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Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.

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(C) 2022 Victor C Hall
GNU AFFERO GENERAL PUBLIC LICENSE
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# EveryDream Trainer 2.0
Welcome to 2.0 of EveryDream trainer! Now with more diffusers and even more features!
Please join us on Discord! https://discord.gg/uheqxU6sXN
If you find this tool useful, please consider subscribing to the project on Patreon or buy me a Ko-fi. The tools are open source and free, but it is a lot of work to maintain and develop and donations will allow me to expand capabilties and spend more time on the project.
## Docs
[Setup and installation](doc/SETUP.md)
[Download and setup base models](doc/BASEMODELS.md)
[Data Preparation](doc/DATA.md)
[Training](doc/TRAINING.md)
[Tweaking](doc/TWEAKING.md)

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call venv/scripts/activate.bat

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
ASPECTS9 = [[1024,1024], # 1048576 1:1
[1088,960],[960,1088], # 1044480 1.125:1
[1152,896],[896,1152], # 1032192 1.286:1
[1216,832],[832,1216], # 1011712 1.462:1
[1344,768],[768,1344], # 1032192 1.75:1
[1472,704],[704,1472], # 1036288 2.09:1
[1600,640],[640,1600], # 1024000 2.5:1
[1792,576],[576,1792], # 1032192 3.111:1
[2048,512],[512,2048], # 1048576 4:1
[2304,448],[448,2304], # 1032192 5.143:1
[2688,384],[384,2688], # 1032192 7:1
]
ASPECTS8 = [[960,960], # 921600 1:1
[1024,896],[896,1024], # 917504 1.143:1
[1088,832],[832,1088], # 905216 1.308:1
[1152,768],[768,1152], # 884736 1.5:1
[1280,704],[704,1280], # 901120 1.818:1
[1408,640],[640,1408], # 901120 2.2:1
[1680,576],[576,1680], # 921600 2.778:1
[1728,512],[512,1728], # 884736 3.375:1
[1792,512],[512,1792], # 917504 3.5:1
[2048,448],[448,2048], # 917504 4.714:1
[2240,384],[384,2240], # 860160 5.833:1
[2368,384],[384,2368], # 909312 6.17:1
]
ASPECTS7 = [[896,896], # 802816 1:1
[960,832],[832,960], # 798720 1.153:1
[1024,768],[768,1024], # 786432 1.333:1
[1088,704],[704,1088], # 765952 1.545:1
[1216,640],[640,1216], # 778240 1.9:1
[1344,576],[576,1344], # 774144 2.333:1
[1536,512],[512,1536], # 786432 3:1
[1792,448],[448,1792], # 802816 4:1
[2048,384],[384,2048], # 786432 5.333:1
]
ASPECTS6 = [[832,832], # 692224 1:1
[896,768],[768,896], # 688128 1.167:1
[960,704],[704,960], # 675840 1.364:1
#[960,640],[640,960], # 614400 1.5:1
[1024,640],[640,1024], # 655360 1.6:1
[1152,576],[576,1152], # 663552 2:1
[1216,512],[512,1216], # 622080 2.375:1
#[1280,512],[512,1280], # 655360 2.5:1
[1344,512],[512,1344], # 688128 2.625:1
[1536,448],[448,1536], # 688128 3.429:1
[1600,384],[384,1600], # 614400 4.167:1
#[1664,384],[384,1664], # 638976 4.333:1
[1728,384],[384,1728], # 663552 4.5:1
[1792,384],[384,1792], # 688128 4.667:1
]
ASPECTS5 = [[768,768], # 589824 1:1
[832,704],[704,832], # 585728 1.181:1
[896,640],[640,896], # 573440 1.4:1
[960,576],[576,960], # 552960 1.6:1
[1024,576],[576,1024], # 524288 1.778:1
[1088,512],[512,1088], # 497664 2.125:1
[1152,512],[512,1152], # 589824 2.25:1
#[1216,448],[448,1216], # 552960 2.714:1
[1280,448],[448,1280], # 573440 2.857:1
#[1344,384],[384,1344], # 518400 3.5:1
[1408,384],[384,1408], # 540672 3.667:1
[1472,320],[320,1472], # 470400 4.6:1
[1536,320],[320,1536], # 491520 4.8:1
]
ASPECTS4 = [[704,704], # 501,376 1:1
[768,640],[640,768], # 491,520 1.2:1
[832,576],[576,832], # 458,752 1.444:1
[896,512],[512,896], # 458,752 1.75:1
[960,512],[512,960], # 491,520 1.875:1
[1024,448],[448,1024], # 458,752 2.286:1
[1088,448],[448,1088], # 487,424 2.429:1
[1152,384],[384,1152], # 442,368 3:1
#[1216,384],[384,1216], # 466,944 3.125:1
[1280,384],[384,1280], # 491,520 3.333:1
[1280,320],[320,1280], # 409,600 4:1
#[1408,320],[320,1408], # 450,560 4.4:1
[1536,320],[320,1536], # 491,520 4.8:1
]
ASPECTS3 = [[640,640], # 409600 1:1
[704,576],[576,704], # 405504 1.25:1
[768,512],[512,768], # 393216 1.5:1
[832,448],[448,832], # 372736 1.857:1
[896,448],[448,896], # 401408 2:1
[1024,384],[384,1024], # 393216 2.667:1
[1152,320],[320,1152], # 368640 3.6:1
[1280,320],[320,1280], # 409600 4:1
[1408,256],[256,1408], # 360448 5.5:1
#[1472,256],[256,1472], # 376832 5.75:1
#[1536,256],[256,1536], # 393216 6:1
#[1600,256],[256,1600], # 409600 6.25:1
]
ASPECTS2 = [[576,576], # 331776 1:1
[640,512],[512,640], # 327680 1.25:1
#[640,448],[448,640], # 286720 1.4286:1
[704,448],[448,704], # 314928 1.5625:1
[832,384],[384,832], # 317440 2.1667:1
[960,320],[320,960], # 307200 3:1
#[1024,320],[320,1024], # 327680 3.2:1
[1280,256],[256,1280], # 327680 5:1
]
ASPECTS = [[512,512], # 262144 1:1
[576,448],[448,576], # 258048 1.29:1
[640,384],[384,640], # 245760 1.667:1
[768,320],[320,768], # 245760 2.4:1
#[832,256],[256,832], # 212992 3.25:1
[896,256],[256,896], # 229376 3.5:1
#[960,256],[256,960], # 245760 3.75:1
[1024,256],[256,1024], # 245760 4:1
]
def get_aspect_buckets(resolution, square_only=False, reduced_buckets=False):
if resolution < 512:
raise ValueError("Resolution must be at least 512")
try:
rounded_resolution = int(resolution / 64) * 64
if square_only:
return [[rounded_resolution, rounded_resolution]]
all_image_sizes = __get_all_aspects()
aspects = next(filter(lambda sizes: sizes[0][0]==rounded_resolution, all_image_sizes), None)
if reduced_buckets:
return aspects[0:2]
return aspects
except Exception as e:
print(f" *** Unsupported resolution of {resolution}, check your resolution config")
print(f" *** Value must be between 512 and 1024")
raise e
def __get_all_aspects():
return [ASPECTS, ASPECTS2, ASPECTS3, ASPECTS4, ASPECTS5, ASPECTS6, ASPECTS7, ASPECTS8, ASPECTS9]

