#!/usr/bin/env python # coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # 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. """Script to fine-tune Stable Video Diffusion and Motion LoRA training.""" import argparse import random import logging import math import os import cv2 import shutil from pathlib import Path from einops import rearrange import numpy as np import torch import torch.utils.checkpoint from torch.utils.data import RandomSampler from torch.utils.data import Dataset import torchvision import accelerate from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration, set_seed from huggingface_hub import create_repo, upload_folder from packaging import version from tqdm.auto import tqdm import transformers from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection import diffusers from diffusers import AutoencoderKLTemporalDecoder from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from diffusers.utils import check_min_version, deprecate, is_wandb_available, load_image, convert_state_dict_to_diffusers, export_to_gif from diffusers.utils.import_utils import is_xformers_available from peft import LoraConfig from peft.utils import get_peft_model_state_dict from svd.pipelines import StableVideoDiffusionPipeline from svd.models import UNetSpatioTemporalConditionModel import decord import imageio # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.24.0.dev0") logger = get_logger(__name__, log_level="INFO") print("imported all modules") def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): """Draws samples from an lognormal distribution.""" u = torch.rand(shape, dtype=dtype, device=device) * (1 - 2e-7) + 1e-7 return torch.distributions.Normal(loc, scale).icdf(u).exp() class TrainDataset(Dataset): def __init__(self, num_samples=None, width=1024, height=576, sample_frames=25, data_path=None): """ Args: num_samples (int): Number of samples in the dataset. If mentioned, we will use this number of samples from the dataset. If not mentioned, all of the datas will used in the data_path. width (int): Width of the video frame. height (int): Height of the video frame. sample_frames (int): Number of frames to sample from the video. data_path (str): Path to the dataset. """ self.width = width self.height = height self.sample_frames = sample_frames self.data_path = data_path self.num_samples = num_samples if num_samples else len(os.listdir(data_path)) self.v_decoder = decord.VideoReader def __len__(self): return self.num_samples def decord_read(self, path): decord_vr = self.v_decoder(path) total_frames = len(decord_vr) # Sampling video frames start_frame_ind = random.randint(0, total_frames - self.sample_frames) end_frame_ind = min(start_frame_ind + self.sample_frames, total_frames) frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, self.sample_frames, dtype=int) video_data = decord_vr.get_batch(frame_indice).asnumpy() video_data = torch.from_numpy(video_data) video_data = video_data.permute(0, 3, 1, 2) # (T,H,W,C) -> (T,C,H,W) return video_data def __getitem__(self, idx): """ Args: idx (int): Index of the sample to return. Returns: dict: A dictionary containing the 'pixel_values' tensor of shape (16, channels, 320, 512). """ files = os.listdir(self.data_path) # Filter out files that end with .mp4 mp4_files = [os.path.join(self.data_path, file) for file in files if file.endswith('.mp4')] if len(mp4_files) == 0: raise ValueError( f"--dataset_path '{self.data_path}' does not contain any .mp4 files.") video = self.decord_read(mp4_files[idx]) video = torchvision.transforms.functional.resize(video, (self.height, self.width)) #normalize the video to range [-1,1] video = video / 127.5 - 1 pixel_values = video return {'pixel_values': pixel_values} # resizing utils # TODO: clean up later def _resize_with_antialiasing(input, size, interpolation="bicubic", align_corners=True): h, w = input.shape[-2:] factors = (h / size[0], w / size[1]) # First, we have to determine sigma # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 sigmas = ( max((factors[0] - 1.0) / 2.0, 0.001), max((factors[1] - 1.0) / 2.0, 0.001), ) # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) # Make sure it is odd if (ks[0] % 2) == 0: ks = ks[0] + 1, ks[1] if (ks[1] % 2) == 0: ks = ks[0], ks[1] + 1 input = _gaussian_blur2d(input, ks, sigmas) output = torch.nn.functional.interpolate( input, size=size, mode=interpolation, align_corners=align_corners) return