EveryDream2trainer/train.py

"""
Copyright [2022] 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 torch.nn.functional as torch_functional
from torch.cuda.amp import autocast
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, DiffusionPipeline, 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 tqdm.auto import tqdm

from data.every_dream import EveryDreamBatch
from utils.convert_diffusers_to_stable_diffusion import convert as converter
from utils.gpu import GPU


_SIGTERM_EXIT_CODE = 130
_VERY_LARGE_NUMBER = 1e9

# def is_notebook() -> bool:
#     try:
#         from IPython import get_ipython
#         shell = get_ipython().__class__.__name__
#         if shell == 'ZMQInteractiveShell':
#             return True   # Jupyter notebook or qtconsole
#         elif shell == 'TerminalInteractiveShell':
#             return False  # Terminal running IPython
#         else:
#             return False  # Other type (?)
#     except NameError:
#         return False      # Probably standard Python interpreter

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 convert_to_hf(ckpt_path):
    hf_cache = os.path.join("ckpt_cache", os.path.basename(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, see readme.")
                exit()
        else:
            logging.info(f"Found cached checkpoint at {hf_cache}")
        
        patch_unet(hf_cache)
        return hf_cache
    elif os.path.isdir(hf_cache):
        patch_unet(hf_cache)
        return hf_cache
    else:
        return ckpt_path

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")

    logfilename = os.path.join(log_path, f"{args.project_name}-train{datetimestamp}.log")
    with open(logfilename, "w") as f:
        f.write(f"Training config:\n{json_config}\n")

    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 main(args):
    """
    Main entry point
    """
    log_time = setup_local_logger(args)
    
    if args.lowvram:
        set_args_12gb(args)

    seed = args.seed if args.seed != -1 else random.randint(0, 2**30)
    set_seed(seed)
    gpu = GPU()
    device = torch.device(f"cuda:{args.gpuid}")
    torch.backends.cudnn.benchmark = 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}")

    total_batch_size = args.batch_size * args.grad_accum

    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.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}")

    log_folder = os.path.join(args.logdir, f"{args.project_name}_{log_time}")
    logging.info(f"Logging to {log_folder}")
    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):
        """
        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)

        logging.info(f" * Saving SD model to {sd_ckpt_full}")
        converter(model_path=save_path, checkpoint_path=sd_ckpt_full, half=True)
        # 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
        )
        if is_xformers_available():
            try:
                pipe.enable_xformers_memory_efficient_attention()
            except Exception as ex:
                logging.warning("failed to load xformers, continuing without it")
                pass
        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}")
        seed = args.seed if args.seed != -1 else random.randint(0, 2**30)
        gen = torch.Generator(device="cuda").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}-{clean_prompt[:100]}.jpg", format="JPEG", quality=95, optimize=True, progressive=False)
            with open(f"{log_folder}/samples/gs{gs:05}-{clean_prompt[:100]}.txt", "w") 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: 
        hf_ckpt_path = convert_to_hf(args.resume_ckpt)
        text_encoder = CLIPTextModel.from_pretrained(hf_ckpt_path, subfolder="text_encoder")
        vae = AutoencoderKL.from_pretrained(hf_ckpt_path, subfolder="vae")
        unet = UNet2DConditionModel.from_pretrained(hf_ckpt_path, subfolder="unet")
        #unet.upcast_attention(True)
        scheduler = DDIMScheduler.from_pretrained(hf_ckpt_path, subfolder="scheduler")
        tokenizer = CLIPTokenizer.from_pretrained(hf_ckpt_path, subfolder="tokenizer", use_fast=False)
    except:
        logging.ERROR(" * Failed to load checkpoint *")

    if args.gradient_checkpointing:
        unet.enable_gradient_checkpointing()
        text_encoder.gradient_checkpointing_enable()

    if is_xformers_available():
        try:
            unet.enable_xformers_memory_efficient_attention()
            print(" Enabled xformers")
        except Exception as e:
            logging.warning(
                "Could not enable memory efficient attention. Make sure xformers is installed"
                f" correctly and a GPU is available: {e}"
            )

    default_lr = 3e-6
    curr_lr = args.lr if args.lr is not None else default_lr

    vae = vae.to(device, dtype=torch.float32 if not args.amp else torch.float16)
    unet = unet.to(device, dtype=torch.float32 if not args.amp else torch.float16)
    text_encoder = text_encoder.to(device, dtype=torch.float32 if not args.amp else torch.float16)

