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Modified data loader - train_dreambooth_lora_sdxl.py
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train_dreambooth_lora_sdxl.py
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. 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
import argparse
import gc
import itertools
import logging
import math
import os
import shutil
import warnings
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import DistributedDataParallelKwargs, ProjectConfiguration, set_seed
from huggingface_hub import create_repo, upload_folder
from huggingface_hub.utils import insecure_hashlib
from packaging import version
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import AutoTokenizer, PretrainedConfig
import diffusers
from diffusers import (
AutoencoderKL,
DDPMScheduler,
DPMSolverMultistepScheduler,
StableDiffusionXLPipeline,
UNet2DConditionModel,
)
from diffusers.loaders import LoraLoaderMixin
from diffusers.optimization import get_scheduler
from diffusers.training_utils import compute_snr
from diffusers.utils import check_min_version, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.25.0.dev0")
logger = get_logger(__name__)
def save_model_card(
repo_id: str,
images=None,
base_model=str,
train_text_encoder=False,
instance_prompt=str,
validation_prompt=str,
repo_folder=None,
vae_path=None,
):
img_str = "widget:\n" if images else ""
for i, image in enumerate(images):
image.save(os.path.join(repo_folder, f"image_{i}.png"))
img_str += f"""
- text: '{validation_prompt if validation_prompt else ' ' }'
output:
url:
"image_{i}.png"
"""
yaml = f"""
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
{img_str}
base_model: {base_model}
instance_prompt: {instance_prompt}
license: openrail++
---
"""
model_card = f"""
# SDXL LoRA DreamBooth - {repo_id}
<Gallery />
## Model description
These are {repo_id} LoRA adaption weights for {base_model}.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: {train_text_encoder}.
Special VAE used for training: {vae_path}.
## Trigger words
You should use {instance_prompt} to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download]({repo_id}/tree/main) them in the Files & versions tab.
"""
with open(os.path.join(repo_folder, "README.md"), "w") as f:
f.write(yaml + model_card)
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--pretrained_vae_model_name_or_path",
type=str,
default=None,
help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) containing the training data of instance images (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that 🤗 Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--instance_data_dir",
type=str,
default=None,
help=("A folder containing the training data. "),
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--image_column",
type=str,
default="image",
help="The column of the dataset containing the target image. By "
"default, the standard Image Dataset maps out 'file_name' "
"to 'image'.",
)
parser.add_argument(
"--caption_column",
type=str,
default=None,
help="The column of the dataset containing the instance prompt for each image",
)
parser.add_argument("--repeats", type=int, default=1, help="How many times to repeat the training data.")
parser.add_argument(
"--class_data_dir",
type=str,
default=None,
required=False,
help="A folder containing the training data of class images.",
)
parser.add_argument(
"--instance_prompt",
type=str,
default=None,
required=True,
help="The prompt with identifier specifying the instance, e.g. 'photo of a TOK dog', 'in the style of TOK'",
)
parser.add_argument(
"--class_prompt",
type=str,
default=None,
help="The prompt to specify images in the same class as provided instance images.",
)
parser.add_argument(
"--validation_prompt",
type=str,
default=None,
help="A prompt that is used during validation to verify that the model is learning.",
)
parser.add_argument(
"--num_validation_images",
type=int,
default=4,
help="Number of images that should be generated during validation with `validation_prompt`.",
)
parser.add_argument(
"--validation_epochs",
type=int,
default=50,
help=(
"Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
" `args.validation_prompt` multiple times: `args.num_validation_images`."
),
)
parser.add_argument(
"--with_prior_preservation",
default=False,
action="store_true",
help="Flag to add prior preservation loss.",
)
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
parser.add_argument(
"--num_class_images",
type=int,
default=100,
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="lora-dreambooth-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(
"--resolution",
type=int,
default=1024,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--crops_coords_top_left_h",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--crops_coords_top_left_w",
type=int,
default=0,
help=("Coordinate for (the height) to be included in the crop coordinate embeddings needed by SDXL UNet."),
)
parser.add_argument(
"--center_crop",
default=False,
action="store_true",
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
" checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--text_encoder_lr",
type=float,
default=5e-6,
help="Text encoder learning rate to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--snr_gamma",
type=float,
default=None,
help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
"More details here: https://arxiv.org/abs/2303.09556.",
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--lr_num_cycles",
type=int,
default=1,
help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
)
parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument(
"--optimizer",
type=str,
default="AdamW",
help=('The optimizer type to use. Choose between ["AdamW", "prodigy"]'),
)
parser.add_argument(
"--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes. Ignored if optimizer is not set to AdamW",
)
parser.add_argument(
"--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam and Prodigy optimizers."
