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Revert "sync trainer with main branch" 

This reverts commit 80e2422967.
This commit is contained in:
chavinlo 2022-11-16 10:44:39 -05:00
parent 80e2422967
commit fed3431f03
1 changed files with 24 additions and 133 deletions
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157
trainer/diffusers_trainer.py
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@ -2,8 +2,8 @@
# `nvcc --version` to get CUDA version.
# `pip install -i https://test.pypi.org/simple/ bitsandbytes-cudaXXX` to install for current CUDA.
# Example Usage:
# Single GPU: torchrun --nproc_per_node=1 trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=1 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Multiple GPUs: torchrun --nproc_per_node=N trainer/diffusers_trainer.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Single GPU: torchrun --nproc_per_node=1 trainer_dist.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
# Multiple GPUs: torchrun --nproc_per_node=N trainer_dist.py --model="CompVis/stable-diffusion-v1-4" --run_name="liminal" --dataset="liminal-dataset" --hf_token="hf_blablabla" --bucket_side_min=64 --use_8bit_adam=True --gradient_checkpointing=True --batch_size=10 --fp16=True --image_log_steps=250 --epochs=20 --resolution=768 --use_ema=True
import argparse
import socket
@ -26,7 +26,6 @@ import numpy as np
import json
import re
import traceback
import shutil
try:
pynvml.nvmlInit()
@ -39,7 +38,6 @@ from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers.optimization import get_scheduler
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from PIL import Image, ImageOps
from PIL.Image import Image as Img
from typing import Dict, List, Generator, Tuple
from scipy.interpolate import interp1d
@ -86,12 +84,8 @@ parser.add_argument('--clip_penultimate', type=bool_t, default='False', help='Us
parser.add_argument('--output_bucket_info', type=bool_t, default='False', help='Outputs bucket information and exits')
parser.add_argument('--resize', type=bool_t, default='False', help="Resizes dataset's images to the appropriate bucket dimensions.")
parser.add_argument('--use_xformers', type=bool_t, default='False', help='Use memory efficient attention')
parser.add_argument('--extended_validation', type=bool_t, default='False', help='Perform extended validation of images to catch truncated or corrupt images.')
parser.add_argument('--no_migration', type=bool_t, default='False', help='Do not perform migration of dataset while the `--resize` flag is active. Migration creates an adjacent folder to the dataset with <dataset_dirname>_cropped.')
parser.add_argument('--skip_validation', type=bool_t, default='False', help='Skip validation of images, useful for speeding up loading of very large datasets that have already been validated.')
parser.add_argument('--wandb', dest='enablewandb', type=str, default='True', help='Enable WeightsAndBiases Reporting')
parser.add_argument('--inference', dest='enableinference', type=str, default='True', help='Enable Inference during training (Consumes 2GB of VRAM)')
args = parser.parse_args()
def setup():
@ -151,137 +145,33 @@ def _sort_by_ratio(bucket: tuple) -> float:
def _sort_by_area(bucket: tuple) -> float:
return bucket[0] * bucket[1]
class Validation():
def __init__(self, is_skipped: bool, is_extended: bool) -> None:
if is_skipped:
self.validate = self.__no_op
return print("Validation: Skipped")
if is_extended:
self.validate = self.__extended_validate
return print("Validation: Extended")
self.validate = self.__validate
print("Validation: Standard")
def __validate(self, fp: str) -> bool:
try:
Image.open(fp)
return True
except:
print(f'WARNING: Image cannot be opened: {fp}')
return False
def __extended_validate(self, fp: str) -> bool:
try:
Image.open(fp).load()
return True
except (OSError) as error:
if 'truncated' in str(error):
print(f'WARNING: Image truncated: {error}')
return False
print(f'WARNING: Image cannot be opened: {error}')
return False
except:
print(f'WARNING: Image cannot be opened: {error}')
return False
def __no_op(self, fp: str) -> bool:
return True
class Resize():
def __init__(self, is_resizing: bool, is_not_migrating: bool) -> None:
if not is_resizing:
self.resize = self.__no_op
return
if not is_not_migrating:
self.resize = self.__migration
dataset_path = os.path.split(args.dataset)
self.__directory = os.path.join(
dataset_path[0],
f'{dataset_path[1]}_cropped'
)
os.makedirs(self.__directory, exist_ok=True)
return print(f"Resizing: Performing migration to '{self.__directory}'.")
