style: format import statements across finetune module

finetune/datasets/__init__.py

@@ -1,6 +1,6 @@
-from .i2v_dataset import I2VDatasetWithResize, I2VDatasetWithBuckets
-from .t2v_dataset import T2VDatasetWithResize, T2VDatasetWithBuckets
 from .bucket_sampler import BucketSampler
+from .i2v_dataset import I2VDatasetWithBuckets, I2VDatasetWithResize
+from .t2v_dataset import T2VDatasetWithBuckets, T2VDatasetWithResize
 
 
 __all__ = [

finetune/datasets/bucket_sampler.py

@@ -1,8 +1,8 @@
-import random
 import logging
+import random
+
+from torch.utils.data import Dataset, Sampler
 
-from torch.utils.data import Sampler
-from torch.utils.data import Dataset
 
 logger = logging.getLogger(__name__)
 

finetune/datasets/i2v_dataset.py

@@ -1,26 +1,26 @@
-import torch
 import hashlib
-
 from pathlib import Path
-from typing import Any, Dict, List, Tuple, TYPE_CHECKING
-from typing_extensions import override
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
 
+import torch
+from accelerate.logging import get_logger
+from safetensors.torch import load_file, save_file
 from torch.utils.data import Dataset
 from torchvision import transforms
-from accelerate.logging import get_logger
-from safetensors.torch import save_file, load_file
+from typing_extensions import override
 
-from finetune.constants import LOG_NAME, LOG_LEVEL
+from finetune.constants import LOG_LEVEL, LOG_NAME
 
 from .utils import (
+    load_images,
     load_prompts,
     load_videos,
-    load_images,
     preprocess_image_with_resize,
-    preprocess_video_with_resize,
     preprocess_video_with_buckets,
+    preprocess_video_with_resize,
 )
 
+
 if TYPE_CHECKING:
     from finetune.trainer import Trainer
 

finetune/datasets/t2v_dataset.py

@@ -1,18 +1,18 @@
 import hashlib
-import torch
-
 from pathlib import Path
-from typing import Any, Dict, List, Tuple, TYPE_CHECKING
-from typing_extensions import override
+from typing import TYPE_CHECKING, Any, Dict, List, Tuple
 
+import torch
+from accelerate.logging import get_logger
+from safetensors.torch import load_file, save_file
 from torch.utils.data import Dataset
 from torchvision import transforms
-from accelerate.logging import get_logger
-from safetensors.torch import save_file, load_file
+from typing_extensions import override
 
-from finetune.constants import LOG_NAME, LOG_LEVEL
+from finetune.constants import LOG_LEVEL, LOG_NAME
+
+from .utils import load_prompts, load_videos, preprocess_video_with_buckets, preprocess_video_with_resize
 
-from .utils import load_prompts, load_videos, preprocess_video_with_resize, preprocess_video_with_buckets
 
 if TYPE_CHECKING:
     from finetune.trainer import Trainer

finetune/datasets/utils.py

@@ -1,11 +1,11 @@
-import torch
-import cv2
-
-from typing import List, Tuple
 from pathlib import Path
-from torchvision import transforms
+from typing import List, Tuple
+
+import cv2
+import torch
 from torchvision.transforms.functional import resize
 
+
 # Must import after torch because this can sometimes lead to a nasty segmentation fault, or stack smashing error
 # Very few bug reports but it happens. Look in decord Github issues for more relevant information.
 import decord  # isort:skip

finetune/models/cogvideox1dot5_i2v/lora_trainer.py

@@ -1,5 +1,5 @@
-from ..utils import register
 from ..cogvideox_i2v.lora_trainer import CogVideoXI2VLoraTrainer
+from ..utils import register
 
 
 class CogVideoX1dot5I2VLoraTrainer(CogVideoXI2VLoraTrainer):

finetune/models/cogvideox_i2v/lora_trainer.py

@@ -1,22 +1,21 @@
-import torch
-
-from typing_extensions import override
 from typing import Any, Dict, List, Tuple
-from PIL import Image
 
-from transformers import AutoTokenizer, T5EncoderModel
-
-from diffusers.models.embeddings import get_3d_rotary_pos_embed
+import torch
 from diffusers import (
-    CogVideoXImageToVideoPipeline,
-    CogVideoXTransformer3DModel,
     AutoencoderKLCogVideoX,
     CogVideoXDPMScheduler,
+    CogVideoXImageToVideoPipeline,
+    CogVideoXTransformer3DModel,
 )
+from diffusers.models.embeddings import get_3d_rotary_pos_embed
+from PIL import Image
+from transformers import AutoTokenizer, T5EncoderModel
+from typing_extensions import override
 
