style: add ruff isort (#183)

lightx2v/models/runners/wan/wan_causvid_runner.py

-import os
 import gc
+import os
+
 import numpy as np
 import torch
+import torch.distributed as dist
 import torchvision.transforms.functional as TF
 from PIL import Image
-from lightx2v.utils.registry_factory import RUNNER_REGISTER
-from lightx2v.models.runners.wan.wan_runner import WanRunner
-from lightx2v.models.runners.default_runner import DefaultRunner
-from lightx2v.models.schedulers.wan.scheduler import WanScheduler
-from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistillScheduler
-from lightx2v.utils.profiler import ProfilingContext4Debug, ProfilingContext
+from loguru import logger
+
 from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
 from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
-from lightx2v.models.networks.wan.model import WanModel
 from lightx2v.models.networks.wan.causvid_model import WanCausVidModel
 from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
+from lightx2v.models.networks.wan.model import WanModel
+from lightx2v.models.runners.default_runner import DefaultRunner
+from lightx2v.models.runners.wan.wan_runner import WanRunner
+from lightx2v.models.schedulers.wan.scheduler import WanScheduler
+from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistillScheduler
 from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
-from loguru import logger
-import torch.distributed as dist
+from lightx2v.utils.profiler import ProfilingContext, ProfilingContext4Debug
+from lightx2v.utils.registry_factory import RUNNER_REGISTER
 
 
 @RUNNER_REGISTER("wan2.1_causvid")

lightx2v/models/runners/wan/wan_distill_runner.py

 import os
+
 import numpy as np
 import torch
 import torchvision.transforms.functional as TF
 from PIL import Image
-from lightx2v.utils.registry_factory import RUNNER_REGISTER
-from lightx2v.models.runners.wan.wan_runner import WanRunner
-from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistillScheduler
-from lightx2v.utils.profiler import ProfilingContext
+from loguru import logger
+
 from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
 from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
-from lightx2v.models.networks.wan.model import WanModel
 from lightx2v.models.networks.wan.distill_model import WanDistillModel
 from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
+from lightx2v.models.networks.wan.model import WanModel
+from lightx2v.models.runners.wan.wan_runner import WanRunner
+from lightx2v.models.schedulers.wan.step_distill.scheduler import WanStepDistillScheduler
 from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
 from lightx2v.models.video_encoders.hf.wan.vae_tiny import WanVAE_tiny
+from lightx2v.utils.profiler import ProfilingContext
+from lightx2v.utils.registry_factory import RUNNER_REGISTER
 from lightx2v.utils.utils import cache_video
-from loguru import logger
 
 
 @RUNNER_REGISTER("wan2.1_distill")

lightx2v/models/runners/wan/wan_runner.py

-import os
 import gc
+import os
+
 import numpy as np
 import torch
-import torchvision.transforms.functional as TF
 import torch.distributed as dist
-from loguru import logger
+import torchvision.transforms.functional as TF
 from PIL import Image
-from lightx2v.utils.registry_factory import RUNNER_REGISTER
+from loguru import logger
+
+from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
+from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
+from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
+from lightx2v.models.networks.wan.model import Wan22MoeModel, WanModel
 from lightx2v.models.runners.default_runner import DefaultRunner
-from lightx2v.models.schedulers.wan.scheduler import WanScheduler
 from lightx2v.models.schedulers.wan.changing_resolution.scheduler import (
     WanScheduler4ChangingResolutionInterface,
 )
@@ -16,16 +20,14 @@ from lightx2v.models.schedulers.wan.feature_caching.scheduler import (
     WanSchedulerCaching,
     WanSchedulerTaylorCaching,
 )
-from lightx2v.utils.utils import *
-from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
-from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
-from lightx2v.models.networks.wan.model import WanModel, Wan22MoeModel
-from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
+from lightx2v.models.schedulers.wan.scheduler import WanScheduler
 from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
 from lightx2v.models.video_encoders.hf.wan.vae_2_2 import Wan2_2_VAE
 from lightx2v.models.video_encoders.hf.wan.vae_tiny import WanVAE_tiny
-from lightx2v.utils.utils import cache_video, best_output_size
 from lightx2v.utils.profiler import ProfilingContext
+from lightx2v.utils.registry_factory import RUNNER_REGISTER
+from lightx2v.utils.utils import *
+from lightx2v.utils.utils import best_output_size, cache_video
 
 
 @RUNNER_REGISTER("wan2.1")

lightx2v/models/runners/wan/wan_skyreels_v2_df_runner.py

-import os
 import gc
+import os
+
 import numpy as np
 import torch
+import torch.distributed as dist
 import torchvision.transforms.functional as TF
 from PIL import Image
-from lightx2v.utils.registry_factory import RUNNER_REGISTER
+from loguru import logger
+
 from lightx2v.models.runners.wan.wan_runner import WanRunner
 from lightx2v.models.schedulers.wan.df.skyreels_v2_df_scheduler import WanSkyreelsV2DFScheduler
-from lightx2v.utils.profiler import ProfilingContext4Debug, ProfilingContext
-from lightx2v.utils.profiler import ProfilingContext
-import torch.distributed as dist
-from loguru import logger
+from lightx2v.utils.profiler import ProfilingContext, ProfilingContext4Debug
+from lightx2v.utils.registry_factory import RUNNER_REGISTER
 
