""" 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 sys import math import signal import argparse import logging import time import gc import random import traceback import shutil import torch.nn.functional as F from torch.cuda.amp import autocast, GradScaler import torchvision.transforms as transforms from colorama import Fore, Style, Cursor import numpy as np import itertools import torch import datetime import json from PIL import Image, ImageDraw, ImageFont from diffusers import StableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, DDIMScheduler, DDPMScheduler, PNDMScheduler, EulerAncestralDiscreteScheduler #from diffusers.models import AttentionBlock from diffusers.optimization import get_scheduler from diffusers.utils.import_utils import is_xformers_available from transformers import CLIPTextModel, CLIPTokenizer #from accelerate import Accelerator from accelerate.utils import set_seed import wandb from torch.utils.tensorboard import SummaryWriter from data.every_dream import EveryDreamBatch from utils.huggingface_downloader import try_download_model_from_hf from utils.convert_diff_to_ckpt import convert as converter from utils.gpu import GPU _SIGTERM_EXIT_CODE = 130 _VERY_LARGE_NUMBER = 1e9 def clean_filename(filename): """ removes all non-alphanumeric characters from a string so it is safe to use as a filename """ return "".join([c for c in filename if c.isalpha() or c.isdigit() or c==' ']).rstrip() 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.patch_unet import patch_unet 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 = patch_unet(hf_cache) return hf_cache, is_sd1attn, yaml elif os.path.isdir(hf_cache): is_sd1attn, yaml = patch_unet(hf_cache) return hf_cache, is_sd1attn, yaml else: is_sd1attn, yaml = patch_unet(ckpt_path) return ckpt_path, is_sd1attn, yaml def setup_local_logger(args): """ configures logger with file and console logging, logs args, and returns the datestamp """ log_path = args.logdir if not os.path.exists(log_path): os.makedirs(log_path) json_config = json.dumps(vars(args), indent=2) datetimestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") with open(os.path.join(log_path, f"{args.project_name}-{datetimestamp}_cfg.json"), "w") as f: f.write(f"{json_config}") logfilename = os.path.join(log_path, 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", ) logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) return datetimestamp def log_optimizer(optimizer: torch.optim.Optimizer, betas, epsilon): """ logs the optimizer settings """ logging.info(f"{Fore.CYAN} * Optimizer: {optimizer.__class__.__name__} *{Style.RESET_ALL}") logging.info(f" betas: {betas}, epsilon: {epsilon} *{Style.RESET_ALL}") def save_optimizer(optimizer: torch.optim.Optimizer, path: str): """ Saves the optimizer state """ torch.save(optimizer.state_dict(), path) def load_optimizer(optimizer, path: str): """ Loads the optimizer state """ optimizer.load_state_dict(torch.load(path)) def get_gpu_memory(nvsmi): """ returns the gpu memory usage """ gpu_query = nvsmi.DeviceQuery('memory.used, memory.total') gpu_used_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['used']) gpu_total_mem = int(gpu_query['gpu'][0]['fb_memory_usage']['total']) return gpu_used_mem, gpu_total_mem def append_epoch_log(global_step: int, epoch_pbar, gpu, log_writer, **logs): """ updates the vram usage for the epoch """ gpu_used_mem, gpu_total_mem = gpu.get_gpu_memory() log_writer.add_scalar("performance/vram", gpu_used_mem, global_step) epoch_mem_color = Style.RESET_ALL if gpu_used_mem > 0.93 * gpu_total_mem: epoch_mem_color = Fore.LIGHTRED_EX elif gpu_used_mem > 0.85 * gpu_total_mem: epoch_mem_color = Fore.LIGHTYELLOW_EX elif gpu_used_mem > 0.7 * gpu_total_mem: epoch_mem_color = Fore.LIGHTGREEN_EX elif gpu_used_mem < 0.5 * gpu_total_mem: epoch_mem_color = Fore.LIGHTBLUE_EX if logs is not None: epoch_pbar.set_postfix(**logs, vram=f"{epoch_mem_color}{gpu_used_mem}/{gpu_total_mem} MB{Style.RESET_ALL} gs:{global_step}") def set_args_12gb(args): logging.info(" Setting args to 12GB mode") if not args.gradient_checkpointing: logging.info(" - Overiding gradient checkpointing to True") args.gradient_checkpointing = True if args.batch_size != 1: logging.info(" - Overiding batch