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import os
from PIL import Image
import random
from data.image_train_item import ImageTrainItem
import data.aspects as aspects
class DataLoaderMultiAspect():
"""
Data loader for multi-aspect-ratio training and bucketing
data_root: root folder of training data
batch_size: number of images per batch
flip_p: probability of flipping image horizontally (i.e. 0-0.5)
"""
def __init__(self, data_root, seed=555, debug_level=0, batch_size=1, flip_p=0.0, resolution=512):
self.image_paths = []
self.debug_level = debug_level
self.flip_p = flip_p
self.aspects = aspects.get_aspect_buckets(resolution=resolution, square_only=False)
print(f"* DLMA resolution {resolution}, buckets: {self.aspects}")
print(" Preloading images...")
self.__recurse_data_root(self=self, recurse_root=data_root)
random.Random(seed).shuffle(self.image_paths)
prepared_train_data = self.__prescan_images(self.image_paths, flip_p) # ImageTrainItem[]
self.image_caption_pairs = self.__bucketize_images(prepared_train_data, batch_size=batch_size, debug_level=debug_level)
#if debug_level > 0: print(f" * DLMA Example: {self.image_caption_pairs[0]} images")
def get_all_images(self):
return self.image_caption_pairs
@staticmethod
def __read_caption_from_file(file_path, fallback_caption):
caption = fallback_caption
try:
with open(file_path, encoding='utf-8', mode='r') as caption_file:
caption = caption_file.read()
except:
print(f" *** Error reading {file_path} to get caption, falling back to filename")
caption = fallback_caption
pass
return caption
def __prescan_images(self, image_paths: list, flip_p=0.0):
"""
Create ImageTrainItem objects with metadata for hydration later
"""
decorated_image_train_items = []
for pathname in image_paths:
caption_from_filename = os.path.splitext(os.path.basename(pathname))[0].split("_")[0]
txt_file_path = os.path.splitext(pathname)[0] + ".txt"
caption_file_path = os.path.splitext(pathname)[0] + ".caption"
if os.path.exists(txt_file_path):
caption = self.__read_caption_from_file(txt_file_path, caption_from_filename)
elif os.path.exists(caption_file_path):
caption = self.__read_caption_from_file(caption_file_path, caption_from_filename)
else:
caption = caption_from_filename
image = Image.open(pathname)
width, height = image.size
image_aspect = width / height
target_wh = min(self.aspects, key=lambda aspects:abs(aspects[0]/aspects[1] - image_aspect))
image_train_item = ImageTrainItem(image=None, caption=caption, target_wh=target_wh, pathname=pathname, flip_p=flip_p)
decorated_image_train_items.append(image_train_item)
return decorated_image_train_items
@staticmethod
def __bucketize_images(prepared_train_data: list, batch_size=1, debug_level=0):
"""
Put images into buckets based on aspect ratio with batch_size*n images per bucket, discards remainder
"""
# TODO: this is not terribly efficient but at least linear time
buckets = {}
for image_caption_pair in prepared_train_data:
target_wh = image_caption_pair.target_wh
if (target_wh[0],target_wh[1]) not in buckets:
buckets[(target_wh[0],target_wh[1])] = []
buckets[(target_wh[0],target_wh[1])].append(image_caption_pair)
print(f" ** Number of buckets used: {len(buckets)}")
if len(buckets) > 1:
for bucket in buckets:
truncate_count = len(buckets[bucket]) % batch_size
current_bucket_size = len(buckets[bucket])
buckets[bucket] = buckets[bucket][:current_bucket_size - truncate_count]
if debug_level > 0:
print(f" ** Bucket {bucket} with {current_bucket_size} will drop {truncate_count} images due to batch size {batch_size}")
# flatten the buckets
image_caption_pairs = []
for bucket in buckets:
image_caption_pairs.extend(buckets[bucket])
return image_caption_pairs
@staticmethod
def __recurse_data_root(self, recurse_root):
for f in os.listdir(recurse_root):
current = os.path.join(recurse_root, f)
if os.path.isfile(current):
ext = os.path.splitext(f)[1]
if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.webp']:
self.image_paths.append(current)
sub_dirs = []
for d in os.listdir(recurse_root):
current = os.path.join(recurse_root, d)
if os.path.isdir(current):
sub_dirs.append(current)
for dir in sub_dirs:
self.__recurse_data_root(self=self, recurse_root=dir)

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# stop lightning's repeated instantiation of batch train/val/test classes causing multiple sweeps of the same data off disk
shared_dataloader = None

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import torch
from torch.utils.data import DataLoader
from data.every_dream import EveryDreamBatch
class EveryDreamDataLoaderWrapper(DataLoader):
"""
Collates image:caption pairs into batches
"""
def __init__(self, batch_size: int, tokenizer, dataset: EveryDreamBatch):
self.dataset = dataset
self.tokenizer = tokenizer
super().__init__(dataset, batch_size, shuffle=False, pin_memory=True)
#super().__init__(dataset, batch_size, shuffle=False, collate_fn=self.collate_fn, pin_memory=True)
def collate_fn(self, batch):
"""
Collates batches of data
based on https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py
"""
print("collate_fn")
print(len(batch))
captions = [example["caption"] for example in batch]
images = [example["image"] for example in batch]
print("collate_fn2")
images = torch.stack(images)
images = images.to(memory_format=torch.contiguous_format).float()
print("collate_fn3")
captions = self.tokenizer.pad(
{"captions": captions},
padding=True,
return_tensors="pt",
).input_ids
batch = {
"captions": captions,
"images": images,
}
print(f"{batch['captions']} {batch['images'].shape}")
return batch

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import numpy as np
from torch.utils.data import Dataset
from ldm.data.data_loader import DataLoaderMultiAspect as dlma
import math
import ldm.data.dl_singleton as dls
from ldm.data.image_train_item import ImageTrainItem
class EDValidateBatch(Dataset):
def __init__(self,
data_root,
flip_p=0.0,
repeats=1,
debug_level=0,
batch_size=1,
set='val',
):
self.data_root = data_root
self.batch_size = batch_size
if not dls.shared_dataloader:
print("Creating new dataloader singleton")
dls.shared_dataloader = dlma(data_root=data_root, debug_level=debug_level, batch_size=self.batch_size, flip_p=flip_p)
self.image_train_items = dls.shared_dataloader.get_all_images()
self.num_images = len(self.image_train_items)
self._length = max(math.trunc(self.num_images * repeats), batch_size) - self.num_images % self.batch_size
print()
print(f" ** Validation Set: {set}, steps: {self._length / batch_size:.0f}, repeats: {repeats} ")
print()
def __len__(self):
return self._length
def __getitem__(self, i):
idx = i % self.num_images
image_train_item = self.image_train_items[idx]
example = self.__get_image_for_trainer(image_train_item)
return example
@staticmethod
def __get_image_for_trainer(image_train_item: ImageTrainItem):
example = {}
image_train_tmp = image_train_item.hydrate()
example["image"] = image_train_tmp.image
example["caption"] = image_train_tmp.caption
return example

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import torch
from torch.utils.data import Dataset
from data.data_loader import DataLoaderMultiAspect as dlma
import math
import data.dl_singleton as dls
from data.image_train_item import ImageTrainItem
import random
from torchvision import transforms
from transformers import CLIPTokenizer
import torch.nn.functional as F
class EveryDreamBatch(Dataset):
"""
data_root: root path of all your training images, will be recursively searched for images
repeats: how many times to repeat each image in the dataset
flip_p: probability of flipping the image horizontally
debug_level: 0=none, 1=print drops due to unfilled batches on aspect ratio buckets, 2=debug info per image, 3=save crops to disk for inspection
batch_size: how many images to return in a batch
conditional_dropout: probability of dropping the caption for a given image
resolution: max resolution (relative to square)
jitter: number of pixels to jitter the crop by, only for non-square images
"""
def __init__(self,
data_root,
flip_p=0.0,
debug_level=0,
batch_size=1,
conditional_dropout=0.02,
resolution=512,
crop_jitter=20,
seed=555,
tokenizer=None,
):
self.data_root = data_root
self.batch_size = batch_size
self.debug_level = debug_level
self.conditional_dropout = conditional_dropout
self.crop_jitter = crop_jitter
self.unloaded_to_idx = 0
self.tokenizer = tokenizer
#print(f"tokenizer: {tokenizer}")
self.max_token_length = self.tokenizer.model_max_length
if seed == -1:
seed = random.randint(0, 99999)
if not dls.shared_dataloader:
print(" * Creating new dataloader singleton")
dls.shared_dataloader = dlma(data_root=data_root, seed=seed, debug_level=debug_level, batch_size=self.batch_size, flip_p=flip_p, resolution=resolution)
self.image_train_items = dls.shared_dataloader.get_all_images()
# for iti in self.image_train_items:
# print(f"iti caption:{iti.caption}")
# exit()
self.num_images = len(self.image_train_items)
self._length = self.num_images
self.image_transforms = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
print()
print(f" ** Trainer Set: {self._length / batch_size:.0f}, num_images: {self.num_images}, batch_size: {self.batch_size}, length w/repeats: {self._length}")
print()
def __len__(self):
return self._length
def __getitem__(self, i):
#print(" * Getting item", i)
# batch = dict()
# batch["images"] = list()
# batch["captions"] = list()
# first = True
# for j in range(i, i + self.batch_size - 1):
# if j < self.num_images:
# example = self.__get_image_for_trainer(self.image_train_items[j], self.debug_level)
# if first:
# print(f"first example {j}", example)
# batch["images"] = [torch.from_numpy(example["image"])]
# batch["captions"] = [example["caption"]]
# first = False
# else:
# print(f"subsiquent example {j}", example)
# batch["images"].extend(torch.from_numpy(example["image"]))
# batch["captions"].extend(example["caption"])
example = {}
train_item = self.__get_image_for_trainer(self.image_train_items[i], self.debug_level)
#example["image"] = torch.from_numpy(train_item["image"])
example["image"] = self.image_transforms(train_item["image"])
# if train_item["caption"] == " ":
# example["tokens"] = [0 for i in range(self.max_token_length-2)]
# else:
if random.random() > self.conditional_dropout:
example["tokens"] = self.tokenizer(train_item["caption"],
#padding="max_length",
truncation=True,
padding=False,
add_special_tokens=False,
max_length=self.max_token_length-2,
).input_ids
example["tokens"] = torch.tensor(example["tokens"])
else:
example["tokens"] = torch.zeros(75, dtype=torch.int)
#print(f"bos: {self.tokenizer.bos_token_id}{self.tokenizer.eos_token_id}")
#print(f"example['tokens']: {example['tokens']}")
pad_amt = self.max_token_length-2 - len(example["tokens"])
example['tokens']= F.pad(example['tokens'],pad=(0,pad_amt),mode='constant',value=0)
example['tokens']= F.pad(example['tokens'],pad=(1,0),mode='constant',value=int(self.tokenizer.bos_token_id))
eos_int = int(self.tokenizer.eos_token_id)
#eos_int = int(0)
example['tokens']= F.pad(example['tokens'],pad=(0,1),mode='constant',value=eos_int)
#print(f"__getitem__ train_item['caption']: {train_item['caption']}")
#print(f"__getitem__ train_item['pathname']: {train_item['pathname']}")
#print(f"__getitem__ example['tokens'] pad: {example['tokens']}")
example["caption"] = train_item["caption"] # for sampling if needed
#print(f"len tokens: {len(example['tokens'])} cap: {example['caption']}")
return example
def __get_image_for_trainer(self, image_train_item: ImageTrainItem, debug_level=0):
example = {}
save = debug_level > 2
image_train_tmp = image_train_item.hydrate(crop=False, save=save, crop_jitter=self.crop_jitter)
example["image"] = image_train_tmp.image
# if random.random() > self.conditional_dropout:
example["caption"] = image_train_tmp.caption
# else:
# example["caption"] = " "
#print(f" {image_train_tmp.pathname}: {image_train_tmp.caption}")
return example

