""" 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) 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: #unet = torch.compile(unet) #text_encoder = torch.compile(text_encoder) #vae = torch.compile(vae) torch.set_float32_matmul_precision('high') torch.backends.cudnn.allow_tf32 = True #logging.info("Successfully compiled models") except Exception as ex: logging.warning(f"Failed to compile model, 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(0, noise_scheduler.config.num_train_timesteps, (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 = F.mse_loss(model_pred.float(), target.float(), reduction="none") loss = loss.mean(dim=list(range(1, len(loss.shape)))) * loss_scale.to(unet.device) loss = loss.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("--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("--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)