output def _compute_padding(kernel_size): """Compute padding tuple.""" # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad if len(kernel_size) < 2: raise AssertionError(kernel_size) computed = [k - 1 for k in kernel_size] # for even kernels we need to do asymmetric padding :( out_padding = 2 * len(kernel_size) * [0] for i in range(len(kernel_size)): computed_tmp = computed[-(i + 1)] pad_front = computed_tmp // 2 pad_rear = computed_tmp - pad_front out_padding[2 * i + 0] = pad_front out_padding[2 * i + 1] = pad_rear return out_padding def _filter2d(input, kernel): # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel[:, None, ...].to( device=input.device, dtype=input.dtype) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) height, width = tmp_kernel.shape[-2:] padding_shape: list[int] = _compute_padding([height, width]) input = torch.nn.functional.pad(input, padding_shape, mode="reflect") # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) # convolve the tensor with the kernel. output = torch.nn.functional.conv2d( input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) out = output.view(b, c, h, w) return out def _gaussian(window_size: int, sigma): if isinstance(sigma, float): sigma = torch.tensor([[sigma]]) batch_size = sigma.shape[0] x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) return gauss / gauss.sum(-1, keepdim=True) def _gaussian_blur2d(input, kernel_size, sigma): if isinstance(sigma, tuple): sigma = torch.tensor([sigma], dtype=input.dtype) else: sigma = sigma.to(dtype=input.dtype) ky, kx = int(kernel_size[0]), int(kernel_size[1]) bs = sigma.shape[0] kernel_x = _gaussian(kx, sigma[:, 1].view(bs, 1)) kernel_y = _gaussian(ky, sigma[:, 0].view(bs, 1)) out_x = _filter2d(input, kernel_x[..., None, :]) out = _filter2d(out_x, kernel_y[..., None]) return out 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 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 f c h w", f=video_length) latents = latents * vae.config.scaling_factor return latents def parse_args(): parser = argparse.ArgumentParser( description="Script to train Stable Diffusion XL for InstructPix2Pix." ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, required=True, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--revision", type=str, default=None, required=False, help="Revision of pretrained model identifier from huggingface.co/models.", ) parser.add_argument( "--validation_prompt", type=str, default=None, help="A prompt that is sampled during training for inference.", ) parser.add_argument( "--num_frames", type=int, default=25, ) parser.add_argument( "--width", type=int, default=576, ) parser.add_argument( "--height", type=int, default=576, ) parser.add_argument( "--num_validation_images", type=int, default=1, help="Number of images that should be generated during validation, set 0 for no validation during training.", ) parser.add_argument( "--validation_steps", type=int, default=500, help=( "Run fine-tuning validation every X epochs. The validation process consists of running the text/image prompt" " multiple times: `args.num_validation_images`." ), ) parser.add_argument( "--output_dir", type=str, default="./outputs", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--seed", type=int, default=None, help="A seed for reproducible training." ) parser.add_argument( "--per_gpu_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader.", ) parser.add_argument("--num_train_epochs", type=int, default=100) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate", type=float, default=1e-4, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=False, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="constant", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup"]' ), ) parser.add_argument( "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler.", ) parser.add_argument( "--conditioning_dropout_prob", type=float, default=None, help="Conditioning dropout probability. Drops out the conditionings (image and edit prompt) used in training InstructPix2Pix. See section 3.2.1 in the paper: https://arxiv.org/abs/2211.09800.", ) parser.add_argument( "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes.", ) parser.add_argument( "--allow_tf32", action="store_true", help=( "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" ), ) parser.add_argument( "--use_ema", action="store_true", help="Whether