train.py

import argparse
import datetime
import logging
import inspect
import math
import os
import random
import gc
import copy

from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf

import cv2
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms as T
import diffusers
import transformers

from torchvision import transforms
from tqdm.auto import tqdm

from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed

from models.unet_3d_condition import UNet3DConditionModel
from diffusers.models import AutoencoderKL
from diffusers import DPMSolverMultistepScheduler, DDPMScheduler, TextToVideoSDPipeline
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version, export_to_video
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention_processor import AttnProcessor2_0, Attention
from diffusers.models.attention import BasicTransformerBlock

from transformers import CLIPTextModel, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPEncoder
from utils.dataset import VideoJsonDataset, SingleVideoDataset, \
    ImageDataset, VideoFolderDataset, CachedDataset
from einops import rearrange, repeat

from utils.lora import (
    extract_lora_ups_down,
    inject_trainable_lora,
    inject_trainable_lora_extended,
    save_lora_weight,
    train_patch_pipe,
    monkeypatch_or_replace_lora,
    monkeypatch_or_replace_lora_extended
)


already_printed_trainables = False

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")

logger = get_logger(__name__, log_level="INFO")

def create_logging(logging, logger, accelerator):
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state, main_process_only=False)

def accelerate_set_verbose(accelerator):
    if accelerator.is_local_main_process:
        transformers.utils.logging.set_verbosity_warning()
        diffusers.utils.logging.set_verbosity_info()
    else:
        transformers.utils.logging.set_verbosity_error()
        diffusers.utils.logging.set_verbosity_error()

def get_train_dataset(dataset_types, train_data, tokenizer):
    train_datasets = []

    # Loop through all available datasets, get the name, then add to list of data to process.
    for DataSet in [VideoJsonDataset, SingleVideoDataset, ImageDataset, VideoFolderDataset]:
        for dataset in dataset_types:
            if dataset == DataSet.__getname__():
                train_datasets.append(DataSet(**train_data, tokenizer=tokenizer))

    if len(train_datasets) > 0:
        return train_datasets
    else:
        raise ValueError("Dataset type not found: 'json', 'single_video', 'folder', 'image'")

def extend_datasets(datasets, dataset_items, extend=False):
    biggest_data_len = max(x.__len__() for x in datasets)
    extended = []
    for dataset in datasets:
        if dataset.__len__() == 0:
            del dataset
            continue
        if dataset.__len__() < biggest_data_len:
            for item in dataset_items:
                if extend and item not in extended and hasattr(dataset, item):
                    print(f"Extending {item}")

                    value = getattr(dataset, item)
                    value *= biggest_data_len
                    value = value[:biggest_data_len]

                    setattr(dataset, item, value)

                    print(f"New {item} dataset length: {dataset.__len__()}")
                    extended.append(item)

def export_to_video(video_frames, output_video_path, fps):
    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    h, w, _ = video_frames[0].shape
    video_writer = cv2.VideoWriter(output_video_path, fourcc, fps=fps, frameSize=(w, h))
    for i in range(len(video_frames)):
        img = cv2.cvtColor(video_frames[i], cv2.COLOR_RGB2BGR)
        video_writer.write(img)

def create_output_folders(output_dir, config):
    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
    out_dir = os.path.join(output_dir, f"train_{now}")
    
    os.makedirs(out_dir, exist_ok=True)
    os.makedirs(f"{out_dir}/samples", exist_ok=True)
    OmegaConf.save(config, os.path.join(out_dir, 'config.yaml'))

    return out_dir

def load_primary_models(pretrained_model_path):
    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
    vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
    unet = UNet3DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet")

    return noise_scheduler, tokenizer, text_encoder, vae, unet

def unet_and_text_g_c(unet, text_encoder, unet_enable, text_enable):
    unet._set_gradient_checkpointing(value=unet_enable)
    text_encoder._set_gradient_checkpointing(CLIPEncoder, value=text_enable)

def freeze_models(models_to_freeze):
    for model in models_to_freeze:
        if model is not None: model.requires_grad_(False) 
            
def is_attn(name):
   return ('attn1' or 'attn2' == name.split('.')[-1])

def set_processors(attentions):
    for attn in attentions: attn.set_processor(AttnProcessor2_0()) 

def set_torch_2_attn(unet):
    optim_count = 0
    
    for name, module in unet.named_modules():
        if is_attn(name):
            if isinstance(module, torch.nn.ModuleList):
                for m in module:
                    if isinstance(m, BasicTransformerBlock):
                        set_processors([m.attn1, m.attn2])
                        optim_count += 1
    if optim_count > 0: 
        print(f"{optim_count} Attention layers using Scaled Dot Product Attention.")

