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EveryDream2trainer/train.py

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"""
Copyright [2022-2023] 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 pprint
import sys
import math
import signal
import argparse
import logging
import threading
import time
import gc
import random
import traceback
import shutil
from typing import Optional
import torch.nn.functional as F
from torch.cuda.amp import autocast
from colorama import Fore, Style
import numpy as np
import itertools
import torch
import datetime
import json
from tqdm.auto import tqdm
from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, DDPMScheduler, \
DPMSolverMultistepScheduler, PNDMScheduler
#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
import webbrowser
from torch.utils.tensorboard import SummaryWriter
from data.data_loader import DataLoaderMultiAspect
from data.every_dream import EveryDreamBatch, build_torch_dataloader
from data.every_dream_validation import EveryDreamValidator
from data.image_train_item import ImageTrainItem, DEFAULT_BATCH_ID
from utils.huggingface_downloader import try_download_model_from_hf
from utils.convert_diff_to_ckpt import convert as converter
from utils.isolate_rng import isolate_rng
from utils.check_git import check_git
from optimizer.optimizers import EveryDreamOptimizer
from copy import deepcopy
if torch.cuda.is_available():
from utils.gpu import GPU
import data.aspects as aspects
import data.resolver as resolver
from utils.sample_generator import SampleGenerator
_SIGTERM_EXIT_CODE = 130
_VERY_LARGE_NUMBER = 1e9
def get_training_noise_scheduler(train_sampler: str, model_root_folder, trained_betas=None):
noise_scheduler = None
if train_sampler.lower() == "pndm":
logging.info(f" * Using PNDM noise scheduler for training: {train_sampler}")
noise_scheduler = PNDMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
elif train_sampler.lower() == "ddim":
logging.info(f" * Using DDIM noise scheduler for training: {train_sampler}")
noise_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
else:
logging.info(f" * Using default (DDPM) noise scheduler for training: {train_sampler}")
noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
return noise_scheduler
def get_hf_ckpt_cache_path(ckpt_path):
return os.path.join("ckpt_cache", os.path.basename(ckpt_path))
def convert_to_hf(ckpt_path):
hf_cache = get_hf_ckpt_cache_path(ckpt_path)
from utils.unet_utils import get_attn_yaml
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")
try:
import utils.convert_original_stable_diffusion_to_diffusers as convert
convert.convert(ckpt_path, f"ckpt_cache/{ckpt_path}")
except:
logging.info("Please manually convert the checkpoint to Diffusers format (one time setup), see readme.")
exit()
else:
logging.info(f"Found cached checkpoint at {hf_cache}")
is_sd1attn, yaml = get_attn_yaml(hf_cache)
return hf_cache, is_sd1attn, yaml
elif os.path.isdir(hf_cache):
is_sd1attn, yaml = get_attn_yaml(hf_cache)
return hf_cache, is_sd1attn, yaml
else:
is_sd1attn, yaml = get_attn_yaml(ckpt_path)
return ckpt_path, is_sd1attn, yaml
class EveryDreamTrainingState:
def __init__(self,
optimizer: EveryDreamOptimizer,
train_batch: EveryDreamBatch,
unet: UNet2DConditionModel,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
scheduler,
vae: AutoencoderKL,
unet_ema: Optional[UNet2DConditionModel],
text_encoder_ema: Optional[CLIPTextModel]
):
self.optimizer = optimizer
self.train_batch = train_batch
self.unet = unet
self.text_encoder = text_encoder
self.tokenizer = tokenizer
self.scheduler = scheduler
self.vae = vae
self.unet_ema = unet_ema
self.text_encoder_ema = text_encoder_ema
@torch.no_grad()
def save_model(save_path, ed_state: EveryDreamTrainingState, global_step: int, save_ckpt_dir, yaml_name,
save_full_precision=False, save_optimizer_flag=False, save_ckpt=True):
"""
Save the model to disk
"""
def save_ckpt_file(diffusers_model_path, sd_ckpt_path):
nonlocal save_ckpt_dir
nonlocal save_full_precision
nonlocal yaml_name
if save_ckpt_dir is not None:
sd_ckpt_full = os.path.join(save_ckpt_dir, sd_ckpt_path)
else:
sd_ckpt_full = os.path.join(os.curdir, sd_ckpt_path)
save_ckpt_dir = os.curdir
half = not save_full_precision
logging.info(f" * Saving SD model to {sd_ckpt_full}")
converter(model_path=diffusers_model_path, checkpoint_path=sd_ckpt_full, half=half)
if yaml_name and yaml_name != "v1-inference.yaml":
yaml_save_path = f"{os.path.join(save_ckpt_dir, os.path.basename(diffusers_model_path))}.yaml"
logging.info(f" * Saving yaml to {yaml_save_path}")
shutil.copyfile(yaml_name, yaml_save_path)
if global_step is None or global_step == 0:
logging.warning(" No model to save, something likely blew up on startup, not saving")
return
if ed_state.unet_ema is not None or ed_state.text_encoder_ema is not None:
pipeline_ema = StableDiffusionPipeline(
vae=ed_state.vae,
text_encoder=ed_state.text_encoder_ema,
tokenizer=ed_state.tokenizer,
unet=ed_state.unet_ema,
scheduler=ed_state.scheduler,
safety_checker=None, # save vram
requires_safety_checker=None, # avoid nag
feature_extractor=None, # must be none of no safety checker
)
diffusers_model_path = save_path + "_ema"
logging.info(f" * Saving diffusers EMA model to {diffusers_model_path}")
pipeline_ema.save_pretrained(diffusers_model_path)
if save_ckpt:
sd_ckpt_path_ema = f"{os.path.basename(save_path)}_ema.safetensors"
save_ckpt_file(diffusers_model_path, sd_ckpt_path_ema)
pipeline = StableDiffusionPipeline(
vae=ed_state.vae,
text_encoder=ed_state.text_encoder,
tokenizer=ed_state.tokenizer,
unet=ed_state.unet,
scheduler=ed_state.scheduler,
safety_checker=None, # save vram
requires_safety_checker=None, # avoid nag
feature_extractor=None, # must be none of no safety checker
)
diffusers_model_path = save_path
logging.info(f" * Saving diffusers model to {diffusers_model_path}")
pipeline.save_pretrained(diffusers_model_path)
if save_ckpt:
sd_ckpt_path = f"{os.path.basename(save_path)}.safetensors"
save_ckpt_file(diffusers_model_path, sd_ckpt_path)
if save_optimizer_flag:
logging.info(f" Saving optimizer state to {save_path}")
ed_state.optimizer.save(save_path)
def setup_local_logger(args):
"""
configures logger with file and console logging, logs args, and returns the datestamp
"""
log_path = args.logdir
os.makedirs(log_path, exist_ok=True)
datetimestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
log_folder = os.path.join(log_path, f"{args.project_name}-{datetimestamp}")
os.makedirs(log_folder, exist_ok=True)
logfilename = os.path.join(log_folder, f"{args.project_name}-{datetimestamp}.log")
print(f" logging to {logfilename}")
logging.basicConfig(filename=logfilename,
level=logging.INFO,
format="%(asctime)s %(message)s",
datefmt="%m/%d/%Y %I:%M:%S %p",
)
console_handler = logging.StreamHandler(sys.stdout)
console_handler.addFilter(lambda msg: "Palette images with Transparency expressed in bytes" not in msg.getMessage())
logging.getLogger().addHandler(console_handler)
import warnings
warnings.filterwarnings("ignore", message="UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images")
#from PIL import Image
return datetimestamp, log_folder
# def save_optimizer(optimizer: torch.optim.Optimizer, path: str):
# """
# Saves the optimizer state
# """
# torch.save(optimizer.state_dict(), path)
# def load_optimizer(optimizer: torch.optim.Optimizer, path: str):
# """
# Loads the optimizer state
# """
# optimizer.load_state_dict(torch.load(path))
def pyramid_noise_like(x, discount=0.8):
b, c, w, h = x.shape # EDIT: w and h get over-written, rename for a different variant!