    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())
        text_encoder.eval()
    else:
        logging.info(f"{Fore.CYAN} * Training Text Encoder *{Style.RESET_ALL}")
        params_to_train = itertools.chain(unet.parameters(), text_encoder.parameters())

    betas = (0.9, 0.999)
    epsilon = 1e-8 if not args.amp else 1e-8
    weight_decay = 0.01
    if args.useadam8bit:
        logging.info(f"{Fore.CYAN} * Using AdamW 8-bit Optimizer *{Style.RESET_ALL}")
        import bitsandbytes as bnb
        optimizer = bnb.optim.AdamW8bit(
            itertools.chain(params_to_train),
            lr=curr_lr,
            betas=betas,
            eps=epsilon,
            weight_decay=weight_decay,
        )
    else:
        logging.info(f"{Fore.CYAN} * Using AdamW standard Optimizer *{Style.RESET_ALL}")
        optimizer = torch.optim.AdamW(
            itertools.chain(params_to_train),
            lr=curr_lr,
            betas=betas,
            eps=epsilon,
            weight_decay=weight_decay,
            amsgrad=False,
        )

    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,
    )

    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 False: #args.wandb is not None and args.wandb: # not yet supported
        log_writer = wandb.init(project="EveryDream2FineTunes", 
                                name=args.project_name, 
                                dir=log_folder,
                               )
    else:
        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 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}")
            __save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, scheduler, vae, args.save_ckpt_dir)
        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")

    # scaler = torch.cuda.amp.GradScaler(
    #     #enabled=False,
    #     enabled=True if args.amp else False,
    #     init_scale=2**1,
    #     growth_factor=1.000001,
    #     backoff_factor=0.9999999,
    #     growth_interval=50,
    # )
    #logging.info(f" Grad scaler enabled: {scaler.is_enabled()}")

    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()
    text_encoder.train()    

    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: {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}total_batch_size: {Style.RESET_ALL}{Fore.LIGHTGREEN_EX}{total_batch_size}")
    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)
    epoch_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Epochs{Style.RESET_ALL}")

    steps_pbar = tqdm(range(epoch_len), position=1, leave=True)
    steps_pbar.set_description(f"{Fore.LIGHTCYAN_EX}Steps{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)

    torch.cuda.empty_cache()
    #loss = torch.tensor(0.0, device=device, dtype=torch.float32)

    try:            
        for epoch in range(args.max_epochs):
            epoch_start_time = time.time()
            steps_pbar.reset()
            images_per_sec_epoch = []

            for step, batch in enumerate(train_dataloader):
                step_start_time = time.time()

                with torch.no_grad():
                    #with autocast():
                    pixel_values = batch["image"].to(memory_format=torch.contiguous_format).to(unet.device)
                    with autocast(enabled=args.amp):                
                        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, 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 = encoder_hidden_states.hidden_states[-args.clip_skip]
                else:
                    encoder_hidden_states = encoder_hidden_states.last_hidden_state
                
                noisy_latents = scheduler.add_noise(latents, noise, timesteps)

                if scheduler.config.prediction_type == "epsilon":
                    target = noise
                elif scheduler.config.prediction_type in ["v_prediction", "v-prediction"]:
                    target = scheduler.get_velocity(latents, noise, timesteps)
                else:
                    raise ValueError(f"Unknown prediction type {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 = torch_functional.mse_loss(model_pred.float(), target.float(), reduction="mean")
                if batch["runt_size"]> 0:
                    loss = loss / (batch["runt_size"] / args.batch_size)
                del target, model_pred


                if args.clip_grad_norm is not None:
                    torch.nn.utils.clip_grad_norm_(parameters=unet.parameters(), max_norm=args.clip_grad_norm)
                    torch.nn.utils.clip_grad_norm_(parameters=text_encoder.parameters(), max_norm=args.clip_grad_norm)