size to 1") args.batch_size = 1 # if args.grad_accum != 1: # logging.info(" Overiding grad accum to 1") args.grad_accum = 1 if args.resolution > 512: logging.info(" - Overiding resolution to 512") args.resolution = 512 if not args.useadam8bit: logging.info(" - Overiding adam8bit to True") args.useadam8bit = True def find_last_checkpoint(logdir): """ 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": 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_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.lowvram: set_args_12gb(args) if not args.shuffle_tags: args.shuffle_tags = False 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}") total_batch_size = args.batch_size * args.grad_accum if args.scale_lr is not None and args.scale_lr: tmp_lr = args.lr args.lr = args.lr * (total_batch_size**0.55) logging.info(f"{Fore.CYAN} * Scaling learning rate {tmp_lr} by {total_batch_size**0.5}, new value: {args.lr}{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}")) return args def update_grad_scaler(scaler: GradScaler, global_step, epoch, step): if global_step == 250 or (epoch >= 4 and step == 1): factor = 1.8 scaler.set_growth_factor(factor) scaler.set_backoff_factor(1/factor) scaler.set_growth_interval(50) if global_step == 500 or (epoch >= 8 and step == 1): factor = 1.6 scaler.set_growth_factor(factor) scaler.set_backoff_factor(1/factor) scaler.set_growth_interval(50) if global_step == 1000 or (epoch >= 10 and step == 1): factor = 1.3 scaler.set_growth_factor(factor) scaler.set_backoff_factor(1/factor) scaler.set_growth_interval(100) if global_step == 3000 or (epoch >= 20 and step == 1): factor = 1.15 scaler.set_growth_factor(factor) scaler.set_backoff_factor(1/factor) scaler.set_growth_interval(100) def main(args): """ Main entry point """ log_time = setup_local_logger(args) args = setup_args(args) if args.notebook: from tqdm.notebook import tqdm else: from tqdm.auto import tqdm seed = args.seed if args.seed != -1 else random.randint(0, 2**30) logging.info(f" Seed: {seed}") set_seed(seed) gpu = GPU() device = torch.device(f"cuda:{args.gpuid}") torch.backends.cudnn.benchmark = True log_folder = os.path.join(args.logdir, f"{args.project_name}_{log_time}") if not os.path.exists(log_folder): os.makedirs(log_folder) @torch.no_grad() def __save_model(save_path, unet, text_encoder, tokenizer, scheduler, vae, save_ckpt_dir, yaml_name, save_full_precision=False): """ Save the model to disk """ global global_step if global_step is None or global_step == 0: logging.warning(" No model to save, something likely blew up on startup, not saving") return logging.info(f" * Saving diffusers model to {save_path}") pipeline = StableDiffusionPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, # save vram requires_safety_checker=None, # avoid nag feature_extractor=None, # must be none of no safety checker ) pipeline.save_pretrained(save_path) sd_ckpt_path = f"{os.path.basename(save_path)}.ckpt" 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=save_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(save_path))}.yaml" logging.info(f" * Saving yaml to {yaml_save_path}") shutil.copyfile(yaml_name, yaml_save_path) # optimizer_path = os.path.join(save_path, "optimizer.pt") # if self.save_optimizer_flag: # logging.info(f" Saving optimizer state to {save_path}") # self.save_optimizer(self.ctx.optimizer, optimizer_path) @torch.no_grad() def __create_inference_pipe(unet, text_encoder, tokenizer, scheduler, vae): """ creates a pipeline for SD inference """ pipe = StableDiffusionPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=None, # save vram requires_safety_checker=None, # avoid nag feature_extractor=None, # must be none of no safety checker ) return pipe def __generate_sample(pipe: StableDiffusionPipeline, prompt : str, cfg: float, resolution: int, gen): """ generates a single sample at a given cfg scale and saves it to disk """ with torch.no_grad(), autocast(): image = pipe(prompt, num_inference_steps=30, num_images_per_prompt=1, guidance_scale=cfg, generator=gen, height=resolution, width=resolution, ).images[0] draw = ImageDraw.Draw(image) try: font = ImageFont.truetype(font="arial.ttf", size=20) except: font = ImageFont.load_default() print_msg = f"cfg:{cfg:.1f}" l, t, r, b = draw.textbbox(xy=(0,0), text=print_msg, font=font) text_width = r - l text_height = b - t x = float(image.width - text_width - 10) y = float(image.height - text_height - 10) draw.rectangle((x, y, image.width, image.height), fill="white") draw.text((x, y), print_msg, fill="black", font=font) del draw, font return image def __generate_test_samples(pipe, prompts, gs, log_writer, log_folder, random_captions=False, resolution=512): """ generates samples at different cfg scales and saves them to disk """ logging.info(f"Generating samples gs:{gs}, for {prompts}") pipe.set_progress_bar_config(disable=True) seed = args.seed if args.seed != -1 else random.randint(0, 2**30) gen = torch.Generator(device=device).manual_seed(seed) i = 0 for prompt in prompts: if prompt is None or len(prompt) < 2: #logging.warning("empty prompt in sample prompts, check your prompts file") continue images = [] for cfg in [7.0, 4.0, 1.01]: image = __generate_sample(pipe, prompt, cfg, resolution=resolution, gen=gen) images.append(image) width = 0 height = 0 for image in images: width += image.width height = max(height, image.height) result = Image.new('RGB', (width, height)) x_offset = 0 for image in images: result.paste(image, (x_offset, 0)) x_offset += image.width clean_prompt = clean_filename(prompt) result.save(f"{log_folder}/samples/gs{gs:05}-{i}-{clean_prompt[:100]}.jpg", format="JPEG", quality=95, optimize=True, progressive=False) with open(f"{log_folder}/samples/gs{gs:05}-{i}-{clean_prompt[:100]}.txt", "w", encoding='utf-8') as f: f.write(prompt) f.write(f"\n seed: {seed}") tfimage = transforms.ToTensor()(result) if random_captions: log_writer.add_image(tag=f"sample_{i}", img_tensor=tfimage, global_step=gs) else: log_writer.add_image(tag=f"sample_{i}_{clean_prompt[:100]}", img_tensor=tfimage, global_step=gs) i += 1 del result del tfimage del images 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) else: # try to download from HF using resume_ckpt as a repo id print(f"local file/folder not found for {args.resume_ckpt}, will try to download from huggingface.co") hf_repo_subfolder = args.hf_repo_subfolder if hasattr(args, 'hf_repo_subfolder') else None model_root_folder, is_sd1attn, yaml = try_download_model_from_hf(repo_id=args.resume_ckpt, subfolder=hf_repo_subfolder) if model_root_folder is None: raise ValueError(f"No local file/folder for {args.resume_ckpt}, and no matching huggingface.co repo could be downloaded") 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") sample_scheduler = DDIMScheduler.from_pretrained(model_root_folder, subfolder="scheduler") noise_scheduler = DDPMScheduler.from_pretrained(model_root_folder, subfolder="scheduler") 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 *") if args.gradient_checkpointing: unet.enable_gradient_checkpointing() text_encoder.gradient_checkpointing_enable() if not args.disable_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 attention slicing instead") unet.set_attention_slice("auto") pass else: logging.info("xformers disabled, using attention slicing instead") unet.set_attention_slice("auto") default_lr = 2e-6 curr_lr = args.lr if args.lr is not None else default_lr 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 args.disable_textenc_training: logging.info(f"{Fore.CYAN} * NOT Training Text Encoder, quality reduced *{Style.RESET_ALL}") params_to_train = itertools.chain(unet.parameters()) elif args.disable_unet_training: logging.info(f"{Fore.CYAN} * Training Text Encoder Only *{Style.RESET_ALL}") params_to_train = itertools.chain(text_encoder.parameters()) else: logging.info(f"{Fore.CYAN} * Training Text and Unet *{Style.RESET_ALL}") params_to_train = itertools.chain(unet.parameters(), text_encoder.parameters()) betas = (0.9, 0.999) epsilon = 1e-8 if args.amp: epsilon = 2e-8 weight_decay = 0.01 if args.useadam8bit: import bitsandbytes as bnb opt_class = bnb.optim.AdamW8bit logging.info(f"{Fore.CYAN} * Using AdamW 8-bit Optimizer *{Style.RESET_ALL}") else: opt_class = torch.optim.AdamW logging.info(f"{Fore.CYAN} * Using AdamW standard Optimizer *{Style.RESET_ALL}") optimizer = opt_class( itertools.chain(params_to_train), lr=curr_lr, betas=betas, eps=epsilon, weight_decay=weight_decay, amsgrad=False, ) log_optimizer(optimizer, betas, epsilon) train_batch = EveryDreamBatch( data_root=args.data_root, flip_p=args.flip_p, debug_level=1, batch_size=args.batch_size, conditional_dropout=args.cond_dropout, resolution=args.resolution, tokenizer=tokenizer, seed = seed, log_folder=log_folder, write_schedule=args.write_schedule, shuffle_tags=args.shuffle_tags, 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 args.lr_decay_steps is None or args.lr_decay_steps < 1: args.lr_decay_steps = int(epoch_len * args.max_epochs * 1.5) lr_warmup_steps = int(args.lr_decay_steps / 50) if args.lr_warmup_steps is None else args.lr_warmup_steps lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=lr_warmup_steps, num_training_steps=args.lr_decay_steps, ) sample_prompts = [] with open(args.sample_prompts, "r") as f: for line in f: sample_prompts.append(line.strip()) if args.wandb is not None and args.wandb: wandb.init(project=args.project_name, sync_tensorboard=True, ) log_writer = SummaryWriter(log_dir=log_folder, flush_secs=5, comment="EveryDream2FineTunes", ) def log_args(log_writer, args): arglog = "args:\n" for arg, value in sorted(vars(args).items()): arglog += f"{arg}={value}, " log_writer.add_text("config", arglog) log_args(log_writer, args) """ 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 """ global interrupted if not interrupted: interrupted=True global global_step #TODO: save model on ctrl-c 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, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision) exit(_SIGTERM_EXIT_CODE) signal.signal(signal.SIGINT, sigterm_handler) if not os.path.exists(f"{log_folder}/samples/"): os.makedirs(f"{log_folder}/samples/") gpu_used_mem, gpu_total_mem = gpu.get_gpu_memory() logging.info(f" Pretraining GPU Memory: {gpu_used_mem} / {gpu_total_mem} MB") logging.info(f" saving ckpts every {args.ckpt_every_n_minutes} minutes") logging.info(f" saving ckpts every {args.save_every_n_epochs } epochs") def collate_fn(batch): """ Collates batches """ images = [example["image"] for example in batch] captions = [example["caption"] for example in batch] tokens = [example["tokens"] for example in batch] runt_size = batch[0]["runt_size"] images = torch.stack(images) images = images.to(memory_format=torch.contiguous_format).float() ret = { "tokens": torch.stack(tuple(tokens)), "image": images, "captions": captions, "runt_size": runt_size, } del batch return ret train_dataloader = torch.utils.data.DataLoader( train_batch, batch_size=args.batch_size, shuffle=False, num_workers=0, collate_fn=collate_fn ) unet.train() if 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}") scaler = GradScaler( enabled=args.amp, init_scale=2**17.5, growth_factor=2, backoff_factor=1.0/2, growth_interval=25, ) logging.info(f" Grad scaler enabled: {scaler.is_enabled()} (amp mode)") epoch_pbar = tqdm(range(args.max_epochs), position=0, leave=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" try: # # dummy batch to pin memory to avoid fragmentation in torch, uses square aspect which is maximum bytes size per aspects.py # pixel_values = torch.randn_like(torch.zeros([args.batch_size, 3, args.resolution, args.resolution])) # pixel_values = pixel_values.to(unet.device) # with autocast(enabled=args.amp): # latents = vae.encode(pixel_values, return_dict=False) # latents = latents[0].sample() * 0.18215 # noise = torch.randn_like(latents) # bsz = latents.shape[0] # timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) # timesteps = timesteps.long() # noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # cuda_caption = torch.linspace(100,177, steps=77, dtype=int).to(text_encoder.device) # encoder_hidden_states = text_encoder(cuda_caption, output_hidden_states=True).last_hidden_state # with autocast(enabled=args.amp): # model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample # # discard the grads, just want to pin memory # optimizer.zero_grad(set_to_none=True) for epoch in range(args.max_epochs): loss_epoch = [] epoch_start_time = time.time() images_per_sec_log_step = [] epoch_len = math.ceil(len(train_batch) / args.batch_size) steps_pbar = tqdm(range(epoch_len), position=1) steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{Style.RESET_ALL}") for step, batch in