u = torch.nn.Upsample(size=(w, h), mode='bilinear')
noise = torch.randn_like(x)
for i in range(10):
r = random.random()*2+2 # Rather than always going 2x,
w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i)))
noise += u(torch.randn(b, c, w, h).to(x)) * discount**i
if w==1 or h==1: break # Lowest resolution is 1x1
return noise/noise.std() # Scaled back to roughly unit variance
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
"""
if gpu is not None:
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 find_last_checkpoint(logdir, is_ema=False):
"""
Finds the last checkpoint in the logdir, recursively
"""
last_ckpt = None
last_date = None
for root, dirs, files in os.walk(logdir):
for file in files:
if os.path.basename(file) == "model_index.json":
if is_ema and (not root.endswith("_ema")):
continue
elif (not is_ema) and root.endswith("_ema"):
continue
curr_date = os.path.getmtime(os.path.join(root,file))
if last_date is None or curr_date > last_date:
last_date = curr_date
last_ckpt = root
assert last_ckpt, f"Could not find last checkpoint in logdir: {logdir}"
assert "errored" not in last_ckpt, f"Found last checkpoint: {last_ckpt}, but it was errored, cancelling"
print(f" {Fore.LIGHTCYAN_EX}Found last checkpoint: {last_ckpt}, resuming{Style.RESET_ALL}")
return last_ckpt
def setup_args(args):
"""
Sets defaults for missing args (possible if missing from json config)
Forces some args to be set based on others for compatibility reasons
"""
if args.disable_amp:
logging.warning(f"{Fore.LIGHTYELLOW_EX} Disabling AMP, not recommended.{Style.RESET_ALL}")
args.amp = False
else:
args.amp = True
if args.disable_unet_training and args.disable_textenc_training:
raise ValueError("Both unet and textenc are disabled, nothing to train")
if args.resume_ckpt == "findlast":
logging.info(f"{Fore.LIGHTCYAN_EX} Finding last checkpoint in logdir: {args.logdir}{Style.RESET_ALL}")
# find the last checkpoint in the logdir
args.resume_ckpt = find_last_checkpoint(args.logdir)
if (args.ema_resume_model != None) and (args.ema_resume_model == "findlast"):
logging.info(f"{Fore.LIGHTCYAN_EX} Finding last EMA decay checkpoint in logdir: {args.logdir}{Style.RESET_ALL}")
args.ema_resume_model = find_last_checkpoint(args.logdir, is_ema=True)
if not args.shuffle_tags:
args.shuffle_tags = False
if not args.keep_tags:
args.keep_tags = 0
args.clip_skip = max(min(4, args.clip_skip), 0)
if args.ckpt_every_n_minutes is None and args.save_every_n_epochs is None:
logging.info(f"{Fore.LIGHTCYAN_EX} No checkpoint saving specified, defaulting to every 20 minutes.{Style.RESET_ALL}")
args.ckpt_every_n_minutes = 20
if args.ckpt_every_n_minutes is None or args.ckpt_every_n_minutes < 1:
args.ckpt_every_n_minutes = _VERY_LARGE_NUMBER
if args.save_every_n_epochs is None or args.save_every_n_epochs < 1:
args.save_every_n_epochs = _VERY_LARGE_NUMBER
if args.save_every_n_epochs < _VERY_LARGE_NUMBER and args.ckpt_every_n_minutes < _VERY_LARGE_NUMBER:
logging.warning(f"{Fore.LIGHTYELLOW_EX}** Both save_every_n_epochs and ckpt_every_n_minutes are set, this will potentially spam a lot of checkpoints{Style.RESET_ALL}")
logging.warning(f"{Fore.LIGHTYELLOW_EX}** save_every_n_epochs: {args.save_every_n_epochs}, ckpt_every_n_minutes: {args.ckpt_every_n_minutes}{Style.RESET_ALL}")
if args.cond_dropout > 0.26:
logging.warning(f"{Fore.LIGHTYELLOW_EX}** cond_dropout is set fairly high: {args.cond_dropout}, make sure this was intended{Style.RESET_ALL}")
if args.grad_accum > 1:
logging.info(f"{Fore.CYAN} Batch size: {args.batch_size}, grad accum: {args.grad_accum}, 'effective' batch size: {args.batch_size * args.grad_accum}{Style.RESET_ALL}")
if args.save_ckpt_dir is not None and not os.path.exists(args.save_ckpt_dir):
os.makedirs(args.save_ckpt_dir)
if args.rated_dataset:
args.rated_dataset_target_dropout_percent = min(max(args.rated_dataset_target_dropout_percent, 0), 100)
logging.info(logging.info(f"{Fore.CYAN} * Activating rated images learning with a target rate of {args.rated_dataset_target_dropout_percent}% {Style.RESET_ALL}"))
args.aspects = aspects.get_aspect_buckets(args.resolution)
if args.timestep_start < 0:
raise ValueError("timestep_start must be >= 0")
if args.timestep_end > 1000:
raise ValueError("timestep_end must be <= 1000")
return args
def report_image_train_item_problems(log_folder: str, items: list[ImageTrainItem], batch_size) -> None:
undersized_items = [item for item in items if item.is_undersized]
if len(undersized_items) > 0:
underized_log_path = os.path.join(log_folder, "undersized_images.txt")
logging.warning(f"{Fore.LIGHTRED_EX} ** Some images are smaller than the target size, consider using larger images{Style.RESET_ALL}")
logging.warning(f"{Fore.LIGHTRED_EX} ** Check {underized_log_path} for more information.{Style.RESET_ALL}")
with open(underized_log_path, "w", encoding='utf-8') as undersized_images_file:
undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:")
for undersized_item in undersized_items:
message = f" *** {undersized_item.pathname} with size: {undersized_item.image_size} is smaller than target size: {undersized_item.target_wh}\n"
undersized_images_file.write(message)
