Cleanup

1 files changed, 71 insertions, 31 deletions

diff --git a/main.py b/main.py
index 8be79e5..51b64c1 100644
--- a/main.py
+++ b/main.py
@@ -9,28 +9,21 @@ from typing import Optional
 
 import numpy as np
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.checkpoint
-from torch.utils.data import Dataset
 
-import PIL
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import LoggerType, set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, PNDMScheduler, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers import AutoencoderKL, DDPMScheduler, LMSDiscreteScheduler, StableDiffusionPipeline, UNet2DConditionModel
 from diffusers.optimization import get_scheduler
-from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-from einops import rearrange
 from pipelines.stable_diffusion.no_check import NoCheck
-from huggingface_hub import HfFolder, Repository, whoami
 from PIL import Image
 from tqdm.auto import tqdm
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from slugify import slugify
 import json
 import os
-import sys
 
 from data import CSVDataModule
 
@@ -39,7 +32,8 @@ logger = get_logger(__name__)
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description="Simple example of a training script.")
+        description="Simple example of a training script."
+    )
     parser.add_argument(
         "--pretrained_model_name_or_path",
         type=str,
@@ -53,7 +47,10 @@ def parse_args():
         help="Pretrained tokenizer name or path if not the same as model_name",
     )
     parser.add_argument(
-        "--train_data_dir", type=str, default=None, help="A folder containing the training data."
+        "--train_data_dir",
+        type=str,
+        default=None,
+        help="A folder containing the training data."
     )
     parser.add_argument(
         "--placeholder_token",
@@ -62,21 +59,33 @@ def parse_args():
         help="A token to use as a placeholder for the concept.",
     )
     parser.add_argument(
-        "--initializer_token", type=str, default=None, help="A token to use as initializer word."
+        "--initializer_token",
+        type=str,
+        default=None,
+        help="A token to use as initializer word."
     )
     parser.add_argument(
-        "--vectors_per_token", type=int, default=1, help="Vectors per token."
+        "--vectors_per_token",
+        type=int,
+        default=1,
+        help="Vectors per token."
     )
-    parser.add_argument("--repeats", type=int, default=100,
-                        help="How many times to repeat the training data.")
+    parser.add_argument(
+        "--repeats",
+        type=int,
+        default=100,
+        help="How many times to repeat the training data.")
     parser.add_argument(
         "--output_dir",
         type=str,
         default="text-inversion-model",
         help="The output directory where the model predictions and checkpoints will be written.",
     )
-    parser.add_argument("--seed", type=int, default=None,
-                        help="A seed for reproducible training.")
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=None,
+        help="A seed for reproducible training.")
     parser.add_argument(
         "--resolution",
         type=int,
@@ -87,12 +96,14 @@ def parse_args():
         ),
     )
     parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+        "--center_crop",
+        action="store_true",
+        help="Whether to center crop images before resizing to resolution"
     )
     parser.add_argument(
-        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
+        "--num_train_epochs",
+        type=int,
+        default=100)
     parser.add_argument(
         "--max_train_steps",
         type=int,
@@ -132,16 +143,35 @@ def parse_args():
         ),
     )
     parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+        "--lr_warmup_steps",
+        type=int,
+        default=500,
+        help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--adam_beta1",
+        type=float,
+        default=0.9,
+        help="The beta1 parameter for the Adam optimizer."
+    )
+    parser.add_argument(
+        "--adam_beta2",
+        type=float,
+        default=0.999,
+        help="The beta2 parameter for the Adam optimizer."
+    )
+    parser.add_argument(
+        "--adam_weight_decay",
+        type=float,
+        default=1e-2,
+        help="Weight decay to use."
+    )
+    parser.add_argument(
+        "--adam_epsilon",
+        type=float,
+        default=1e-08,
+        help="Epsilon value for the Adam optimizer"
     )
-    parser.add_argument("--adam_beta1", type=float, default=0.9,
-                        help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999,
-                        help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float,
-                        default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08,
-                        help="Epsilon value for the Adam optimizer")
     parser.add_argument(
         "--mixed_precision",
         type=str,
@@ -153,8 +183,12 @@ def parse_args():
             "and an Nvidia Ampere GPU."
         ),
     )
-    parser.add_argument("--local_rank", type=int, default=-1,
-                        help="For distributed training: local_rank")
+    parser.add_argument(
+        "--local_rank",
+        type=int,
+        default=-1,
+        help="For distributed training: local_rank"
+    )
     parser.add_argument(
         "--checkpoint_frequency",
         type=int,
@@ -186,6 +220,12 @@ def parse_args():
         help="Number of samples to generate per batch",
     )
     parser.add_argument(
+        "--train_batch_size",
+        type=int,
+        default=1,
+        help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
         "--sample_steps",
         type=int,
         default=50,