)
parser.add_argument(
"--prodigy_beta3",
type=float,
default=None,
help="coefficients for computing the Prodidy stepsize using running averages. If set to None, "
"uses the value of square root of beta2. Ignored if optimizer is adamW",
)
parser.add_argument("--prodigy_decouple", type=bool, default=True, help="Use AdamW style decoupled weight decay")
parser.add_argument("--adam_weight_decay", type=float, default=1e-04, help="Weight decay to use for unet params")
parser.add_argument(
"--adam_weight_decay_text_encoder", type=float, default=1e-03, help="Weight decay to use for text_encoder"
)
parser.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer and Prodigy optimizers.",
)
parser.add_argument(
"--prodigy_use_bias_correction",
type=bool,
default=True,
help="Turn on Adam's bias correction. True by default. Ignored if optimizer is adamW",
)
parser.add_argument(
"--prodigy_safeguard_warmup",
type=bool,
default=True,
help="Remove lr from the denominator of D estimate to avoid issues during warm-up stage. True by default. "
"Ignored if optimizer is adamW",
)
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--prior_generation_precision",
type=str,
default=None,
choices=["no", "fp32", "fp16", "bf16"],
help=(
"Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument(
"--rank",
type=int,
default=4,
help=("The dimension of the LoRA update matrices."),
)
if input_args is not None:
args = parser.parse_args(input_args)
else:
args = parser.parse_args()
if args.dataset_name is None and args.instance_data_dir is None:
raise ValueError("Specify either `--dataset_name` or `--instance_data_dir`")
if args.dataset_name is not None and args.instance_data_dir is not None:
raise ValueError("Specify only one of `--dataset_name` or `--instance_data_dir`")
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.with_prior_preservation:
if args.class_data_dir is None:
raise ValueError("You must specify a data directory for class images.")
if args.class_prompt is None:
raise ValueError("You must specify prompt for class images.")
else:
# logger is not available yet
if args.class_data_dir is not None:
warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
if args.class_prompt is not None:
warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
return args
def load_image(example):
image_path = f"{args.dataset_name}/{example['file_name']}"
example['image'] = Image.open(image_path).convert('RGB')
return example
class DreamBoothDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
class_prompt,
class_data_root=None,
class_num=None,
size=1024,
repeats=1,
center_crop=False,
):
self.size = size
self.center_crop = center_crop
self.instance_prompt = instance_prompt
self.custom_instance_prompts = None
self.class_prompt = class_prompt
# if --dataset_name is provided or a metadata jsonl file is provided in the local --instance_data directory,
# we load the training data using load_dataset
if args.dataset_name is not None:
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"You are trying to load your data using the datasets library. If you wish to train using custom "
"captions please install the datasets library: `pip install datasets`. If you wish to load a "
"local folder containing images only, specify --instance_data_dir instead."
)
# Downloading and loading a dataset from the hub.
# See more about loading custom images at
# https://huggingface.co/docs/datasets/v2.0.0/en/dataset_script
dataset = load_dataset(
"json",
data_files=f"{args.dataset_name}/metadata.jsonl",
cache_dir=args.cache_dir,
)
dataset = dataset.map(load_image)
# Preprocessing the datasets.
column_names = dataset["train"].column_names
# 6. Get the column names for input/target.
if args.image_column is None:
image_column = column_names[0]
logger.info(f"image column defaulting to {image_column}")
else:
image_column = args.image_column
if image_column not in column_names:
raise ValueError(
f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
instance_images = dataset["train"][image_column]
if args.caption_column is None:
logger.info(
"No caption column provided, defaulting to instance_prompt for all images. If your dataset "
"contains captions/prompts for the images, make sure to specify the "
"column as --caption_column"
)
self.custom_instance_prompts = None
else:
if args.caption_column not in column_names:
raise ValueError(
f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}"
)
custom_instance_prompts = dataset["train"][args.caption_column]
# create final list of captions according to --repeats
self.custom_instance_prompts = []
for caption in custom_instance_prompts:
self.custom_instance_prompts.extend(itertools.repeat(caption, repeats))
else:
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError("Instance images root doesn't exists.")