# warn on underfilled aspect ratio buckets
# Intuition: if there are too few images to fill a batch, duplicates will be appended.
# this is not a problem for large image counts but can seriously distort training if there
# are just a handful of images for a given aspect ratio.
# at a dupe ratio of 0.5, all images in this bucket have effective multiplier 1.5,
# at a dupe ratio 1.0, all images in this bucket have effective multiplier 2.0
warn_bucket_dupe_ratio = 0.5
def make_bucket_key(item):
return (item.batch_id, int(item.target_wh[0]), int(item.target_wh[1]))
ar_buckets = set(make_bucket_key(i) for i in items)
for ar_bucket in ar_buckets:
count = len([i for i in items if make_bucket_key(i) == ar_bucket])
runt_size = batch_size - (count % batch_size)
bucket_dupe_ratio = runt_size / count
if bucket_dupe_ratio > warn_bucket_dupe_ratio:
aspect_ratio_rational = aspects.get_rational_aspect_ratio((ar_bucket[1], ar_bucket[2]))
aspect_ratio_description = f"{aspect_ratio_rational[0]}:{aspect_ratio_rational[1]}"
batch_id_description = "" if ar_bucket[0] == DEFAULT_BATCH_ID else f" for batch id '{ar_bucket[0]}'"
effective_multiplier = round(1 + bucket_dupe_ratio, 1)
logging.warning(f" * {Fore.LIGHTRED_EX}Aspect ratio bucket {ar_bucket} has only {count} "
f"images{Style.RESET_ALL}. At batch size {batch_size} this makes for an effective multiplier "
f"of {effective_multiplier}, which may cause problems. Consider adding {runt_size} or "
f"more images with aspect ratio {aspect_ratio_description}{batch_id_description}, or reducing your batch_size.")
def resolve_image_train_items(args: argparse.Namespace) -> list[ImageTrainItem]:
logging.info(f"* DLMA resolution {args.resolution}, buckets: {args.aspects}")
logging.info(" Preloading images...")
resolved_items = resolver.resolve(args.data_root, args)
image_paths = set(map(lambda item: item.pathname, resolved_items))
# Remove erroneous items
for item in resolved_items:
if item.error is not None:
logging.error(f"{Fore.LIGHTRED_EX} *** Error opening {Fore.LIGHTYELLOW_EX}{item.pathname}{Fore.LIGHTRED_EX} to get metadata. File may be corrupt and will be skipped.{Style.RESET_ALL}")
logging.error(f" *** exception: {item.error}")
image_train_items = [item for item in resolved_items if item.error is None]
print (f" * Found {len(image_paths)} files in '{args.data_root}'")
return image_train_items
def write_batch_schedule(log_folder: str, train_batch: EveryDreamBatch, epoch: int):
with open(f"{log_folder}/ep{epoch}_batch_schedule.txt", "w", encoding='utf-8') as f:
for i in range(len(train_batch.image_train_items)):
try:
item = train_batch.image_train_items[i]
f.write(f"step:{int(i / train_batch.batch_size):05}, wh:{item.target_wh}, r:{item.runt_size}, path:{item.pathname}\n")
except Exception as e:
logging.error(f" * Error writing to batch schedule for file path: {item.pathname}")
def read_sample_prompts(sample_prompts_file_path: str):
sample_prompts = []
with open(sample_prompts_file_path, "r") as f:
for line in f:
sample_prompts.append(line.strip())
return sample_prompts
def log_args(log_writer, args, optimizer_config, log_folder, log_time):
arglog = "args:\n"
for arg, value in sorted(vars(args).items()):
arglog += f"{arg}={value}, "
log_writer.add_text("config", arglog)
args_as_json = json.dumps(vars(args), indent=2)
with open(os.path.join(log_folder, f"{args.project_name}-{log_time}_main.json"), "w") as f:
f.write(args_as_json)
optimizer_config_as_json = json.dumps(optimizer_config, indent=2)
with open(os.path.join(log_folder, f"{args.project_name}-{log_time}_opt.json"), "w") as f:
f.write(optimizer_config_as_json)
def update_ema(model, ema_model, decay, default_device, ema_device):
with torch.no_grad():
original_model_on_proper_device = model
need_to_delete_original = False
if ema_device != default_device:
original_model_on_other_device = deepcopy(model)
original_model_on_proper_device = original_model_on_other_device.to(ema_device, dtype=model.dtype)
del original_model_on_other_device
need_to_delete_original = True
params = dict(original_model_on_proper_device.named_parameters())
ema_params = dict(ema_model.named_parameters())
for name in ema_params:
#ema_params[name].data.mul_(decay).add_(params[name].data, alpha=1 - decay)
ema_params[name].data = ema_params[name] * decay + params[name].data * (1.0 - decay)
if need_to_delete_original:
del(original_model_on_proper_device)
def compute_snr(timesteps, noise_scheduler):
"""
Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
"""
minimal_value = 1e-9
alphas_cumprod = noise_scheduler.alphas_cumprod
# Use .any() to check if any elements in the tensor are zero
if (alphas_cumprod[:-1] == 0).any():
logging.warning(
f"Alphas cumprod has zero elements! Resetting to {minimal_value}.."
)
alphas_cumprod[alphas_cumprod[:-1] == 0] = minimal_value
sqrt_alphas_cumprod = alphas_cumprod**0.5
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
# Expand the tensors.
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
timesteps
].float()
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
device=timesteps.device
)[timesteps].float()
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
# Compute SNR, first without epsilon
snr = (alpha / sigma) ** 2
# Check if the first element in SNR tensor is zero
if torch.any(snr == 0):
snr[snr == 0] = minimal_value
return snr
def load_train_json_from_file(args, report_load = False):
try:
if report_load:
print(f"Loading training config from {args.config}.")
with open(args.config, 'rt') as f:
read_json = json.load(f)
args.__dict__.update(read_json)
except Exception as config_read:
print(f"Error on loading training config from {args.config}.")
def main(args):
"""
Main entry point
"""
if os.name == 'nt':
print(" * Windows detected, disabling Triton")
os.environ['XFORMERS_FORCE_DISABLE_TRITON'] = "1"
log_time, log_folder = setup_local_logger(args)
args = setup_args(args)
print(f" Args:")
pprint.pprint(vars(args))
if args.seed == -1:
args.seed = random.randint(0, 2**30)
seed = args.seed
logging.info(f" Seed: {seed}")
set_seed(seed)
if torch.cuda.is_available():
device = torch.device(f"cuda:{args.gpuid}")
gpu = GPU(device)
torch.backends.cudnn.benchmark = True
else:
logging.warning("*** Running on CPU. This is for testing loading/config parsing code only.")
