import argparse import datetime import logging import inspect import math import os from typing import Dict, Optional, Tuple from omegaconf import OmegaConf from collections import OrderedDict import torch import torch.nn.functional as F import torch.utils.checkpoint import diffusers import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version from diffusers.utils.import_utils import is_xformers_available from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from animatediff.models.unet import UNet3DConditionModel from tuneavideo.data.frames_dataset import FramesDataset from animatediff.data.dataset import ImgSeqDataset from tuneavideo.data.multi_dataset import MultiTuneAVideoDataset from animatediff.pipelines.pipeline_animation import AnimationPipeline from tuneavideo.util import save_videos_grid, ddim_inversion from einops import rearrange, repeat # 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 main( pretrained_model_path: str, output_dir: str, train_data: Dict, validation_data: Dict, validation_steps: int = 100, train_whole_module: bool = False, trainable_modules: Tuple[str] = ( "to_q", ), train_batch_size: int = 1, max_train_steps: int = 500, learning_rate: float = 3e-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 = True, checkpointing_steps: int = 500, start_global_step: int = 0, resume_from_checkpoint: Optional[str] = None, mixed_precision: Optional[str] = "fp16", use_8bit_adam: bool = False, enable_xformers_memory_efficient_attention: bool = True, seed: Optional[int] = None, motion_module: str = "models/Motion_Module/mm_sd_v15.ckpt", inference_config_path: str = "configs/inference/inference-v3.yaml", motion_module_pe_multiplier: int = 1, dataset_class: str = 'MultiTuneAVideoDataset', # extra args image_finetune: bool = False, name: str = "scenefusion", use_wandb: bool = True, launcher: str = "launcher", cfg_random_null_text: bool = True, cfg_random_null_text_ratio: float = 0.1, unet_checkpoint_path: str = "", unet_additional_kwargs: Dict = {}, ema_decay: float = 0.9999, noise_scheduler_kwargs = None, max_train_epoch: int = -1, validation_steps_tuple: Tuple = (-1,), num_workers: int = 32, checkpointing_epochs: int = 5, mixed_precision_training: bool = True, global_seed: int = 42, is_debug: bool = False, ): *_, config = inspect.getargvalues(inspect.currentframe()) inference_config = OmegaConf.load(inference_config_path) accelerator = Accelerator( gradient_accumulation_steps=gradient_accumulation_steps, mixed_precision=mixed_precision, ) # Make one log on every process with the configuration for debugging. 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) 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() # 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: # now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") # output_dir = os.path.join(output_dir, now) os.makedirs(output_dir, exist_ok=True) os.makedirs(f"{output_dir}/samples", exist_ok=True) os.makedirs(f"{output_dir}/inv_latents", exist_ok=True) OmegaConf.save(config, os.path.join(output_dir, 'config.yaml')) # Load scheduler, tokenizer and models. 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_2d( pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs) ) # unet_path = "unet" # unet = UNet3DConditionModel.from_pretrained_2d( # unet_path, subfolder="unet", # unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs) # ) motion_module_state_dict = torch.load(motion_module, map_location="cpu") # Multiply pe weights by multiplier for training more than 24 frames if motion_module_pe_multiplier > 1: for key in motion_module_state_dict: if 'pe' in key: t = motion_module_state_dict[key] t = repeat(t, "b f d -> b (f m) d", m=motion_module_pe_multiplier) motion_module_state_dict[key] = t if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]}) missing, unexpected = unet.load_state_dict(motion_module_state_dict, strict=False) assert len(unexpected) == 0 # Freeze vae and text_encoder vae.requires_grad_(False) text_encoder.requires_grad_(False) unet.requires_grad_(False) for name, module in unet.named_modules(): if "motion_modules" in name and (train_whole_module or name.endswith(tuple(trainable_modules))): for params in module.parameters(): params.requires_grad = True if enable_xformers_memory_efficient_attention: if is_xformers_available(): unet.enable_xformers_memory_efficient_attention() else: raise ValueError("xformers is not available. Make sure it is installed correctly") if gradient_checkpointing: unet.enable_gradient_checkpointing() if scale_lr: learning_rate = ( learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes ) print("optimizer values", learning_rate, adam_beta1, adam_beta2, adam_weight_decay, adam_epsilon) # Initialize the optimizer 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`" ) optimizer_cls = bnb.optim.AdamW8bit else: optimizer_cls = torch.optim.AdamW optimizer = optimizer_cls( unet.parameters(), lr=learning_rate, betas=(adam_beta1, adam_beta2), weight_decay=adam_weight_decay, eps=adam_epsilon, ) # Get the training dataset train_dataset = None if dataset_class == 