 
 @RUNNER_REGISTER("wan2.1_skyreels_v2_df")

lightx2v/models/schedulers/cogvideox/scheduler.py

+import numpy as np
 import torch
-from diffusers.utils.torch_utils import randn_tensor
 from diffusers.models.embeddings import get_3d_rotary_pos_embed
-import numpy as np
+from diffusers.utils.torch_utils import randn_tensor
 
 from lightx2v.models.schedulers.scheduler import BaseScheduler
 

lightx2v/models/schedulers/hunyuan/feature_caching/scheduler.py

-from ..scheduler import HunyuanScheduler
 import torch
 
+from ..scheduler import HunyuanScheduler
+
 
 class HunyuanSchedulerTeaCaching(HunyuanScheduler):
     def __init__(self, config):

lightx2v/models/schedulers/hunyuan/scheduler.py

-import torch
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
 import numpy as np
+import torch
 from diffusers.utils.torch_utils import randn_tensor
-from typing import Union, Tuple, List
-from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+
 from lightx2v.models.schedulers.scheduler import BaseScheduler
 
 

lightx2v/models/schedulers/scheduler.py

 import torch
+
 from lightx2v.utils.envs import *
 
 

lightx2v/models/schedulers/wan/audio/scheduler.py

-import os
 import gc
 import math
-import numpy as np
-import torch
+import os
 from typing import List, Optional, Tuple, Union
-from lightx2v.utils.envs import *
-from lightx2v.models.schedulers.scheduler import BaseScheduler
-from loguru import logger
 
-from diffusers.configuration_utils import register_to_config
-from torch import Tensor
+import numpy as np
+import torch
 from diffusers import (
     FlowMatchEulerDiscreteScheduler as FlowMatchEulerDiscreteSchedulerBase,  # pyright: ignore
 )
+from diffusers.configuration_utils import register_to_config
+from loguru import logger
+from torch import Tensor
+
+from lightx2v.models.schedulers.scheduler import BaseScheduler
+from lightx2v.utils.envs import *
 
 
 def unsqueeze_to_ndim(in_tensor: Tensor, tgt_n_dim: int):

lightx2v/models/schedulers/wan/changing_resolution/scheduler.py

 import torch
+
 from lightx2v.models.schedulers.wan.scheduler import WanScheduler
 
 

lightx2v/models/schedulers/wan/df/skyreels_v2_df_scheduler.py

-import os
 import math
+import os
+
 import numpy as np
 import torch
 

lightx2v/models/schedulers/wan/scheduler.py

+import gc
 import math
+from typing import List, Optional, Union
+
 import numpy as np
 import torch
-import gc
-from typing import List, Optional, Union
+
 from lightx2v.models.schedulers.scheduler import BaseScheduler
 from lightx2v.utils.utils import masks_like
 

lightx2v/models/schedulers/wan/step_distill/scheduler.py

 import math
+from typing import List, Optional, Tuple, Union
+
 import numpy as np
 import torch
-from typing import List, Optional, Tuple, Union
+
 from lightx2v.models.schedulers.wan.scheduler import WanScheduler
 
 

lightx2v/models/vfi/rife/model/loss.py

-import torch
 import numpy as np
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision.models as models

lightx2v/models/vfi/rife/model/pytorch_msssim/__init__.py

-import torch
-import torch.nn.functional as F
 from math import exp
+
 import numpy as np
+import torch
+import torch.nn.functional as F
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 

lightx2v/models/vfi/rife/rife_comfyui_wrapper.py

 import os
 from typing import List, Optional, Tuple
 
-from loguru import logger
 import torch
+from loguru import logger
 from torch.nn import functional as F
 
 from lightx2v.utils.profiler import ProfilingContext

lightx2v/models/vfi/rife/train_log/IFNet_HDv3.py

@@ -3,6 +3,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from ..model.warplayer import warp
+
 # from train_log.refine import *
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

lightx2v/models/video_encoders/hf/autoencoder_kl_causal_3d/autoencoder_kl_causal_3d.py

@@ -16,12 +16,11 @@
 # Modified from diffusers==0.29.2
 #
 # ==============================================================================
-from typing import Dict, Optional, Tuple, Union
 from dataclasses import dataclass
+from typing import Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 
 try:
@@ -30,7 +29,6 @@ try:
 except ImportError:
     # Use this to be compatible with the original diffusers.
     from diffusers.loaders.single_file_model import FromOriginalModelMixin as FromOriginalVAEMixin
-from diffusers.utils.accelerate_utils import apply_forward_hook
 from diffusers.models.attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
     CROSS_ATTENTION_PROCESSORS,
@@ -41,7 +39,9 @@ from diffusers.models.attention_processor import (
 )
 from diffusers.models.modeling_outputs import AutoencoderKLOutput
 from diffusers.models.modeling_utils import ModelMixin
-from .vae import DecoderCausal3D, BaseOutput, DecoderOutput, DiagonalGaussianDistribution, EncoderCausal3D
+from diffusers.utils.accelerate_utils import apply_forward_hook
+
+from .vae import BaseOutput, DecoderCausal3D, DecoderOutput, DiagonalGaussianDistribution, EncoderCausal3D
 
 
 @dataclass

lightx2v/models/video_encoders/hf/autoencoder_kl_causal_3d/model.py

 import os
+
 import torch
+
 from .autoencoder_kl_causal_3d import AutoencoderKLCausal3D, DiagonalGaussianDistribution
 
 

lightx2v/models/video_encoders/hf/autoencoder_kl_causal_3d/unet_causal_3d_blocks.py

@@ -21,15 +21,12 @@ from typing import Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
-from torch import nn
-from einops import rearrange
-
-from diffusers.utils import logging
 from diffusers.models.activations import get_activation
-from diffusers.models.attention_processor import SpatialNorm
-from diffusers.models.attention_processor import Attention
-from diffusers.models.normalization import AdaGroupNorm
-from diffusers.models.normalization import RMSNorm
+from diffusers.models.attention_processor import Attention, SpatialNorm
+from diffusers.models.normalization import AdaGroupNorm, RMSNorm
+from diffusers.utils import logging
+from einops import rearrange
+from torch import nn
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name