device = 'cpu'
gpu = None
#log_folder = os.path.join(args.logdir, f"{args.project_name}_{log_time}")
if not os.path.exists(log_folder):
os.makedirs(log_folder)
def release_memory(model_to_delete, original_device):
del model_to_delete
gc.collect()
if 'cuda' in original_device.type:
torch.cuda.empty_cache()
use_ema_dacay_training = (args.ema_decay_rate != None) or (args.ema_strength_target != None)
ema_model_loaded_from_file = False
if use_ema_dacay_training:
ema_device = torch.device(args.ema_device)
optimizer_state_path = None
try:
# check for a local file
hf_cache_path = get_hf_ckpt_cache_path(args.resume_ckpt)
if os.path.exists(hf_cache_path) or os.path.exists(args.resume_ckpt):
model_root_folder, is_sd1attn, yaml = convert_to_hf(args.resume_ckpt)
text_encoder = CLIPTextModel.from_pretrained(model_root_folder, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(model_root_folder, subfolder="vae")
unet = UNet2DConditionModel.from_pretrained(model_root_folder, subfolder="unet")
else:
# try to download from HF using resume_ckpt as a repo id
downloaded = try_download_model_from_hf(repo_id=args.resume_ckpt)
if downloaded is None:
raise ValueError(f"No local file/folder for {args.resume_ckpt}, and no matching huggingface.co repo could be downloaded")
pipe, model_root_folder, is_sd1attn, yaml = downloaded
text_encoder = pipe.text_encoder
vae = pipe.vae
unet = pipe.unet
del pipe
if use_ema_dacay_training and args.ema_resume_model:
print(f"Loading EMA model: {args.ema_resume_model}")
ema_model_loaded_from_file=True
hf_cache_path = get_hf_ckpt_cache_path(args.ema_resume_model)
if os.path.exists(hf_cache_path) or os.path.exists(args.ema_resume_model):
ema_model_root_folder, ema_is_sd1attn, ema_yaml = convert_to_hf(args.resume_ckpt)
text_encoder_ema = CLIPTextModel.from_pretrained(ema_model_root_folder, subfolder="text_encoder")
unet_ema = UNet2DConditionModel.from_pretrained(ema_model_root_folder, subfolder="unet")
else:
# try to download from HF using ema_resume_model as a repo id
ema_downloaded = try_download_model_from_hf(repo_id=args.ema_resume_model)
if ema_downloaded is None:
raise ValueError(
f"No local file/folder for ema_resume_model {args.ema_resume_model}, and no matching huggingface.co repo could be downloaded")
ema_pipe, ema_model_root_folder, ema_is_sd1attn, ema_yaml = ema_downloaded
text_encoder_ema = ema_pipe.text_encoder
unet_ema = ema_pipe.unet
del ema_pipe
# Make sure EMA model is on proper device, and memory released if moved
unet_ema_current_device = next(unet_ema.parameters()).device
if ema_device != unet_ema_current_device:
unet_ema_on_wrong_device = unet_ema
unet_ema = unet_ema.to(ema_device)
release_memory(unet_ema_on_wrong_device, unet_ema_current_device)
# Make sure EMA model is on proper device, and memory released if moved
text_encoder_ema_current_device = next(text_encoder_ema.parameters()).device
if ema_device != text_encoder_ema_current_device:
text_encoder_ema_on_wrong_device = text_encoder_ema
text_encoder_ema = text_encoder_ema.to(ema_device)
release_memory(text_encoder_ema_on_wrong_device, text_encoder_ema_current_device)
if args.enable_zero_terminal_snr:
# Use zero terminal SNR
from utils.unet_utils import enforce_zero_terminal_snr
temp_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
trained_betas = enforce_zero_terminal_snr(temp_scheduler.betas).numpy().tolist()
inference_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler", trained_betas=trained_betas)
noise_scheduler = get_training_noise_scheduler(args.train_sampler, model_root_folder, trained_betas=trained_betas)
else:
inference_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler")
noise_scheduler = get_training_noise_scheduler(args.train_sampler, model_root_folder)
tokenizer = CLIPTokenizer.from_pretrained(model_root_folder, subfolder="tokenizer", use_fast=False)
except Exception as e:
traceback.print_exc()
logging.error(" * Failed to load checkpoint *")
raise
if args.gradient_checkpointing:
unet.enable_gradient_checkpointing()
text_encoder.gradient_checkpointing_enable()
if args.attn_type == "xformers":
if (args.amp and is_sd1attn) or (not is_sd1attn):
try:
unet.enable_xformers_memory_efficient_attention()
logging.info("Enabled xformers")
except Exception as ex:
logging.warning("failed to load xformers, using default SDP attention instead")
pass
elif (args.disable_amp and is_sd1attn):
logging.info("AMP is disabled but model is SD1.X, xformers is incompatible so using default attention")
elif args.attn_type == "slice":
unet.set_attention_slice("auto")
else:
logging.info("* Using SDP attention *")
vae = vae.to(device, dtype=torch.float16 if args.amp else torch.float32)
unet = unet.to(device, dtype=torch.float32)
if args.disable_textenc_training and args.amp:
text_encoder = text_encoder.to(device, dtype=torch.float16)
else:
text_encoder = text_encoder.to(device, dtype=torch.float32)
if use_ema_dacay_training:
if not ema_model_loaded_from_file:
logging.info(f"EMA decay enabled, creating EMA model.")
with torch.no_grad():
if args.ema_device == device:
unet_ema = deepcopy(unet)
text_encoder_ema = deepcopy(text_encoder)
else:
unet_ema_first = deepcopy(unet)
text_encoder_ema_first = deepcopy(text_encoder)
unet_ema = unet_ema_first.to(ema_device, dtype=unet.dtype)
text_encoder_ema = text_encoder_ema_first.to(ema_device, dtype=text_encoder.dtype)
del unet_ema_first
del text_encoder_ema_first
else:
# Make sure correct types are used for models
unet_ema = unet_ema.to(ema_device, dtype=unet.dtype)
text_encoder_ema = text_encoder_ema.to(ema_device, dtype=text_encoder.dtype)
else:
unet_ema = None
text_encoder_ema = None
try:
print()
#unet = torch.compile(unet, mode="max-autotune")
#text_encoder = torch.compile(text_encoder, mode="max-autotune")
#vae = torch.compile(vae, mode="max-autotune")
#logging.info("Successfully compiled models")
except Exception as ex:
logging.warning(f"Failed to compile model, continuing anyway, ex: {ex}")
pass
try:
torch.set_float32_matmul_precision('high')
torch.backends.cudnn.allow_tf32 = True
except Exception as ex:
logging.warning(f"Failed to set float32 matmul precision, continuing anyway, ex: {ex}")
pass
optimizer_config = None
optimizer_config_path = args.optimizer_config if args.optimizer_config else "optimizer.json"
if os.path.exists(os.path.join(os.curdir, optimizer_config_path)):
with open(os.path.join(os.curdir, optimizer_config_path), "r") as f:
optimizer_config = json.load(f)
if args.wandb:
wandb.tensorboard.patch(root_logdir=log_folder, pytorch=False, tensorboard_x=False, save=False)
wandb_run = wandb.init(
project=args.project_name,
config={"main_cfg": vars(args), "optimizer_cfg": optimizer_config},
name=args.run_name,
)
try:
if webbrowser.get():
webbrowser.open(wandb_run.url, new=2)
except Exception:
pass
log_writer = SummaryWriter(log_dir=log_folder,
flush_secs=20,
comment=args.run_name if args.run_name is not None else log_time,
)
image_train_items = resolve_image_train_items(args)
validator = None
if args.validation_config is not None:
validator = EveryDreamValidator(args.validation_config,
default_batch_size=args.batch_size,
resolution=args.resolution,
log_writer=log_writer,
)
# the validation dataset may need to steal some items from image_train_items
image_train_items = validator.prepare_validation_splits(image_train_items, tokenizer=tokenizer)
report_image_train_item_problems(log_folder, image_train_items, batch_size=args.batch_size)
from plugins.plugins import load_plugin
if args.plugins is not None:
plugins = [load_plugin(name) for name in args.plugins]
else:
logging.info("No plugins specified")
plugins = []
from plugins.plugins import PluginRunner
plugin_runner = PluginRunner(plugins=plugins)
data_loader = DataLoaderMultiAspect(
image_train_items=image_train_items,
seed=seed,
batch_size=args.batch_size,
grad_accum=args.grad_accum
)
train_batch = EveryDreamBatch(
data_loader=data_loader,
debug_level=1,
conditional_dropout=args.cond_dropout,
tokenizer=tokenizer,
seed = seed,
shuffle_tags=args.shuffle_tags,
keep_tags=args.keep_tags,
plugin_runner=plugin_runner,
rated_dataset=args.rated_dataset,
rated_dataset_dropout_target=(1.0 - (args.rated_dataset_target_dropout_percent / 100.0))
)
torch.cuda.benchmark = False
epoch_len = math.ceil(len(train_batch) / args.batch_size)
if use_ema_dacay_training:
args.ema_update_interval = args.ema_update_interval * args.grad_accum
if args.ema_strength_target != None:
total_number_of_steps: float = epoch_len * args.max_epochs
total_number_of_ema_update: float = total_number_of_steps / args.ema_update_interval
args.ema_decay_rate = args.ema_strength_target ** (1 / total_number_of_ema_update)
logging.info(f"ema_strength_target is {args.ema_strength_target}, calculated ema_decay_rate will be: {args.ema_decay_rate}.")
