style: add ruff isort (#183)

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

@@ -4,10 +4,10 @@ from typing import Optional, Tuple
 import numpy as np
 import torch
 import torch.nn as nn
-
+from diffusers.models.attention_processor import SpatialNorm
 from diffusers.utils import BaseOutput, is_torch_version
 from diffusers.utils.torch_utils import randn_tensor
-from diffusers.models.attention_processor import SpatialNorm
+
 from .unet_causal_3d_blocks import (
     CausalConv3d,
     UNetMidBlockCausal3D,

lightx2v/models/video_encoders/hf/cogvideox/autoencoder_ks_cogvidex.py

@@ -19,18 +19,16 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 from diffusers.loaders.single_file_model import FromOriginalModelMixin
-from diffusers.utils import logging
-from diffusers.utils.accelerate_utils import apply_forward_hook
 from diffusers.models.activations import get_activation
+from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
+from diffusers.models.downsampling import CogVideoXDownsample3D
 from diffusers.models.modeling_outputs import AutoencoderKLOutput
 from diffusers.models.modeling_utils import ModelMixin
 from diffusers.models.upsampling import CogVideoXUpsample3D
-from diffusers.models.downsampling import CogVideoXDownsample3D
-from diffusers.models.autoencoders.vae import DecoderOutput, DiagonalGaussianDistribution
-
+from diffusers.utils import logging
+from diffusers.utils.accelerate_utils import apply_forward_hook
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 

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

-import os
 import glob
+import os
+
 import torch  # type: ignore
-from safetensors import safe_open  # type: ignore
 from diffusers.video_processor import VideoProcessor  # type: ignore
+from safetensors import safe_open  # type: ignore
 
 from lightx2v.models.video_encoders.hf.cogvideox.autoencoder_ks_cogvidex import AutoencoderKLCogVideoX
 

lightx2v/models/video_encoders/hf/tae.py

+import gc
+import os
+from collections import namedtuple
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from tqdm.auto import tqdm
-from collections import namedtuple
-import gc
-import os
 
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:32,expandable_segments:True"
 
@@ -266,6 +267,7 @@ class TAEHV(nn.Module):
 def main():
     """Run TAEHV roundtrip reconstruction on the given video paths."""
     import sys
+
     import cv2  # no highly esteemed deed is commemorated here
 
     class VideoTensorReader:

lightx2v/models/video_encoders/hf/wan/vae.py

@@ -3,9 +3,9 @@ import logging
 
 import torch
 import torch.cuda.amp as amp
+import torch.distributed as dist
 import torch.nn as nn
 import torch.nn.functional as F
-import torch.distributed as dist
 from einops import rearrange
 from loguru import logger
 

lightx2v/models/video_encoders/hf/wan/vae_tiny.py

 import torch
 import torch.nn as nn
-from ..tae import TAEHV
+
 from lightx2v.utils.memory_profiler import peak_memory_decorator
 
+from ..tae import TAEHV
+
 
 class DotDict(dict):
     __getattr__ = dict.__getitem__

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

 import os
+
 import torch
 
 from lightx2v.models.video_encoders.hf.autoencoder_kl_causal_3d.autoencoder_kl_causal_3d import AutoencoderKLCausal3D

lightx2v/models/video_encoders/trt/autoencoder_kl_causal_3d/trt_vae_infer.py

@@ -3,10 +3,10 @@ from pathlib import Path
 from subprocess import Popen
 
 import numpy as np
-import torch
 import tensorrt as trt
-from cuda import cudart
+import torch
 import torch.nn as nn
+from cuda import cudart
 from loguru import logger
 
 from lightx2v.common.backend_infer.trt import common

lightx2v/server/api.py

 import asyncio
-from fastapi import FastAPI, UploadFile, HTTPException, Form, File, APIRouter
-from fastapi.responses import StreamingResponse
-from loguru import logger
-import threading
 import gc
-import torch
-from pathlib import Path
+import threading
 import uuid
+from pathlib import Path
 from typing import Optional
+
+import torch
+from fastapi import APIRouter, FastAPI, File, Form, HTTPException, UploadFile
+from fastapi.responses import StreamingResponse
+from loguru import logger
+
 from .schema import (
-    TaskRequest,
-    TaskResponse,
     ServiceStatusResponse,
     StopTaskResponse,
+    TaskRequest,
+    TaskResponse,
 )
-from .service import FileService, DistributedInferenceService, VideoGenerationService
+from .service import DistributedInferenceService, FileService, VideoGenerationService
 from .utils import ServiceStatus
 
