"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py """ #!/usr/bin/env python # coding=utf-8 # Copyright 2024 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 import argparse import gc import logging import math import os import pickle import shutil import sys import accelerate import diffusers import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.state import AcceleratorState from accelerate.utils import ProjectConfiguration, set_seed from diffusers import AutoencoderKL, DDPMScheduler from diffusers.models.embeddings import get_3d_rotary_pos_embed from diffusers.optimization import get_scheduler from diffusers.training_utils import EMAModel from diffusers.utils import check_min_version, deprecate, is_wandb_available from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.torch_utils import is_compiled_module from einops import rearrange from omegaconf import OmegaConf from packaging import version from PIL import Image from torch.utils.data import RandomSampler from torch.utils.tensorboard import SummaryWriter from torchvision import transforms from tqdm.auto import tqdm from transformers import (BertModel, BertTokenizer, CLIPImageProcessor, CLIPVisionModelWithProjection, T5Tokenizer, T5EncoderModel, T5Tokenizer) from transformers.utils import ContextManagers import datasets current_file_path = os.path.abspath(__file__) project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path))] for project_root in project_roots: sys.path.insert(0, project_root) if project_root not in sys.path else None from easyanimate.data.bucket_sampler import (ASPECT_RATIO_512, ASPECT_RATIO_RANDOM_CROP_512, ASPECT_RATIO_RANDOM_CROP_PROB, AspectRatioBatchImageVideoSampler, RandomSampler, get_closest_ratio) from easyanimate.data.dataset_image_video import (ImageVideoControlDataset, ImageVideoSampler) from easyanimate.models import (name_to_autoencoder_magvit, name_to_transformer3d) from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import (get_2d_rotary_pos_embed, get_3d_rotary_pos_embed, get_resize_crop_region_for_grid) from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_control import \ EasyAnimatePipeline_Multi_Text_Encoder_Control from easyanimate.utils import gaussian_diffusion as gd from easyanimate.utils.discrete_sampler import DiscreteSampling from easyanimate.utils.respace import SpacedDiffusion, space_timesteps from easyanimate.utils.utils import get_video_to_video_latent, save_videos_grid if is_wandb_available(): import wandb rotary_pos_embed_cache = {} def _get_2d_rotary_pos_embed_cached(embed_dim, crops_coords, grid_size): tmp_key = (embed_dim, crops_coords, grid_size) print('embed_dim=%d crops_coords=%s grid_size=%s in_cache=%d cache_size=%d' % ( embed_dim, crops_coords, grid_size, tmp_key in rotary_pos_embed_cache, len(rotary_pos_embed_cache))) if tmp_key not in rotary_pos_embed_cache: tmp_embed = get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size) tmp_embed_gpu = (tmp_embed[0].cuda(), tmp_embed[1].cuda()) print('\tembed_dim=%d data_size=%s' % (embed_dim, tmp_embed[0].shape)) rotary_pos_embed_cache[tmp_key] = tmp_embed_gpu return tmp_embed_gpu else: return rotary_pos_embed_cache[tmp_key] def _get_3d_rotary_pos_embed_cached(embed_dim, crops_coords, grid_size, temporal_size): tmp_key = (embed_dim, crops_coords, grid_size, temporal_size) print('embed_dim=%d crops_coords=%s grid_size=%s in_cache=%d cache_size=%d' % ( embed_dim, crops_coords, grid_size, tmp_key in rotary_pos_embed_cache, len(rotary_pos_embed_cache))) if tmp_key not in rotary_pos_embed_cache: tmp_embed = get_3d_rotary_pos_embed( embed_dim, crops_coords, grid_size=grid_size, temporal_size=temporal_size, use_real=True, ) tmp_embed_gpu = (tmp_embed[0].cuda(), tmp_embed[1].cuda()) print('\tembed_dim=%d data_size=%s' % (embed_dim, tmp_embed[0].shape)) rotary_pos_embed_cache[tmp_key] = tmp_embed_gpu return tmp_embed_gpu else: return rotary_pos_embed_cache[tmp_key] def encode_prompt( tokenizer, text_encoder, prompt: str, device: torch.device, dtype: torch.dtype, max_sequence_length = None, tokenizer_max_length = 256, add_special_tokens = False, enable_text_attention_mask = True, ): if max_sequence_length is None: max_length = min(tokenizer.model_max_length, tokenizer_max_length) else: max_length = max_sequence_length text_inputs = tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, add_special_tokens=add_special_tokens, return_tensors="pt", ) if device is not None: text_input_ids = text_inputs.input_ids.to(device) prompt_attention_mask = text_inputs.attention_mask.to(device) else: text_input_ids = text_inputs.input_ids prompt_attention_mask = text_inputs.attention_mask if enable_text_attention_mask: prompt_embeds = text_encoder( text_input_ids, attention_mask=prompt_attention_mask, )[0] else: prompt_embeds = text_encoder( text_input_ids )[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) prompt_attention_mask = prompt_attention_mask.to(dtype=dtype, device=device) return prompt_embeds, prompt_attention_mask def get_random_downsample_ratio(sample_size, image_ratio=[], all_choices=False, rng=None): def _create_special_list(length): if length == 1: return [1.0] if length >= 2: first_element = 0.75 remaining_sum = 1.0 - first_element other_elements_value = remaining_sum / (length - 1) special_list = [first_element] + [other_elements_value] * (length - 1) return special_list if sample_size >= 1536: number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio elif sample_size >= 1024: number_list = [1, 1.25, 1.5, 2] + image_ratio elif sample_size >= 768: number_list = [1, 1.25, 1.5] + image_ratio elif sample_size >= 512: number_list = [1] + image_ratio else: number_list = [1] if all_choices: return number_list number_list_prob = np.array(_create_special_list(len(number_list))) if rng is None: return np.random.choice(number_list, p = number_list_prob) else: return rng.choice(number_list, p = number_list_prob) # Will error if the minimal version of diffusers is not installed. Remove at your own risks. check_min_version("0.18.0.dev0") logger = get_logger(__name__, log_level="INFO") def log_validation( vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, config, args, accelerator, weight_dtype, global_step ): try: logger.info("Running validation... ") # Get New Transformer Choosen_Transformer3DModel = name_to_transformer3d[ config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') ] transformer3d_val = Choosen_Transformer3DModel.from_pretrained_2d( args.pretrained_model_name_or_path, subfolder="transformer", transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) ).to(weight_dtype) transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict()) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Control.from_pretrained( args.pretrained_model_name_or_path, vae=accelerator.unwrap_model(vae).to(weight_dtype), text_encoder=accelerator.unwrap_model(text_encoder), text_encoder_2=accelerator.unwrap_model(text_encoder_2), tokenizer=tokenizer, tokenizer_2=tokenizer_2, transformer=transformer3d_val, torch_dtype=weight_dtype, ) else: raise ValueError("enable_multi_text_encoder == False is not support now") pipeline = pipeline.to(accelerator.device) if args.enable_xformers_memory_efficient_attention \ and config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') == 'Transformer3DModel': pipeline.enable_xformers_memory_efficient_attention() if args.seed is None: generator = None else: generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) images = [] for i in range(len(args.validation_prompts)): with torch.no_grad(): with torch.autocast("cuda", dtype=weight_dtype): if vae.cache_mag_vae: video_length = int((args.video_sample_n_frames - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1 if args.video_sample_n_frames != 1 else 1 else: video_length = int(args.video_sample_n_frames // vae.mini_batch_encoder * vae.mini_batch_encoder) if args.video_sample_n_frames != 1 else 1 input_video, input_video_mask, clip_image = get_video_to_video_latent(args.validation_paths[i], video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size]) sample = pipeline( args.validation_prompts[i], video_length = video_length, negative_prompt = "bad detailed", height = args.video_sample_size, width = args.video_sample_size, generator = generator, control_video = input_video, ).videos os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True) save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif")) del pipeline del transformer3d_val gc.collect() torch.cuda.empty_cache() torch.cuda.ipc_collect() return images except Exception as e: gc.collect() torch.cuda.empty_cache() torch.cuda.ipc_collect() print(f"Eval error with info {e}") return None def linear_decay(initial_value, final_value, total_steps, current_step): if current_step >= total_steps: return final_value current_step = max(0, current_step) step_size = (final_value - initial_value) / total_steps current_value = initial_value + step_size * current_step return current_value def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None): u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator) t = 1 / (1 + torch.exp(-u)) * (high - low) + low return torch.clip(t.to(torch.int32), low, high - 1) def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") parser.add_argument( "--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1." ) 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( "--variant", type=str, default=None, help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16", ) parser.add_argument( "--train_data_dir", type=str, default=None, help=( "A folder containing the training data. " ), ) parser.add_argument( "--train_data_meta", type=str, default=None, help=( "A csv containing the training data. " ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--validation_paths", type=str, default=None, nargs="+", help=("A set of control videos evaluated every `--validation_epochs` and logged to `--report_to`."), ) parser.add_argument( "--validation_prompts", type=str, default=None, nargs="+", help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."), ) parser.add_argument( "--output_dir", type=str, default="sd-model-finetuned", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--cache_dir", type=str, default=None, help="The directory where the downloaded models and datasets will be stored.", ) parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--random_flip", action="store_true", help="whether to randomly flip images horizontally", ) parser.add_argument( "--use_came", action="store_true", help="whether to use came", ) parser.add_argument( "--multi_stream", action="store_true", help="whether to use cuda multi-stream", ) parser.add_argument( "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader." ) parser.add_argument( "--vae_mini_batch", type=int, default=32, help="mini batch size for vae." ) 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( "--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( "--dataloader_num_workers", type=int, default=0, 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( "--prediction_type", type=str, default=None, help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.", ) 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( "--report_model_info", action="store_true", help="Whether or not to report more info about model (such as norm, grad)." ) 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="tensorboard", 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=None, 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("--noise_offset", type=float, default=0, help="The scale of noise offset.") parser.add_argument( "--validation_epochs", type=int, default=5, help="Run validation every X epochs.", ) parser.add_argument( "--validation_steps", type=int, default=2000, help="Run validation every X steps.", ) parser.add_argument( "--tracker_project_name", type=str, default="text2image-fine-tune", help=( "The `project_name` argument passed to Accelerator.init_trackers for" " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator" ), ) parser.add_argument( "--snr_loss", action="store_true", help="Whether or not to use snr_loss." ) parser.add_argument( "--uniform_sampling", action="store_true", help="Whether or not to use uniform_sampling." ) parser.add_argument( "--not_sigma_loss", action="store_true", help="Whether or not to not use sigma_loss." ) parser.add_argument( "--enable_text_encoder_in_dataloader", action="store_true", help="Whether or not to use text encoder in dataloader." ) parser.add_argument( "--enable_bucket", action="store_true", help="Whether enable bucket sample in datasets." ) parser.add_argument( "--random_ratio_crop", action="store_true", help="Whether enable random ratio crop sample in datasets." ) parser.add_argument( "--random_frame_crop", action="store_true", help="Whether enable random frame crop sample in datasets." ) parser.add_argument( "--random_hw_adapt", action="store_true", help="Whether enable random adapt height and width in datasets." ) parser.add_argument( "--training_with_video_token_length", action="store_true", help="The training stage of the model in training.", ) parser.add_argument( "--noise_share_in_frames", action="store_true", help="Whether enable noise share in frames." ) parser.add_argument( "--noise_share_in_frames_ratio", type=float, default=0.5, help="Noise