # type: ignore
 
-from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 from refiners.fluxion.utils import load_from_safetensors
+from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
 
 
 @pytest.fixture(scope="module")

tests/foundationals/clip/test_text_encoder.py

@@ -1,15 +1,13 @@
-import torch
-import pytest
-
-from warnings import warn
 from pathlib import Path
+from warnings import warn
 
-from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
+import pytest
-from refiners.fluxion.utils import load_from_safetensors
+import torch
-
 import transformers  # type: ignore
-from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
+from refiners.fluxion.utils import load_from_safetensors
+from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 long_prompt = """
 Above these apparent hieroglyphics was a figure of evidently pictorial intent,

tests/foundationals/latent_diffusion/test_auto_encoder.py

@@ -1,15 +1,14 @@
-import torch
-import pytest
-
-from warnings import warn
-from PIL import Image
 from pathlib import Path
+from warnings import warn
+
+import pytest
+import torch
+from PIL import Image
+from tests.utils import ensure_similar_images
 
 from refiners.fluxion.utils import load_from_safetensors
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 
-from tests.utils import ensure_similar_images
-
 
 @pytest.fixture(scope="module")
 def ref_path() -> Path:

tests/foundationals/latent_diffusion/test_controlnet.py

@@ -1,11 +1,11 @@
 from typing import Iterator
 
-import torch
 import pytest
+import torch
 
 import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import lookup_top_adapter
-from refiners.foundationals.latent_diffusion import SD1UNet, SD1ControlnetAdapter
+from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter, SD1UNet
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import Controlnet