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January 30, 2023 11:40
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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2022 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 logging | |
import math | |
import os | |
import random | |
from pathlib import Path | |
from typing import Optional | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
import torch.utils.checkpoint | |
import datasets | |
import diffusers | |
import transformers | |
from accelerate import Accelerator | |
from accelerate.logging import get_logger | |
from accelerate.utils import set_seed | |
from datasets import load_dataset | |
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel | |
from diffusers.optimization import get_scheduler | |
from diffusers.training_utils import EMAModel | |
from diffusers.utils import check_min_version | |
from diffusers.utils.import_utils import is_xformers_available | |
from huggingface_hub import HfFolder, Repository, create_repo, whoami | |
from torchvision import transforms | |
from tqdm.auto import tqdm | |
from transformers import CLIPTextModel, CLIPTokenizer | |
# Will error if the minimal version of diffusers is not installed. Remove at your own risks. | |
check_min_version("0.13.0.dev0") | |
logger = get_logger(__name__, log_level="INFO") | |
def parse_args(): | |
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( | |
"--revision", | |
type=str, | |
default=None, | |
required=False, | |
help="Revision of pretrained model identifier from huggingface.co/models.", | |
) | |
parser.add_argument( | |
"--dataset_name", | |
type=str, | |
default=None, | |
help=( | |
"The name of the Dataset (from the HuggingFace hub) to train on (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( | |
"--train_data_dir", | |
type=str, | |
default=None, | |
help=( | |
"A folder containing the training data. Folder contents must follow the structure described in" | |
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" | |
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified." | |
), | |
) | |
parser.add_argument( | |
"--image_column", type=str, default="image", help="The column of the dataset containing an image." | |
) | |
parser.add_argument( | |
"--caption_column", | |
type=str, | |
default="text", | |
help="The column of the dataset containing a caption or a list of captions.", | |
) | |
parser.add_argument( | |
"--validation_prompt", | |
type=str, | |
default=None, | |
help="A prompt that is sampled during training for inference.", | |
) | |
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=1, | |
help=( | |
"Run fine-tuning validation every X epochs. The validation process consists of running the prompt" | |
" `args.validation_prompt` multiple times: `args.num_validation_images`." | |
), | |
) | |
parser.add_argument( | |
"--max_train_samples", | |
type=int, | |
default=None, | |
help=( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
), | |
) | |
parser.add_argument( | |
"--output_dir", | |
type=str, | |
default="sd-model-finetuned", | |
help="The output directory where the model predictions and checkpoints will be written.", | |
) | |
parser.add_argument( | |
"--cache_dir", | |
type=str, | |
default=None, | |
help="The directory where the downloaded models and datasets will be stored.", | |
) | |
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") | |
parser.add_argument( | |
"--resolution", | |
type=int, | |
default=512, | |
help=( | |
"The resolution for input images, all the images in the train/validation dataset will be resized to this" | |
" resolution" | |
), | |
) | |
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( | |
"--random_flip", | |
action="store_true", | |
help="whether to randomly flip images horizontally", | |
) | |
parser.add_argument( | |
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." | |
) | |
parser.add_argument("--num_train_epochs", type=int, default=100) | |
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( | |
"--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( | |
"--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( | |
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler." | |
) | |
parser.add_argument( | |
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes." | |
) | |
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("--use_ema", action="store_true", help="Whether to use EMA model.") | |
parser.add_argument( | |
"--non_ema_revision", | |
type=str, | |
default=None, | |
required=False, | |
help=( | |
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" | |
" remote repository specified with --pretrained_model_name_or_path." | |
), | |
) | |
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("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") | |
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") | |
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") | |
parser.add_argument("--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( | |
"--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( | |
"--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("--local_rank", type=int, default=-1, help="For distributed training: local_rank") | |
parser.add_argument( | |
"--checkpointing_steps", | |
type=int, | |
default=500, | |
help=( | |
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" | |
" training using `--resume_from_checkpoint`." | |
), | |
) | |
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( | |
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers." | |
) | |
args = parser.parse_args() | |
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 | |
# Sanity checks | |
if args.dataset_name is None and args.train_data_dir is None: | |
raise ValueError("Need either a dataset name or a training folder.") | |
# default to using the same revision for the non-ema model if not specified | |
if args.non_ema_revision is None: | |
args.non_ema_revision = args.revision | |
return args | |
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): | |