share ratio.", ) parser.add_argument( "--motion_sub_loss", action="store_true", help="Whether enable motion sub loss." ) parser.add_argument( "--motion_sub_loss_ratio", type=float, default=0.25, help="The ratio of motion sub loss." ) parser.add_argument( "--train_sampling_steps", type=int, default=1000, help="Run train_sampling_steps.", ) parser.add_argument( "--keep_all_node_same_token_length", action="store_true", help="Reference of the length token.", ) parser.add_argument( "--token_sample_size", type=int, default=512, help="Sample size of the token.", ) parser.add_argument( "--video_sample_size", type=int, default=512, help="Sample size of the video.", ) parser.add_argument( "--image_sample_size", type=int, default=512, help="Sample size of the video.", ) parser.add_argument( "--video_sample_stride", type=int, default=4, help="Sample stride of the video.", ) parser.add_argument( "--video_sample_n_frames", type=int, default=17, help="Num frame of video.", ) parser.add_argument( "--video_repeat", type=int, default=0, help="Num of repeat video.", ) parser.add_argument( "--config_path", type=str, default=None, help=( "The config of the model in training." ), ) parser.add_argument( "--transformer_path", type=str, default=None, help=("If you want to load the weight from other transformers, input its path."), ) parser.add_argument( "--vae_path", type=str, default=None, help=("If you want to load the weight from other vaes, input its path."), ) parser.add_argument( '--trainable_modules', nargs='+', help='Enter a list of trainable modules' ) parser.add_argument( '--trainable_modules_low_learning_rate', nargs='+', default=[], help='Enter a list of trainable modules with lower learning rate' ) parser.add_argument( '--tokenizer_max_length', type=int, default=256, help='Max length of tokenizer' ) parser.add_argument( "--use_deepspeed", action="store_true", help="Whether or not to use deepspeed." ) parser.add_argument( "--low_vram", action="store_true", help="Whether enable low_vram mode." ) parser.add_argument( "--abnormal_norm_clip_start", type=int, default=1000, help=( 'When do we start doing additional processing on abnormal gradients. ' ), ) parser.add_argument( "--initial_grad_norm_ratio", type=int, default=5, help=( 'The initial gradient is relative to the multiple of the max_grad_norm. ' ), ) 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.report_to == "wandb" and args.hub_token is not None: raise ValueError( "You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token." " Please use `huggingface-cli login` to authenticate with the Hub." ) 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) config = OmegaConf.load(args.config_path) accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir) accelerator = Accelerator( gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision, log_with=args.report_to, project_config=accelerator_project_config, ) if accelerator.is_main_process: writer = SummaryWriter(log_dir=logging_dir) # 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: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_warning() diffusers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() 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) rng = np.random.default_rng(np.random.PCG64(args.seed + accelerator.process_index)) torch_rng = torch.Generator(accelerator.device).manual_seed(args.seed + accelerator.process_index) else: rng = None torch_rng = None index_rng = np.random.default_rng(np.random.PCG64(43)) print(f"Init rng with seed {args.seed + accelerator.process_index}. Process_index is {accelerator.process_index}") # Handle the repository creation if accelerator.is_main_process: if args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer3d) to half-precision # as these weights 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 args.mixed_precision = accelerator.mixed_precision elif accelerator.mixed_precision == "bf16": weight_dtype = torch.bfloat16 args.mixed_precision = accelerator.mixed_precision # Load scheduler, tokenizer and models. if args.not_sigma_loss: noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") else: train_diffusion = SpacedDiffusion( use_timesteps=space_timesteps(1000, str(args.train_sampling_steps)), betas=gd.get_named_beta_schedule("linear", 1000), model_mean_type=(gd.ModelMeanType.EPSILON), model_var_type=((gd.ModelVarType.LEARNED_RANGE)), loss_type=gd.LossType.MSE, snr=args.snr_loss, return_startx=False, ) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): print("Init BertTokenizer") tokenizer = BertTokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision ) print("Init T5Tokenizer") tokenizer_2 = T5Tokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision ) else: print("Init T5Tokenizer") tokenizer = T5Tokenizer.from_pretrained( args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision ) tokenizer_2 = None def deepspeed_zero_init_disabled_context_manager(): """ returns either a context list that includes one that will disable zero.Init or an empty context list """ deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None if deepspeed_plugin is None: return [] return [deepspeed_plugin.zero3_init_context_manager(enable=False)] # Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3. # For this to work properly all models must be run through `accelerate.prepare`. But accelerate # will try to assign the same optimizer with the same weights to all models during # `deepspeed.initialize`, which of course doesn't work. # # For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2 # frozen models from being partitioned during `zero.Init` which gets called during # `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding # across multiple gpus and only UNet2DConditionModel will get ZeRO sharded. with ContextManagers(deepspeed_zero_init_disabled_context_manager()): if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): text_encoder = BertModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant, torch_dtype=weight_dtype ) text_encoder_2 = T5EncoderModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant, torch_dtype=weight_dtype ) else: text_encoder = T5EncoderModel.from_pretrained( args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant, torch_dtype=weight_dtype ) text_encoder_2 = None # Get Vae Choosen_AutoencoderKL = name_to_autoencoder_magvit[ config['vae_kwargs'].get('vae_type', 'AutoencoderKL') ] vae = Choosen_AutoencoderKL.from_pretrained( args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant, vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs']) ) # Get Transformer Choosen_Transformer3DModel = name_to_transformer3d[ config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') ] transformer3d = Choosen_Transformer3DModel.from_pretrained_2d( args.pretrained_model_name_or_path, subfolder="transformer", transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) ) # Freeze vae and text_encoder and set transformer3d to trainable vae.requires_grad_(False) text_encoder.requires_grad_(False) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): text_encoder_2.requires_grad_(False) transformer3d.requires_grad_(False) # Load transformer and vae from path if it needs. if args.transformer_path is not None: print(f"From checkpoint: {args.transformer_path}") if args.transformer_path.endswith("safetensors"): from safetensors.torch import load_file, safe_open state_dict = load_file(args.transformer_path) else: state_dict = torch.load(args.transformer_path, map_location="cpu") state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict m, u = transformer3d.load_state_dict(state_dict, strict=False) print(f"missing keys: {len(m)}, unexpected keys: {len(u)}") if args.vae_path is not None: print(f"From checkpoint: {args.vae_path}") if args.vae_path.endswith("safetensors"): from safetensors.torch import load_file, safe_open state_dict = load_file(args.vae_path) else: state_dict = torch.load(args.vae_path, map_location="cpu") state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict m, u = vae.load_state_dict(state_dict, strict=False) print(f"missing keys: {len(m)}, unexpected keys: {len(u)}") # A good trainable modules is showed below now. transformer3d.train() if accelerator.is_main_process: accelerator.print( f"Trainable modules '{args.trainable_modules}'." ) for name, param in transformer3d.named_parameters(): for trainable_module_name in args.trainable_modules + args.trainable_modules_low_learning_rate: if trainable_module_name in name: param.requires_grad = True break for name, param in transformer3d.named_parameters(): for trainable_module_name in args.trainable_modules + args.trainable_modules_low_learning_rate: if trainable_module_name in name: param.requires_grad = True break # Create EMA for the transformer3d. if args.use_ema: ema_transformer3d = Choosen_Transformer3DModel.from_pretrained_2d( args.pretrained_model_name_or_path, subfolder="transformer", transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) ) ema_transformer3d = EMAModel(ema_transformer3d.parameters(), model_cls=Choosen_Transformer3DModel, model_config=ema_transformer3d.config) if args.enable_xformers_memory_efficient_attention \ and config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') == 'Transformer3DModel': 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." ) transformer3d.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 accelerator.is_main_process: if args.use_ema: ema_transformer3d.save_pretrained(os.path.join(output_dir, "transformer_ema")) models[0].save_pretrained(os.path.join(output_dir, "transformer")) if not args.use_deepspeed: weights.pop() with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file: pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file) def load_model_hook(models, input_dir): if args.use_ema: ema_path = os.path.join(input_dir, "transformer_ema") _, ema_kwargs = Choosen_Transformer3DModel.load_config(ema_path, return_unused_kwargs=True) load_model = Choosen_Transformer3DModel.from_pretrained_2d( input_dir, subfolder="transformer_ema", transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) ) load_model = EMAModel(load_model.parameters(), model_cls=Choosen_Transformer3DModel, model_config=load_model.config) load_model.load_state_dict(ema_kwargs) ema_transformer3d.load_state_dict(load_model.state_dict()) ema_transformer3d.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 = Choosen_Transformer3DModel.from_pretrained_2d( input_dir, subfolder="transformer", transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs']) ) model.register_to_config(**load_model.config) model.load_state_dict(load_model.state_dict()) del load_model pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl") if os.path.exists(pkl_path): with open(pkl_path, 'rb') as file: loaded_number, _ = pickle.load(file) batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0) print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.") accelerator.register_save_state_pre_hook(save_model_hook) accelerator.register_load_state_pre_hook(load_model_hook) if args.gradient_checkpointing: transformer3d.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.train_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 elif args.use_came: try: from came_pytorch import CAME except: raise ImportError( "Please install came_pytorch to use CAME. You can do so by running `pip install came_pytorch`" ) optimizer_cls = CAME else: optimizer_cls = torch.optim.AdamW # Select trainable params trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters())) trainable_params_optim = [ {'params': [], 'lr': args.learning_rate}, {'params': [], 'lr': args.learning_rate / 2}, ] in_already = [] for name, param in transformer3d.named_parameters(): high_lr_flag = False if name in in_already: continue for trainable_module_name in args.trainable_modules: if trainable_module_name in name: in_already.append(name) high_lr_flag = True trainable_params_optim[0]['params'].append(param) if accelerator.is_main_process: print(f"Set {name} to lr : {args.learning_rate}") break if high_lr_flag: continue for trainable_module_name in args.trainable_modules_low_learning_rate: if trainable_module_name in name: in_already.append(name) trainable_params_optim[1]['params'].append(param) if accelerator.is_main_process: print(f"Set {name} to lr : {args.learning_rate / 2}") break # Init optimizer if args.use_came: optimizer = optimizer_cls( trainable_params_optim, lr=args.learning_rate, betas=(0.9, 0.999, 0.9999), eps=(1e-30, 1e-16) ) else: optimizer = optimizer_cls( trainable_params_optim, lr=args.learning_rate, betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) # Get the training dataset sample_n_frames_bucket_interval = vae.mini_batch_encoder if vae.quant_conv is None or vae.quant_conv.weight.ndim==5 else 4 # Get the dataset train_dataset = ImageVideoControlDataset( args.train_data_meta, args.train_data_dir, video_sample_size=args.video_sample_size, video_sample_stride=args.video_sample_stride, video_sample_n_frames=args.video_sample_n_frames, video_repeat=args.video_repeat, image_sample_size=args.image_sample_size, enable_bucket=args.enable_bucket, enable_inpaint=False, ) if args.enable_bucket: aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()} batch_sampler_generator = torch.Generator().manual_seed(args.seed) batch_sampler = AspectRatioBatchImageVideoSampler( sampler=RandomSampler(train_dataset, generator=batch_sampler_generator), dataset=train_dataset.dataset, batch_size=args.train_batch_size, train_folder = args.train_data_dir, drop_last=True, aspect_ratios=aspect_ratio_sample_size, ) # Get the frame length at different resolutions according to token_length def get_length_to_frame_num(token_length): if args.image_sample_size > args.video_sample_size: sample_sizes = list(range(args.video_sample_size, args.image_sample_size + 1, 128)) if sample_sizes[-1] != args.image_sample_size: sample_sizes.append(args.image_sample_size) else: sample_sizes = [args.image_sample_size] if vae.cache_mag_vae: length_to_frame_num = { sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1 for sample_size in sample_sizes } else: length_to_frame_num = { sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval for sample_size in sample_sizes } return length_to_frame_num def collate_fn(examples): # Get token length target_token_length = args.video_sample_n_frames * args.token_sample_size * args.token_sample_size length_to_frame_num = get_length_to_frame_num(target_token_length) # Create new output new_examples = {} new_examples["target_token_length"] = target_token_length new_examples["pixel_values"] = [] new_examples["text"] = [] # Used in Control Mode new_examples["control_pixel_values"] = [] # Get downsample ratio in image and videos pixel_value = examples[0]["pixel_values"] data_type = examples[0]["data_type"] f, h, w, c = np.shape(pixel_value) if data_type == 'image': random_downsample_ratio = 1 if not args.random_hw_adapt else get_random_downsample_ratio(args.image_sample_size, image_ratio=[args.image_sample_size / args.video_sample_size], rng=rng) aspect_ratio_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()} aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()} batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval else: if args.random_hw_adapt: if args.training_with_video_token_length: local_min_size = np.min(np.array([np.mean(np.array([np.shape(example["pixel_values"])[1], np.shape(example["pixel_values"])[2]])) for example in examples])) # The video will be resized to a lower resolution than its own. choice_list = [length for length in list(length_to_frame_num.keys()) if length < local_min_size] if len(choice_list) == 0: choice_list = list(length_to_frame_num.keys()) if rng is None: local_video_sample_size = np.random.choice(choice_list) else: local_video_sample_size = rng.choice(choice_list) batch_video_length = length_to_frame_num[local_video_sample_size] random_downsample_ratio = args.video_sample_size / local_video_sample_size else: random_downsample_ratio = get_random_downsample_ratio( args.video_sample_size, rng=rng) batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval else: random_downsample_ratio = 1 batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()} aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()} closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size) closest_size = [int(x / 16) * 16 for x in closest_size] if args.random_ratio_crop: if rng is None: random_sample_size = aspect_ratio_random_crop_sample_size[ np.random.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB) ] else: random_sample_size = aspect_ratio_random_crop_sample_size[ rng.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB) ] random_sample_size = [int(x / 16) * 16 for x in random_sample_size] for example in examples: if args.random_ratio_crop: # To 0~1 pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous() pixel_values = pixel_values / 255. control_pixel_values = torch.from_numpy(example["control_pixel_values"]).permute(0, 3, 1, 2).contiguous() control_pixel_values = control_pixel_values / 255. # Get adapt hw for resize b, c, h, w = pixel_values.size() th, tw = random_sample_size if th / tw > h / w: nh = int(th) nw = int(w / h * nh) else: nw = int(tw) nh = int(h / w * nw) transform = transforms.Compose([ transforms.Resize([nh, nw]), transforms.CenterCrop([int(x) for x in random_sample_size]), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), ]) else: # To 0~1 pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous() pixel_values = pixel_values / 255. control_pixel_values = torch.from_numpy(example["control_pixel_values"]).permute(0, 3, 1, 2).contiguous() control_pixel_values = control_pixel_values / 255. # Get adapt hw for resize closest_size = list(map(lambda x: int(x), closest_size)) if closest_size[0] / h > closest_size[1] / w: resize_size = closest_size[0], int(w * closest_size[0] / h) else: resize_size = int(h * closest_size[1] / w), closest_size[1] transform = transforms.Compose([ transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC transforms.CenterCrop(closest_size), transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True), ]) new_examples["pixel_values"].append(transform(pixel_values)) new_examples["control_pixel_values"].append(transform(control_pixel_values)) new_examples["text"].append(example["text"]) # Magvae needs the number of frames to be 4n + 1. if vae.cache_mag_vae: batch_video_length = int( min( batch_video_length, (len(pixel_values) - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1, ) ) else: batch_video_length = int( min( batch_video_length, len(pixel_values) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval, ) ) if batch_video_length == 0: batch_video_length = 1 # Limit the number of frames to the same new_examples["pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["pixel_values"]]) new_examples["control_pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["control_pixel_values"]]) # Encode prompts when enable_text_encoder_in_dataloader=True if args.enable_text_encoder_in_dataloader: if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): prompt_embeds, prompt_attention_mask = \ encode_prompt( tokenizer, text_encoder, new_examples['text'], None, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True) ) prompt_embeds_2, prompt_attention_mask_2 = \ encode_prompt( tokenizer_2, text_encoder_2, new_examples['text'], None, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True) ) new_examples['prompt_embeds'] = prompt_embeds new_examples['prompt_attention_mask'] = prompt_attention_mask new_examples['prompt_embeds_2'] = prompt_embeds_2 new_examples['prompt_attention_mask_2'] = prompt_attention_mask_2 else: prompt_embeds, prompt_attention_mask = \ encode_prompt( tokenizer, text_encoder, new_examples['text'], None, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, add_special_tokens=True, enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True) ) new_examples['prompt_embeds'] = prompt_embeds