'MultiTuneAVideoDataset': train_dataset = ImgSeqDataset(**train_data) # Preprocessing the dataset train_dataset.prompt_ids = [None] * len(train_dataset.prompt) for index, prompt in enumerate(train_dataset.prompt): train_dataset.prompt_ids[index] = tokenizer( prompt,max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ).input_ids[0] else: train_dataset = FramesDataset(tokenizer=tokenizer, **train_data) train_dataset.load() # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=train_batch_size ) # Get the validation pipeline validation_pipeline = AnimationPipeline( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs['DDIMScheduler'])), ) validation_pipeline.enable_vae_slicing() ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler') ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps) # 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, ) # Prepare everything with our `accelerator`. unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( unet, optimizer, train_dataloader, lr_scheduler ) # 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 = torch.float32 if accelerator.mixed_precision == "fp16": weight_dtype = torch.float16 elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 # Move text_encode and vae to gpu and cast to weight_dtype text_encoder.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) print("DATA LEN:", len(train_dataloader)) # 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 if start_global_step > 0: global_step = start_global_step first_epoch = global_step // num_update_steps_per_epoch resume_step = global_step % num_update_steps_per_epoch # Potentially load in the weights and states from a previous save if resume_from_checkpoint: if resume_from_checkpoint != "latest": path = os.path.basename(resume_from_checkpoint) else: # Get the most recent checkpoint dirs = os.listdir(output_dir) dirs = [d for d in dirs if d.startswith("checkpoint")] dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) path = dirs[-1] accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(output_dir, path)) global_step = int(path.split("-")[1]) first_epoch = global_step // num_update_steps_per_epoch resume_step = global_step % num_update_steps_per_epoch # 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") for epoch in range(first_epoch, num_train_epochs): unet.train() 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): # Convert videos to latent space pixel_values = batch["pixel_values"].to(weight_dtype) video_length = pixel_values.shape[1] pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w") latents = vae.encode(pixel_values).latent_dist.sample() latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length) latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn_like(latents) 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) # Get the text embedding for conditioning encoder_hidden_states = text_encoder(batch["prompt_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}") # Predict the noise residual and compute loss model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") # 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 accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # 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: if accelerator.is_main_process: save_path = os.path.join(output_dir, f"mm-{global_step}.pth") save_checkpoint(unet, save_path) logger.info(f"Saved state to {save_path}") if global_step % validation_steps == 0: if accelerator.is_main_process: samples = [] generator = torch.Generator(device=latents.device) generator.manual_seed(seed) ddim_inv_latent = None if validation_data.use_inv_latent: inv_latents_path = os.path.join(output_dir, f"inv_latents/ddim_latent-{global_step}.pt") ddim_inv_latent = ddim_inversion( validation_pipeline, ddim_inv_scheduler, video_latent=latents, num_inv_steps=validation_data.num_inv_steps, prompt="")[-1].to(weight_dtype) torch.save(ddim_inv_latent, inv_latents_path) for idx, prompt in enumerate(set(validation_data.prompts)): sample = validation_pipeline(prompt, generator=generator, latents=ddim_inv_latent, fp16=True, **validation_data).videos save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{idx}.gif", fps=1) samples.append(sample) samples = torch.concat(samples) save_path = f"{output_dir}/samples/sample-{global_step}.gif" save_videos_grid(samples, save_path, fps=1) logger.info(f"Saved samples to {save_path}") logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} 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: unet = accelerator.unwrap_model(unet) pipeline = AnimationPipeline.from_pretrained( pretrained_model_path, text_encoder=text_encoder, vae=vae, unet=unet, ) mm_path = "%s/mm.pth" % output_dir save_checkpoint(unet, mm_path) accelerator.end_training() def save_checkpoint(unet, mm_path): mm_state_dict = OrderedDict() state_dict = unet.state_dict() for key in state_dict: if "motion_module" in key: mm_state_dict[key] = state_dict[key] torch.save(mm_state_dict, mm_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml") args = parser.parse_args() main(**OmegaConf.load(args.config))