-from finetune.trainer import Trainer
 from finetune.schemas import Components
+from finetune.trainer import Trainer
 from finetune.utils import unwrap_model
+
 from ..utils import register
 
 

finetune/models/cogvideox_t2v/lora_trainer.py

@@ -1,23 +1,21 @@
-import torch
-
-from typing_extensions import override
 from typing import Any, Dict, List, Tuple
 
-from PIL import Image
-
-from transformers import AutoTokenizer, T5EncoderModel
-
-from diffusers.models.embeddings import get_3d_rotary_pos_embed
+import torch
 from diffusers import (
-    CogVideoXPipeline,
-    CogVideoXTransformer3DModel,
     AutoencoderKLCogVideoX,
     CogVideoXDPMScheduler,
+    CogVideoXPipeline,
+    CogVideoXTransformer3DModel,
 )
+from diffusers.models.embeddings import get_3d_rotary_pos_embed
+from PIL import Image
+from transformers import AutoTokenizer, T5EncoderModel
+from typing_extensions import override
 
-from finetune.trainer import Trainer
 from finetune.schemas import Components
+from finetune.trainer import Trainer
 from finetune.utils import unwrap_model
+
 from ..utils import register
 
 

finetune/models/utils.py

@@ -1,4 +1,4 @@
-from typing import Literal, Dict
+from typing import Dict, Literal
 
 from finetune.trainer import Trainer
 

finetune/schemas/__init__.py

@@ -1,5 +1,6 @@
 from .args import Args
-from .state import State
 from .components import Components
+from .state import State
+
 
 __all__ = ["Args", "State", "Components"]

finetune/schemas/args.py

@@ -1,9 +1,9 @@
-import datetime
 import argparse
-from typing import Dict, Any, Literal, List, Tuple
-from pydantic import BaseModel, field_validator, ValidationInfo
-
+import datetime
 from pathlib import Path
+from typing import Any, List, Literal, Tuple
+
+from pydantic import BaseModel, ValidationInfo, field_validator
 
 
 class Args(BaseModel):

finetune/schemas/components.py

@@ -1,4 +1,5 @@
 from typing import Any
+
 from pydantic import BaseModel
 
 

finetune/schemas/state.py

@@ -1,8 +1,8 @@
-import torch
-
 from pathlib import Path
-from typing import List, Dict, Any
-from pydantic import BaseModel, field_validator
+from typing import Any, Dict, List
+
+import torch
+from pydantic import BaseModel
 
 
 class State(BaseModel):

finetune/scripts/extract_images.py

@@ -1,6 +1,7 @@
 import argparse
 import os
 from pathlib import Path
+
 import cv2
 
 

finetune/train.py

@@ -1,10 +1,11 @@
 import sys
 from pathlib import Path
 
+
 sys.path.append(str(Path(__file__).parent.parent))
 
-from finetune.schemas import Args
 from finetune.models.utils import get_model_cls
+from finetune.schemas import Args
 
 
 def main():

finetune/trainer.py

@@ -1,56 +1,52 @@
+import json
 import logging
 import math
-import json
-
-import torch
-import transformers
-import diffusers
-import wandb
-
 from datetime import timedelta
 from pathlib import Path
-from tqdm import tqdm
-from typing import Dict, Any, List, Tuple
-from PIL import Image
+from typing import Any, Dict, List, Tuple
 
-from torch.utils.data import Dataset, DataLoader
-from accelerate.logging import get_logger
+import diffusers
+import torch
+import transformers
+import wandb
 from accelerate.accelerator import Accelerator, DistributedType
+from accelerate.logging import get_logger
 from accelerate.utils import (
     DistributedDataParallelKwargs,
     InitProcessGroupKwargs,
     ProjectConfiguration,
-    set_seed,
     gather_object,
+    set_seed,
 )
-
-from diffusers.pipelines import DiffusionPipeline
 from diffusers.optimization import get_scheduler
+from diffusers.pipelines import DiffusionPipeline
 from diffusers.utils.export_utils import export_to_video
 from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
 