enumerate(train_dataloader): step_start_time = time.time() with torch.no_grad(): with autocast(enabled=args.amp): pixel_values = batch["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) bsz = latents.shape[0] timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() cuda_caption = batch["tokens"].to(text_encoder.device) #with autocast(enabled=args.amp): 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): model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample #del timesteps, encoder_hidden_states, noisy_latents #with autocast(enabled=args.amp): loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") del target, model_pred scaler.scale(loss).backward() if args.clip_grad_norm is not None: if not args.disable_unet_training: torch.nn.utils.clip_grad_norm_(parameters=unet.parameters(), max_norm=args.clip_grad_norm) if not args.disable_textenc_training: torch.nn.utils.clip_grad_norm_(parameters=text_encoder.parameters(), max_norm=args.clip_grad_norm) if batch["runt_size"] > 0: grad_scale = batch["runt_size"] / args.batch_size with torch.no_grad(): # not required? just in case for now, needs more testing for param in unet.parameters(): if param.grad is not None: param.grad *= grad_scale if text_encoder.training: for param in text_encoder.parameters(): if param.grad is not None: param.grad *= grad_scale if ((global_step + 1) % args.grad_accum == 0) or (step == epoch_len - 1): scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) lr_scheduler.step() 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: curr_lr = lr_scheduler.get_last_lr()[0] loss_local = sum(loss_log_step) / len(loss_log_step) loss_log_step = [] logs = {"loss/log_step": loss_local, "lr": curr_lr, "img/s": images_per_sec} log_writer.add_scalar(tag="hyperparamater/lr", scalar_value=curr_lr, global_step=global_step) log_writer.add_scalar(tag="loss/log_step", scalar_value=loss_local, 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="hyperparamater/grad scale", scalar_value=scaler.get_scale(), global_step=global_step) log_writer.add_scalar(tag="performance/images per second", scalar_value=avg, global_step=global_step) append_epoch_log(global_step=global_step, epoch_pbar=epoch_pbar, gpu=gpu, log_writer=log_writer, **logs) torch.cuda.empty_cache() if (global_step + 1) % args.sample_steps == 0: pipe = __create_inference_pipe(unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=sample_scheduler, vae=vae) pipe = pipe.to(device) with torch.no_grad(): if sample_prompts is not None and len(sample_prompts) > 0 and len(sample_prompts[0]) > 1: __generate_test_samples(pipe=pipe, prompts=sample_prompts, log_writer=log_writer, log_folder=log_folder, gs=global_step, resolution=args.resolution) else: max_prompts = min(4,len(batch["captions"])) prompts=batch["captions"][:max_prompts] __generate_test_samples(pipe=pipe, prompts=prompts, log_writer=log_writer, log_folder=log_folder, gs=global_step, random_captions=True, resolution=args.resolution) del pipe gc.collect() torch.cuda.empty_cache() min_since_last_ckpt = (time.time() - last_epoch_saved_time) / 60 if args.ckpt_every_n_minutes is not None and (min_since_last_ckpt > args.ckpt_every_n_minutes): last_epoch_saved_time = time.time() logging.info(f"Saving model, {args.ckpt_every_n_minutes} mins at step {global_step}") save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}") __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, yaml, args.save_full_precision) if epoch > 0 and epoch % args.save_every_n_epochs == 0 and step == 1 and epoch < args.max_epochs - 1: logging.info(f" Saving model, {args.save_every_n_epochs} epochs at step {global_step}") save_path = os.path.join(f"{log_folder}/ckpts/{args.project_name}-ep{epoch:02}-gs{global_step:05}") __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, yaml, args.save_full_precision) del batch global_step += 1 update_grad_scaler(scaler, global_step, epoch, step) if args.amp else None # 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) epoch_pbar.update(1) if epoch < args.max_epochs - 1: train_batch.shuffle(epoch_n=epoch, max_epochs = args.max_epochs) loss_local = sum(loss_epoch) / len(loss_epoch) log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_local, global_step=global_step) gc.collect() # end