new_examples['prompt_attention_mask'] = prompt_attention_mask return new_examples # DataLoaders creation: train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_sampler=batch_sampler, collate_fn=collate_fn, persistent_workers=True if args.dataloader_num_workers != 0 else False, num_workers=args.dataloader_num_workers, ) else: # DataLoaders creation: batch_sampler_generator = torch.Generator().manual_seed(args.seed) batch_sampler = ImageVideoSampler(RandomSampler(train_dataset, generator=batch_sampler_generator), train_dataset, args.train_batch_size) train_dataloader = torch.utils.data.DataLoader( train_dataset, batch_sampler=batch_sampler, persistent_workers=True if args.dataloader_num_workers != 0 else False, num_workers=args.dataloader_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`. transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( transformer3d, optimizer, train_dataloader, lr_scheduler ) if args.use_ema: ema_transformer3d.to(accelerator.device) # Move text_encode and vae to gpu and cast to weight_dtype vae.to(accelerator.device, dtype=weight_dtype) if not args.enable_text_encoder_in_dataloader: text_encoder.to(accelerator.device) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): text_encoder_2.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: tracker_config = dict(vars(args)) tracker_config.pop("validation_prompts") tracker_config.pop("validation_paths") tracker_config.pop("trainable_modules") tracker_config.pop("trainable_modules_low_learning_rate") accelerator.init_trackers(args.tracker_project_name, tracker_config) # Function for unwrapping if model was compiled with `torch.compile`. def unwrap_model(model): model = accelerator.unwrap_model(model) model = model._orig_mod if is_compiled_module(model) else model return model # Train! total_batch_size = args.train_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.train_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 # 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 initial_global_step = 0 else: global_step = int(path.split("-")[1]) initial_global_step = global_step pkl_path = os.path.join(os.path.join(args.output_dir, path), "sampler_pos_start.pkl") if os.path.exists(pkl_path): with open(pkl_path, 'rb') as file: _, first_epoch = pickle.load(file) else: first_epoch = global_step // num_update_steps_per_epoch print(f"Load pkl from {pkl_path}. Get first_epoch = {first_epoch}.") accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) else: initial_global_step = 0 progress_bar = tqdm( range(0, args.max_train_steps), initial=initial_global_step, desc="Steps", # Only show the progress bar once on each machine. disable=not accelerator.is_local_main_process, ) if args.multi_stream: # create extra cuda streams to speedup inpaint vae computation vae_stream_1 = torch.cuda.Stream() vae_stream_2 = torch.cuda.Stream() else: vae_stream_1 = None vae_stream_2 = None idx_sampling = DiscreteSampling(args.train_sampling_steps, uniform_sampling=args.uniform_sampling) for epoch in range(first_epoch, args.num_train_epochs): train_loss = 0.0 batch_sampler.sampler.generator = torch.Generator().manual_seed(args.seed + epoch) for step, batch in enumerate(train_dataloader): # Data batch sanity check if epoch == first_epoch and step == 0: pixel_values, texts = batch['pixel_values'].cpu(), batch['text'] control_pixel_values = batch["control_pixel_values"].cpu() pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w") control_pixel_values = rearrange(control_pixel_values, "b f c h w -> b c f h w") os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True) for idx, (pixel_value, control_pixel_value, text) in enumerate(zip(pixel_values, control_pixel_values, texts)): pixel_value = pixel_value[None, ...] control_pixel_value = control_pixel_value[None, ...] gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}' save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}.gif", rescale=True) save_videos_grid(control_pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}_control.gif", rescale=True) with accelerator.accumulate(transformer3d): # Convert images to latent space pixel_values = batch["pixel_values"].to(weight_dtype) control_pixel_values = batch["control_pixel_values"].to(weight_dtype) # Increase the batch size when the length of the latent sequence of the current sample is small if args.training_with_video_token_length: if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: pixel_values = torch.tile(pixel_values, (4, 1, 1, 1, 1)) control_pixel_values = torch.tile(control_pixel_values, (4, 1, 1, 1, 1)) if args.enable_text_encoder_in_dataloader: batch['prompt_embeds'] = torch.tile(batch['prompt_embeds'], (4, 1, 1)) batch['prompt_attention_mask'] = torch.tile(batch['prompt_attention_mask'], (4, 1)) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): batch['prompt_embeds_2'] = torch.tile(batch['prompt_embeds_2'], (4, 1, 1)) batch['prompt_attention_mask_2'] = torch.tile(batch['prompt_attention_mask_2'], (4, 1)) else: batch['text'] = batch['text'] * 4 elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]: pixel_values = torch.tile(pixel_values, (2, 1, 1, 1, 1)) control_pixel_values = torch.tile(control_pixel_values, (2, 1, 1, 1, 1)) if args.enable_text_encoder_in_dataloader: batch['prompt_embeds'] = torch.tile(batch['prompt_embeds'], (2, 1, 1)) batch['prompt_attention_mask'] = torch.tile(batch['prompt_attention_mask'], (2, 1)) if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): batch['prompt_embeds_2'] = torch.tile(batch['prompt_embeds_2'], (2, 1, 1)) batch['prompt_attention_mask_2'] = torch.tile(batch['prompt_attention_mask_2'], (2, 1)) else: batch['text'] = batch['text'] * 2 # Random crop number of frames to adapt different frames of video. if args.random_frame_crop: def _create_special_list(length): if length == 1: return [1.0] if length >= 2: last_element = 0.90 remaining_sum = 1.0 - last_element other_elements_value = remaining_sum / (length - 1) special_list = [other_elements_value] * (length - 1) + [last_element] return special_list if vae.cache_mag_vae: select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval + 1, args.video_sample_n_frames + sample_n_frames_bucket_interval, sample_n_frames_bucket_interval))] else: select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval, args.video_sample_n_frames + sample_n_frames_bucket_interval, sample_n_frames_bucket_interval))] select_frames_prob = np.array(_create_special_list(len(select_frames))) if rng is None: temp_n_frames = np.random.choice(select_frames, p = select_frames_prob) else: temp_n_frames = rng.choice(select_frames, p = select_frames_prob) pixel_values = pixel_values[:, :temp_n_frames, :, :] control_pixel_values = control_pixel_values[:, :temp_n_frames, :, :] # Keep all node same token length to accelerate the traning when resolution grows. if args.keep_all_node_same_token_length: if args.token_sample_size > 256: numbers_list = list(range(256, args.token_sample_size + 1, 128)) if numbers_list[-1] != args.token_sample_size: numbers_list.append(args.token_sample_size) else: numbers_list = [256] numbers_list = [_number * _number * args.video_sample_n_frames for _number in numbers_list] actual_token_length = index_rng.choice(numbers_list) if vae.cache_mag_vae: actual_video_length = (min( actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames ) - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1 else: actual_video_length = min( actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames ) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval actual_video_length = int(max(actual_video_length, 1)) pixel_values = pixel_values[:, :actual_video_length, :, :] control_pixel_values = control_pixel_values[:, :actual_video_length, :, :] # Reduce the vram by offload vae and text encoders if args.low_vram: torch.cuda.empty_cache() vae.to(accelerator.device) if not args.enable_text_encoder_in_dataloader: text_encoder.to(accelerator.device) if text_encoder_2 is not None: text_encoder_2.to(accelerator.device) with torch.no_grad(): video_length = pixel_values.shape[1] # This way is quicker when batch grows up def _batch_encode_vae(pixel_values): if vae.quant_conv is None or vae.quant_conv.weight.ndim==5: pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w") bs = args.vae_mini_batch new_pixel_values = [] for i in range(0, pixel_values.shape[0], bs): pixel_values_bs = pixel_values[i : i + bs] pixel_values_bs = vae.encode(pixel_values_bs)[0] pixel_values_bs = pixel_values_bs.sample() new_pixel_values.append(pixel_values_bs) latents = torch.cat(new_pixel_values, dim = 0) else: pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w") bs = args.vae_mini_batch new_pixel_values = [] for i in range(0, pixel_values.shape[0], bs): pixel_values_bs = pixel_values[i : i + bs] pixel_values_bs = vae.encode(pixel_values_bs.to(dtype=weight_dtype)).latent_dist pixel_values_bs = pixel_values_bs.sample() new_pixel_values.append(pixel_values_bs) latents = torch.cat(new_pixel_values, dim = 0) latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length) return latents # Encode latents. if vae_stream_1 is not None: vae_stream_1.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(vae_stream_1): latents = _batch_encode_vae(pixel_values) else: latents = _batch_encode_vae(pixel_values) latents = latents * vae.config.scaling_factor control_latents = _batch_encode_vae(control_pixel_values) control_latents = control_latents * vae.config.scaling_factor # wait for latents = vae.encode(pixel_values) to complete if vae_stream_1 is not None: torch.cuda.current_stream().wait_stream(vae_stream_1) # Reduce the vram by offload vae and text encoders if args.low_vram: vae.to('cpu') torch.cuda.empty_cache() if not args.enable_text_encoder_in_dataloader: text_encoder.to(accelerator.device) if text_encoder_2 is not None: text_encoder_2.to(accelerator.device) # Get text embeds if args.enable_text_encoder_in_dataloader: if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): prompt_embeds = batch['prompt_embeds'].to(device=latents.device) prompt_attention_mask = batch['prompt_attention_mask'].to(device=latents.device) prompt_embeds_2 = batch['prompt_embeds_2'].to(device=latents.device) prompt_attention_mask_2 = batch['prompt_attention_mask_2'].to(device=latents.device) else: prompt_embeds = batch['prompt_embeds'].to(device=latents.device) prompt_attention_mask = batch['prompt_attention_mask'].to(device=latents.device) prompt_embeds_2 = None prompt_attention_mask_2 = None else: if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False): with torch.no_grad(): prompt_embeds, prompt_attention_mask = \ encode_prompt( tokenizer, text_encoder, batch['text'], latents.device, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True) ) prompt_embeds_2, prompt_attention_mask_2 = \ encode_prompt( tokenizer_2, text_encoder_2, batch['text'], latents.device, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True) ) else: with torch.no_grad(): prompt_embeds, prompt_attention_mask = \ encode_prompt( tokenizer, text_encoder, batch['text'], latents.device, tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype, add_special_tokens=True, enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True) ) prompt_embeds_2 = None prompt_attention_mask_2 = None # Reduce the vram by offload vae and text encoders if args.low_vram and not args.enable_text_encoder_in_dataloader: text_encoder.to('cpu') if text_encoder_2 is not None: text_encoder_2.to('cpu') torch.cuda.empty_cache() # Get Noise bsz = latents.shape[0] if args.noise_share_in_frames: def generate_noise(bs, channel, length, height, width, ratio=0.5, generator=None, device="cuda", dtype=None): noise = torch.randn(bs, channel, length, height, width, generator=generator, device=device, dtype=dtype) for i in range(1, length): noise[:, :, i, :, :] = ratio * noise[:, :, i - 1, :, :] + (1 - ratio) * noise[:, :, i, :, :] return noise noise = generate_noise(*latents.size(), ratio=args.noise_share_in_frames_ratio, device=latents.device, generator=torch_rng, dtype=weight_dtype) else: noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype) # Sample a random timestep for each image # timesteps = generate_timestep_with_lognorm(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng) # timesteps = torch.randint(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng) timesteps = idx_sampling(bsz, generator=torch_rng, device=latents.device) timesteps = timesteps.long() if args.not_sigma_loss: # Create image_rotary_emb, style embedding & time ids height, width = batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1] # Get 3D rope or 2d rope grid_height = height // 8 // unwrap_model(transformer3d).config.patch_size grid_width = width // 8 // unwrap_model(transformer3d).config.patch_size if unwrap_model(transformer3d).config.get("time_position_encoding_type", "2d_rope") == "3d_rope": base_size_width = 720 // 8 // unwrap_model(transformer3d).config.patch_size base_size_height = 480 // 8 // unwrap_model(transformer3d).config.patch_size grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size_width, base_size_height ) image_rotary_emb = _get_3d_rotary_pos_embed_cached( unwrap_model(transformer3d).config.attention_head_dim, grid_crops_coords, (grid_height, grid_width), latents.size()[2], ) else: base_size = 512 // 8 // unwrap_model(transformer3d).config.patch_size grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size, base_size ) image_rotary_emb = _get_2d_rotary_pos_embed_cached( unwrap_model(transformer3d).config.attention_head_dim, grid_crops_coords, (grid_height, grid_width) ) # Get other hunyuan params style = torch.tensor([0], device=latents.device) target_size = (height, width) add_time_ids = list((1024, 1024) + target_size + (0, 0)) add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype) # To latents.device prompt_embeds = prompt_embeds.to(device=latents.device) prompt_attention_mask = prompt_attention_mask.to(device=latents.device) if prompt_embeds_2 is not None: prompt_embeds_2 = prompt_embeds_2.to(device=latents.device) prompt_attention_mask_2 = prompt_attention_mask_2.to(device=latents.device) add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=latents.device).repeat( bsz, 1 ) style = style.to(device=latents.device).repeat(bsz) # Add noise noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) 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}") # Predict the noise residual noise_pred = transformer3d( noisy_latents, timesteps.to(noisy_latents.dtype), encoder_hidden_states=prompt_embeds, text_embedding_mask=prompt_attention_mask, encoder_hidden_states_t5=prompt_embeds_2, text_embedding_mask_t5=prompt_attention_mask_2, image_meta_size=add_time_ids, style=style, image_rotary_emb=image_rotary_emb, control_latents=control_latents, return_dict=False )[0] if noise_pred.size()[1] != vae.config.latent_channels: noise_pred, _ = noise_pred.chunk(2, dim=1) def custom_mse_loss(noise_pred, target, threshold=50): noise_pred = noise_pred.float() target = target.float() diff = noise_pred - target mse_loss = F.mse_loss(noise_pred, target, reduction='none') mask = (diff.abs() <= threshold).float() masked_loss = mse_loss * mask final_loss = masked_loss.mean() return final_loss loss = custom_mse_loss(noise_pred.float(), target.float()) if args.motion_sub_loss and noise_pred.size()[2] > 2: gt_sub_noise = noise_pred[:, :, 1:].float() - noise_pred[:, :, :-1].float() pre_sub_noise = target[:, :, 1:].float() - target[:, :, :-1].float() sub_loss = F.mse_loss(gt_sub_noise, pre_sub_noise, reduction="mean") loss = loss * (1 - args.motion_sub_loss_ratio) + sub_loss * args.motion_sub_loss_ratio else: added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if unwrap_model(transformer3d).config.sample_size == 128: bs, height, width = bsz, batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1] resolution = torch.tensor([height, width]).repeat(bs, 1) aspect_ratio = torch.tensor([float(height / width)]).repeat(bs, 1) resolution = resolution.to(accelerator.device, dtype=weight_dtype) aspect_ratio = aspect_ratio.to(accelerator.device, dtype=weight_dtype) added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} loss_term = train_diffusion.training_losses( transformer3d, latents, timesteps, noise=noise, model_kwargs=dict( encoder_hidden_states=prompt_embeds, encoder_attention_mask=prompt_attention_mask, added_cond_kwargs=added_cond_kwargs, control_latents=control_latents, return_dict=False ) ) loss = loss_term['loss'].mean() # Gather the losses across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: if not args.use_deepspeed: trainable_params_grads = [p.grad for p in trainable_params if p.grad is not None] trainable_params_total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2) for g in trainable_params_grads]), 2) max_grad_norm = linear_decay(args.max_grad_norm * args.initial_grad_norm_ratio, args.max_grad_norm, args.abnormal_norm_clip_start, global_step) if trainable_params_total_norm / max_grad_norm > 5 and global_step > args.abnormal_norm_clip_start: actual_max_grad_norm = max_grad_norm / min((trainable_params_total_norm / max_grad_norm), 10) else: actual_max_grad_norm = max_grad_norm else: actual_max_grad_norm = args.max_grad_norm if not args.use_deepspeed and args.report_model_info and accelerator.is_main_process: if trainable_params_total_norm > 1 and global_step > args.abnormal_norm_clip_start: for name, param in transformer3d.named_parameters(): if param.requires_grad: writer.add_scalar(f'gradients/before_clip_norm/{name}', param.grad.norm(), global_step=global_step) norm_sum = accelerator.clip_grad_norm_(trainable_params, actual_max_grad_norm) if not args.use_deepspeed and args.report_model_info and accelerator.is_main_process: writer.add_scalar(f'gradients/norm_sum', norm_sum, global_step=global_step) writer.add_scalar(f'gradients/actual_max_grad_norm', actual_max_grad_norm, global_step=global_step) 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_transformer3d.step(transformer3d.parameters()) progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss}, step=global_step) train_loss = 0.0 if global_step % args.checkpointing_steps == 0: if args.use_deepspeed or accelerator.is_main_process: # _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}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") if accelerator.is_main_process: if args.validation_prompts is not None and global_step % args.validation_steps == 0: if args.use_ema: # Store the UNet parameters temporarily and load the EMA parameters to perform inference. ema_transformer3d.store(transformer3d.parameters()) ema_transformer3d.copy_to(transformer3d.parameters()) log_validation( vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, config, args, accelerator, weight_dtype, global_step, ) if args.use_ema: # Switch back to the original transformer3d parameters. ema_transformer3d.restore(transformer3d.parameters()) 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 if accelerator.is_main_process: if args.validation_prompts is not None and epoch % args.validation_epochs == 0: if args.use_ema: # Store the UNet parameters temporarily and load the EMA parameters to perform inference. ema_transformer3d.store(transformer3d.parameters()) ema_transformer3d.copy_to(transformer3d.parameters()) log_validation( vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, config, args, accelerator, weight_dtype, global_step, ) if args.use_ema: # Switch back to the original transformer3d parameters. ema_transformer3d.restore(transformer3d.parameters()) # Create the pipeline using the trained modules and save it. accelerator.wait_for_everyone() if accelerator.is_main_process: transformer3d = unwrap_model(transformer3d) if args.use_ema: ema_transformer3d.copy_to(transformer3d.parameters()) if args.use_deepspeed or accelerator.is_main_process: save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(save_path) logger.info(f"Saved state to {save_path}") accelerator.end_training() if __name__ == "__main__": main()