to use EMA model." ) parser.add_argument( "--non_ema_revision", type=str, default=None, required=False, help=( "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" " remote repository specified with --pretrained_model_name_or_path." ), ) parser.add_argument( "--num_workers", type=int, default=8, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.", ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.", ) parser.add_argument( "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use." ) parser.add_argument( "--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer", ) parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.", ) parser.add_argument( "--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.", ) parser.add_argument( "--hub_model_id", type=str, default=None, help="The name of the repository to keep in sync with the local `output_dir`.", ) parser.add_argument( "--logging_dir", type=str, default="logs", help=( "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." ), ) parser.add_argument( "--mixed_precision", type=str, default=None, choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." ), ) parser.add_argument( "--report_to", type=str, default=None, help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' ), ) parser.add_argument( "--local_rank", type=int, default=-1, help="For distributed training: local_rank", ) parser.add_argument( "--checkpointing_steps", type=int, default=500, help=( "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" " training using `--resume_from_checkpoint`." ), ) parser.add_argument( "--checkpoints_total_limit", type=int, default=2, help=("Max number of checkpoints to store."), ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help=( "Whether training should be resumed from a previous checkpoint. Use a path saved by" ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' ), ) parser.add_argument( "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers.", ) parser.add_argument( "--pretrain_unet", type=str, default=None, help="use weight for unet block", ) parser.add_argument( "--rank", type=int, default=128, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--dataset_path", type=str, default=None, help=("The path to the dataset."), ) parser.add_argument( "--train_lora", action="store_true", help="Whether to train the temporal LoRA.", ) parser.add_argument( "--validation_image_path", type=str, default=None, help="The path to validation images.", ) args = parser.parse_args() env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) if env_local_rank != -1 and env_local_rank != args.local_rank: args.local_rank = env_local_rank # default to using the same revision for the non-ema model if not specified if args.non_ema_revision is None: args.non_ema_revision = args.revision return args def main(): args = parse_args() if args.non_ema_revision is not None: deprecate( "non_ema_revision!=None", "0.15.0", message=( "Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to" " use `--variant=non_ema` instead." ), ) logging_dir = os.path.join(args.output_dir, args.logging_dir) accelerator_project_config = ProjectConfiguration( project_dir=args.output_dir, logging_dir=logging_dir) # ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, # kwargs_handlers=[ddp_kwargs] ) generator = torch.Generator( device=accelerator.device).manual_seed(args.seed) if args.report_to == "wandb": if not is_wandb_available(): raise ImportError( "Make sure to install wandb if you want to use it for logging during training.") import wandb # 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 args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) if args.push_to_hub: repo_id = create_repo( repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token ).repo_id # Load scheduler, tokenizer and models. We do not load the scheduler since I implemented it feature_extractor = CLIPImageProcessor.from_pretrained( args.pretrained_model_name_or_path, subfolder="feature_extractor", revision=args.revision ) image_encoder = CLIPVisionModelWithProjection.from_pretrained( args.pretrained_model_name_or_path, subfolder="image_encoder", revision=args.revision ) vae = AutoencoderKLTemporalDecoder.