instance_images = [Image.open(path) for path in list(Path(instance_data_root).iterdir())]
self.custom_instance_prompts = None
self.instance_images = []
for img in instance_images:
self.instance_images.extend(itertools.repeat(img, repeats))
self.num_instance_images = len(self.instance_images)
self._length = self.num_instance_images
if class_data_root is not None:
self.class_data_root = Path(class_data_root)
self.class_data_root.mkdir(parents=True, exist_ok=True)
self.class_images_path = list(self.class_data_root.iterdir())
if class_num is not None:
self.num_class_images = min(len(self.class_images_path), class_num)
else:
self.num_class_images = len(self.class_images_path)
self._length = max(self.num_class_images, self.num_instance_images)
else:
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
def __getitem__(self, index):
example = {}
instance_image = self.instance_images[index % self.num_instance_images]
instance_image = exif_transpose(instance_image)
if not instance_image.mode == "RGB":
instance_image = instance_image.convert("RGB")
example["instance_images"] = self.image_transforms(instance_image)
if self.custom_instance_prompts:
caption = self.custom_instance_prompts[index % self.num_instance_images]
if caption:
example["instance_prompt"] = caption
else:
example["instance_prompt"] = self.instance_prompt
else: # costum prompts were provided, but length does not match size of image dataset
example["instance_prompt"] = self.instance_prompt
if self.class_data_root:
class_image = Image.open(self.class_images_path[index % self.num_class_images])
class_image = exif_transpose(class_image)
if not class_image.mode == "RGB":
class_image = class_image.convert("RGB")
example["class_images"] = self.image_transforms(class_image)
example["class_prompt"] = self.class_prompt
return example
def collate_fn(examples, with_prior_preservation=False):
pixel_values = [example["instance_images"] for example in examples]
prompts = [example["instance_prompt"] for example in examples]
# Concat class and instance examples for prior preservation.
# We do this to avoid doing two forward passes.
if with_prior_preservation:
pixel_values += [example["class_images"] for example in examples]
prompts += [example["class_prompt"] for example in examples]
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
batch = {"pixel_values": pixel_values, "prompts": prompts}
return batch
class PromptDataset(Dataset):
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
def __init__(self, prompt, num_samples):
self.prompt = prompt
self.num_samples = num_samples
def __len__(self):
return self.num_samples
def __getitem__(self, index):
example = {}
example["prompt"] = self.prompt
example["index"] = index
return example
def tokenize_prompt(tokenizer, prompt):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
return text_input_ids
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
prompt_embeds_list = []
for i, text_encoder in enumerate(text_encoders):
if tokenizers is not None:
tokenizer = tokenizers[i]
text_input_ids = tokenize_prompt(tokenizer, prompt)
else:
assert text_input_ids_list is not None
text_input_ids = text_input_ids_list[i]
prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
)
# We are only ALWAYS interested in the pooled output of the final text encoder
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return prompt_embeds, pooled_prompt_embeds
def main(args):
logging_dir = Path(args.output_dir, args.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
kwargs_handlers=[kwargs],
)
if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
import wandb
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Generate class images if prior preservation is enabled.
if args.with_prior_preservation:
class_images_dir = Path(args.class_data_dir)
if not class_images_dir.exists():
class_images_dir.mkdir(parents=True)
cur_class_images = len(list(class_images_dir.iterdir()))
if cur_class_images < args.num_class_images:
torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
if args.prior_generation_precision == "fp32":
torch_dtype = torch.float32
elif args.prior_generation_precision == "fp16":
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
revision=args.revision,
variant=args.variant,
)
pipeline.set_progress_bar_config(disable=True)
num_new_images = args.num_class_images - cur_class_images
logger.info(f"Number of class images to sample: {num_new_images}.")