-from finetune.schemas import Args, State, Components
-from finetune.utils import (
-    unwrap_model,
-    cast_training_params,
-    get_optimizer,
-    get_memory_statistics,
-    free_memory,
-    unload_model,
-    get_latest_ckpt_path_to_resume_from,
-    get_intermediate_ckpt_path,
-    string_to_filename,
-)
+from finetune.constants import LOG_LEVEL, LOG_NAME
 from finetune.datasets import I2VDatasetWithResize, T2VDatasetWithResize
 from finetune.datasets.utils import (
-    load_prompts,
     load_images,
+    load_prompts,
     load_videos,
     preprocess_image_with_resize,
     preprocess_video_with_resize,
 )
-
-from finetune.constants import LOG_NAME, LOG_LEVEL
+from finetune.schemas import Args, Components, State
+from finetune.utils import (
+    cast_training_params,
+    free_memory,
+    get_intermediate_ckpt_path,
+    get_latest_ckpt_path_to_resume_from,
+    get_memory_statistics,
+    get_optimizer,
+    string_to_filename,
+    unload_model,
+    unwrap_model,
+)
 
 
 logger = get_logger(LOG_NAME, LOG_LEVEL)

finetune/utils/__init__.py

@@ -1,5 +1,5 @@
-from .torch_utils import *
-from .optimizer_utils import *
-from .memory_utils import *
 from .checkpointing import *
 from .file_utils import *
+from .memory_utils import *
+from .optimizer_utils import *
+from .torch_utils import *

finetune/utils/checkpointing.py

@@ -1,10 +1,12 @@
 import os
 from pathlib import Path
 from typing import Tuple
+
 from accelerate.logging import get_logger
 
-from finetune.constants import LOG_NAME, LOG_LEVEL
-from ..utils.file_utils import find_files, delete_files
+from finetune.constants import LOG_LEVEL, LOG_NAME
+
+from ..utils.file_utils import delete_files, find_files
 
 
 logger = get_logger(LOG_NAME, LOG_LEVEL)

finetune/utils/file_utils.py

@@ -1,11 +1,12 @@
 import logging
 import os
 import shutil
-
 from pathlib import Path
 from typing import Any, Dict, List, Union
+
 from accelerate.logging import get_logger
-from finetune.constants import LOG_NAME, LOG_LEVEL
+
+from finetune.constants import LOG_LEVEL, LOG_NAME
 
 
 logger = get_logger(LOG_NAME, LOG_LEVEL)

finetune/utils/memory_utils.py

@@ -1,10 +1,10 @@
 import gc
-import torch
-
 from typing import Any, Dict, Union
+
+import torch
 from accelerate.logging import get_logger
 
-from finetune.constants import LOG_NAME, LOG_LEVEL
+from finetune.constants import LOG_LEVEL, LOG_NAME
 
 
 logger = get_logger(LOG_NAME, LOG_LEVEL)

finetune/utils/optimizer_utils.py

@@ -1,9 +1,9 @@
 import inspect
-import torch
 
+import torch
 from accelerate.logging import get_logger
 
-from finetune.constants import LOG_NAME, LOG_LEVEL
+from finetune.constants import LOG_LEVEL, LOG_NAME
 
 
 logger = get_logger(LOG_NAME, LOG_LEVEL)

finetune/utils/torch_utils.py

@@ -1,4 +1,4 @@
-from typing import Dict, Optional, Union, List
+from typing import Dict, List, Optional, Union
 
 import torch
 from accelerate import Accelerator

inference/cli_demo.py

@@ -17,20 +17,20 @@ $ python cli_demo.py --prompt "A girl riding a bike." --model_path THUDM/CogVide
 Additional options are available to specify the model path, guidance scale, number of inference steps, video generation type, and output paths.
 """
 
-import logging
 import argparse
+import logging
 from typing import Literal, Optional
 
 import torch
 from diffusers import (
-    CogVideoXPipeline,
     CogVideoXDPMScheduler,
     CogVideoXImageToVideoPipeline,
+    CogVideoXPipeline,
     CogVideoXVideoToVideoPipeline,
 )
-
 from diffusers.utils import export_to_video, load_image, load_video
 
+
 logging.basicConfig(level=logging.INFO)
 
 # Recommended resolution for each model (width, height)
@@ -38,7 +38,6 @@ RESOLUTION_MAP = {
     # cogvideox1.5-*
     "cogvideox1.5-5b-i2v": (1360, 768),
     "cogvideox1.5-5b": (1360, 768),
-
     # cogvideox-*
     "cogvideox-5b-i2v": (720, 480),
     "cogvideox-5b": (720, 480),
@@ -100,10 +99,14 @@ def generate_video(
     elif (width, height) != desired_resolution:
         if generate_type == "i2v":
             # For i2v models, use user-defined width and height
-            logging.warning(f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m")
+            logging.warning(
+                f"\033[1;31mThe width({width}) and height({height}) are not recommended for {model_name}. The best resolution is {desired_resolution}.\033[0m"
+            )
         else:
             # Otherwise, use the recommended width and height
-            logging.warning(f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m")
+            logging.warning(
+                f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m"
+            )
             width, height = desired_resolution
 
     if generate_type == "i2v":