def handle_memory_attention(enable_xformers_memory_efficient_attention, enable_torch_2_attn, unet): 
    try:
        is_torch_2 = hasattr(F, 'scaled_dot_product_attention')

        if enable_xformers_memory_efficient_attention and not is_torch_2:
            if is_xformers_available():
                from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
                unet.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
            else:
                raise ValueError("xformers is not available. Make sure it is installed correctly")
        
        if enable_torch_2_attn and is_torch_2:
            set_torch_2_attn(unet)
    except:
        print("Could not enable memory efficient attention for xformers or Torch 2.0.")

def inject_lora(use_lora, model, replace_modules, is_extended=False, dropout=0.0, lora_path='', r=16):    
    injector = (
        inject_trainable_lora if not is_extended 
    else 
        inject_trainable_lora_extended
    )

    params = None
    negation = None

    if os.path.exists(lora_path):
        try:
            for f in os.listdir(lora_path):
                if f.endswith('.pt'):
                    lora_file = os.path.join(lora_path, f)
                    
                    if 'text_encoder' in f and isinstance(model, CLIPTextModel):
                        monkeypatch_or_replace_lora(
                            model,
                            torch.load(lora_file),
                            target_replace_module=replace_modules,
                            r=r
                        )
                        print("Successfully loaded Text Encoder LoRa.")

                    if 'unet' in f and isinstance(model, UNet3DConditionModel):
                        monkeypatch_or_replace_lora_extended(
                            model,
                            torch.load(lora_file),
                            target_replace_module=replace_modules,
                            r=r
                        )
                        print("Successfully loaded UNET LoRa.")

        except Exception as e:
            print(e)
            print("Could not load LoRAs. Injecting new ones instead...")

    if use_lora:
        REPLACE_MODULES = replace_modules
        injector_args = {
            "model": model, 
            "target_replace_module": REPLACE_MODULES,
            "r": r
        }
        if not is_extended: injector_args['dropout_p'] = dropout

        params, negation = injector(**injector_args)   
        for _up, _down in extract_lora_ups_down(
            model, 
            target_replace_module=REPLACE_MODULES):

            if all(x is not None for x in [_up, _down]):
                print(f"Lora successfully injected into {model.__class__.__name__}.")

            break
        
    return params, negation

def save_lora(model, name, condition, replace_modules, step, save_path): 
    if condition and replace_modules is not None:
        save_path = f"{save_path}/{step}_{name}.pt"
        save_lora_weight(model, save_path, replace_modules)

def handle_lora_save(
    use_unet_lora, 
    use_text_lora, 
    model, 
    save_path, 
    checkpoint_step, 
    unet_target_modules, 
    text_encoder_target_modules
):
    
    save_path = f"{save_path}/lora"
    os.makedirs(save_path, exist_ok=True)

    save_lora(
        model.unet, 
        'unet', 
        use_unet_lora, 
        unet_target_modules, 
        checkpoint_step,
        save_path, 
    )
    save_lora(
        model.text_encoder, 
        'text_encoder', 
        use_text_lora, 
        text_encoder_target_modules, 
        checkpoint_step, 
        save_path
    )

    train_patch_pipe(model, use_unet_lora, use_text_lora)

def param_optim(model, condition, extra_params=None, is_lora=False, negation=None):
    return {
        "model": model, 
        "condition": condition, 
        'extra_params': extra_params,
        'is_lora': is_lora,
        "negation": negation
    }
    

def create_optim_params(name='param', params=None, lr=5e-6, extra_params=None):
    params = {
        "name": name, 
        "params": params, 
        "lr": lr
    }