logging.info(
f"EMA decay enabled, with ema_decay_rate {args.ema_decay_rate}, ema_update_interval: {args.ema_update_interval}, ema_device: {args.ema_device}.")
ed_optimizer = EveryDreamOptimizer(args,
optimizer_config,
text_encoder,
unet,
epoch_len,
log_writer)
log_args(log_writer, args, optimizer_config, log_folder, log_time)
sample_generator = SampleGenerator(log_folder=log_folder, log_writer=log_writer,
default_resolution=args.resolution, default_seed=args.seed,
config_file_path=args.sample_prompts,
batch_size=max(1,args.batch_size//2),
default_sample_steps=args.sample_steps,
use_xformers=args.attn_type == "xformers",
use_penultimate_clip_layer=(args.clip_skip >= 2),
guidance_rescale=0.7 if args.enable_zero_terminal_snr else 0
)
"""
Train the model
"""
print(f" {Fore.LIGHTGREEN_EX}** Welcome to EveryDream trainer 2.0!**{Style.RESET_ALL}")
print(f" (C) 2022-2023 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
"""
is_main_thread = (torch.utils.data.get_worker_info() == None)
if is_main_thread:
global interrupted
if not interrupted:
interrupted=True
global global_step
interrupted_checkpoint_path = os.path.join(f"{log_folder}/ckpts/interrupted-gs{global_step}")
print()
logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
time.sleep(2) # give opportunity to ctrl-C again to cancel save
save_model(interrupted_checkpoint_path, global_step=global_step, ed_state=make_current_ed_state(),
save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
exit(_SIGTERM_EXIT_CODE)
else:
# non-main threads (i.e. dataloader workers) should exit cleanly
exit(0)
signal.signal(signal.SIGINT, sigterm_handler)
if not os.path.exists(f"{log_folder}/samples/"):
os.makedirs(f"{log_folder}/samples/")
if gpu is not None:
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")
logging.info(f" saving ckpts every {args.save_every_n_epochs } epochs")
train_dataloader = build_torch_dataloader(train_batch, batch_size=args.batch_size)
unet.train() if (args.gradient_checkpointing or not args.disable_unet_training) else unet.eval()
text_encoder.train() if not args.disable_textenc_training else text_encoder.eval()
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: {noise_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}{args.grad_accum}{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}epoch_len: {Fore.LIGHTGREEN_EX}{epoch_len}{Style.RESET_ALL}")
epoch_pbar = tqdm(range(args.max_epochs), position=0, leave=True, dynamic_ncols=True)
epoch_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Epochs{Style.RESET_ALL}")
epoch_times = []
global global_step
global_step = 0
training_start_time = time.time()
last_epoch_saved_time = training_start_time
append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer)
loss_log_step = []
assert len(train_batch) > 0, "train_batch is empty, check that your data_root is correct"
# actual prediction function - shared between train and validate
def get_model_prediction_and_target(image, tokens, zero_frequency_noise_ratio=0.0, return_loss=False, loss_scale=None):
with torch.no_grad():
with autocast(enabled=args.amp):
pixel_values = image.to(memory_format=torch.contiguous_format).to(unet.device)
latents = vae.encode(pixel_values, return_dict=False)
del pixel_values
latents = latents[0].sample() * 0.18215
noise = torch.randn_like(latents)
if args.pyramid_noise_discount != None:
if 0 < args.pyramid_noise_discount:
noise = pyramid_noise_like(noise, discount=args.pyramid_noise_discount)
if zero_frequency_noise_ratio != None:
if zero_frequency_noise_ratio < 0:
zero_frequency_noise_ratio = 0
# see https://www.crosslabs.org//blog/diffusion-with-offset-noise
zero_frequency_noise = zero_frequency_noise_ratio * torch.randn(latents.shape[0], latents.shape[1], 1, 1, device=latents.device)
noise = noise + zero_frequency_noise
bsz = latents.shape[0]
timesteps = torch.randint(args.timestep_start, args.timestep_end, (bsz,), device=latents.device)
timesteps = timesteps.long()
cuda_caption = tokens.to(text_encoder.device)
encoder_hidden_states = text_encoder(cuda_caption, output_hidden_states=True)
if args.clip_skip > 0:
encoder_hidden_states = text_encoder.text_model.final_layer_norm(
encoder_hidden_states.hidden_states[-args.clip_skip])
else:
encoder_hidden_states = encoder_hidden_states.last_hidden_state
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type in ["v_prediction", "v-prediction"]:
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
del noise, latents, cuda_caption
with autocast(enabled=args.amp):
#print(f"types: {type(noisy_latents)} {type(timesteps)} {type(encoder_hidden_states)}")
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
if return_loss:
if loss_scale is None:
loss_scale = torch.ones(model_pred.shape[0], dtype=torch.float)
if args.min_snr_gamma is not None:
snr = compute_snr(timesteps, noise_scheduler)
mse_loss_weights = (
torch.stack(
[snr, args.min_snr_gamma * torch.ones_like(timesteps)], dim=1
).min(dim=1)[0]
/ snr
)
mse_loss_weights[snr == 0] = 1.0
loss_scale = loss_scale * mse_loss_weights.to(loss_scale.device)
loss_mse = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss_scale = loss_scale.view(-1, 1, 1, 1).expand_as(loss_mse)
if args.loss_type == "mse_huber":
early_timestep_bias = (timesteps / noise_scheduler.config.num_train_timesteps)
early_timestep_bias = torch.tensor(early_timestep_bias, dtype=torch.float).to(unet.device)
early_timestep_bias = early_timestep_bias.view(-1, 1, 1, 1).expand_as(loss_mse)
loss_huber = F.huber_loss(model_pred.float(), target.float(), reduction="none", delta=1.0)
loss_mse = loss_mse * loss_scale.to(unet.device) * early_timestep_bias
loss_huber = loss_huber * loss_scale.to(unet.device) * (1.0 - early_timestep_bias)
loss = loss_mse.mean() + loss_huber.mean()
elif args.loss_type == "huber_mse":
early_timestep_bias = (timesteps / noise_scheduler.config.num_train_timesteps)
early_timestep_bias = torch.tensor(early_timestep_bias, dtype=torch.float).to(unet.device)
early_timestep_bias = early_timestep_bias.view(-1, 1, 1, 1).expand_as(loss_mse)
loss_huber = F.huber_loss(model_pred.float(), target.float(), reduction="none", delta=1.0)