of epoch # end of training save_path = os.path.join(f"{log_folder}/ckpts/last-{args.project_name}-ep{epoch:02}-gs{global_step:05}") __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, yaml, args.save_full_precision) 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 = os.path.join(f"{log_folder}/ckpts/errored-{args.project_name}-ep{epoch:02}-gs{global_step:05}") __save_model(save_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, yaml, args.save_full_precision) 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}") def update_old_args(t_args): """ Update old args to new args to deal with json config loading and missing args for compatibility """ if not hasattr(t_args, "shuffle_tags"): print(f" Config json is missing 'shuffle_tags' flag") t_args.__dict__["shuffle_tags"] = False if not hasattr(t_args, "save_full_precision"): print(f" Config json is missing 'save_full_precision' flag") t_args.__dict__["save_full_precision"] = False if not hasattr(t_args, "notebook"): print(f" Config json is missing 'notebook' flag") t_args.__dict__["notebook"] = False if not hasattr(t_args, "disable_unet_training"): print(f" Config json is missing 'disable_unet_training' flag") t_args.__dict__["disable_unet_training"] = False if not hasattr(t_args, "rated_dataset"): print(f" Config json is missing 'rated_dataset' flag") t_args.__dict__["rated_dataset"] = False if not hasattr(t_args, "rated_dataset_target_dropout_percent"): print(f" Config json is missing 'rated_dataset_target_dropout_percent' flag") t_args.__dict__["rated_dataset_target_dropout_percent"] = 50 if __name__ == "__main__": supported_resolutions = [256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152] supported_precisions = ['fp16', 'fp32'] 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, _ = argparser.parse_known_args() if args.config is not None: print(f"Loading training config from {args.config}, all other command options will be ignored!") with open(args.config, 'rt') as f: t_args = argparse.Namespace() t_args.__dict__.update(json.load(f)) update_old_args(t_args) # update args to support older configs print(f" args: \n{t_args.__dict__}") args = argparser.parse_args(namespace=t_args) else: print("No config file specified, using command line args") argparser = argparse.ArgumentParser(description="EveryDream2 Training options") argparser.add_argument("--amp", action="store_true", default=False, help="Enables automatic mixed precision compute, recommended on") 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_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("--disable_xformers", action="store_true", default=False, help="disable xformers, may reduce performance (def: False)") 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)") 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("--hf_repo_subfolder", type=str, default=None, help="Subfolder inside the huggingface repo to download, if the model is not in the root of the repo.") 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("--lowvram", action="store_true", default=False, help="automatically overrides various args to support 12GB gpu") 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("--notebook", action="store_true", default=False, help="disable keypresses and uses tqdm.notebook for jupyter notebook (def: False)") 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=True, 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("--sample_prompts", type=str, default="sample_prompts.txt", help="File with prompts to generate test samples from (def: sample_prompts.txt)") argparser.add_argument("--sample_steps", type=int, default=250, help="Number of steps between samples (def: 250)") argparser.add_argument("--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_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("--scale_lr", action="store_true", default=False, help="automatically scale up learning rate based on batch size and grad accumulation (def: False)") 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("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!") 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("--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)") args, _ = argparser.parse_known_args() main(args)