if token is None: | |
token = HfFolder.get_token() | |
if organization is None: | |
username = whoami(token)["name"] | |
return f"{username}/{model_id}" | |
else: | |
return f"{organization}/{model_id}" | |
dataset_name_mapping = { | |
"lambdalabs/pokemon-blip-captions": ("image", "text"), | |
} | |
def main(): | |
args = parse_args() | |
logging_dir = os.path.join(args.output_dir, args.logging_dir) | |
accelerator = Accelerator( | |
gradient_accumulation_steps=args.gradient_accumulation_steps, | |
mixed_precision=args.mixed_precision, | |
log_with=args.report_to, | |
logging_dir=logging_dir, | |
) | |
# 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: | |
datasets.utils.logging.set_verbosity_warning() | |
transformers.utils.logging.set_verbosity_warning() | |
diffusers.utils.logging.set_verbosity_info() | |
else: | |
datasets.utils.logging.set_verbosity_error() | |
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) | |
# Handle the repository creation | |
if accelerator.is_main_process: | |
if args.push_to_hub: | |
if args.hub_model_id is None: | |
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) | |
else: | |
repo_name = args.hub_model_id | |
create_repo(repo_name, exist_ok=True, token=args.hub_token) | |
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token) | |
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: | |
if "step_*" not in gitignore: | |
gitignore.write("step_*\n") | |
if "epoch_*" not in gitignore: | |
gitignore.write("epoch_*\n") | |
elif args.output_dir is not None: | |
os.makedirs(args.output_dir, exist_ok=True) | |
# Load scheduler, tokenizer and models. | |
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") | |
tokenizer = CLIPTokenizer.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision | |
) | |
text_encoder = CLIPTextModel.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision | |
) | |
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) | |
unet = UNet2DConditionModel.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision | |
) | |
# Freeze vae and text_encoder | |
vae.requires_grad_(False) | |
text_encoder.requires_grad_(False) | |
# Create EMA for the unet. | |
if args.use_ema: | |
ema_unet = UNet2DConditionModel.from_pretrained( | |
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision | |
) | |
ema_unet = EMAModel(ema_unet.parameters()) | |
if args.enable_xformers_memory_efficient_attention: | |
if is_xformers_available(): | |
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() | |
# 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 | |
) | |
# Initialize the optimizer | |
if args.use_8bit_adam: | |
try: | |
import bitsandbytes as bnb | |
except ImportError: | |
raise ImportError( | |
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" | |
) | |
optimizer_cls = bnb.optim.AdamW8bit | |
else: | |
optimizer_cls = torch.optim.AdamW | |
optimizer = optimizer_cls( | |
unet.parameters(), | |
lr=args.learning_rate, | |
betas=(args.adam_beta1, args.adam_beta2), | |
weight_decay=args.adam_weight_decay, | |
eps=args.adam_epsilon, | |
) | |
# Get the datasets: you can either provide your own training and evaluation files (see below) | |
# or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). | |
# In distributed training, the load_dataset function guarantees that only one local process can concurrently | |
# download the dataset. | |
if args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
dataset = load_dataset( | |
args.dataset_name, | |
args.dataset_config_name, | |
cache_dir=args.cache_dir, | |
) | |
else: | |
data_files = {} | |
if args.train_data_dir is not None: | |
data_files["train"] = os.path.join(args.train_data_dir, "**") | |
dataset = load_dataset( | |
"imagefolder", | |
data_files=data_files, | |
cache_dir=args.cache_dir, | |
) | |
# See more about loading custom images at | |
# https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder | |
# Preprocessing the datasets. | |
# We need to tokenize inputs and targets. | |
column_names = dataset["train"].column_names | |
# 6. Get the column names for input/target. | |
dataset_columns = dataset_name_mapping.get(args.dataset_name, None) | |
if args.image_column is None: | |
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] | |
else: | |
image_column = args.image_column | |
if image_column not in column_names: | |
raise ValueError( | |
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
if args.caption_column is None: | |
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] | |
else: | |
caption_column = args.caption_column | |
if caption_column not in column_names: | |
raise ValueError( | |
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" | |
) | |
# Preprocessing the datasets. | |
# We need to tokenize input captions and transform the images. | |
def tokenize_captions(examples, is_train=True): | |
captions = [] | |
for caption in examples[caption_column]: | |
if isinstance(caption, str): | |
captions.append(caption) | |
elif isinstance(caption, (list, np.ndarray)): | |
# take a random caption if there are multiple | |
captions.append(random.choice(caption) if is_train else caption[0]) | |
else: | |
raise ValueError( | |
f"Caption column `{caption_column}` should contain either strings or lists of strings." | |
) | |
inputs = tokenizer( | |
captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" | |
) | |
return inputs.input_ids | |
# Preprocessing the datasets. | |
train_transforms = transforms.Compose( | |
[ | |
transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), | |
transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution), | |
transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x), | |
transforms.ToTensor(), | |
transforms.Normalize([0.5], [0.5]), | |
] | |
) | |
def preprocess_train(examples): | |
images = [image.convert("RGB") for image in examples[image_column]] | |
examples["pixel_values"] = [train_transforms(image) for image in images] | |
examples["input_ids"] = tokenize_captions(examples) | |
return examples | |