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
sample_dataloader = accelerator.prepare(sample_dataloader)
pipeline.to(accelerator.device)
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if args.push_to_hub:
repo_id = create_repo(
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
).repo_id
# Load the tokenizers
tokenizer_one = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
use_fast=False,
)
tokenizer_two = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=args.revision,
use_fast=False,
)
# import correct text encoder classes
text_encoder_cls_one = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision
)
text_encoder_cls_two = import_model_class_from_model_name_or_path(
args.pretrained_model_name_or_path, args.revision, subfolder="text_encoder_2"
)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant
)
vae_path = (
args.pretrained_model_name_or_path
if args.pretrained_vae_model_name_or_path is None
else args.pretrained_vae_model_name_or_path
)
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
variant=args.variant,
)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, variant=args.variant
)
# We only train the additional adapter LoRA layers
vae.requires_grad_(False)
text_encoder_one.requires_grad_(False)
text_encoder_two.requires_grad_(False)
unet.requires_grad_(False)
# For mixed precision training we cast all non-trainable weights (vae, non-lora text_encoder and non-lora unet) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move unet, vae and text_encoder to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
# The VAE is always in float32 to avoid NaN losses.
vae.to(accelerator.device, dtype=torch.float32)
text_encoder_one.to(accelerator.device, dtype=weight_dtype)
text_encoder_two.to(accelerator.device, dtype=weight_dtype)
if args.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, "
"please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
if args.train_text_encoder:
text_encoder_one.gradient_checkpointing_enable()
text_encoder_two.gradient_checkpointing_enable()
# now we will add new LoRA weights to the attention layers
unet_lora_config = LoraConfig(
r=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"]
)
unet.add_adapter(unet_lora_config)
# The text encoder comes from 🤗 transformers, so we cannot directly modify it.
# So, instead, we monkey-patch the forward calls of its attention-blocks.
if args.train_text_encoder:
text_lora_config = LoraConfig(
r=args.rank, init_lora_weights="gaussian", target_modules=["q_proj", "k_proj", "v_proj", "out_proj"]
)
text_encoder_one.add_adapter(text_lora_config)
text_encoder_two.add_adapter(text_lora_config)
# Make sure the trainable params are in float32.
if args.mixed_precision == "fp16":
models = [unet]
if args.train_text_encoder:
models.extend([text_encoder_one, text_encoder_two])
for model in models:
for param in model.parameters():
# only upcast trainable parameters (LoRA) into fp32
if param.requires_grad:
param.data = param.to(torch.float32)
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
# there are only two options here. Either are just the unet attn processor layers
# or there are the unet and text encoder atten layers
unet_lora_layers_to_save = None
text_encoder_one_lora_layers_to_save = None
text_encoder_two_lora_layers_to_save = None
for model in models:
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_lora_layers_to_save = get_peft_model_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_lora_layers_to_save = get_peft_model_state_dict(model)
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_lora_layers_to_save = get_peft_model_state_dict(model)
else:
raise ValueError(f"unexpected save model: {model.__class__}")
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
StableDiffusionXLPipeline.save_lora_weights(
output_dir,
unet_lora_layers=unet_lora_layers_to_save,
text_encoder_lora_layers=text_encoder_one_lora_layers_to_save,
text_encoder_2_lora_layers=text_encoder_two_lora_layers_to_save,
)
def load_model_hook(models, input_dir):
unet_ = None
text_encoder_one_ = None
text_encoder_two_ = None
while len(models) > 0:
model = models.pop()
if isinstance(model, type(accelerator.unwrap_model(unet))):
unet_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_one))):
text_encoder_one_ = model
elif isinstance(model, type(accelerator.unwrap_model(text_encoder_two))):
text_encoder_two_ = model
else:
raise ValueError(f"unexpected save model: {model.__class__}")
lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
LoraLoaderMixin.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
text_encoder_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_one_
)
text_encoder_2_state_dict = {k: v for k, v in lora_state_dict.items() if "text_encoder_2." in k}
LoraLoaderMixin.load_lora_into_text_encoder(
text_encoder_2_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_two_
)
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if args.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if args.scale_lr:
args.learning_rate = (
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
)
unet_lora_parameters = list(filter(lambda p: p.requires_grad, unet.parameters()))
if args.train_text_encoder:
text_lora_parameters_one = list(filter(lambda p: p.requires_grad, text_encoder_one.parameters()))
text_lora_parameters_two = list(filter(lambda p: p.requires_grad, text_encoder_two.parameters()))
# Optimization parameters
unet_lora_parameters_with_lr = {"params": unet_lora_parameters, "lr": args.learning_rate}
if args.train_text_encoder:
# different learning rate for text encoder and unet
text_lora_parameters_one_with_lr = {
"params": text_lora_parameters_one,
"weight_decay": args.adam_weight_decay_text_encoder,
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
}
text_lora_parameters_two_with_lr = {
"params": text_lora_parameters_two,
"weight_decay": args.adam_weight_decay_text_encoder,
"lr": args.text_encoder_lr if args.text_encoder_lr else args.learning_rate,
}
params_to_optimize = [
unet_lora_parameters_with_lr,
text_lora_parameters_one_with_lr,
text_lora_parameters_two_with_lr,
]
else:
params_to_optimize = [unet_lora_parameters_with_lr]
# Optimizer creation
if not (args.optimizer.lower() == "prodigy" or args.optimizer.lower() == "adamw"):
logger.warn(
f"Unsupported choice of optimizer: {args.optimizer}.Supported optimizers include [adamW, prodigy]."
"Defaulting to adamW"
)
args.optimizer = "adamw"
if args.use_8bit_adam and not args.optimizer.lower() == "adamw":
logger.warn(
f"use_8bit_adam is ignored when optimizer is not set to 'AdamW'. Optimizer was "
f"set to {args.optimizer.lower()}"
)
if args.optimizer.lower() == "adamw":
if args.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
)
optimizer_class = bnb.optim.AdamW8bit
else:
optimizer_class = torch.optim.AdamW
optimizer = optimizer_class(
params_to_optimize,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
if args.optimizer.lower() == "prodigy":
try:
import prodigyopt
except ImportError:
raise ImportError("To use Prodigy, please install the prodigyopt library: `pip install prodigyopt`")
optimizer_class = prodigyopt.Prodigy
optimizer = optimizer_class(
params_to_optimize,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
decouple=args.prodigy_decouple,
use_bias_correction=args.prodigy_use_bias_correction,
safeguard_warmup=args.prodigy_safeguard_warmup,
)
# Dataset and DataLoaders creation:
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_prompt=args.class_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_num=args.num_class_images,
size=args.resolution,
repeats=args.repeats,
center_crop=args.center_crop,
)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
num_workers=args.dataloader_num_workers,
)
# Computes additional embeddings/ids required by the SDXL UNet.
# regular text embeddings (when `train_text_encoder` is not True)
# pooled text embeddings
# time ids
def compute_time_ids():
# Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
original_size = (args.resolution, args.resolution)
target_size = (args.resolution, args.resolution)
crops_coords_top_left = (args.crops_coords_top_left_h, args.crops_coords_top_left_w)
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids])
add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
return add_time_ids
if not args.train_text_encoder:
tokenizers = [tokenizer_one, tokenizer_two]
text_encoders = [text_encoder_one, text_encoder_two]
def compute_text_embeddings(prompt, text_encoders, tokenizers):
with torch.no_grad():
prompt_embeds, pooled_prompt_embeds = encode_prompt(text_encoders, tokenizers, prompt)
prompt_embeds = prompt_embeds.to(accelerator.device)
pooled_prompt_embeds = pooled_prompt_embeds.to(accelerator.device)
return prompt_embeds, pooled_prompt_embeds
# Handle instance prompt.
instance_time_ids = compute_time_ids()