 

lightx2v/server/distributed_utils.py

 import os
+
 import torch
 import torch.distributed as dist
 from loguru import logger

lightx2v/server/schema.py

-from pydantic import BaseModel, Field
-from typing import Optional
 from datetime import datetime
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
 from ..utils.generate_task_id import generate_task_id
 
 

lightx2v/server/service.py

@@ -9,12 +9,11 @@ import httpx
 import torch.multiprocessing as mp
 from loguru import logger
 
-from ..utils.set_config import set_config
 from ..infer import init_runner
-from .utils import ServiceStatus
-from .schema import TaskRequest, TaskResponse
+from ..utils.set_config import set_config
 from .distributed_utils import create_distributed_worker
-
+from .schema import TaskRequest, TaskResponse
+from .utils import ServiceStatus
 
 mp.set_start_method("spawn", force=True)
 

lightx2v/server/utils.py

+import base64
+import io
+import signal
 import sys
+import threading
+from datetime import datetime
+from typing import Optional
+
 import psutil
-import signal
-import base64
+import torch
 from PIL import Image
 from loguru import logger
-from typing import Optional
-from datetime import datetime
 from pydantic import BaseModel
-import threading
-import torch
-import io
 
 
 class ProcessManager:

lightx2v/utils/async_io.py

-import aiofiles
 import asyncio
-from PIL import Image
 import io
-from typing import Union
 from pathlib import Path
+from typing import Union
+
+import aiofiles
+from PIL import Image
 from loguru import logger
 
 

lightx2v/utils/profiler.py

-import time
-import torch
 import asyncio
+import time
 from functools import wraps
-from lightx2v.utils.envs import *
+
+import torch
 from loguru import logger
 
+from lightx2v.utils.envs import *
+
 
 class _ProfilingContext:
     def __init__(self, name):

lightx2v/utils/prompt_enhancer.py

 import argparse
+
 import torch
 from loguru import logger
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from lightx2v.utils.profiler import ProfilingContext4Debug, ProfilingContext
+
+from lightx2v.utils.profiler import ProfilingContext, ProfilingContext4Debug
 
 sys_prompt = """
 Transform the short prompt into a detailed video-generation caption using this structure:

lightx2v/utils/service_utils.py

+import base64
+import io
+import signal
 import sys
+import threading
+from datetime import datetime
+from typing import List, Optional
+
 import psutil
-import signal
-import base64
+import torch
 from PIL import Image
 from loguru import logger
-from typing import Optional, List
-from datetime import datetime
 from pydantic import BaseModel
-import threading
-import torch
-import io
 
 
 class ProcessManager:

lightx2v/utils/set_config.py

 import json
 import os
+
+import torch.distributed as dist
 from easydict import EasyDict
 from loguru import logger
-import torch.distributed as dist
 from torch.distributed.tensor.device_mesh import init_device_mesh
 
 

lightx2v/utils/utils.py

+import glob
 import os
 import random
 import subprocess
-import glob
+from typing import Optional
+
 import imageio
 import imageio_ffmpeg as ffmpeg
 import numpy as np
 import torch
 import torchvision
-from typing import Optional
 from einops import rearrange
 from loguru import logger
 

pyproject.toml

 [tool.ruff]
-exclude = [".git", ".mypy_cache", ".ruff_cache", ".venv", "dist"]
+exclude = [
+    ".git",
+    ".mypy_cache",
+    ".ruff_cache",
+    ".venv",
+    "dist",
+    "build",
+    "__pycache__",
+    "*.egg-info",
+    ".pytest_cache",
+    ".cluade",
+    ".cursor",
+    "lightx2v_kernel",
+]
 target-version = "py311"
 line-length = 200
 indent-width = 4
-lint.ignore =["F"]
 
 
-[tool.ruff.format]
-line-ending = "lf"
-quote-style = "double"
-indent-style = "space"
+[tool.ruff.lint]
+extend-select = ["I"]
+ignore = ["F"]
+
+[tool.ruff.lint.per-file-ignores]
+"**/__init__.py" = ["F401"]
+"**/lightx2v_kernel/*" = ["F401"]
+"**/{cookbook,docs}/*" = ["E402", "F401", "F811", "F841"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["lightx2v"]
+case-sensitive = true