    if extra_params is not None:
        for k, v in extra_params.items():
            params[k] = v
    
    return params

def negate_params(name, negation):
    # We have to do this if we are co-training with LoRA.
    # This ensures that parameter groups aren't duplicated.
    if negation is None: return False
    for n in negation:
        if n in name and 'temp' not in name:
            return True
    return False


def create_optimizer_params(model_list, lr):
    import itertools
    optimizer_params = []

    for optim in model_list:
        model, condition, extra_params, is_lora, negation = optim.values()
        # Check if we are doing LoRA training.
        if is_lora and condition: 
            params = create_optim_params(
                params=itertools.chain(*model), 
                extra_params=extra_params
            )
            optimizer_params.append(params)
            continue

        # If this is true, we can train it.
        if condition:
            for n, p in model.named_parameters():
                should_negate = 'lora' in n
                if should_negate: continue

                params = create_optim_params(n, p, lr, extra_params)
                optimizer_params.append(params)

    return optimizer_params

def get_optimizer(use_8bit_adam):
    if use_8bit_adam:
        try:
            import bitsandbytes as bnb
        except ImportError:
            raise ImportError(
                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
            )

        return bnb.optim.AdamW8bit
    else:
        return torch.optim.AdamW

def is_mixed_precision(accelerator):
    weight_dtype = torch.float32

    if accelerator.mixed_precision == "fp16":
        weight_dtype = torch.float16

    elif accelerator.mixed_precision == "bf16":
        weight_dtype = torch.bfloat16

    return weight_dtype

def cast_to_gpu_and_type(model_list, accelerator, weight_dtype):
    for model in model_list:
        if model is not None: model.to(accelerator.device, dtype=weight_dtype)

def handle_cache_latents(
        should_cache, 
        output_dir, 
        train_dataloader, 
        train_batch_size, 
        vae, 
        cached_latent_dir=None
    ):

    # Cache latents by storing them in VRAM. 
    # Speeds up training and saves memory by not encoding during the train loop.
    if not should_cache: return None
    vae.to('cuda', dtype=torch.float16)
    vae.enable_slicing()
    
    cached_latent_dir = (
        os.path.abspath(cached_latent_dir) if cached_latent_dir is not None else None 
        )

    if cached_latent_dir is None:
        cache_save_dir = f"{output_dir}/cached_latents"
        os.makedirs(cache_save_dir, exist_ok=True)

        for i, batch in enumerate(tqdm(train_dataloader, desc="Caching Latents.")):

            save_name = f"cached_{i}"
            full_out_path =  f"{cache_save_dir}/{save_name}.pt"

            pixel_values = batch['pixel_values'].to('cuda', dtype=torch.float16)
            batch['pixel_values'] = tensor_to_vae_latent(pixel_values, vae)
            for k, v in batch.items(): batch[k] = v[0]
        
            torch.save(batch, full_out_path)
            del pixel_values
            del batch

            # We do this to avoid fragmentation from casting latents between devices.
            torch.cuda.empty_cache()
    else:
        cache_save_dir = cached_latent_dir
        

    return torch.utils.data.DataLoader(
        CachedDataset(cache_dir=cache_save_dir), 
        batch_size=train_batch_size, 
        shuffle=True,
        num_workers=0
    ) 

def handle_trainable_modules(model, trainable_modules=None, is_enabled=True, negation=None):
    global already_printed_trainables

    # This can most definitely be refactored :-)
    unfrozen_params = 0
    if trainable_modules is not None:
        for name, module in model.named_modules():
            for tm in tuple(trainable_modules):
                if tm == 'all':
                    model.requires_grad_(is_enabled)
                    unfrozen_params =len(list(model.parameters()))
                    break
                    
                if tm in name and 'lora' not in name:
                    for m in module.parameters():
                        m.requires_grad_(is_enabled)
                        if is_enabled: unfrozen_params +=1

    if unfrozen_params > 0 and not already_printed_trainables:
        already_printed_trainables = True 
        print(f"{unfrozen_params} params have been unfrozen for training.")