# If no type of tuning is done on the text_encoder and custom instance prompts are NOT
# provided (i.e. the --instance_prompt is used for all images), we encode the instance prompt once to avoid
# the redundant encoding.
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
instance_prompt_hidden_states, instance_pooled_prompt_embeds = compute_text_embeddings(
args.instance_prompt, text_encoders, tokenizers
)
# Handle class prompt for prior-preservation.
if args.with_prior_preservation:
class_time_ids = compute_time_ids()
if not args.train_text_encoder:
class_prompt_hidden_states, class_pooled_prompt_embeds = compute_text_embeddings(
args.class_prompt, text_encoders, tokenizers
)
# Clear the memory here
if not args.train_text_encoder and not train_dataset.custom_instance_prompts:
del tokenizers, text_encoders
gc.collect()
torch.cuda.empty_cache()
# If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
# pack the statically computed variables appropriately here. This is so that we don't
# have to pass them to the dataloader.
add_time_ids = instance_time_ids
if args.with_prior_preservation:
add_time_ids = torch.cat([add_time_ids, class_time_ids], dim=0)
if not train_dataset.custom_instance_prompts:
if not args.train_text_encoder:
prompt_embeds = instance_prompt_hidden_states
unet_add_text_embeds = instance_pooled_prompt_embeds
if args.with_prior_preservation:
prompt_embeds = torch.cat([prompt_embeds, class_prompt_hidden_states], dim=0)
unet_add_text_embeds = torch.cat([unet_add_text_embeds, class_pooled_prompt_embeds], dim=0)
# if we're optmizing the text encoder (both if instance prompt is used for all images or custom prompts) we need to tokenize and encode the
# batch prompts on all training steps
else:
tokens_one = tokenize_prompt(tokenizer_one, args.instance_prompt)
tokens_two = tokenize_prompt(tokenizer_two, args.instance_prompt)
if args.with_prior_preservation:
class_tokens_one = tokenize_prompt(tokenizer_one, args.class_prompt)
class_tokens_two = tokenize_prompt(tokenizer_two, args.class_prompt)
tokens_one = torch.cat([tokens_one, class_tokens_one], dim=0)
tokens_two = torch.cat([tokens_two, class_tokens_two], dim=0)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
num_cycles=args.lr_num_cycles,
power=args.lr_power,
)
# Prepare everything with our `accelerator`.
if args.train_text_encoder:
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, text_encoder_one, text_encoder_two, optimizer, train_dataloader, lr_scheduler
)
else:
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("dreambooth-lora-sd-xl", config=vars(args))
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num batches each epoch = {len(train_dataloader)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the mos recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
else:
initial_global_step = 0
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
for epoch in range(first_epoch, args.num_train_epochs):
unet.train()
if args.train_text_encoder:
text_encoder_one.train()
text_encoder_two.train()
# set top parameter requires_grad = True for gradient checkpointing works
text_encoder_one.text_model.embeddings.requires_grad_(True)
text_encoder_two.text_model.embeddings.requires_grad_(True)
for step, batch in enumerate(train_dataloader):
with accelerator.accumulate(unet):
pixel_values = batch["pixel_values"].to(dtype=vae.dtype)
prompts = batch["prompts"]
# encode batch prompts when custom prompts are provided for each image -
if train_dataset.custom_instance_prompts:
if not args.train_text_encoder:
prompt_embeds, unet_add_text_embeds = compute_text_embeddings(
prompts, text_encoders, tokenizers
)
else:
tokens_one = tokenize_prompt(tokenizer_one, prompts)
tokens_two = tokenize_prompt(tokenizer_two, prompts)
# Convert images to latent space
model_input = vae.encode(pixel_values).latent_dist.sample()
model_input = model_input * vae.config.scaling_factor
if args.pretrained_vae_model_name_or_path is None:
model_input = model_input.to(weight_dtype)
# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
bsz = model_input.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
)
timesteps = timesteps.long()