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import PIL
import numpy as np
from torchvision import transforms, utils
import random
import math
import os
_RANDOM_TRIM = 0.04
class ImageTrainItem():
"""
image: PIL.Image
identifier: caption,
target_aspect: (width, height),
pathname: path to image file
flip_p: probability of flipping image (0.0 to 1.0)
"""
def __init__(self, image: PIL.Image, caption: str, target_wh: list, pathname: str, flip_p=0.0):
self.caption = caption
self.target_wh = target_wh
self.pathname = pathname
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.cropped_img = None
if image is None:
self.image = []
else:
self.image = image
def hydrate(self, crop=False, save=False, crop_jitter=20):
"""
crop: hard center crop to 512x512
save: save the cropped image to disk, for manual inspection of resize/crop
crop_jitter: randomly shift cropp by N pixels when using multiple aspect ratios to improve training quality
"""
if not hasattr(self, 'image') or len(self.image) == 0:
self.image = PIL.Image.open(self.pathname).convert('RGB')
width, height = self.image.size
if crop:
cropped_img = self.__autocrop(self.image)
self.image = cropped_img.resize((512,512), resample=PIL.Image.BICUBIC)
else:
width, height = self.image.size
jitter_amount = random.randint(0,crop_jitter)
if self.target_wh[0] == self.target_wh[1]:
if width > height:
left = random.randint(0, width - height)
self.image = self.image.crop((left, 0, height+left, height))
width = height
elif height > width:
top = random.randint(0, height - width)
self.image = self.image.crop((0, top, width, width+top))
height = width
elif width > self.target_wh[0]:
slice = min(int(self.target_wh[0] * _RANDOM_TRIM), width-self.target_wh[0])
slicew_ratio = random.random()
left = int(slice*slicew_ratio)
right = width-int(slice*(1-slicew_ratio))
sliceh_ratio = random.random()
top = int(slice*sliceh_ratio)
bottom = height- int(slice*(1-sliceh_ratio))
self.image = self.image.crop((left, top, right, bottom))
else:
image_aspect = width / height
target_aspect = self.target_wh[0] / self.target_wh[1]
if image_aspect > target_aspect:
new_width = int(height * target_aspect)
jitter_amount = max(min(jitter_amount, int(abs(width-new_width)/2)), 0)
left = jitter_amount
right = left + new_width
self.image = self.image.crop((left, 0, right, height))
else:
new_height = int(width / target_aspect)
jitter_amount = max(min(jitter_amount, int(abs(height-new_height)/2)), 0)
top = jitter_amount
bottom = top + new_height
self.image = self.image.crop((0, top, width, bottom))
self.image = self.image.resize(self.target_wh, resample=PIL.Image.BICUBIC)
self.image = self.flip(self.image)
if type(self.image) is not np.ndarray:
if save:
base_name = os.path.basename(self.pathname)
if not os.path.exists("test/output"):
os.makedirs("test/output")
self.image.save(f"test/output/{base_name}")
self.image = np.array(self.image).astype(np.uint8)
self.image = (self.image / 127.5 - 1.0).astype(np.float32)
#print(self.image.shape)
return self
@staticmethod
def __autocrop(image: PIL.Image, q=.404):
"""
crops image to a random square inside small axis using a truncated gaussian distribution across the long axis
"""
x, y = image.size
if x != y:
if (x>y):
rand_x = x-y
sigma = max(rand_x*q,1)
else:
rand_y = y-x
sigma = max(rand_y*q,1)
if (x>y):
x_crop_gauss = abs(random.gauss(0, sigma))
x_crop = min(x_crop_gauss,(x-y)/2)
x_crop = math.trunc(x_crop)
y_crop = 0
else:
y_crop_gauss = abs(random.gauss(0, sigma))
x_crop = 0
y_crop = min(y_crop_gauss,(y-x)/2)
y_crop = math.trunc(y_crop)
min_xy = min(x, y)
image = image.crop((x_crop, y_crop, x_crop + min_xy, y_crop + min_xy))
return image

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import torch
import os
import hashlib
import io
from PIL import Image, ImageOps
import random
from aspects import get_aspect_buckets
from torchvision import transforms
class LatentCacheItem():
"""
caches image/caption latent pairs and index value to select appropriate random crop jitter
"""
def __init__(self, imagelatent, captionembedding, cropjitteridx, resolution = tuple):
"""
imagelatent: image tensor
captionembedding: caption embedding tensor
cropjitteridx: index of random crop jitter to use
"""
self.imagelatent = imagelatent
self.captionembedding = captionembedding
self.cropjitteridx = cropjitteridx
self.resolution = resolution
def __repr__(self):
return f"lat: {self.imagelatent.shape} emb:{self.captionembedding.shape} cj:{self.cropjitteridx}"
class LatentCacheManager():
"""
Manages a cache of latent vectors for a dataset.
"""
def __init__(self, latent_cache_path="/.cache/latents", device=torch.device("cuda"), jitter_lim=8, vae=None):
"""
Manages caching of image latents to disk,
latent_cache_path: path to latent cache folder
device: device to use for creating latents (torch.device)
vae: vae to use for creating latents
jitter_lim: number of random crop jitters to use per image (default: 8)
"""
assert vae is not None, "LatentCacheManager requires a vae to be passed in"
self.cache = dict(str, []) # key: sha256 hash of image path, value: list of LatentCacheItem
self.latentcachepath = latent_cache_path
self.jitter_lim = jitter_lim
self.device = device
self.vae = vae
# create pt file if it doesn't exist
if not os.path.exists(self.latentcachepath):
torch.save(self.cache, self.latentcachepath)
self.vae_on_device = False
def set_vae(self, vae):
self.vae = vae
def delete_vae(self):
self.vae = None
def vae_to_device(self, device):
self.vae.to(self.device)
self.vae_on_device = True
def vae_to_cpu(self):
self.vae.to("cpu")
self.vae_on_device = False
@staticmethod
def __hash(imagepath):
return hashlib.sha256(imagepath.encode("utf-8")).hexdigest()
def add(self, imagepath: io, captionembedding: torch.tensor, target_resolution=(512,512)):
"""
adds aan item to the cache
"""
if not self.vae_on_device: self.vae_to_gpu()
hash = self.__hash(imagepath)
image = Image.open(imagepath)
image_aspects = get_aspect_buckets(resolution=target_resolution)
for i in range(self.jitter_lim):
bleed = random.uniform(0.0, 0.02)
centering = (random.uniform(0.0, 0.02), random.uniform(0.0, 0.02))
jittered_image = ImageOps.fit(image, target_resolution, method=Image.BICUBIC, bleed=bleed, centering=centering)
# convert to tensor
latent = self.vae(jittered_image)
# add to cache
self.cache[hash].append(LatentCacheItem(imagelatent=latent,
captionembedding=captionembedding,
i,
resolution=self.vae.resolution))
# append to pt file
torch.save(self.cache, os.path.join(self.latentcachepath, f"{hash}.pt"))
def __getitem__(self, imagepath, cropjitteridx=0):
"""
returns a LatentCacheItem by imagepath key
"""
hash = self.__hash(imagepath)
item = self.cache[hash][cropjitteridx]
return item