loss_mse = loss_mse * loss_scale.to(unet.device) * (1.0 - early_timestep_bias)
loss_huber = loss_huber * loss_scale.to(unet.device) * early_timestep_bias
loss = loss_huber.mean() + loss_mse.mean()
elif args.loss_type == "huber":
loss_huber = F.huber_loss(model_pred.float(), target.float(), reduction="none", delta=1.0)
loss_huber = loss_huber * loss_scale.to(unet.device)
loss = loss_huber.mean()
else:
loss_mse = loss_mse * loss_scale.to(unet.device)
loss = loss_mse.mean()
return model_pred, target, loss
else:
return model_pred, target
def generate_samples(global_step: int, batch):
nonlocal unet
nonlocal text_encoder
nonlocal unet_ema
nonlocal text_encoder_ema
with isolate_rng():
prev_sample_steps = sample_generator.sample_steps
sample_generator.reload_config()
if prev_sample_steps != sample_generator.sample_steps:
next_sample_step = math.ceil((global_step + 1) / sample_generator.sample_steps) * sample_generator.sample_steps
print(f" * SampleGenerator config changed, now generating images samples every " +
f"{sample_generator.sample_steps} training steps (next={next_sample_step})")
sample_generator.update_random_captions(batch["captions"])
models_info = []
if (args.ema_decay_rate is None) or args.ema_sample_nonema_model:
models_info.append({"is_ema": False, "swap_required": False})
if (args.ema_decay_rate is not None) and args.ema_sample_ema_model:
models_info.append({"is_ema": True, "swap_required": ema_device != device})
for model_info in models_info:
extra_info: str = ""
if model_info["is_ema"]:
current_unet, current_text_encoder = unet_ema, text_encoder_ema
extra_info = "_ema"
else:
current_unet, current_text_encoder = unet, text_encoder
torch.cuda.empty_cache()
if model_info["swap_required"]:
with torch.no_grad():
unet_unloaded = unet.to(ema_device)
del unet
text_encoder_unloaded = text_encoder.to(ema_device)
del text_encoder
current_unet = unet_ema.to(device)
del unet_ema
current_text_encoder = text_encoder_ema.to(device)
del text_encoder_ema
gc.collect()
torch.cuda.empty_cache()
inference_pipe = sample_generator.create_inference_pipe(unet=current_unet,
text_encoder=current_text_encoder,
tokenizer=tokenizer,
vae=vae,
diffusers_scheduler_config=inference_scheduler.config
).to(device)
sample_generator.generate_samples(inference_pipe, global_step, extra_info=extra_info)
# Cleanup
del inference_pipe
if model_info["swap_required"]:
with torch.no_grad():
unet = unet_unloaded.to(device)
del unet_unloaded
text_encoder = text_encoder_unloaded.to(device)
del text_encoder_unloaded
unet_ema = current_unet.to(ema_device)
del current_unet
text_encoder_ema = current_text_encoder.to(ema_device)
del current_text_encoder
gc.collect()
torch.cuda.empty_cache()
def make_save_path(epoch, global_step, prepend=""):
basename = f"{prepend}{args.project_name}"
if epoch is not None:
basename += f"-ep{epoch:02}"
if global_step is not None:
basename += f"-gs{global_step:05}"
return os.path.join(log_folder, "ckpts", basename)
# Pre-train validation to establish a starting point on the loss graph
if validator:
validator.do_validation(global_step=0,
get_model_prediction_and_target_callable=get_model_prediction_and_target)
# the sample generator might be configured to generate samples before step 0
if sample_generator.generate_pretrain_samples:
_, batch = next(enumerate(train_dataloader))
generate_samples(global_step=0, batch=batch)
def make_current_ed_state() -> EveryDreamTrainingState:
return EveryDreamTrainingState(optimizer=ed_optimizer,
train_batch=train_batch,
unet=unet,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=noise_scheduler,
vae=vae,
unet_ema=unet_ema,
text_encoder_ema=text_encoder_ema)
epoch = None
try:
plugin_runner.run_on_training_start(log_folder=log_folder, project_name=args.project_name)
for epoch in range(args.max_epochs):
write_batch_schedule(log_folder, train_batch, epoch) if args.write_schedule else None
if args.load_settings_every_epoch:
load_train_json_from_file(args)
epoch_len = math.ceil(len(train_batch) / args.batch_size)
def update_arg(arg: str, newValue):
if arg == "grad_accum":
args.grad_accum = newValue
data_loader.grad_accum = newValue
else:
raise("Unrecognized arg: " + arg)
plugin_runner.run_on_epoch_start(
epoch=epoch,
global_step=global_step,
epoch_length=epoch_len,
project_name=args.project_name,
log_folder=log_folder,
data_root=args.data_root,
arg_update_callback=update_arg
)
loss_epoch = []
epoch_start_time = time.time()
images_per_sec_log_step = []
steps_pbar = tqdm(range(epoch_len), position=1, leave=False, dynamic_ncols=True)
steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}")
validation_steps = (
[] if validator is None
else validator.get_validation_step_indices(epoch, len(train_dataloader))
)
for step, batch in enumerate(train_dataloader):
step_start_time = time.time()
plugin_runner.run_on_step_start(epoch=epoch,
local_step=step,
global_step=global_step,
project_name=args.project_name,
log_folder=log_folder,
batch=batch,
ed_state=make_current_ed_state())
model_pred, target, loss = get_model_prediction_and_target(batch["image"],
batch["tokens"],
args.zero_frequency_noise_ratio,
return_loss=True,
loss_scale=batch["loss_scale"])
del target, model_pred
if batch["runt_size"] > 0:
runt_loss_scale = (batch["runt_size"] / args.batch_size)**1.5 # further discount runts by **1.5
loss = loss * runt_loss_scale
ed_optimizer.step(loss, step, global_step)
if args.ema_decay_rate != None:
if ((global_step + 1) % args.ema_update_interval) == 0:
# debug_start_time = time.time() # Measure time
if args.disable_unet_training != True:
update_ema(unet, unet_ema, args.ema_decay_rate, default_device=device, ema_device=ema_device)
if args.disable_textenc_training != True:
update_ema(text_encoder, text_encoder_ema, args.ema_decay_rate, default_device=device, ema_device=ema_device)
# debug_end_time = time.time() # Measure time
# debug_elapsed_time = debug_end_time - debug_start_time # Measure time
# print(f"Command update_EMA unet and TE took {debug_elapsed_time:.3f} seconds.") # Measure time
loss_step = loss.detach().item()
steps_pbar.set_postfix({"loss/step": loss_step}, {"gs": global_step})
steps_pbar.update(1)