with accelerator.main_process_first(): | |
if args.max_train_samples is not None: | |
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) | |
# Set the training transforms | |
train_dataset = dataset["train"].with_transform(preprocess_train) | |
def collate_fn(examples): | |
pixel_values = torch.stack([example["pixel_values"] for example in examples]) | |
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() | |
input_ids = torch.stack([example["input_ids"] for example in examples]) | |
return {"pixel_values": pixel_values, "input_ids": input_ids} | |
# DataLoaders creation: | |
train_dataloader = torch.utils.data.DataLoader( | |
train_dataset, | |
shuffle=True, | |
collate_fn=collate_fn, | |
batch_size=args.train_batch_size, | |
num_workers=args.dataloader_num_workers, | |
) | |
# 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 * args.gradient_accumulation_steps, | |
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, | |
) | |
# Prepare everything with our `accelerator`. | |
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( | |
unet, optimizer, train_dataloader, lr_scheduler | |
) | |
if args.use_ema: | |
accelerator.register_for_checkpointing(ema_unet) | |
# For mixed precision training we cast the text_encoder and vae weights to half-precision | |
# as these models 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 text_encode and vae to gpu and cast to weight_dtype | |
text_encoder.to(accelerator.device, dtype=weight_dtype) | |
vae.to(accelerator.device, dtype=weight_dtype) | |
if args.use_ema: | |
ema_unet.to(accelerator.device) | |
# 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("text2image-fine-tune", 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 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 most 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 | |
else: | |
accelerator.print(f"Resuming from checkpoint {path}") | |
accelerator.load_state(os.path.join(args.output_dir, path)) | |
global_step = int(path.split("-")[1]) | |
resume_global_step = global_step * args.gradient_accumulation_steps | |
first_epoch = global_step // num_update_steps_per_epoch | |
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps) | |
# Only show the progress bar once on each machine. | |
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) | |
progress_bar.set_description("Steps") | |
for epoch in range(first_epoch, args.num_train_epochs): | |
unet.train() | |
train_loss = 0.0 | |
for step, batch in enumerate(train_dataloader): | |
# Skip steps until we reach the resumed step | |
if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: | |
if step % args.gradient_accumulation_steps == 0: | |
progress_bar.update(1) | |
continue | |
with accelerator.accumulate(unet): | |
# Convert images to latent space | |
latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample() | |
latents = latents * vae.config.scaling_factor | |
# Sample noise that we'll add to the latents | |
noise = torch.randn_like(latents) | |
bsz = latents.shape[0] | |
# Sample a random timestep for each image | |
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device) | |
timesteps = timesteps.long() | |
# Add noise to the latents according to the noise magnitude at each timestep | |
# (this is the forward diffusion process) | |
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) | |
# Get the text embedding for conditioning | |
encoder_hidden_states = text_encoder(batch["input_ids"])[0] | |
# 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(latents, noise, timesteps) | |
else: | |
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") | |
# Predict the noise residual and compute loss | |
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample | |
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") | |
# Gather the losses across all processes for logging (if we use distributed training). | |
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() | |
train_loss += avg_loss.item() / args.gradient_accumulation_steps | |
# Backpropagate | |
accelerator.backward(loss) | |
if accelerator.sync_gradients: | |
accelerator.clip_grad_norm_(unet.parameters(), 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: | |
if args.use_ema: | |
ema_unet.step(unet.parameters()) | |
progress_bar.update(1) | |
global_step += 1 | |
accelerator.log({"train_loss": train_loss}, step=global_step) | |
train_loss = 0.0 | |
if global_step % args.checkpointing_steps == 0: | |
if accelerator.is_main_process: | |
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 = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} | |
progress_bar.set_postfix(**logs) | |
if global_step >= args.max_train_steps: | |
break | |
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 | |
temp_unet = accelerator.unwrap_model(unet) | |
pipeline = StableDiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
unet=temp_unet, | |
revision=args.revision, | |
torch_dtype=weight_dtype | |
) | |
pipeline = pipeline.to(accelerator.device) | |
pipeline.set_progress_bar_config(disable=True) | |
# run inference | |
generator = torch.Generator(device=accelerator.device).manual_seed( | |
args.seed | |
) | |
images = [] | |
for _ in range(args.num_validation_images): | |
images.append( | |
pipeline( | |
args.validation_prompt, | |
num_inference_steps=30, | |
generator=generator, | |
).images[0] | |
) | |
# Create the pipeline using the trained modules and save it. | |
accelerator.wait_for_everyone() | |
if accelerator.is_main_process: | |
unet = accelerator.unwrap_model(unet) | |
if args.use_ema: | |
ema_unet.copy_to(unet.parameters()) | |
pipeline = StableDiffusionPipeline.from_pretrained( | |
args.pretrained_model_name_or_path, | |
text_encoder=text_encoder, | |
vae=vae, | |
unet=unet, | |
revision=args.revision, | |
) | |
pipeline.save_pretrained(args.output_dir) | |
if args.push_to_hub: | |
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) | |
accelerator.end_training() | |
if __name__ == "__main__": | |
main() |
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