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision) unet = UNetSpatioTemporalConditionModel.from_pretrained( args.pretrained_model_name_or_path if args.pretrain_unet is None else args.pretrain_unet, subfolder="unet", ) # 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 image_encoder and vae to gpu and cast to weight_dtype image_encoder.to(accelerator.device, dtype=weight_dtype) vae.to(accelerator.device, dtype=weight_dtype) # unet will be sent with accelerator.prepare() unet_lora_config = LoraConfig( r=args.rank, lora_alpha=args.rank, init_lora_weights="gaussian", target_modules=["to_k", "to_q", "to_v", "to_out.0"], ) # Create EMA for the unet. if args.use_ema: ema_unet = EMAModel(unet.parameters( ), model_cls=UNetSpatioTemporalConditionModel, model_config=unet.config) if args.enable_xformers_memory_efficient_attention: if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) if xformers_version == version.parse("0.0.16"): logger.warn( "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." ) unet.enable_xformers_memory_efficient_attention() else: raise ValueError( "xformers is not available. Make sure it is installed correctly") # `accelerate` 0.16.0 will have better support for customized saving if version.parse(accelerate.__version__) >= version.parse("0.16.0"): # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format def save_model_hook(models, weights, output_dir): if args.use_ema: ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema")) for i, model in enumerate(models): model.save_pretrained(os.path.join(output_dir, "unet")) # make sure to pop weight so that corresponding model is not saved again weights.pop() def load_model_hook(models, input_dir): if args.use_ema: load_model = EMAModel.from_pretrained(os.path.join( input_dir, "unet_ema"), UNetSpatioTemporalConditionModel) ema_unet.load_state_dict(load_model.state_dict()) ema_unet.to(accelerator.device) del load_model for i in range(len(models)): # pop models so that they are not loaded again model = models.pop() # load diffusers style into model load_model = UNetSpatioTemporalConditionModel.from_pretrained( input_dir, subfolder="unet") model.register_to_config(**load_model.config) model.load_state_dict(load_model.state_dict()) del load_model accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() # Enable TF32 for faster training on Ampere GPUs, # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices if args.allow_tf32: torch.backends.cuda.matmul.allow_tf32 = True if args.scale_lr: args.learning_rate = ( args.learning_rate * args.gradient_accumulation_steps * args.per_gpu_batch_size * accelerator.num_processes ) # Initialize the optimizer if args.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 parameters_list = [] # Always freeze vae and image_encoder vae.requires_grad_(False) image_encoder.requires_grad_(False) train_lora = args.train_lora if train_lora: # Unet is freezed when we train the lora unet.requires_grad_(False) # Add adapter and make sure the trainable params are in float32. unet.add_adapter(unet_lora_config) # The added parameters are the only parameters with requires_grad=True lora_layers = list(filter(lambda p: p.requires_grad, unet.parameters())) print(f"number of lora layers: {len(lora_layers)}") # Set the parameters_list to the lora_layers parameters_list = lora_layers else: # Only train the temporal parameters of the unet for image information preservation for name, para in unet.named_parameters(): if 'temporal_transformer_block' in name: parameters_list.append(para) para.requires_grad = True else: para.requires_grad = False optimizer = optimizer_cls( parameters_list, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # check parameters if accelerator.is_main_process: rec_txt1 = open('rec_para.txt', 'w') rec_txt2 = open('rec_para_train.txt', 'w') for name, para in unet.named_parameters(): if para.requires_grad is False: rec_txt1.write(f'{name}\n') else: rec_txt2.write(f'{name}\n') rec_txt1.close() rec_txt2.close() # DataLoaders creation: args.global_batch_size = args.per_gpu_batch_size * accelerator.num_processes train_dataset = TrainDataset(num_samples=None, width=args.width, height=args.height, sample_frames=args.num_frames, data_path = args.dataset_path) sampler = RandomSampler(train_dataset) train_dataloader = torch.utils.data.DataLoader( train_dataset, sampler=sampler, batch_size=args.per_gpu_batch_size, num_workers=args.num_workers, ) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil( len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes, num_training_steps=args.max_train_steps * accelerator.num_processes, ) # Prepare everything with our `accelerator`. unet, optimizer, lr_scheduler, train_dataloader = accelerator.prepare( unet, optimizer, lr_scheduler, train_dataloader ) if args.use_ema: ema_unet.to(accelerator.device) # 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) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil( args.