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# Download and setup base models
In order to train, you need a base model on which to train. This is a one-time setup to configure base models when you want to use a particular base.
Make sure the trainer is installed properly first. See [SETUP.md](doc/SETUP.md) for more details.
When you finish you should see something like this, come back to reference this picture as you go through the steps below:
![models](ckptcache.png)
## Download models
You need some sort of base model to start training. I suggest these two:
Stable Diffusion 1.5 with improved VAE:
https://huggingface.co/panopstor/EveryDream/blob/main/sd_v1-5_vae.ckpt
SD2.1 768:
https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.ckpt
You can use SD2.0 512 as well, but typically SD1.5 is going to be better.
https://huggingface.co/stabilityai/stable-diffusion-2-base/blob/main/512-base-ema.ckpt
Place these in the root folder of EveryDream2.
Run these commands *one time* to prepare them. **It's very important to use the correct YAML!**
For SD1.x models, use this (note it will spill a lot of warnings to the console, but its fine):
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v1-inference.yaml ^
--image_size 512 ^
--checkpoint_path sd_v1-5_vae.ckpt ^
--prediction_type epsilon ^
--upcast_attn False ^
--dump_path "ckpt_cache/sd_v1-5_vae"
And the SD2.1 768 model (uses v2-v yaml and "v_prediction" prediction type):
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v2-inference-v.yaml ^
--image_size 768 ^
--checkpoint_path v2-1_768-ema-pruned.ckpt ^
--prediction_type v_prediction ^
--upcast_attn False ^
--dump_path "ckpt_cache/v2-1_768-ema-pruned"
And finally the SD2.0 512 base model (generally not recommended base model):
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v2-inference.yaml ^
--image_size 512 ^
--checkpoint_path 512-base-ema.ckpt ^
--prediction_type epsilon ^
--upcast_attn False ^
--dump_path "ckpt_cache/512-base-ema"
If you have other models, you need to know the base model that was used for them, **in particular use the correct yaml (original_config_file) or it will not properly convert.** Make sure to put some sort of name in the dump_path after "ckpt_cache/" so you can reference it later.
All of the above is one time. After running, you will use --resume_ckpt and just name the file without "ckpt_cache/"
ex.
python train.py --resume_ckpt "sd_v1-5_vae" ...
python train.py --resume_ckpt "v2-1_768-ema-pruned" ...
python train.py --resume_ckpt "512-base-ema" ...

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# Data organization
Since this trainer relies on having captions for your training images you will need to decide how you want deal with this.
There are two currently supported methods to retrieve captions:
1. Name the files with the caption. Underscore marks the end of the captoin (ex. "john smith on a boat_999.jpg")
2. Put your captions for each image in a .txt file with the same name as the image. All UTF-8 text is supported with no reserved or special case characters. (ex. 00001.jpg, 00001.txt)
You will need to place all your images and captions into a folder. Inside that folder, you can use subfolders to organize data as you please. The trainer will recursively search for images and captions. It may be useful, for instance, to split each character into a subfolder, and have other subfolders for cityscapes, etc.
When you train, you will use "--data_root" to point to the root folder of your data. All images in that folder and its subfolders will be used for training.
# Data preparation
## Image size
The trainer will automatically fit your images to the best possible size. It is best to leave your images larger tham you may think for typical Stable Diffusion training. Even 4K images will be handled fine so just don't sweat it if you have large images. The only downside is they take a bit more disk space.
Current recommendation is 1 megapixel (ex 1100x100, 1300x900, etc) or larger, but thinking ahead to future technology advancements you may wish to keep them at even larger resolutions. Again, don't worry about the trainer squeezing or cropping, it will handle it!
Aspect ratios up to 4:1 or 1:4 are supported. Again, just don't worry about this too much. The trainer will handle it.
## Cropping
You can crop your images in an image editor *if you need, in order to get good close ups of things like faces, or to split images up that contain multiple characters.* As above, make sure **after** cropping your images are still fairly large. It is ok to use a full shot of two characters in one image and also a cropped version of each character separately, but make sure every image is captioned appropriately for what is actually present in each image.
## Captions
For most use cases, use a sane English sentence to describe the image. Try to put your character or main object name close to the start.
### Styles
For style, consider adding a suffix on the caption that describes the style. Examples would be "by claude monet" or "in the style of gta box art" at the end of the caption. This will help the model learn recall style at inference time so you can style other subjects you did not train with the style. You may also consider "drawing of" or "painting of" at the start of the caption when appropriate.
Consider also including a style tag as above if you are training anything besides photos. For instance, if you are training a few characters from a video game you can consider "cloud strife holding a buster sword, screenshot from final fantasy for ps5" if you wish to capture the style of the game along with the characters.
### Context
Include the surroundings and context in your captions. Ex. "cloud strife standing on a dirt path in midgar city slums district" Again, this will allow you to recall the "dirt path in midgar city slums district" style at inference time, and will even pick up on pieces of that like "midgar city" (if enough samples are present with similar words) as a style you can apply later!
Also consider some basic mention of pose. ex. "clouds strife sitting on a blue wooden bench in front of a concrete wall" or "barrett wallace holding his fist in front of his face with an angry look on his face, looking at the camera." Captions can capture value not only for the character's look, but also for the pose, the background scene, and the camera angle. You can be creative here, there is a lot of potential!

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# Installation
## Windows
* Open a normal windows command prompt and run `windows_setup.bat` from the command line.
*Do **not** double click the file from Windows File Explorer*, you need the command window open.
* While that is running, download the official xformers windows wheel from this URL:
https://github.com/facebookresearch/xformers/suites/9544395581/artifacts/454051141
* Unzip the xformers file to the EveryDream2 folder
* Check your command line window to make sure no errors occured. If you have errors, please post them in the Discord and ask for assistance.
* Once the command line is done with no errors, paste this command into the command prompt:
`pip install xformers-0.0.15.dev0+303e613.d20221128-cp310-cp310-win_amd64.whl`
* When you want to train in the future after closing the command line, run `activate_venv.bat` from the command line to activate the virtual environment again. (hint: you can type `a` then press tab, then press enter)
## Next step
Read the documentation to setup your base models from which you will train.
[Base Model setup](doc/BASEMODELS.md)

