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 torch import torch.nn.functional as F import torch.utils.checkpoint 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 DDIMScheduler, TextToVideoSDPipeline from diffusers.optimization import get_scheduler 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_handler import LoraHandler from utils.lora import extract_lora_child_module from utils.ddim_utils import ddim_inversion import imageio import numpy as np already_printed_trainables = False 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): video_writer = imageio.get_writer(output_video_path, fps=fps) for img in video_frames: video_writer.append_data(np.array(img)) video_writer.close() 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 = DDIMScheduler.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') enable_torch_2 = is_torch_2 and enable_torch_2_attn if enable_xformers_memory_efficient_attention and not enable_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: set_torch_2_attn(unet) except: print("Could not enable memory efficient attention for xformers or Torch 2.0.") def param_optim(model, condition, extra_params=None, is_lora=False, negation=None): extra_params = extra_params if len(extra_params.keys()) > 0 else 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 and isinstance(model, list): params = create_optim_params( params=itertools.chain(*model), extra_params=extra_params ) optimizer_params.append(params) continue if is_lora and condition and not isinstance(model, list): for n, p in model.named_parameters(): if 'lora' in n: params = create_optim_params(n, p, lr, 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 and not is_lora 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 inverse_video(pipe, latents, num_steps): ddim_inv_scheduler = DDIMScheduler.from_config(pipe.scheduler.config) ddim_inv_scheduler.set_timesteps(num_steps) ddim_inv_latent = ddim_inversion( pipe, ddim_inv_scheduler, video_latent=latents.to(pipe.device), num_inv_steps=num_steps, prompt="")[-1] return ddim_inv_latent def handle_cache_latents( should_cache, output_dir, train_dataloader, train_batch_size, vae, unet, pretrained_model_path, noise_prior, 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() pipe = TextToVideoSDPipeline.from_pretrained( pretrained_model_path, vae=vae, unet=copy.deepcopy(unet).to('cuda', dtype=torch.float16) ) pipe.text_encoder.to('cuda', dtype=torch.float16) 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['latents'] = tensor_to_vae_latent(pixel_values, vae) if noise_prior > 0.: batch['inversion_noise'] = inverse_video(pipe, batch['latents'], 50) 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=False): b, c, f, *_ = latents.shape noise_latents = torch.randn_like(latents, device=latents.device) 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 enforce_zero_terminal_snr(betas): """ Corrects noise in diffusion schedulers. From: Common Diffusion Noise Schedules and Sample Steps are Flawed https://arxiv.org/pdf/2305.08891.pdf """ # Convert betas to alphas_bar_sqrt alphas = 1 - betas alphas_bar = alphas.cumprod(0) alphas_bar_sqrt = alphas_bar.sqrt() # Store old values. alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() # Shift so the last timestep is zero. alphas_bar_sqrt -= alphas_bar_sqrt_T # Scale so the first timestep is back to the old value. alphas_bar_sqrt *= alphas_bar_sqrt_0 / ( alphas_bar_sqrt_0 - alphas_bar_sqrt_T ) # Convert alphas_bar_sqrt to betas alphas_bar = alphas_bar_sqrt ** 2 alphas = alphas_bar[1:] / alphas_bar[:-1] alphas = torch.cat([alphas_bar[0:1], alphas]) betas = 1 - alphas return betas def should_sample(global_step, validation_steps, validation_data): return global_step % validation_steps == 0 and validation_data.sample_preview def save_pipe( path, global_step, accelerator, unet, text_encoder, vae, output_dir, lora_manager_spatial: LoraHandler, lora_manager_temporal: LoraHandler, unet_target_replace_module=None, text_target_replace_module=None, is_checkpoint=False, save_pretrained_model=True ): 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.cpu(), keep_fp32_wrapper=False)) text_encoder_out = copy.deepcopy(accelerator.unwrap_model(text_encoder.cpu(), keep_fp32_wrapper=False)) pipeline = TextToVideoSDPipeline.from_pretrained( path, unet=unet_out, text_encoder=text_encoder_out, vae=vae, ).to(torch_dtype=torch.float32) lora_manager_spatial.save_lora_weights(model=copy.deepcopy(pipeline), save_path=save_path+'/spatial', step=global_step) lora_manager_temporal.save_lora_weights(model=copy.deepcopy(pipeline), save_path=save_path+'/temporal', step=global_step) if save_pretrained_model: 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 main( pretrained_model_path: str, output_dir: str, train_data: Dict, validation_data: Dict, extra_train_data: list = [], dataset_types: Tuple[str] = ('json'), validation_steps: int = 100, trainable_modules: Tuple[str] = None, # Eg: ("attn1", "attn2") extra_unet_params=None, train_batch_size: int = 1, max_train_steps: int = 500, learning_rate: float = 5e-5, 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, 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, resume_step: Optional[int] = 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, use_offset_noise: bool = False, rescale_schedule: 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, unet_lora_modules: Tuple[str] = [], text_encoder_lora_modules: Tuple[str] = [], save_pretrained_model: bool = True, lora_rank: int = 16, lora_path: str = '', lora_unet_dropout: float = 0.1, logger_type: str = 'tensorboard', **kwargs ): *_, config = inspect.getargvalues(inspect.currentframe()) accelerator = Accelerator( gradient_accumulation_steps=gradient_accumulation_steps, mixed_precision=mixed_precision, log_with=logger_type, project_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) # 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) # Initialize the optimizer optimizer_cls = get_optimizer(use_8bit_adam) # Get the training dataset based on types (json, single_video, image) train_datasets = get_train_dataset(dataset_types, train_data, tokenizer) # If you have extra train data, you can add a list of however many you would like. # Eg: extra_train_data: [{: {dataset_types, train_data: {etc...}}}] try: if extra_train_data is not None and len(extra_train_data) > 0: for dataset in extra_train_data: d_t, t_d = dataset['dataset_types'], dataset['train_data'] train_datasets += get_train_dataset(d_t, t_d, tokenizer) except Exception as e: print(f"Could not process extra train datasets due to an error : {e}") # 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) # Create parameters to optimize over with a condition (if "condition" is true, optimize it) extra_unet_params = extra_unet_params if extra_unet_params is not None else {} extra_text_encoder_params = extra_unet_params if extra_unet_params is not None else {} # Use LoRA if enabled. # one temporal lora lora_manager_temporal = LoraHandler(use_unet_lora=use_unet_lora, unet_replace_modules=["TransformerTemporalModel"]) unet_lora_params_temporal, unet_negation_temporal = lora_manager_temporal.add_lora_to_model( use_unet_lora, unet, lora_manager_temporal.unet_replace_modules, lora_unet_dropout, lora_path + '/temporal/lora/', r=lora_rank) optimizer_temporal = optimizer_cls( create_optimizer_params([param_optim(unet_lora_params_temporal, use_unet_lora, is_lora=True, extra_params={**{"lr": learning_rate}, **extra_text_encoder_params} )], learning_rate), lr=learning_rate, betas=(adam_beta1, adam_beta2), weight_decay=adam_weight_decay, eps=adam_epsilon, ) lr_scheduler_temporal = get_scheduler( lr_scheduler, optimizer=optimizer_temporal, num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps, num_training_steps=max_train_steps * gradient_accumulation_steps, ) # one spatial lora for each video if 'folder' in dataset_types: spatial_lora_num = train_dataset.__len__() else: spatial_lora_num = 1 lora_manager_spatials = [] unet_lora_params_spatial_list = [] optimizer_spatial_list = [] lr_scheduler_spatial_list = [] for i in range(spatial_lora_num): lora_manager_spatial = LoraHandler(use_unet_lora=use_unet_lora, unet_replace_modules=["Transformer2DModel"]) lora_manager_spatials.append(lora_manager_spatial) unet_lora_params_spatial, unet_negation_spatial = lora_manager_spatial.add_lora_to_model( use_unet_lora, unet, lora_manager_spatial.unet_replace_modules, lora_unet_dropout, lora_path + '/spatial/lora/', r=lora_rank) unet_lora_params_spatial_list.append(unet_lora_params_spatial) optimizer_spatial = optimizer_cls( create_optimizer_params([param_optim(unet_lora_params_spatial, use_unet_lora, is_lora=True, extra_params={**{"lr": learning_rate}, **extra_text_encoder_params} )], learning_rate), lr=learning_rate, betas=(adam_beta1, adam_beta2), weight_decay=adam_weight_decay, eps=adam_epsilon, ) optimizer_spatial_list.append(optimizer_spatial) # Scheduler lr_scheduler_spatial = get_scheduler( lr_scheduler, optimizer=optimizer_spatial, num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps, num_training_steps=max_train_steps * gradient_accumulation_steps, ) lr_scheduler_spatial_list.append(lr_scheduler_spatial) unet_negation_all = unet_negation_spatial + unet_negation_temporal # 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, unet, pretrained_model_path, validation_data.noise_prior, cached_latent_dir, ) if cached_data_loader is not None: train_dataloader = cached_data_loader # Prepare everything with our `accelerator`. unet, optimizer_spatial_list, optimizer_temporal, train_dataloader, lr_scheduler_spatial_list, lr_scheduler_temporal, text_encoder = accelerator.prepare( unet, optimizer_spatial_list, optimizer_temporal, train_dataloader, lr_scheduler_spatial_list, lr_scheduler_temporal, 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) # Fix noise schedules to predcit light and dark areas if available. if not use_offset_noise and rescale_schedule: noise_scheduler.betas = enforce_zero_terminal_snr(noise_scheduler.betas) # 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, step, mask_spatial_lora=False, mask_temporal_lora=False): nonlocal use_offset_noise nonlocal rescale_schedule # 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_all ) # Convert videos to latent space if not cache_latents: latents = tensor_to_vae_latent(batch["pixel_values"], vae) else: latents = batch["latents"] # Sample noise that we'll add to the latents use_offset_noise = use_offset_noise and not rescale_schedule 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.config.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) # *Potentially* Fixes gradient checkpointing training. # See: https://github.com/prigoyal/pytorch_memonger/blob/master/tutorial/Checkpointing_for_PyTorch_models.ipynb if kwargs.get('eval_train', False): unet.eval() text_encoder.eval() # Encode text embeddings token_ids = batch['prompt_ids'] encoder_hidden_states = text_encoder(token_ids)[0] detached_encoder_state = encoder_hidden_states.clone().detach() # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") encoder_hidden_states = detached_encoder_state if mask_spatial_lora: loras = extract_lora_child_module(unet, target_replace_module=["Transformer2DModel"]) for lora_i in loras: lora_i.scale = 0. loss_spatial = None else: loras = extract_lora_child_module(unet, target_replace_module=["Transformer2DModel"]) for lora_i in loras: lora_i.scale = 1. for lora_idx in range(0, len(loras), spatial_lora_num): loras[lora_idx + step].scale = 1. loras = extract_lora_child_module(unet, target_replace_module=["TransformerTemporalModel"]) for lora_i in loras: lora_i.scale = 0. ran_idx = torch.randint(0, noisy_latents.shape[2], (1,)).item() if random.uniform(0, 1) < -0.5: pixel_values_spatial = transforms.functional.hflip( batch["pixel_values"][:, ran_idx, :, :, :]).unsqueeze(1) latents_spatial = tensor_to_vae_latent(pixel_values_spatial, vae) noise_spatial = sample_noise(latents_spatial, offset_noise_strength, use_offset_noise) noisy_latents_input = noise_scheduler.add_noise(latents_spatial, noise_spatial, timesteps) target_spatial = noise_spatial model_pred_spatial = unet(noisy_latents_input, timesteps, encoder_hidden_states=encoder_hidden_states).sample loss_spatial = F.mse_loss(model_pred_spatial[:, :, 0, :, :].float(), target_spatial[:, :, 0, :, :].float(), reduction="mean") else: noisy_latents_input = noisy_latents[:, :, ran_idx, :, :] target_spatial = target[:, :, ran_idx, :, :] model_pred_spatial = unet(noisy_latents_input.unsqueeze(2), timesteps, encoder_hidden_states=encoder_hidden_states).sample loss_spatial = F.mse_loss(model_pred_spatial[:, :, 0, :, :].float(), target_spatial.float(), reduction="mean") if mask_temporal_lora: loras = extract_lora_child_module(unet, target_replace_module=["TransformerTemporalModel"]) for lora_i in loras: lora_i.scale = 0. loss_temporal = None else: loras = extract_lora_child_module(unet, target_replace_module=["TransformerTemporalModel"]) for