delete duplicated lines

led/models/ema.py

@@ -1,9 +1,5 @@
 import copy
-import os
-import random
 from typing import Any, Dict, Iterable, Optional, Union
-
-import numpy as np
 import torch
 from diffusers.utils import deprecate
 

led/models/unet.py

@@ -1,21 +1,14 @@
-from dataclasses import dataclass
 from typing import Optional, Tuple, Union
-
 import torch
 import torch.nn as nn
-
 from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.utils import BaseOutput
 from diffusers.models.embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
 from diffusers.models.modeling_utils import ModelMixin
 from diffusers.models.unet_2d_blocks import get_down_block, UNetMidBlock2D, get_up_block
 from diffusers.models.unet_2d import UNet2DOutput
-import torch.nn.functional as F
 from omegaconf import OmegaConf
-
 _default_config = OmegaConf.load("led/models/default_config.yaml")
 
-
 class UNet2DGenerator(ModelMixin, ConfigMixin):
     r"""
     UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.

led/pipelines/led_pipeline.py

@@ -1,17 +1,3 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
 from typing import  List, Optional, Union
 import numpy as np
 import torch

led/trainers/led_trainer.py

@@ -2,9 +2,8 @@
 from accelerate import Accelerator
 import os
 from tqdm import tqdm
-from diffusers import DDPMScheduler, DDPMPipeline, DDIMPipeline, StableDiffusionImg2ImgPipeline
+from diffusers import DDPMScheduler
 from accelerate.utils import ProjectConfiguration
-from diffusers.optimization import get_cosine_schedule_with_warmup
 from led.models import build_model
 import torch.nn.functional as F
 from led.data import build_dataset
@@ -16,8 +15,7 @@
 import math
 import numpy as np
 from accelerate.logging import get_logger
-from diffusers.utils import check_min_version, is_wandb_available
-import tensorboard
+from diffusers.utils import  is_wandb_available
 import logging
 import diffusers
 import datasets

script/train.py

@@ -1,14 +1,11 @@
-import yaml
 import argparse
 import os
 import sys
 # add current path to workspace
 sys.path.append(os.getcwd())
 from led.trainers.led_trainer import Trainer
-from bunch import Bunch
 import os
-import torch
-from omegaconf import OmegaConf, listconfig
+from omegaconf import OmegaConf