# Add noise to the model input according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
# Calculate the elements to repeat depending on the use of prior-preservation and custom captions.
if not train_dataset.custom_instance_prompts:
elems_to_repeat_text_embeds = bsz // 2 if args.with_prior_preservation else bsz
elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
else:
elems_to_repeat_text_embeds = 1
elems_to_repeat_time_ids = bsz // 2 if args.with_prior_preservation else bsz
# Predict the noise residual
if not args.train_text_encoder:
unet_added_conditions = {
"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1),
"text_embeds": unet_add_text_embeds.repeat(elems_to_repeat_text_embeds, 1),
}
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
noisy_model_input,
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
).sample
else:
unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1)}
prompt_embeds, pooled_prompt_embeds = encode_prompt(
text_encoders=[text_encoder_one, text_encoder_two],
tokenizers=None,
prompt=None,
text_input_ids_list=[tokens_one, tokens_two],
)
unet_added_conditions.update(
{"text_embeds": pooled_prompt_embeds.repeat(elems_to_repeat_text_embeds, 1)}
)
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(model_input, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if args.with_prior_preservation:
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
target, target_prior = torch.chunk(target, 2, dim=0)
# Compute prior loss
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
if args.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(noise_scheduler, timesteps)
base_weight = (
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
if noise_scheduler.config.prediction_type == "v_prediction":
# Velocity objective needs to be floored to an SNR weight of one.
mse_loss_weights = base_weight + 1
else:
# Epsilon and sample both use the same loss weights.
mse_loss_weights = base_weight
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
if args.with_prior_preservation:
# Add the prior loss to the instance loss.
loss = loss + args.prior_loss_weight * prior_loss
accelerator.backward(loss)
if accelerator.sync_gradients:
params_to_clip = (
itertools.chain(unet_lora_parameters, text_lora_parameters_one, text_lora_parameters_two)
if args.train_text_encoder
else unet_lora_parameters
)
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if accelerator.is_main_process:
if global_step % args.checkpointing_steps == 0:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= args.max_train_steps:
break
if accelerator.is_main_process:
if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
if not args.train_text_encoder:
text_encoder_one = text_encoder_cls_one.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
variant=args.variant,
)
text_encoder_two = text_encoder_cls_two.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder_2",
revision=args.revision,
variant=args.variant,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
text_encoder=accelerator.unwrap_model(text_encoder_one),
text_encoder_2=accelerator.unwrap_model(text_encoder_two),
unet=accelerator.unwrap_model(unet),
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if "variance_type" in pipeline.scheduler.config:
variance_type = pipeline.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
pipeline.scheduler.config, **scheduler_args
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
pipeline_args = {"prompt": args.validation_prompt}
with torch.cuda.amp.autocast():
images = [
pipeline(**pipeline_args, generator=generator).images[0]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
del pipeline
torch.cuda.empty_cache()
# Save the lora layers
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
unet = unet.to(torch.float32)
unet_lora_layers = get_peft_model_state_dict(unet)
if args.train_text_encoder:
text_encoder_one = accelerator.unwrap_model(text_encoder_one)
text_encoder_lora_layers = get_peft_model_state_dict(text_encoder_one.to(torch.float32))
text_encoder_two = accelerator.unwrap_model(text_encoder_two)
text_encoder_2_lora_layers = get_peft_model_state_dict(text_encoder_two.to(torch.float32))
else:
text_encoder_lora_layers = None
text_encoder_2_lora_layers = None
StableDiffusionXLPipeline.save_lora_weights(
save_directory=args.output_dir,
unet_lora_layers=unet_lora_layers,
text_encoder_lora_layers=text_encoder_lora_layers,
text_encoder_2_lora_layers=text_encoder_2_lora_layers,
)
# Final inference
# Load previous pipeline
vae = AutoencoderKL.from_pretrained(
vae_path,
subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = StableDiffusionXLPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=vae,
revision=args.revision,
variant=args.variant,
torch_dtype=weight_dtype,
)
# We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
scheduler_args = {}
if "variance_type" in pipeline.scheduler.config:
variance_type = pipeline.scheduler.config.variance_type
if variance_type in ["learned", "learned_range"]:
variance_type = "fixed_small"
scheduler_args["variance_type"] = variance_type
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
# load attention processors
pipeline.load_lora_weights(args.output_dir)
# run inference
images = []
if args.validation_prompt and args.num_validation_images > 0:
pipeline = pipeline.to(accelerator.device)
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
for _ in range(args.num_validation_images)
]
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"test": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
train_text_encoder=args.train_text_encoder,
instance_prompt=args.instance_prompt,
validation_prompt=args.validation_prompt,
repo_folder=args.output_dir,
vae_path=args.pretrained_vae_model_name_or_path,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)
accelerator.end_training()
if __name__ == "__main__":
args = parse_args()
main(args)
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