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## Install Python
Install Python 3.10 from here:
https://www.python.org/downloads/release/python-3109/
https://www.python.org/ftp/python/3.10.9/python-3.10.9-amd64.exe
Download and install Git from [git-scm.com](https://git-scm.com/).
or [Git for windows](https://gitforwindows.org/)
## Clone this repo
Clone the repo from normal command line then change into the directory:
git clone https://github.com/victorchall/EveryDream-trainer2
cd EveryDream-trainer2
## Download models
You need some sort of base model to start training. I suggest these two:
Stable Diffusion 1.5 with improved VAE:
https://huggingface.co/panopstor/EveryDream/blob/main/sd_v1-5_vae.ckpt
SD2.1 768:
https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-nonema-pruned.ckpt
You can use SD2.0 512 as well, but typically SD1.5 is going to be better.
https://huggingface.co/stabilityai/stable-diffusion-2-base/blob/main/512-base-ema.ckpt
Place these in the root folder of EveryDream2.
Run these commands *one time* to prepare them:
For SD1.x models, use this:
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v1-inference.yaml ^
--image_size 768 ^
--checkpoint_path sd_v1-5_vae.ckpt ^
--prediction_type epsilon ^
--upcast_attn False ^
--pipeline_type FrozenOpenCLIPEmbedder ^
--dump_path "ckpt_cache/sd_v1-5_vae"
And the SD2.1 768 model:
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v2-inference-v.yaml ^
--image_size 768 ^
--checkpoint_path v2-1_768-ema-pruned.ckpt ^
--prediction_type v_prediction ^
--upcast_attn False ^
--pipeline_type FrozenOpenCLIPEmbedder ^
--dump_path "ckpt_cache/v2-1_768-ema-pruned"
And finally the SD2.0 512 base model (generally not recommended base model):
python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim ^
--original_config_file v2-inference.yaml ^
--image_size 768 ^
--checkpoint_path 512-base-ema.ckpt ^
--prediction_type epsilon ^
--upcast_attn False ^
--pipeline_type FrozenOpenCLIPEmbedder ^
--dump_path "ckpt_cache/512-base-ema"
If you have other models, you need to know the base model that was used for them, in particular use the correct yaml (original_config_file) or it will not properly convert.
All of the above is one time. After running, you will use --resume_ckpt and just name the file without "ckpt_cache"
ex.
python train.py --resume_ckpt "sd_v1-5_vae" ...
python train.py --resume_ckpt "v2-1_768-ema-pruned" ...
python train.py --resume_ckpt "512-base-ema" ...
## Windows
Run windows_setup.bat to create your venv and install dependencies.
windows_setup.bat
## Linux, Linux containers, or WSL
TBD

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Here are some example commands to get you started, you can copy paste them into your command line and press enter.
Make sure the last line does not have ^ but all other lines do.
Training examples:
Resuming from a checkpoint, 50 epochs, 6 batch size, 3e-6 learning rate, cosine scheduler, generate samples evern 200 steps, 10 minute checkpoint interval, adam8bit, and using the default "input" folder for training data:
python train.py --resume_ckpt "sd_v1-5_vae" ^
--max_epochs 50 ^
--data_root "R:\everydream-trainer\training_samples\mega\ff7r\man_ff7r\cloud" ^
--lr_scheduler cosine ^
--lr_decay_steps 1500 ^
--project_name myproj ^
--batch_size 6 ^
--sample_steps 200 ^
--lr 3e-6 ^
--ckpt_every_n_minutes 10 ^
--useadam8bit
Training from SD2 512 base model, 18 epochs, 4 batch size, 1.2e-6 learning rate, constant LR, generate samples evern 100 steps, 30 minute checkpoint interval, adam8bit, using imagesin the x:\mydata folder, training at resolution class of 640:
python train.py --resume_ckpt "512-base-ema" ^
--data_root "x:\mydata" ^
--max_epochs 18 ^
--lr_scheduler constant ^
--project_name myproj ^
--batch_size 4 ^
--sample_steps 100 ^
--lr 1.2e-6 ^
--resolution 640 ^
--clip_grad_norm 1 ^
--ckpt_every_n_minutes 30 ^
--useadam8bit
python train.py --resume_ckpt "SD21" ^
--data_root "R:\everydream-trainer\training_samples\mega\gt\objects\jets" ^
--max_epochs 50 ^
--lr_scheduler cosine ^
--lr_decay_steps 1500 ^
--lr_warmup_steps 20 ^
--project_name myproj ^
--batch_size 6 ^
--sample_steps 100 ^
--lr 1.5e-6 ^
--ckpt_every_n_minutes 15 ^
--useadam8bit ^
--clip_grad_norm 1 ^
Copy paste the above to your command line and press enter.
Make sure the last line does not have ^ but all other lines do
Scheduler example, note warmup and decay dont work with constant (default), warmup is set automatically to 5% of decay if not set
--lr_scheduler cosine
--lr_warmup_steps 100
--lr_decay_steps 2500
Warmup and decay only count for some schedulers! Constant is not one of them.
Currently "constant" and "cosine" are recommended. I'll add support to others upon request.
How to resume:
Point your resume_ckpt to the path in logs like so:
--resume_ckpt "R:\ed3\logs\myproj20221213-161620\ckpts\myproj-ep22-gs01099" ^