self.resize = self.__no_migration
def __no_migration(self, image_path: str, w: int, h: int) -> Img:
return ImageOps.fit(
Image.open(image_path),
(w, h),
bleed=0.0,
centering=(0.5, 0.5),
method=Image.Resampling.LANCZOS
).convert(mode='RGB')
def __migration(self, image_path: str, w: int, h: int) -> Img:
filename = re.sub('\.[^/.]+$', '', os.path.split(image_path)[1])
image = ImageOps.fit(
Image.open(image_path),
(w, h),
bleed=0.0,
centering=(0.5, 0.5),
method=Image.Resampling.LANCZOS
).convert(mode='RGB')
image.save(
os.path.join(f'{self.__directory}', f'{filename}.jpg'),
optimize=True
)
try:
shutil.copy(
os.path.join(args.dataset, f'{filename}.txt'),
os.path.join(self.__directory, f'{filename}.txt'),
follow_symlinks=False
)
except (FileNotFoundError):
f = open(
os.path.join(self.__directory, f'{filename}.txt'),
'w',
encoding='UTF-8'
)
f.close()
return image
def __no_op(self, image_path: str, w: int, h: int) -> Img:
return Image.open(image_path)
class ImageStore:
def __init__(self, data_dir: str) -> None:
self.data_dir = data_dir
self.image_files = []
[self.image_files.extend(glob.glob(f'{data_dir}' + '/*.' + e)) for e in ['jpg', 'jpeg', 'png', 'bmp', 'webp']]
self.validator = Validation(
args.skip_validation,
args.extended_validation
).validate
self.resizer = Resize(args.resize, args.no_migration).resize
self.image_files = [x for x in self.image_files if self.validator(x)]
self.image_files = [x for x in self.image_files if self.__valid_file(x)]
def __len__(self) -> int:
return len(self.image_files)
def __valid_file(self, f) -> bool:
try:
Image.open(f)
return True
except:
print(f'WARNING: Unable to open file: {f}')
return False
# iterator returns images as PIL images and their index in the store
def entries_iterator(self) -> Generator[Tuple[Img, int], None, None]:
def entries_iterator(self) -> Generator[Tuple[Image.Image, int], None, None]:
for f in range(len(self)):
yield Image.open(self.image_files[f]), f
yield Image.open(self.image_files[f]).convert(mode='RGB'), f
# get image by index
def get_image(self, ref: Tuple[int, int, int]) -> Img:
return self.resizer(
self.image_files[ref[0]],
ref[1],
ref[2]
)
def get_image(self, ref: Tuple[int, int, int]) -> Image.Image:
return Image.open(self.image_files[ref[0]]).convert(mode='RGB')
# gets caption by removing the extension from the filename and replacing it with .txt
def get_caption(self, ref: Tuple[int, int, int]) -> str:
@ -509,6 +399,15 @@ class AspectDataset(torch.utils.data.Dataset):
image_file = self.store.get_image(item)
if args.resize:
image_file = ImageOps.fit(
image_file,
(item[1], item[2]),
bleed=0.0,
centering=(0.5, 0.5),
method=Image.Resampling.LANCZOS
)
return_dict['pixel_values'] = self.transforms(image_file)
if random.random() > self.ucg:
caption_file = self.store.get_caption(item)
@ -700,7 +599,6 @@ def main():
beta_end=0.012,
beta_schedule='scaled_linear',
num_train_timesteps=1000,
clip_sample=False
)
# load dataset
@ -722,13 +620,6 @@ def main():
collate_fn=dataset.collate_fn
)
# Migrate dataset
if args.resize and not args.no_migration:
for _, batch in enumerate(train_dataloader):
continue
print(f"Completed resize and migration to '{args.dataset}_cropped' please relaunch the trainer without the --resize argument and train on the migrated dataset.")
exit(0)
weight_dtype = torch.float16 if args.fp16 else torch.float32
# move models to device