                loss.backward()

                if ((global_step + 1) % args.grad_accum == 0) or (step == epoch_len - 1):
                    optimizer.step()
                    optimizer.zero_grad(set_to_none=True)

                lr_scheduler.step()

                steps_pbar.update(1)
                global_step += 1

                images_per_sec = args.batch_size / (time.time() - step_start_time)
                images_per_sec_epoch.append(images_per_sec)

                if (global_step + 1) % args.log_step == 0:
                    curr_lr = lr_scheduler.get_last_lr()[0]
                    loss_local = loss.detach().item()
                    logs = {"loss/step": loss_local, "lr": curr_lr, "img/s": images_per_sec}
                    log_writer.add_scalar(tag="loss/step", scalar_value=loss_local, global_step=global_step)
                    log_writer.add_scalar(tag="hyperparamater/lr", scalar_value=curr_lr, global_step=global_step)
                    sum_img = sum(images_per_sec_epoch)
                    avg = sum_img / len(images_per_sec_epoch)
                    images_per_sec_epoch = []
                    #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)

                if (global_step + 1) % args.sample_steps == 0:
                    pipe = __create_inference_pipe(unet=unet, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=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
                    torch.cuda.empty_cache()
                    gc.collect()

                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, scheduler, vae, args.save_ckpt_dir)

                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, scheduler, vae, args.save_ckpt_dir)

                # end of step

            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+1)
            # 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, scheduler, vae, args.save_ckpt_dir)

        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:.2f} seconds, 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, scheduler, vae, args.save_ckpt_dir)
        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__":
    supported_resolutions = [448, 512, 576, 640, 704, 768, 832, 896, 960, 1024, 1088, 1152]
    argparser = argparse.ArgumentParser(description="EveryDream2 Training options")
    argparser.add_argument("--resume_ckpt", type=str, required=True, default="sd_v1-5_vae.ckpt")
    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("--lr_decay_steps", type=int, default=0, help="Steps to reach minimum LR, default: automatically set")
    argparser.add_argument("--log_step", type=int, default=25, help="How often to log training stats, def: 25, recommend default!")
    argparser.add_argument("--max_epochs", type=int, default=300, help="Maximum number of epochs to train for")
    argparser.add_argument("--ckpt_every_n_minutes", type=int, default=None, help="Save checkpoint every n minutes, def: 20")
    argparser.add_argument("--save_every_n_epochs", type=int, default=None, help="Save checkpoint every n epochs, def: 0 (disabled)")
    argparser.add_argument("--lr", type=float, default=None, help="Learning rate, if using scheduler is maximum LR at top of curve")
    argparser.add_argument("--useadam8bit", action="store_true", default=False, help="Use AdamW 8-Bit optimizer, recommended!")
    argparser.add_argument("--project_name", type=str, default="myproj", help="Project name for logs and checkpoints, ex. 'tedbennett', 'superduperV1'")
    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("--disable_textenc_training", action="store_true", default=False, help="disables training of text encoder (def: False) NOT RECOMMENDED")
    argparser.add_argument("--batch_size", type=int, default=2, help="Batch size (def: 2)")
    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("--grad_accum", type=int, default=1, help="Gradient accumulation factor (def: 1), (ex, 2)")
    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("--data_root", type=str, default="input", help="folder where your training images are")
    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("--save_optimizer", action="store_true", default=False, help="saves optimizer state with ckpt, useful for resuming training later")
    argparser.add_argument("--resolution", type=int, default=512, help="resolution to train", choices=supported_resolutions)
    argparser.add_argument("--amp", action="store_true", default=False, help="use floating point 16 bit training, experimental, reduces quality")
    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("--logdir", type=str, default="logs", help="folder to save logs to (def: logs)")
    argparser.add_argument("--save_ckpt_dir", type=str, default=None, help="folder to save checkpoints to (def: root training folder)")
    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("--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("--write_schedule", action="store_true", default=False, help="write schedule of images and their batches to file (def: False)")
    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("--lowvram", action="store_true", default=False, help="automatically overrides various args to support 12GB gpu")

    args = argparser.parse_args()

    main(args)