images_per_sec = args.batch_size / (time.time() - step_start_time)
images_per_sec_log_step.append(images_per_sec)
loss_log_step.append(loss_step)
loss_epoch.append(loss_step)
if (global_step + 1) % args.log_step == 0:
loss_step = sum(loss_log_step) / len(loss_log_step)
lr_unet = ed_optimizer.get_unet_lr()
lr_textenc = ed_optimizer.get_textenc_lr()
loss_log_step = []
log_writer.add_scalar(tag="hyperparameter/lr unet", scalar_value=lr_unet, global_step=global_step)
log_writer.add_scalar(tag="hyperparameter/lr text encoder", scalar_value=lr_textenc, global_step=global_step)
log_writer.add_scalar(tag="loss/log_step", scalar_value=loss_step, global_step=global_step)
sum_img = sum(images_per_sec_log_step)
avg = sum_img / len(images_per_sec_log_step)
images_per_sec_log_step = []
if args.amp:
log_writer.add_scalar(tag="hyperparameter/grad scale", scalar_value=ed_optimizer.get_scale(), global_step=global_step)
log_writer.add_scalar(tag="performance/images per second", scalar_value=avg, global_step=global_step)
logs = {"loss/log_step": loss_step, "lr_unet": lr_unet, "lr_te": lr_textenc, "img/s": images_per_sec}
append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer, **logs)
torch.cuda.empty_cache()
if validator and step in validation_steps:
validator.do_validation(global_step, get_model_prediction_and_target)
if (global_step + 1) % sample_generator.sample_steps == 0:
generate_samples(global_step=global_step, batch=batch)
min_since_last_ckpt = (time.time() - last_epoch_saved_time) / 60
needs_save = False
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, {args.ckpt_every_n_minutes} mins at step {global_step}")
needs_save = True
if epoch > 0 and epoch % args.save_every_n_epochs == 0 and step == 0 and epoch < args.max_epochs and epoch >= args.save_ckpts_from_n_epochs:
logging.info(f" Saving model, {args.save_every_n_epochs} epochs at step {global_step}")
needs_save = True
if needs_save:
save_path = make_save_path(epoch, global_step)
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
save_ckpt_dir=args.save_ckpt_dir, yaml_name=None,
save_full_precision=args.save_full_precision,
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
plugin_runner.run_on_step_end(epoch=epoch,
global_step=global_step,
local_step=step,
project_name=args.project_name,
log_folder=log_folder,
data_root=args.data_root,
batch=batch,
ed_state=make_current_ed_state())
del batch
global_step += 1
# end of step
steps_pbar.close()
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)
plugin_runner.run_on_epoch_end(epoch=epoch,
global_step=global_step,
project_name=args.project_name,
log_folder=log_folder,
data_root=args.data_root,
arg_update_callback=update_arg)
epoch_pbar.update(1)
if epoch < args.max_epochs - 1:
train_batch.shuffle(epoch_n=epoch, max_epochs = args.max_epochs)
if len(loss_epoch) > 0:
loss_epoch = sum(loss_epoch) / len(loss_epoch)
log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_epoch, global_step=global_step)
gc.collect()
# end of epoch
# end of training
epoch = args.max_epochs
plugin_runner.run_on_training_end()
save_path = make_save_path(epoch, global_step, prepend=("" if args.no_prepend_last else "last-"))
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
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/60:.2f} minutes, total steps: {global_step}")
logging.info(f"Average epoch time: {np.mean([t['time'] for t in epoch_times]):.2f} minutes")
except Exception as ex:
logging.error(f"{Fore.LIGHTYELLOW_EX}Something went wrong, attempting to save model{Style.RESET_ALL}")
save_path = make_save_path(epoch, global_step, prepend="errored-")
save_model(save_path, global_step=global_step, ed_state=make_current_ed_state(),
save_ckpt_dir=args.save_ckpt_dir, yaml_name=yaml, save_full_precision=args.save_full_precision,
save_optimizer_flag=args.save_optimizer, save_ckpt=not args.no_save_ckpt)
logging.info(f"{Fore.LIGHTYELLOW_EX}Model saved, re-raising exception and exiting. Exception was:{Style.RESET_ALL}{Fore.LIGHTRED_EX} {ex} {Style.RESET_ALL}")
raise ex
logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
logging.info(f"{Fore.LIGHTWHITE_EX} **** Finished training ****{Style.RESET_ALL}")
logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
if __name__ == "__main__":
check_git()
supported_resolutions = aspects.get_supported_resolutions()
argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
argparser.add_argument("--config", type=str, required=False, default=None, help="JSON config file to load options from")
args, argv = argparser.parse_known_args()
load_train_json_from_file(args, report_load=True)
argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
argparser.add_argument("--amp", action="store_true", default=True, help="deprecated, use --disable_amp if you wish to disable AMP")
argparser.add_argument("--attn_type", type=str, default="sdp", help="Attention mechanismto use", choices=["xformers", "sdp", "slice"])
argparser.add_argument("--batch_size", type=int, default=2, help="Batch size (def: 2)")
argparser.add_argument("--ckpt_every_n_minutes", type=int, default=None, help="Save checkpoint every n minutes, def: 20")
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("--clip_skip", type=int, default=0, help="Train using penultimate layer (def: 0) (2 is 'penultimate')", choices=[0, 1, 2, 3, 4])
argparser.add_argument("--cond_dropout", type=float, default=0.04, help="Conditional drop out as decimal 0.0-1.0, see docs for more info (def: 0.04)")
argparser.add_argument("--data_root", type=str, default="input", help="folder where your training images are")
argparser.add_argument("--disable_amp", action="store_true", default=False, help="disables automatic mixed precision (def: False)")
argparser.add_argument("--disable_textenc_training", action="store_true", default=False, help="disables training of text encoder (def: False)")
argparser.add_argument("--disable_unet_training", action="store_true", default=False, help="disables training of unet (def: False) NOT RECOMMENDED")
argparser.add_argument("--flip_p", type=float, default=0.0, help="probability of flipping image horizontally (def: 0.0) use 0.0 to 1.0, ex 0.5, not good for specific faces!")