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("SVD-motion-lora", config=vars(args)) # Train! total_batch_size = args.per_gpu_batch_size * \ accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info( f" Instantaneous batch size per device = {args.per_gpu_batch_size}") logger.info( f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info( f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") global_step = 0 first_epoch = 0 def encode_image(pixel_values): # pixel: [-1, 1] pixel_values = _resize_with_antialiasing(pixel_values, (224, 224)) # We unnormalize it after resizing. pixel_values = (pixel_values + 1.0) / 2.0 # Normalize the image with for CLIP input pixel_values = feature_extractor( images=pixel_values, do_normalize=True, do_center_crop=False, do_resize=False, do_rescale=False, return_tensors="pt", ).pixel_values pixel_values = pixel_values.to( device=accelerator.device, dtype=weight_dtype) image_embeddings = image_encoder(pixel_values).image_embeds return image_embeddings def _get_add_time_ids( fps, motion_bucket_id, noise_aug_strength, dtype, ): add_time_ids = [] for noise in noise_aug_strength: add_time_ids.append([fps, motion_bucket_id, noise]) # If using multi-gpu, unet.module. ... should be done, for single-gpu, 'module' should be erased. if accelerator.num_processes > 1: passed_add_embed_dim = unet.module.config.addition_time_embed_dim * \ len(add_time_ids[0]) expected_add_embed_dim = unet.module.add_embedding.linear_1.in_features else: passed_add_embed_dim = unet.config.addition_time_embed_dim * \ len(add_time_ids[0]) expected_add_embed_dim = unet.add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor(add_time_ids, dtype=dtype) return add_time_ids # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint != "latest": path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = os.listdir(args.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] if len(dirs) > 0 else None if path is None: accelerator.print( f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run." ) args.resume_from_checkpoint = None else: accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) global_step = int(path.split("-")[1]) resume_global_step = global_step * args.gradient_accumulation_steps first_epoch = global_step // num_update_steps_per_epoch resume_step = resume_global_step % ( num_update_steps_per_epoch * args.gradient_accumulation_steps) # Only show the progress bar once on each machine. progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process) progress_bar.set_description("Steps") for epoch in range(first_epoch, args.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 args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: if step % args.gradient_accumulation_steps == 0: progress_bar.update(1) continue with accelerator.accumulate(unet): # First, convert images to latent space. pixel_values = batch["pixel_values"].to(weight_dtype).to( accelerator.device, non_blocking=True ) #(1, 21, 3, 512, 512) conditional_pixel_values = pixel_values[:, 0:1, :, :, :] latents = tensor_to_vae_latent(pixel_values, vae) # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] cond_sigmas = rand_log_normal(shape=[bsz,], loc=-3.0, scale=0.5).to(latents) noise_aug_strength = cond_sigmas cond_sigmas = cond_sigmas[:, None, None, None, None] conditional_pixel_values = \ torch.randn_like(conditional_pixel_values) * cond_sigmas + conditional_pixel_values conditional_latents = tensor_to_vae_latent(conditional_pixel_values, vae)[:, 0, :, :, :] conditional_latents = conditional_latents / vae.config.scaling_factor #since in inference, image scaling is not done # Sample a random timestep for each image # P_mean=0.7 P_std=1.6 sigmas = rand_log_normal(shape=[bsz,], loc=0.7, scale=1.6).to(latents.device) # Add noise to the latents according to the noise magnitude at each timestep sigmas = sigmas[:, None, None, None, None] noisy_latents = latents + noise * sigmas timesteps = torch.Tensor( [0.25 * sigma.log() for sigma in sigmas]).to(accelerator.device) inp_noisy_latents = noisy_latents / ((sigmas**2 + 1) ** 0.5) # Get the image embedding for conditioning. encoder_hidden_states = encode_image( pixel_values[:, 0, :, :, :].float()) #to work properly, pixel_values should be in range of [-1.1] # Here I input a fixed numerical value for 'motion_bucket_id', which is not reasonable. # However, I am unable to fully align with the calculation method of the motion score, # so I adopted this approach. The same applies to the 'fps' (frames per second). added_time_ids = _get_add_time_ids( 7, # fixed 127, # motion_bucket_id = 127, fixed noise_aug_strength, # noise_aug_strength == cond_sigmas encoder_hidden_states.dtype, ) added_time_ids = added_time_ids.to(latents.device) # Conditioning dropout to support classifier-free guidance during inference. For more details # check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800. if args.conditioning_dropout_prob is not None: random_p = torch.rand( bsz, device=latents.device, generator=generator) # Sample masks for the edit prompts. prompt_mask = random_p < 2 * args.conditioning_dropout_prob prompt_mask = prompt_mask.reshape(bsz, 1, 1) # Final text conditioning. null_conditioning = torch.zeros_like(encoder_hidden_states) encoder_hidden_states = torch.where( prompt_mask, null_conditioning.unsqueeze(1), encoder_hidden_states.unsqueeze(1)) # Sample masks for the original images. image_mask_dtype = conditional_latents.dtype image_mask = 1 - ( (random_p >= args.conditioning_dropout_prob).to( image_mask_dtype) * (random_p < 3 * args.conditioning_dropout_prob).to(image_mask_dtype) ) image_mask = image_mask.reshape(bsz, 1, 1, 1) # Final image conditioning. conditional_latents = image_mask * conditional_latents else: encoder_hidden_states = encoder_hidden_states.unsqueeze(1) # Concatenate the `conditional_latents` with the `noisy_latents`. conditional_latents = conditional_latents.unsqueeze( 1).repeat(1, noisy_latents.shape[1], 1, 1, 1) inp_noisy_latents = torch.cat( [inp_noisy_latents, conditional_latents], dim=2) # check https://arxiv.org/abs/2206.00364(the EDM-framework) for more details. target = latents model_pred = unet( inp_noisy_latents, timesteps, encoder_hidden_states, added_time_ids=added_time_ids).sample # Denoise the latents c_out = -sigmas / ((sigmas**2 + 1)**0.5) c_skip = 1 / (sigmas**2 + 1) denoised_latents = model_pred * c_out + c_skip * noisy_latents weighing = (1 + sigmas ** 2) * (sigmas**-2.0) # MSE loss loss = torch.mean( (weighing.float() * (denoised_latents.float() - target.float()) ** 2).reshape(target.shape[0], -1), dim=1, ) loss = loss.mean() # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather( loss.repeat(args.per_gpu_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: accelerator.clip_grad_norm_(unet.parameters(), args.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: if args.use_ema: ema_unet.step(unet.parameters()) progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if accelerator.is_main_process: # save checkpoints! if global_step % args.checkpointing_steps == 0: # _before_ saving state, check if this save would set us over the `checkpoints_total_limit` if args.checkpoints_total_limit is not None: checkpoints = os.listdir(args.output_dir) checkpoints = [ d for d in checkpoints if d.startswith("checkpoint")] checkpoints = sorted( checkpoints, key=lambda x: int(x.split("-")[1])) # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints if len(checkpoints) >= args.checkpoints_total_limit: num_to_remove = len( checkpoints) - args.checkpoints_total_limit + 1 removing_checkpoints = checkpoints[0:num_to_remove] logger.info( f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints" ) logger.info( f"removing checkpoints: {', '.join(removing_checkpoints)}") for removing_checkpoint in removing_checkpoints: removing_checkpoint = os.path.join( args.output_dir, removing_checkpoint) shutil.rmtree(removing_checkpoint) save_path = os.path.join( args.output_dir, f"checkpoint-{global_step}") if train_lora: unwrapped_unet = accelerator.unwrap_model(unet) unet_lora_state_dict = convert_state_dict_to_diffusers( get_peft_model_state_dict(unwrapped_unet) ) StableVideoDiffusionPipeline.save_lora_weights( save_directory=save_path, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) else: accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") # Log