def tensor_to_vae_latent(t, vae):
    video_length = t.shape[1]

    t = rearrange(t, "b f c h w -> (b f) c h w")
    latents = vae.encode(t).latent_dist.sample()
    latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
    latents = latents * 0.18215

    return latents

def sample_noise(latents, noise_strength, use_offset_noise):
    b ,c, f, *_ = latents.shape
    noise_latents = torch.randn_like(latents, device=latents.device)
    offset_noise = None

    if use_offset_noise:
        offset_noise = torch.randn(b, c, f, 1, 1, device=latents.device)
        noise_latents = noise_latents + noise_strength * offset_noise

    return noise_latents

def should_sample(global_step, validation_steps, validation_data):
    return (global_step % validation_steps == 0 or global_step == 1)  \
    and validation_data.sample_preview

def save_pipe(
        path, 
        global_step,
        accelerator, 
        unet, 
        text_encoder, 
        vae, 
        output_dir,
        use_unet_lora,
        use_text_lora,
        unet_target_replace_module=None,
        text_target_replace_module=None,
        is_checkpoint=False,
    ):

    if is_checkpoint:
        save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
        os.makedirs(save_path, exist_ok=True)
    else:
        save_path = output_dir

    # Save the dtypes so we can continue training at the same precision.
    u_dtype, t_dtype, v_dtype = unet.dtype, text_encoder.dtype, vae.dtype 

   # Copy the model without creating a reference to it. This allows keeping the state of our lora training if enabled.
    unet_out = copy.deepcopy(accelerator.unwrap_model(unet, keep_fp32_wrapper=False))
    text_encoder_out = copy.deepcopy(accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False))

    pipeline = TextToVideoSDPipeline.from_pretrained(
        path,
        unet=unet_out,
        text_encoder=text_encoder_out,
        vae=vae,
    ).to(torch_dtype=torch.float16)
    
    handle_lora_save(
        use_unet_lora, 
        use_text_lora, 
        pipeline, 
        output_dir, 
        global_step,
        unet_target_replace_module, 
        text_target_replace_module
    )

    pipeline.save_pretrained(save_path)
    
    if is_checkpoint:
        unet, text_encoder = accelerator.prepare(unet, text_encoder)
        models_to_cast_back = [(unet, u_dtype), (text_encoder, t_dtype), (vae, v_dtype)]
        [x[0].to(accelerator.device, dtype=x[1]) for x in models_to_cast_back]

    logger.info(f"Saved model at {save_path} on step {global_step}")
    
    del pipeline
    del unet_out
    del text_encoder_out
    torch.cuda.empty_cache()
    gc.collect()


def replace_prompt(prompt, token, wlist):
    for w in wlist:
        if w in prompt: return prompt.replace(w, token)
    return prompt 

def main(
    pretrained_model_path: str,
    output_dir: str,
    train_data: Dict,
    validation_data: Dict,
    dataset_types: Tuple[str] = ('json'),
    validation_steps: int = 100,
    trainable_modules: Tuple[str] = ("attn1", "attn2"),
    trainable_text_modules: Tuple[str] = ("all"),
    extra_unet_params = None,
    extra_text_encoder_params = None,
    train_batch_size: int = 1,
    max_train_steps: int = 500,
    learning_rate: float = 5e-5,
    scale_lr: bool = False,
    lr_scheduler: str = "constant",
    lr_warmup_steps: int = 0,
    adam_beta1: float = 0.9,
    adam_beta2: float = 0.999,
    adam_weight_decay: float = 1e-2,
    adam_epsilon: float = 1e-08,
    max_grad_norm: float = 1.0,
    gradient_accumulation_steps: int = 1,
    gradient_checkpointing: bool = False,
    text_encoder_gradient_checkpointing: bool = False,
    checkpointing_steps: int = 500,
    resume_from_checkpoint: Optional[str] = None,
    mixed_precision: Optional[str] = "fp16",
    use_8bit_adam: bool = False,
    enable_xformers_memory_efficient_attention: bool = True,
    enable_torch_2_attn: bool = False,
    seed: Optional[int] = None,
    train_text_encoder: bool = False,
    use_offset_noise: bool = False,
    offset_noise_strength: float = 0.1,
    extend_dataset: bool = False,
    cache_latents: bool = False,
    cached_latent_dir = None,
    use_unet_lora: bool = False,
    use_text_lora: bool = False,
    unet_lora_modules: Tuple[str] = ["ResnetBlock2D"],
    text_encoder_lora_modules: Tuple[str] = ["CLIPEncoderLayer"],
    lora_rank: int = 16,
    lora_path: str = '',
    **kwargs
):