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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
import os
import sys
import math
import signal
import argparse
import logging
import time
import torch.nn.functional as torch_functional
from torch.cuda.amp import autocast
import torchvision.transforms as transforms
from colorama import Fore, Style, Cursor
import numpy as np
import itertools
import torch
import datetime
import json
from PIL import Image, ImageDraw, ImageFont
from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, DiffusionPipeline, DDPMScheduler, PNDMScheduler, EulerAncestralDiscreteScheduler
#from diffusers.models import AttentionBlock
from diffusers.optimization import get_scheduler
from diffusers.utils.import_utils import is_xformers_available
from transformers import CLIPTextModel, CLIPTokenizer
#from accelerate import Accelerator
from accelerate.utils import set_seed
import wandb
from torch.utils.tensorboard import SummaryWriter
from tqdm.auto import tqdm
from data.every_dream import EveryDreamBatch
from utils.convert_diffusers_to_stable_diffusion import convert as converter
from utils.gpu import GPU
_GRAD_ACCUM_STEPS = 1 # future use...
_SIGTERM_EXIT_CODE = 130
def convert_to_hf(ckpt_path):
hf_cache = os.path.join("ckpt_cache", os.path.basename(ckpt_path))
if os.path.isfile(ckpt_path):
if not os.path.exists(hf_cache):
os.makedirs(hf_cache)
logging.info(f"Converting {ckpt_path} to Diffusers format")
import utils.convert_original_stable_diffusion_to_diffusers as convert
convert.convert(ckpt_path, f"ckpt_cache/{ckpt_path}")
return hf_cache
elif os.path.isdir(hf_cache):
return hf_cache
else:
return ckpt_path
def setup_local_logger(args):
"""
configures logger with file and console logging, logs args, and returns the datestamp
"""
log_path = "logs"
if not os.path.exists(log_path):
os.makedirs(log_path)
json_config = json.dumps(vars(args), indent=2)
# write current time and date stamp to string
datetimestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
logfilename = os.path.join(log_path, f"{args.project_name}-train{datetimestamp}.log")
with open(logfilename, "w") as f:
f.write(f"Training config:\n{json_config}\n")
logging.basicConfig(filename=logfilename,
level=logging.INFO,
format="%(asctime)s %(message)s",
datefmt="%m/%d/%Y %I:%M:%S %p",
)
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
return datetimestamp
def log_optimizer(optimizer: torch.optim.Optimizer, betas, epsilon):
logging.info(f"{Fore.CYAN} * Optimizer: {optimizer.__class__.__name__} *{Style.RESET_ALL}")
logging.info(f" betas: {betas}, epsilon: {epsilon} *{Style.RESET_ALL}")
def save_optimizer(optimizer: torch.optim.Optimizer, path: str):
"""
Saves the optimizer state
"""
torch.save(optimizer.state_dict(), path)
def load_optimizer(optimizer, path: str):
"""
Loads the optimizer state
"""
optimizer.load_state_dict(torch.load(path))
def get_gpu_memory(nvsmi):
"""
returns the gpu memory usage
"""
gpu_query = nvsmi.DeviceQuery('memory.used, memory.total')
gpu_used_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['used'])
gpu_total_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['total'])
return gpu_used_mem, gpu_total_mem
def append_epoch_log(global_step: int, epoch_pbar, gpu, log_writer, **logs):
"""
updates the vram usage for the epoch
"""
gpu_used_mem, gpu_total_mem = gpu.get_gpu_memory()
log_writer.add_scalar("performance/vram", gpu_used_mem, global_step)
epoch_mem_color = Style.RESET_ALL
if gpu_used_mem > 0.93 * gpu_total_mem:
epoch_mem_color = Fore.LIGHTRED_EX
elif gpu_used_mem > 0.85 * gpu_total_mem:
epoch_mem_color = Fore.LIGHTYELLOW_EX
elif gpu_used_mem > 0.7 * gpu_total_mem:
epoch_mem_color = Fore.LIGHTGREEN_EX
elif gpu_used_mem < 0.5 * gpu_total_mem:
epoch_mem_color = Fore.LIGHTBLUE_EX
if logs is not None:
epoch_pbar.set_postfix(**logs, vram=f"{epoch_mem_color}{gpu_used_mem}/{gpu_total_mem} MB{Style.RESET_ALL} gs:{global_step}")
def main(args):
"""
Main entry point
"""
log_time = setup_local_logger(args)
seed = 555
set_seed(seed)
gpu = GPU()
@torch.no_grad()
def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae):
"""
Save the model to disk
"""
global global_step
if global_step is None or global_step == 0:
logging.warning(" No model to save, something likely blew up on startup, not saving")
return
logging.info(f" * Saving diffusers model to {save_path}")
pipeline = StableDiffusionPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=None, # save vram
requires_safety_checker=None, # avoid nag
feature_extractor=None, # must be none of no safety checker
)
pipeline.save_pretrained(save_path)
sd_ckpt_path = f"{os.path.basename(save_path)}.ckpt"
sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
logging.info(f" * Saving SD model to {sd_ckpt_full}")
converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=True)
# optimizer_path = os.path.join(save_path, "optimizer.pt")
# if self.save_optimizer_flag:
# logging.info(f" Saving optimizer state to {save_path}")
# self.save_optimizer(self.ctx.optimizer, optimizer_path)
@torch.no_grad()
def __create_inference_pipe(unet, text_encoder, tokenizer, scheduler, vae):
"""
creates a pipeline for SD inference
"""
pipe = StableDiffusionPipeline(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=None, # save vram
requires_safety_checker=None, # avoid nag
feature_extractor=None, # must be none of no safety checker
)
if is_xformers_available():
try:
pipe.enable_xformers_memory_efficient_attention()
except Exception as ex:
pass
return pipe
def __generate_sample(pipe: StableDiffusionPipeline, prompt : str, cfg: float, resolution: int):
"""
generates a single sample at a given cfg scale and saves it to disk
"""
gen = torch.Generator(device="cuda").manual_seed(555)
with torch.no_grad(), autocast():
image = pipe(prompt,
num_inference_steps=30,
num_images_per_prompt=1,
guidance_scale=cfg,
generator=gen,
height=resolution,
width=resolution,
).images[0]
draw = ImageDraw.Draw(image)
font = ImageFont.truetype(font="arial.ttf", size=24)
print_msg = f"cfg:{cfg:.1f}"
l, t, r, b = draw.textbbox(xy=(0,0), text=print_msg, font=font)
text_width = r - l
text_height = b - t
x = float(image.width - text_width - 10)
y = float(image.height - text_height - 10)
draw.rectangle((x, y, image.width, image.height), fill="white")
draw.text((x, y), print_msg, fill="black", font=font)
del draw, font
return image
#@torch.no_grad()
def __generate_test_samples(pipe, prompts, gs, log_writer, log_folder, random_captions=False, resolution=512):
"""
generates samples at different cfg scales and saves them to disk
"""
logging.info(f"Generating samples gs:{gs}, for {prompts}")
#with torch.inference_mode(), suppress_stdout():
#with autocast():
i = 0
for prompt in prompts:
if prompt is None or len(prompt) < 2:
logging.warning("empty prompt in sample prompts, check your prompts file")
continue
images = []
for cfg in [7.0, 4.0, 1.01]:
image = __generate_sample(pipe, prompt, cfg, resolution=resolution)
images.append(image)
width = 0
height = 0
for image in images:
width += image.width
height = max(height, image.height)
result = Image.new('RGB', (width, height))
x_offset = 0
for image in images:
result.paste(image, (x_offset, 0))
x_offset += image.width
result.save(f"{log_folder}/samples/gs{gs:05}-{prompt[:150]}.png")
tfimage = transforms.ToTensor()(result)
if random_captions:
log_writer.add_image(tag=f"sample_{i}", img_tensor=tfimage, global_step=gs)
i += 1
else:
log_writer.add_image(tag=f"sample_{prompt[:150]}", img_tensor=tfimage, global_step=gs)
del result
del tfimage
del images
try:
hf_ckpt_path = convert_to_hf(args.resume_ckpt)
text_encoder = CLIPTextModel.from_pretrained(hf_ckpt_path, subfolder="text_encoder", torch_dtype=torch.float32)
vae = AutoencoderKL.from_pretrained(hf_ckpt_path, subfolder="vae", torch_dtype=torch.float32)
unet = UNet2DConditionModel.from_pretrained(hf_ckpt_path, subfolder="unet", torch_dtype=torch.float32)
scheduler = DDIMScheduler.from_pretrained(hf_ckpt_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(hf_ckpt_path, subfolder="tokenizer", use_fast=False)
except:
logging.ERROR(" * Failed to load checkpoint *")
if is_xformers_available():
try:
unet.enable_xformers_memory_efficient_attention()
logging.info(" Enabled memory efficient attention (xformers)")
except Exception as e:
logging.warning(
"Could not enable memory efficient attention. Make sure xformers is installed"
f" correctly and a GPU is available: {e}"
)
default_lr = 2e-6 if args.useadam8bit else 2e-6
lr = args.lr if args.lr is not None else default_lr
vae = vae.to(torch.device("cuda"), dtype=torch.float16 if args.sd1 else torch.float32)
unet = unet.to(torch.device("cuda"))
text_encoder = text_encoder.to(torch.device("cuda"))
if args.disable_textenc_training:
logging.info(f"{Fore.CYAN} * NOT Training Text Encoder, quality reduced *{Style.RESET_ALL}")
params_to_train = itertools.chain(unet.parameters())
text_encoder.eval()
else:
logging.info(f"{Fore.CYAN} * Training Text Encoder *{Style.RESET_ALL}")
params_to_train = itertools.chain(unet.parameters(), text_encoder.parameters())
betas = (0.9, 0.999)
epsilon = 1e-8 if args.mixed_precision == "NO" else 1e-7
weight_decay = 0.01
if args.useadam8bit:
logging.info(f"{Fore.CYAN} * Using AdamW 8-bit Optimizer *{Style.RESET_ALL}")
import bitsandbytes as bnb
optimizer = bnb.optim.AdamW8bit(
itertools.chain(params_to_train),
lr=lr,
betas=betas,
eps=epsilon,
weight_decay=weight_decay,
)
else:
logging.info(f"{Fore.CYAN} * Using AdamW8 standard Optimizer *{Style.RESET_ALL}")
optimizer = torch.optim.AdamW(
itertools.chain(params_to_train),
lr=lr,
betas=betas,
eps=epsilon,
weight_decay=weight_decay,
amsgrad=False,
)
log_optimizer(optimizer, betas, epsilon)
train_batch = EveryDreamBatch(
data_root=args.data_root,
flip_p=0.0,
debug_level=1,
batch_size=args.batch_size,
conditional_dropout=0.03,
resolution=args.resolution,
tokenizer=tokenizer,
)
lr_warmup_steps = int(args.lr_decay_steps / 20) if args.lr_warmup_steps is None else args.lr_warmup_steps
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=lr_warmup_steps * _GRAD_ACCUM_STEPS,
num_training_steps=args.lr_decay_steps * _GRAD_ACCUM_STEPS,
)
# read prompts from prompts_file.txt
sample_prompts = []
with open(args.sample_prompts, "r") as f:
for line in f:
sample_prompts.append(line.strip())
log_folder = os.path.join("logs", f"{args.project_name}{log_time}")
if False: #args.wandb is not None and args.wandb: # not yet supported
log_writer = wandb.init(project="EveryDream2FineTunes",
name=args.project_name,
dir=log_folder,
)
else:
log_writer = SummaryWriter(log_dir=log_folder,
flush_secs=5,
comment="EveryDream2FineTunes",
)
def log_args(log_writer, args):
arglog = "args:\n"
for arg, value in sorted(vars(args).items()):
arglog += f"{arg}={value}, "
log_writer.add_text("config", arglog)
log_args(log_writer, args)
args.clip_skip = max(min(2, args.clip_skip), 0)
"""
Train the model
"""
print(f" {Fore.LIGHTGREEN_EX}** Welcome to EveryDream trainer 2.0!**{Style.RESET_ALL}")
print(f" (C) 2022 Victor C Hall This program is licensed under AGPL 3.0 https://www.gnu.org/licenses/agpl-3.0.en.html")
print()
print("** Trainer Starting **")
global interrupted
interrupted = False
def sigterm_handler(signum, frame):
"""
handles sigterm
"""
global interrupted
if not interrupted:
interrupted=True
global global_step
#TODO: save model on ctrl-c
interrupted_checkpoint_path = os.path.join(f"logs/{log_folder}/interrupted-gs{global_step}.ckpt")
print()
logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, exiting{Style.RESET_ALL}")
logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
save_path = os.path.join(f"logs/interrupted.ckpt")