argparser.add_argument("--gpuid", type=int, default=0, help="id of gpu to use for training, (def: 0) (ex: 1 to use GPU_ID 1), use nvidia-smi to find your GPU ids")
argparser.add_argument("--gradient_checkpointing", action="store_true", default=False, help="enable gradient checkpointing to reduce VRAM use, may reduce performance (def: False)")
argparser.add_argument("--grad_accum", type=int, default=1, help="Gradient accumulation factor (def: 1), (ex, 2)")
argparser.add_argument("--logdir", type=str, default="logs", help="folder to save logs to (def: logs)")
argparser.add_argument("--log_step", type=int, default=25, help="How often to log training stats, def: 25, recommend default!")
argparser.add_argument("--loss_type", type=str, default="mse_huber", help="type of loss, 'huber', 'mse', or 'mse_huber' for interpolated (def: mse_huber)", choices=["huber", "mse", "mse_huber"])
argparser.add_argument("--lr", type=float, default=None, help="Learning rate, if using scheduler is maximum LR at top of curve")
argparser.add_argument("--lr_decay_steps", type=int, default=0, help="Steps to reach minimum LR, default: automatically set")
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: 0.02 of lr_decay_steps), non-functional for constant")
argparser.add_argument("--max_epochs", type=int, default=300, help="Maximum number of epochs to train for")
argparser.add_argument("--no_prepend_last", action="store_true", help="Do not prepend 'last-' to the final checkpoint filename")
argparser.add_argument("--no_save_ckpt", action="store_true", help="Save only diffusers files, not .safetensors files (save disk space if you do not need LDM-style checkpoints)" )
argparser.add_argument("--optimizer_config", default="optimizer.json", help="Path to a JSON configuration file for the optimizer. Default is 'optimizer.json'")
argparser.add_argument('--plugins', nargs='+', help='Names of plugins to use')
argparser.add_argument("--project_name", type=str, default="myproj", help="Project name for logs and checkpoints, ex. 'tedbennett', 'superduperV1'")
argparser.add_argument("--resolution", type=int, default=512, help="resolution to train", choices=supported_resolutions)
argparser.add_argument("--resume_ckpt", type=str, required=not ('resume_ckpt' in args), default="sd_v1-5_vae.ckpt", help="The checkpoint to resume from, either a local .ckpt file, a converted Diffusers format folder, or a Huggingface.co repo id such as stabilityai/stable-diffusion-2-1 ")
argparser.add_argument("--run_name", type=str, required=False, default=None, help="Run name for wandb (child of project name), and comment for tensorboard, (def: None)")
argparser.add_argument("--sample_prompts", type=str, default="sample_prompts.txt", help="Text file with prompts to generate test samples from, or JSON file with sample generator settings (default: sample_prompts.txt)")
argparser.add_argument("--sample_steps", type=int, default=250, help="Number of steps between samples (def: 250)")
argparser.add_argument("--save_ckpt_dir", type=str, default=None, help="folder to save checkpoints to (def: root training folder)")
argparser.add_argument("--save_every_n_epochs", type=int, default=None, help="Save checkpoint every n epochs, def: 0 (disabled)")
argparser.add_argument("--save_ckpts_from_n_epochs", type=int, default=0, help="Only saves checkpoints starting an N epochs, def: 0 (disabled)")
argparser.add_argument("--save_full_precision", action="store_true", default=False, help="save ckpts at full FP32")
argparser.add_argument("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
argparser.add_argument("--seed", type=int, default=555, help="seed used for samples and shuffling, use -1 for random")
argparser.add_argument("--shuffle_tags", action="store_true", default=False, help="randomly shuffles CSV tags in captions, for booru datasets")
argparser.add_argument("--timestep_start", type=int, default=0, help="Noising timestep minimum (def: 0)")
argparser.add_argument("--timestep_end", type=int, default=1000, help="Noising timestep (def: 1000)")
argparser.add_argument("--train_sampler", type=str, default="ddpm", help="noise sampler used for training, (default: ddpm)", choices=["ddpm", "pndm", "ddim"])
argparser.add_argument("--keep_tags", type=int, default=0, help="Number of tags to keep when shuffle, used to randomly select subset of tags when shuffling is enabled, def: 0 (shuffle all)")
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("--validation_config", default=None, help="Path to a JSON configuration file for the validator. Default is no validation.")
argparser.add_argument("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
argparser.add_argument("--rated_dataset", action="store_true", default=False, help="enable rated image set training, to less often train on lower rated images through the epochs")
argparser.add_argument("--rated_dataset_target_dropout_percent", type=int, default=50, help="how many images (in percent) should be included in the last epoch (Default 50)")
argparser.add_argument("--zero_frequency_noise_ratio", type=float, default=0.02, help="adds zero frequency noise, for improving contrast (def: 0.0) use 0.0 to 0.15")
argparser.add_argument("--enable_zero_terminal_snr", action="store_true", default=None, help="Use zero terminal SNR noising beta schedule")
argparser.add_argument("--load_settings_every_epoch", action="store_true", default=None, help="Enable reloading of 'train.json' at start of every epoch.")
argparser.add_argument("--min_snr_gamma", type=int, default=None, help="min-SNR-gamma parameter is the loss function into individual tasks. Recommended values: 5, 1, 20. Disabled by default and enabled when used. More info: https://arxiv.org/abs/2303.09556")
argparser.add_argument("--ema_decay_rate", type=float, default=None, help="EMA decay rate. EMA model will be updated with (1 - ema_rate) from training, and the ema_rate from previous EMA, every interval. Values less than 1 and not so far from 1. Using this parameter will enable the feature.")
argparser.add_argument("--ema_strength_target", type=float, default=None, help="EMA decay target value in range (0,1). emarate will be calculated from equation: 'ema_decay_rate=ema_strength_target^(total_steps/ema_update_interval)'. Using this parameter will enable the ema feature and overide ema_decay_rate.")
argparser.add_argument("--ema_update_interval", type=int, default=500, help="How many steps between optimizer steps that EMA decay updates. EMA model will be update on every step modulo grad_accum times ema_update_interval.")
argparser.add_argument("--ema_device", type=str, default='cpu', help="EMA decay device values: cpu, cuda. Using 'cpu' is taking around 4 seconds per update vs fraction of a second on 'cuda'. Using 'cuda' will reserve around 3.2GB VRAM for a model, with 'cpu' the system RAM will be used.")
argparser.add_argument("--ema_sample_nonema_model", action="store_true", default=False, help="Will show samples from non-EMA trained model, just like regular training. Can be used with: --ema_sample_ema_model")
argparser.add_argument("--ema_sample_ema_model", action="store_true", default=False, help="Will show samples from EMA model. May be slower when using ema cpu offloading. Can be used with: --ema_sample_nonema_model")
argparser.add_argument("--ema_resume_model", type=str, default=None, help="The EMA decay checkpoint to resume from for EMA decay, either a local .ckpt file, a converted Diffusers format folder, or a Huggingface.co repo id such as stabilityai/stable-diffusion-2-1-ema-decay")
argparser.add_argument("--pyramid_noise_discount", type=float, default=None, help="Enables pyramid noise and use specified discount factor for it")
# load CLI args to overwrite existing config args
args = argparser.parse_args(args=argv, namespace=args)
main(args)
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