    *_, config = inspect.getargvalues(inspect.currentframe())

    accelerator = Accelerator(
        gradient_accumulation_steps=gradient_accumulation_steps,
        mixed_precision=mixed_precision,
        log_with="tensorboard",
        logging_dir=output_dir
    )

    # Make one log on every process with the configuration for debugging.
    create_logging(logging, logger, accelerator)

    # Initialize accelerate, transformers, and diffusers warnings
    accelerate_set_verbose(accelerator)

    # If passed along, set the training seed now.
    if seed is not None:
        set_seed(seed)

    # Handle the output folder creation
    if accelerator.is_main_process:
       output_dir = create_output_folders(output_dir, config)

    # Load scheduler, tokenizer and models.
    noise_scheduler, tokenizer, text_encoder, vae, unet = load_primary_models(pretrained_model_path)

    # Freeze any necessary models
    freeze_models([vae, text_encoder, unet])
    
    # Enable xformers if available
    handle_memory_attention(enable_xformers_memory_efficient_attention, enable_torch_2_attn, unet)

    if scale_lr:
        learning_rate = (
            learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
        )

    # Initialize the optimizer
    optimizer_cls = get_optimizer(use_8bit_adam)

    # Use LoRA if enabled.    
    unet_lora_params, unet_negation = inject_lora(
        use_unet_lora, unet, unet_lora_modules, is_extended=True,
        r=lora_rank, lora_path=lora_path
        )

    text_encoder_lora_params, text_encoder_negation = inject_lora(
        use_text_lora, text_encoder, text_encoder_lora_modules,
        r=lora_rank, lora_path=lora_path
        )

    # Create parameters to optimize over with a condition (if "condition" is true, optimize it)
    optim_params = [
        param_optim(unet, trainable_modules is not None, extra_params=extra_unet_params, negation=unet_negation),
        param_optim(text_encoder, train_text_encoder and not use_text_lora, extra_params=extra_text_encoder_params, 
                    negation=text_encoder_negation
                   ),
        param_optim(text_encoder_lora_params, use_text_lora, is_lora=True, extra_params={"lr": 1e-5}),
        param_optim(unet_lora_params, use_unet_lora, is_lora=True, extra_params={"lr": 1e-5})
    ]

    params = create_optimizer_params(optim_params, learning_rate)
    
    # Create Optimizer
    optimizer = optimizer_cls(
        params,
        lr=learning_rate,
        betas=(adam_beta1, adam_beta2),
        weight_decay=adam_weight_decay,
        eps=adam_epsilon,
    )

    # Scheduler
    lr_scheduler = get_scheduler(
        lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
        num_training_steps=max_train_steps * gradient_accumulation_steps,
    )

    # Get the training dataset based on types (json, single_video, image)
    train_datasets = get_train_dataset(dataset_types, train_data, tokenizer)

    # Extend datasets that are less than the greatest one. This allows for more balanced training.
    attrs = ['train_data', 'frames', 'image_dir', 'video_files']
    extend_datasets(train_datasets, attrs, extend=extend_dataset)

    # Process one dataset
    if len(train_datasets) == 1:
        train_dataset = train_datasets[0]
    
    # Process many datasets
    else:
        train_dataset = torch.utils.data.ConcatDataset(train_datasets) 

    # DataLoaders creation:
    train_dataloader = torch.utils.data.DataLoader(
        train_dataset, 
        batch_size=train_batch_size,
        shuffle=True
    )

     # Latents caching
    cached_data_loader = handle_cache_latents(
        cache_latents, 
        output_dir,
        train_dataloader, 
        train_batch_size, 
        vae,
        cached_latent_dir
    ) 

    if cached_data_loader is not None: 
        train_dataloader = cached_data_loader