#__save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, scheduler, vae)
exit(_SIGTERM_EXIT_CODE)
signal.signal(signal.SIGINT, sigterm_handler)
if not os.path.exists(f"{log_folder}/samples/"):
os.makedirs(f"{log_folder}/samples/")
gpu_used_mem, gpu_total_mem = gpu.get_gpu_memory()
logging.info(f" Pretraining GPU Memory: {gpu_used_mem} / {gpu_total_mem} MB")
logging.info(f" saving ckpts every {args.ckpt_every_n_minutes} minutes")
scaler = torch.cuda.amp.GradScaler(
enabled=False,
#enabled=True if args.sd1 else False,
init_scale=2**16,
growth_factor=1.000001,
backoff_factor=0.9999999,
growth_interval=50,
)
logging.info(f" Grad scaler enabled: {scaler.is_enabled()}")
def collate_fn(batch):
"""
Collates batches
"""
images = [example["image"] for example in batch]
captions = [example["caption"] for example in batch]
tokens = [example["tokens"] for example in batch]
images = torch.stack(images)
images = images.to(memory_format=torch.contiguous_format).float()
batch = {
"tokens": torch.stack(tuple(tokens)),
"image": images,
"captions": captions,
}
return batch
train_dataloader = torch.utils.data.DataLoader(
train_batch,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
collate_fn=collate_fn
)
total_batch_size = args.batch_size * _GRAD_ACCUM_STEPS
epoch_len = math.ceil(len(train_batch) / args.batch_size)
unet.train()
text_encoder.requires_grad_(True)
text_encoder.train()
logging.info(f" unet device: {unet.device}, precision: {unet.dtype}, training: {unet.training}")
logging.info(f" text_encoder device: {text_encoder.device}, precision: {text_encoder.dtype}, training: {text_encoder.training}")
logging.info(f" vae device: {vae.device}, precision: {vae.dtype}, training: {vae.training}")
logging.info(f" scheduler: {scheduler.__class__}")
logging.info(f" {Fore.GREEN}Project name: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{args.project_name}{Style.RESET_ALL}")
logging.info(f" {Fore.GREEN}grad_accum: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{_GRAD_ACCUM_STEPS}{Style.RESET_ALL}"),
logging.info(f" {Fore.GREEN}batch_size: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{args.batch_size}{Style.RESET_ALL}")
logging.info(f" {Fore.GREEN}total_batch_size: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{total_batch_size}")
logging.info(f" {Fore.GREEN}epoch_len: {Fore.LIGHTGREEN_EX}{epoch_len}{Style.RESET_ALL}")
epoch_pbar = tqdm(range(args.max_epochs), position=0)
epoch_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Epochs{Style.RESET_ALL}")
steps_pbar = tqdm(range(epoch_len), position=1)
steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}")
epoch_times = []
global global_step
global_step = 0
training_start_time = time.time()
last_epoch_saved_time = training_start_time
# (global_step: int, epoch_pbar, gpu, log_writer, **logs):
append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer)
torch.cuda.empty_cache()
try:
for epoch in range(args.max_epochs):
if epoch > 0 and epoch % args.save_every_n_epochs == 0:
logging.info(f" Saving model")
save_path = os.path.join(f"logs/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
__save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae)
epoch_start_time = time.time()
steps_pbar.reset()
images_per_sec_epoch = []
#for step, batch in enumerate(self.ctx.train_dataloader):
for step, batch in enumerate(train_dataloader):
step_start_time = time.time()
with torch.no_grad():
with autocast():
pixel_values = batch["image"].to(memory_format=torch.contiguous_format).to(unet.device)
latents = vae.encode(pixel_values, return_dict=False)
latent = latents[0]
latents = latent.sample()
latents = latents * 0.18215
noise = torch.randn_like(latents)
bsz = latents.shape[0]
timesteps = torch.randint(0, scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
timesteps = timesteps.long()
cuda_caption = batch["tokens"].to(text_encoder.device)
encoder_hidden_states = text_encoder(cuda_caption)
# if clip_skip > 0: #TODO
# encoder_hidden_states = encoder_hidden_states['last_hidden_state'][-clip_skip]
noisy_latents = scheduler.add_noise(latents, noise, timesteps)
if scheduler.config.prediction_type == "epsilon":
target = noise
elif scheduler.config.prediction_type in ["v_prediction", "v-prediction"]:
target = scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {scheduler.config.prediction_type}")
#del noise, latents
#with torch.cuda.amp.autocast(enabled=lowvram):
with autocast(): # xformers requires fp16
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states.last_hidden_state).sample
with autocast(enabled=args.sd1):
loss = torch_functional.mse_loss(model_pred.float(), target.float(), reduction="mean")
#del timesteps, encoder_hidden_states, noisy_latents
if args.clip_grad_norm is not None:
torch.nn.utils.clip_grad_norm_(parameters=unet.parameters(), max_norm=args.clip_grad_norm)
torch.nn.utils.clip_grad_norm_(parameters=text_encoder.parameters(), max_norm=args.clip_grad_norm)
#with torch.cuda.amp(enabled=False):
#if args.mixed_precision in ['bf16','fp16']:
if args.sd1:
with autocast():
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
steps_pbar.update(1)
global_step += 1
images_per_sec = args.batch_size / (time.time() - step_start_time)
images_per_sec_epoch.append(images_per_sec)
#with torch.no_grad():
if (global_step + 1) % args.log_step == 0:
lr = lr_scheduler.get_last_lr()[0]
logs = {"loss/step": loss.detach().item(), "lr": lr, "img/s": images_per_sec, "scale": scaler.get_scale()}
log_writer.add_scalar(tag="loss/step", scalar_value=loss, global_step=global_step)
log_writer.add_scalar(tag="hyperparamater/lr", scalar_value=lr, global_step=global_step)
sum_img = sum(images_per_sec_epoch)
avg = sum_img / len(images_per_sec_epoch)
images_per_sec_epoch = []
log_writer.add_scalar(tag="hyperparamater/grad scale", scalar_value=scaler.get_scale(), global_step=global_step)
log_writer.add_scalar(tag="performance/images per second", scalar_value=avg, global_step=global_step)
append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer, **logs)
if (global_step + 1) % args.sample_steps == 0:
#(unet, text_encoder, tokenizer, scheduler):
pipe = __create_inference_pipe(unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler, vae=vae)
pipe = pipe.to(torch.device("cuda"))
with torch.no_grad():
if sample_prompts is not None and len(sample_prompts) > 0 and len(sample_prompts[0]) > 1:
#(pipe, prompts, gs, log_writer, log_folder, random_captions=False):
__generate_test_samples(pipe=pipe, prompts=sample_prompts, log_writer=log_writer, log_folder=log_folder, gs=global_step, resolution=args.resolution)
else:
max_prompts = min(4,len(batch["captions"]))
prompts=batch["captions"][:max_prompts]
__generate_test_samples(pipe=pipe, prompts=prompts, log_writer=log_writer, log_folder=log_folder, gs=global_step, random_captions=True)
del pipe
torch.cuda.empty_cache()
min_since_last_ckpt = (time.time() - last_epoch_saved_time) / 60
if args.ckpt_every_n_minutes is not None and (min_since_last_ckpt > args.ckpt_every_n_minutes):
last_epoch_saved_time = time.time()
logging.info(f"Saving model at {args.ckpt_every_n_minutes} mins at step {global_step}")
save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}")
__save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae)
# end of step
# end of epoch
elapsed_epoch_time = (time.time() - epoch_start_time) / 60
epoch_times.append(dict(epoch=epoch, time=elapsed_epoch_time))
log_writer.add_scalar("performance/minutes per epoch", elapsed_epoch_time, global_step)
epoch_pbar.update(1)
# end of training
save_path = os.path.join(f"{log_folder}/ckpts/last-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
__save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae)
total_elapsed_time = time.time() - training_start_time
logging.info(f"{Fore.CYAN}Training complete{Style.RESET_ALL}")
logging.info(f"Total training time took {total_elapsed_time:.2f} seconds, total steps: {global_step}")
logging.info(f"Average epoch time: {np.mean([t['time'] for t in epoch_times]) / 60:.2f} minutes")
except Exception as ex:
logging.error(f"{Fore.LIGHTYELLOW_EX}Something went wrong, attempting to save model{Style.RESET_ALL}")
save_path = os.path.join(f"{log_folder}/ckpts/errored-{args.project_name}-ep{epoch:02}-gs{global_step:05}")
__save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae)
raise ex
logging.info(f"{Fore.LIGHTWHITE_EX} *Finished training *{Style.RESET_ALL}")
if __name__ == "__main__":
supported_resolutions = [512, 576, 640, 704, 768, 832, 896, 960, 1024]
argparser = argparse.ArgumentParser(description="EveryDream Training options")
argparser.add_argument("--resume_ckpt", type=str, required=True, default="sd_v1-5_vae.ckpt")
argparser.add_argument("--lr_scheduler", type=str, default="constant", help="LR scheduler, (default: constant)", choices=["constant", "linear", "cosine", "polynomial"])
argparser.add_argument("--lr_warmup_steps", type=int, default=None, help="Steps to reach max LR during warmup (def: 0.10x of lr_decay_steps), nonfunctional for constant scheduler")
argparser.add_argument("--lr_decay_steps", type=int, default=1500, help="Steps to reach minimum LR")
argparser.add_argument("--log_step", type=int, default=25, help="How often to log training stats, def: 25, recommend default")
argparser.add_argument("--max_epochs", type=int, default=300, help="Maximum number of epochs to train for")
argparser.add_argument("--ckpt_every_n_minutes", type=int, default=20, help="Save checkpoint every n minutes, def: 20")
argparser.add_argument("--save_every_n_epochs", type=int, default=9999, help="Save checkpoint every n epochs, def: 9999")
argparser.add_argument("--lr", type=float, default=None, help="Learning rate, if using scheduler is maximum LR at top of curve")
argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer")
argparser.add_argument("--project_name", type=str, default="myproj", help="Project name for logs and checkpoints, ex. 'tedbennett', 'superduperV1'")
argparser.add_argument("--sample_prompts", type=str, default="sample_prompts.txt", help="File with prompts to generate test samples from (def: sample_prompts.txt)")
argparser.add_argument("--sample_steps", type=int, default=250, help="Number of steps between samples (def: 250)")
argparser.add_argument("--disable_textenc_training", action="store_true", default=False, help="disables training of text encoder (def: False)")
argparser.add_argument("--batch_size", type=int, default=2, help="Batch size (def: 2)")
argparser.add_argument("--clip_grad_norm", type=float, default=None, help="Clip gradient norm (def: disabled) (ex: 1.5), useful if loss=nan?")
argparser.add_argument("--grad_accum", type=int, default=1, help="NONFUNCTIONING. Gradient accumulation factor (def: 1), (ex, 2)")
argparser.add_argument("--clip_skip", type=int, default=0, help="NONFUNCTIONING. Train using penultimate layers (def: 0)", choices=[0, 1, 2])
argparser.add_argument("--data_root", type=str, default="input", help="folder where your training images are")
argparser.add_argument("--mixed_precision", default="no", help="NONFUNCTIONING. precision, (default: NO for fp32)", choices=["NO", "fp16", "bf16"])
argparser.add_argument("--wandb", action="store_true", default=False, help="enable wandb logging instead of tensorboard, requires env var WANDB_API_KEY")
argparser.add_argument("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
argparser.add_argument("--resolution", type=int, default=512, help="resolution to train", choices=supported_resolutions)
argparser.add_argument("--sd1", action="store_true", default=False, help="set if training SD1.x, else SD2 is assumed")
args = argparser.parse_args()
main(args)