lora_i in loras: lora_i.scale = 1. model_pred = unet(noisy_latents, timesteps, encoder_hidden_states=encoder_hidden_states).sample loss_temporal = F.mse_loss(model_pred.float(), target.float(), reduction="mean") beta = 1 alpha = (beta ** 2 + 1) ** 0.5 ran_idx = torch.randint(0, model_pred.shape[2], (1,)).item() model_pred_decent = alpha * model_pred - beta * model_pred[:, :, ran_idx, :, :].unsqueeze(2) target_decent = alpha * target - beta * target[:, :, ran_idx, :, :].unsqueeze(2) loss_ad_temporal = F.mse_loss(model_pred_decent.float(), target_decent.float(), reduction="mean") loss_temporal = loss_temporal + loss_ad_temporal return loss_spatial, loss_temporal, latents, noise for epoch in range(first_epoch, num_train_epochs): train_loss_spatial = 0.0 train_loss_temporal = 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] for optimizer_spatial in optimizer_spatial_list: optimizer_spatial.zero_grad(set_to_none=True) optimizer_temporal.zero_grad(set_to_none=True) mask_temporal_lora = False # mask_spatial_lora = False mask_spatial_lora = random.uniform(0, 1) < 0.1 and not mask_temporal_lora with accelerator.autocast(): loss_spatial, loss_temporal, latents, init_noise = finetune_unet(batch, step, mask_spatial_lora=mask_spatial_lora, mask_temporal_lora=mask_temporal_lora) # Gather the losses across all processes for logging (if we use distributed training). if not mask_spatial_lora: avg_loss_spatial = accelerator.gather(loss_spatial.repeat(train_batch_size)).mean() train_loss_spatial += avg_loss_spatial.item() / gradient_accumulation_steps if not mask_temporal_lora: avg_loss_temporal = accelerator.gather(loss_temporal.repeat(train_batch_size)).mean() train_loss_temporal += avg_loss_temporal.item() / gradient_accumulation_steps # Backpropagate if not mask_spatial_lora: accelerator.backward(loss_spatial, retain_graph = True) optimizer_spatial_list[step].step() if not mask_temporal_lora: accelerator.backward(loss_temporal) optimizer_temporal.step() lr_scheduler_spatial_list[step].step() lr_scheduler_temporal.step() # 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_temporal}, step=global_step) train_loss_temporal = 0.0 if global_step % checkpointing_steps == 0 and global_step > 0: save_pipe( pretrained_model_path, global_step, accelerator, unet, text_encoder, vae, output_dir, lora_manager_spatial, lora_manager_temporal, unet_lora_modules, text_encoder_lora_modules, is_checkpoint=True, save_pretrained_model=save_pretrained_model ) if should_sample(global_step, validation_steps, validation_data): if accelerator.is_main_process: with accelerator.autocast(): unet.eval() text_encoder.eval() unet_and_text_g_c(unet, text_encoder, False, False) loras = extract_lora_child_module(unet, target_replace_module=["Transformer2DModel"]) for lora_i in loras: lora_i.scale = validation_data.spatial_scale if validation_data.noise_prior > 0: preset_noise = (validation_data.noise_prior) ** 0.5 * batch['inversion_noise'] + ( 1-validation_data.noise_prior) ** 0.5 * torch.randn_like(batch['inversion_noise']) else: preset_noise = None pipeline = TextToVideoSDPipeline.from_pretrained( pretrained_model_path, text_encoder=text_encoder, vae=vae, unet=unet ) diffusion_scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) pipeline.scheduler = diffusion_scheduler prompt_list = text_prompt if len(validation_data.prompt) <= 0 else validation_data.prompt for prompt in prompt_list: save_filename = f"{global_step}_{prompt.replace('.', '')}" 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, latents=preset_noise ).frames export_to_video(video_frames, out_file, train_data.get('fps', 8)) logger.info(f"Saved a new sample to {out_file}") del pipeline torch.cuda.empty_cache() unet_and_text_g_c( unet, text_encoder, gradient_checkpointing, text_encoder_gradient_checkpointing ) accelerator.log({"loss_temporal": loss_temporal.detach().item()}, step=step) 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, lora_manager_spatial, lora_manager_temporal, unet_lora_modules, text_encoder_lora_modules, is_checkpoint=False, save_pretrained_model=save_pretrained_model ) accelerator.end_training() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default='./configs/config_multi_videos.yaml') args = parser.parse_args() main(**OmegaConf.load(args.config))