    # Prepare everything with our `accelerator`.
    unet, optimizer,train_dataloader, lr_scheduler, text_encoder = accelerator.prepare(
        unet, 
        optimizer, 
        train_dataloader, 
        lr_scheduler, 
        text_encoder
    )

    # Use Gradient Checkpointing if enabled.
    unet_and_text_g_c(
        unet, 
        text_encoder, 
        gradient_checkpointing, 
        text_encoder_gradient_checkpointing
    )
    
    # Enable VAE slicing to save memory.
    vae.enable_slicing()

    # For mixed precision training we cast the text_encoder and vae weights to half-precision
    # as these models are only used for inference, keeping weights in full precision is not required.
    weight_dtype = is_mixed_precision(accelerator)

    # Move text encoders, and VAE to GPU
    models_to_cast = [text_encoder, vae]
    cast_to_gpu_and_type(models_to_cast, accelerator, weight_dtype)

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)

    # Afterwards we recalculate our number of training epochs
    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if accelerator.is_main_process:
        accelerator.init_trackers("text2video-fine-tune")

    # Train!
    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {max_train_steps}")
    global_step = 0
    first_epoch = 0

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
    progress_bar.set_description("Steps")

    def finetune_unet(batch, train_encoder=False):
        
        # Check if we are training the text encoder
        text_trainable = (train_text_encoder or use_text_lora)
        
        # Unfreeze UNET Layers
        if global_step == 0: 
            already_printed_trainables = False
            unet.train()
            handle_trainable_modules(
                unet, 
                trainable_modules, 
                is_enabled=True,
                negation=unet_negation
            )

        # Convert videos to latent space
        pixel_values = batch["pixel_values"]

        if not cache_latents:
            latents = tensor_to_vae_latent(pixel_values, vae)
        else:
            latents = pixel_values

        # Get video length
        video_length = latents.shape[2]

        # Sample noise that we'll add to the latents
        noise = sample_noise(latents, offset_noise_strength, use_offset_noise)
        bsz = latents.shape[0]

        # Sample a random timestep for each video
        timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
        timesteps = timesteps.long()

        # Add noise to the latents according to the noise magnitude at each timestep
        # (this is the forward diffusion process)
        noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
    
        # Enable text encoder training
        if text_trainable:
            text_encoder.train()

            if use_text_lora: 
                text_encoder.text_model.embeddings.requires_grad_(True)

            if global_step == 0 and train_text_encoder:
                handle_trainable_modules(
                    text_encoder, 
                    trainable_modules=trainable_text_modules,
                    negation=text_encoder_negation
            )
            cast_to_gpu_and_type([text_encoder], accelerator, torch.float32)
                
        # Fixes gradient checkpointing training.
        # See: https://github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb
        if gradient_checkpointing or text_encoder_gradient_checkpointing:
            unet.eval()
            text_encoder.eval()
            
        # Encode text embeddings
        token_ids = batch['prompt_ids']
        encoder_hidden_states = text_encoder(token_ids)[0]

        # Get the target for loss depending on the prediction type
        if noise_scheduler.prediction_type == "epsilon":
            target = noise

        elif noise_scheduler.prediction_type == "v_prediction":
            target = noise_scheduler.get_velocity(latents, noise, timesteps)

        else:
            raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}")

        
        # Here we do two passes for video and text training.
        # If we are on the second iteration of the loop, get one frame.
        # This allows us to train text information only on the spatial layers.
        losses = []
        should_truncate_video = (video_length > 1 and text_trainable)

        # We detach the encoder hidden states for the first pass (video frames > 1)
        # Then we make a clone of the initial state to ensure we can train it in the loop.
        detached_encoder_state = encoder_hidden_states.clone().detach()
        trainable_encoder_state = encoder_hidden_states.clone()

        for i in range(2):

            should_detach = noisy_latents.shape[2] > 1 and i == 0

            if should_truncate_video and i == 1:
                noisy_latents = noisy_latents[:,:,1,:,:].unsqueeze(2)
                target = target[:,:,1,:,:].unsqueeze(2)
                       
            encoder_hidden_states = (
                detached_encoder_state if should_detach else trainable_encoder_state
            )

            model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample
            loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")

            losses.append(loss)
            