27
utils/get_yamls.py Normal file
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@ -0,0 +1,27 @@
import sys
import requests
_V2V_URL = ["v2-inference-v.yaml","https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"]
_V2_URL = ["v2-inference.yaml","https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml"]
_V1_URL = ["v1-inference.yaml","https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"]
# download https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml
def download_all():
list = [_V2V_URL,_V2_URL,_V1_URL]
for file in list:
get_yaml(file)
def get_yaml(file):
res = requests.request(method="GET", url=file[1])
with open(file[0],"wb") as f:
f.write(res.content)
print(f" downloaded: {file[0]}")
def isWindows():
return sys.platform.startswith('win')
if __name__ == '__main__':
download_all()
print("SD1.x and SD2.x yamls downloaded")

30
utils/gpu.py Normal file
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@ -0,0 +1,30 @@
"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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 pynvml.smi import nvidia_smi
class GPU:
def __init__(self):
self.nvsmi = nvidia_smi.getInstance()
def get_gpu_memory(self):
"""
returns a tuple of [gpu_used_mem, gpu_total_mem]
"""
gpu_query = self.nvsmi.DeviceQuery('memory.used, memory.total')
#print(gpu_query)
gpu_used_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['used'])
gpu_total_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['total'])
return gpu_used_mem, gpu_total_mem

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utils/patch_bnb.py Normal file
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"""
Copyright [2022] Victor C Hall
Licensed under the GNU Affero General Public License;
You may not use this code except in compliance with the License.
You may obtain a copy of the License at
https://www.gnu.org/licenses/agpl-3.0.en.html
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.
"""
# see: https://github.com/TimDettmers/bitsandbytes/issues/30 for explanation
import sys
import os
from subprocess import check_output
import shutil
_CEXT_PATCH = " self.lib = ct.cdll.LoadLibrary(str(binary_path))"
_MAIN_PATCH = " return 'libbitsandbytes_cuda116.dll'"
def patch_main():
bnbpath_main = "venv/Lib/site-packages/bitsandbytes/cuda_setup/main.py"
try:
with open(bnbpath_main, "r") as f:
contents = f.read()
contents = contents.split('\n')
except Exception as ex:
print(f"cannot find bitsandbytes install, aborting, error: {ex}")
return False
main_patched = False
for i, line in enumerate(contents):
if i == 112:
if line != _MAIN_PATCH:
contents[i] = _MAIN_PATCH
main_patched = True
else:
print(" *** Already patched!")
main_patched = True
assert main_patched, "unable to patch bitsandbytes, may be mismatched version, requires 0.35.0"
with open(bnbpath_main, "w") as f:
for line in contents:
f.write(line + "\n")
#print(contents)
return main_patched
def patch_cext():
bnbpath_cextension = "venv/Lib/site-packages/bitsandbytes/cextension.py"
try:
with open(bnbpath_cextension, "r") as f:
contents = f.read()
contents = contents.split('\n')
except Exception as ex:
print(f"cannot find bitsandbytes install, aborting, error: {ex}")
return False
cext_patched = False
for i, line in enumerate(contents):
# update both lines 28 and 31 to be sure correct dll is returned
if (i == 30 or i == 27):
if line != _CEXT_PATCH:
contents[i] = _CEXT_PATCH
cext_patched = True
else:
cext_patched = True
assert cext_patched, "unable to patch bitsandbytes, died midprocess, something broke and may need to reinstall bitsandbytes==0.35.0"
with open(bnbpath_cextension, "w") as f:
for line in contents:
f.write(line + "\n")
#print(contents)
return cext_patched
def iswindows():
return sys.platform.startswith('win')
def error():
print("Somethnig went wrong trying to patch bitsandbytes, aborting")
print("make sure your venv is activated and try again")
print("or if activated try: ")
print(" pip install bitsandbytes==0.35.0")
raise RuntimeError("** FATAL ERROR: unable to patch bitsandbytes for Windows env")
def check_dlls():
dll_exists = os.path.exists("venv/Lib/site-packages/bitsandbytes/libbitsandbytes_cuda116.dll")
if not dll_exists:
if not os.path.exists("tmp/bnb_cache"):
check_output("git clone https://github.com/DeXtmL/bitsandbytes-win-prebuilt tmp/bnb_cache", shell=True)
shutil.copy("tmp/bnb_cache/libbitsandbytes_cuda116.dll", "venv/Lib/site-packages/bitsandbytes/libbitsandbytes_cuda116.dll")
dll_exists = os.path.exists("venv/Lib/site-packages/bitsandbytes/libbitsandbytes_cuda116.dll")
return dll_exists
def main():
"""
applies a patch for windows compatibility for bitsandbytes 0.35.0 for using their AdamW8bit optimizer
"""
if iswindows():
print()
print(" *** Applying bitsandbytes patch for windows ***")
if not check_dlls():
print("unable to find bitsandbytes dll or clone them from git, aborting")
raise RuntimeError("** FATAL ERROR: unable to patch bitsandbytes for Windows env")
main_patched = patch_main()
cext_patched = patch_cext()
if main_patched and cext_patched:
try:
print(" *************************************************************")
print(" *** bitsandbytes windows patch applied, attempting import *** ")
import bitsandbytes
print(f" *** bitsandbytes patch succeeded, everything looks good! ***")
except:
error()
else:
error()
else:
print(" *** not using windows environment, skipping bitsandbytes patch ***")
return
if __name__ == "__main__":
main()