GT training video every validation_steps if ( (global_step % args.validation_steps == 0) or (global_step == 1) ): if accelerator.is_main_process and args.report_to == "wandb": try: # pixel_values: (B, T, C, H, W) in [-1, 1] gt_vid = pixel_values[0].detach().cpu().float() # (T, C, H, W) gt_vid_np = ((gt_vid.permute(0, 2, 3, 1).numpy() + 1) * 127.5).clip(0, 255).astype(np.uint8) # (T, H, W, C) gt_vid_wandb = gt_vid_np.transpose(0, 3, 1, 2) # (T, C, H, W) wandb.log({ "gt_training_video": wandb.Video(gt_vid_wandb, caption=f"GT training batch step {global_step}", fps=8, format="mp4"), }, step=global_step) logger.info(f"Logged GT training video at step {global_step}") except Exception as e: logger.warning(f"Failed to log GT video: {e}") # sample images! if ( (global_step % args.validation_steps == 0) or (global_step == 1) ): logger.info( f"Running validation... \n Generating {args.num_validation_images} videos." ) # create pipeline if args.use_ema: # Store the UNet parameters temporarily and load the EMA parameters to perform inference. ema_unet.store(unet.parameters()) ema_unet.copy_to(unet.parameters()) # The models need unwrapping because for compatibility in distributed training mode. pipeline = StableVideoDiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, unet=accelerator.unwrap_model(unet), image_encoder=accelerator.unwrap_model( image_encoder), vae=accelerator.unwrap_model(vae), revision=args.revision, torch_dtype=weight_dtype, ) pipeline = pipeline.to(accelerator.device) val_save_dir = os.path.join(args.output_dir, "validation_images") # run inference val_save_dir = os.path.join( args.output_dir, "validation") if not os.path.exists(val_save_dir): os.makedirs(val_save_dir) with torch.autocast( str(accelerator.device).replace(":0", ""), enabled=(accelerator.mixed_precision in ["bf16", "fp16"]) ): validation_images = sorted(os.listdir(args.validation_image_path)) for idx, val_img in enumerate(validation_images): if idx >= args.num_validation_images: break val_image_path = os.path.join(args.validation_image_path, val_img) val_image = load_image(val_image_path).resize((args.width, args.height)) video_frames = pipeline( val_image, height=args.height, width=args.width, num_frames=args.num_frames, decode_chunk_size=8, motion_bucket_id=127, fps=7, noise_aug_strength=0.02, max_guidance_scale=3.0 # generator=generator, ).frames[0] save_folder = os.path.join(val_save_dir, f"{os.path.splitext(val_img)[0]}") os.makedirs(save_folder, exist_ok=True) imageio.imwrite(os.path.join(save_folder, "input.jpg"), val_image) naive_out_file = os.path.join( save_folder, f"navieSVD.gif", ) out_file = os.path.join( save_folder, f"step_{global_step}.gif", ) export_to_gif(video_frames, out_file, 8) for tracker in accelerator.trackers: if tracker.name == "tensorboard": np_videos = np.stack([np.asarray(vid) for vid in video_frames]) tracker.writer.add_video("validation", np_videos, global_step, fps=10) if tracker.name == "wandb": exporting_video = np.array(video_frames) exporting_video = np.transpose(exporting_video, (0, 3, 1, 2)) tracker.log( { f"val_video_{idx}": wandb.Video(exporting_video, caption=f"step {global_step}", fps=8, format="mp4"), f"val_image_{idx}": wandb.Image(val_image, caption=f"Input {idx}"), } ) if args.use_ema: # Switch back to the original UNet parameters. ema_unet.restore(unet.parameters()) del pipeline torch.cuda.empty_cache() logs = {"step_loss": loss.detach().item( ), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.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) if args.use_ema: ema_unet.copy_to(unet.parameters()) if train_lora: unet_lora_state_dict = convert_state_dict_to_diffusers(get_peft_model_state_dict(unwrapped_unet)) StableVideoDiffusionPipeline.save_lora_weights( save_directory=args.output_dir, unet_lora_layers=unet_lora_state_dict, safe_serialization=True, ) else: pipeline = StableVideoDiffusionPipeline.from_pretrained( args.pretrained_model_name_or_path, image_encoder=accelerator.unwrap_model(image_encoder), vae=accelerator.unwrap_model(vae), unet=unet, revision=args.revision, ) pipeline.save_pretrained(args.output_dir) if args.push_to_hub: upload_folder( repo_id=repo_id, folder_path=args.output_dir, commit_message="End of training", ignore_patterns=["step_*", "epoch_*"], ) accelerator.end_training() if __name__ == "__main__": print("inside main") main()