            # This was most likely single frame training or a single image.
            if video_length == 1 and i == 0: break

        loss = losses[0] if len(losses) == 1 else losses[0] + losses[1] 

        return loss, latents

    for epoch in range(first_epoch, num_train_epochs):
        train_loss = 0.0
        
        for step, batch in enumerate(train_dataloader):
            # Skip steps until we reach the resumed step
            if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
                if step % gradient_accumulation_steps == 0:
                    progress_bar.update(1)
                continue
            
            with accelerator.accumulate(unet) ,accelerator.accumulate(text_encoder):

                text_prompt = batch['text_prompt'][0]
                
                with accelerator.autocast():
                    loss, latents = finetune_unet(batch, train_encoder=train_text_encoder)
                
                # Gather the losses across all processes for logging (if we use distributed training).
                avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
                train_loss += avg_loss.item() / gradient_accumulation_steps

                # Backpropagate
                try:
                    accelerator.backward(loss)
                    params_to_clip = (
                        unet.parameters() if not train_text_encoder 
                    else 
                        list(unet.parameters()) + list(text_encoder.parameters())
                    )
                    accelerator.clip_grad_norm_(params_to_clip, max_grad_norm)
                
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad(set_to_none=True)
                    
                except Exception as e:
                    print(f"An error has occured during backpropogation! {e}") 
                    continue

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1
                accelerator.log({"train_loss": train_loss}, step=global_step)
                train_loss = 0.0
            
                if global_step % checkpointing_steps == 0:
                    save_pipe(
                        pretrained_model_path, 
                        global_step, 
                        accelerator, 
                        unet, 
                        text_encoder, 
                        vae, 
                        output_dir, 
                        use_unet_lora, 
                        use_text_lora,
                        unet_lora_modules,
                        text_encoder_lora_modules,
                        is_checkpoint=True
                    )

                if should_sample(global_step, validation_steps, validation_data):
                    if global_step == 1: print("Performing validation prompt.")
                    if accelerator.is_main_process:

                        with accelerator.autocast():
                            unet.eval()
                            text_encoder.eval()
                            unet_and_text_g_c(unet, text_encoder, False, False)
                            
                            pipeline = TextToVideoSDPipeline.from_pretrained(
                                pretrained_model_path,
                                text_encoder=text_encoder,
                                vae=vae,
                                unet=unet
                            )

                            diffusion_scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
                            pipeline.scheduler = diffusion_scheduler

                            prompt = text_prompt if len(validation_data.prompt) <= 0 else validation_data.prompt

                            curr_dataset_name = batch['dataset']
                            save_filename = f"{global_step}_dataset-{curr_dataset_name}_{prompt}"

                            out_file = f"{output_dir}/samples/{save_filename}.mp4"
                            
                            with torch.no_grad():
                                video_frames = pipeline(
                                    prompt,
                                    width=validation_data.width,
                                    height=validation_data.height,
                                    num_frames=validation_data.num_frames,
                                    num_inference_steps=validation_data.num_inference_steps,
                                    guidance_scale=validation_data.guidance_scale
                                ).frames
                            export_to_video(video_frames, out_file, train_data.get('fps', 8))

                            del pipeline
                            torch.cuda.empty_cache()

                    logger.info(f"Saved a new sample to {out_file}")

                    unet_and_text_g_c(
                        unet, 
                        text_encoder, 
                        gradient_checkpointing, 
                        text_encoder_gradient_checkpointing
                    )

            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
            accelerator.log({"training_loss": loss.detach().item()}, step=step)
            progress_bar.set_postfix(**logs)

            if global_step >= max_train_steps:
                break

    # Create the pipeline using the trained modules and save it.
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        save_pipe(
                pretrained_model_path, 
                global_step, 
                accelerator, 
                unet, 
                text_encoder, 
                vae, 
                output_dir, 
                use_unet_lora, 
                use_text_lora,
                unet_lora_modules,
                text_encoder_lora_modules,
                is_checkpoint=False
        )     
    accelerator.end_training()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--config", type=str, default="./configs/my_config.yaml")
    args = parser.parse_args()

    main(**OmegaConf.load(args.config))