"""Modified from EasyAnimate/scripts/train_lora.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 json from contextlib import contextmanager import random from typing import Optional, List import accelerate import diffusers import numpy as np import torch import torch.utils.checkpoint import torchvision.transforms as transforms 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 DDIMScheduler from diffusers.optimization import get_scheduler 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 decord import VideoReader from einops import rearrange from omegaconf import OmegaConf from packaging import version from tqdm.auto import tqdm from transformers import BertModel, BertTokenizer, 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 transformers import T5EncoderModel, T5Tokenizer from transformers.utils import ContextManagers import easyanimate.reward.reward_fn as reward_fn from easyanimate.models import (name_to_autoencoder_magvit, name_to_transformer3d) from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import get_3d_rotary_pos_embed, get_resize_crop_region_for_grid from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import EasyAnimatePipeline_Multi_Text_Encoder_Inpaint from easyanimate.utils import gaussian_diffusion as gd from easyanimate.utils.lora_utils import create_network, merge_lora from easyanimate.utils.respace import SpacedDiffusion, space_timesteps from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid if is_wandb_available(): import wandb # 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") @contextmanager def video_reader(*args, **kwargs): """A context manager to solve the memory leak of decord. """ vr = VideoReader(*args, **kwargs) try: yield vr finally: del vr gc.collect() def log_validation( vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, network, loss_fn, config, args, accelerator, weight_dtype, global_step, validation_prompts_idx ): 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()) scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.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, scheduler=scheduler, torch_dtype=weight_dtype, ) pipeline = pipeline.to(accelerator.device) pipeline = merge_lora( pipeline, None, 1, accelerator.device, state_dict=accelerator.unwrap_model(network).state_dict(), transformer_only=True ) to_tensor = transforms.ToTensor() validation_loss, validation_reward = 0, 0 if args.enable_xformers_memory_efficient_attention \ and config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') == 'Transformer3DModel': pipeline.enable_xformers_memory_efficient_attention() for i in range(len(validation_prompts_idx)): validation_idx, validation_prompt = validation_prompts_idx[i] with torch.no_grad(): with torch.autocast("cuda", dtype=weight_dtype): if vae.cache_mag_vae: video_length = int((args.video_length - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1 if args.video_length != 1 else 1 else: video_length = int(args.video_length // vae.mini_batch_encoder * vae.mini_batch_encoder) if args.video_length != 1 else 1 sample_size = [args.validation_sample_height, args.validation_sample_width] input_video, input_video_mask, clip_image = get_image_to_video_latent( None, None, video_length=args.video_length, sample_size=sample_size ) if args.seed is None: generator = None else: generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) sample = pipeline( validation_prompt, video_length = video_length, negative_prompt = "bad detailed", height = args.validation_sample_height, width = args.validation_sample_width, guidance_scale = 7, generator = generator, video = input_video, mask_video = input_video_mask, clip_image = clip_image, ).videos sample_saved_path = os.path.join(args.output_dir, f"validation_sample/sample-{global_step}-{validation_idx}.mp4") save_videos_grid(sample, sample_saved_path, fps=8) num_sampled_frames = 4 sampled_frames_list = [] with video_reader(sample_saved_path) as vr: sampled_frame_idx_list = np.linspace(0, len(vr), num_sampled_frames, endpoint=False, dtype=int) sampled_frame_list = vr.get_batch(sampled_frame_idx_list).asnumpy() sampled_frames = torch.stack([to_tensor(frame) for frame in sampled_frame_list], dim=0) sampled_frames_list.append(sampled_frames) sampled_frames = torch.stack(sampled_frames_list) sampled_frames = rearrange(sampled_frames, "b t c h w -> b c t h w") loss, reward = loss_fn(sampled_frames, [validation_prompt]) validation_loss, validation_reward = validation_loss + loss, validation_reward + reward validation_loss = validation_loss / len(validation_prompts_idx) validation_reward = validation_reward / len(validation_prompts_idx) del pipeline del transformer3d_val gc.collect() torch.cuda.empty_cache() torch.cuda.ipc_collect() return validation_loss, validation_reward def load_prompts(prompt_path, prompt_column="prompt", start_idx=None, end_idx=None): prompt_list = [] if prompt_path.endswith(".txt"): with open(prompt_path, "r") as f: for line in f: prompt_list.append(line.strip()) elif prompt_path.endswith(".jsonl"): with open(prompt_path, "r") as f: for line in f.readlines(): item = json.loads(line) prompt_list.append(item[prompt_column]) else: raise ValueError("The prompt_path must end with .txt or .jsonl.") prompt_list = prompt_list[start_idx:end_idx] return prompt_list # Modified from EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.encode_prompt def encode_prompt( tokenizer, tokenizer_2, text_encoder, text_encoder_2, prompt: str, device: torch.device, dtype: torch.dtype, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: Optional[str] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, max_sequence_length: Optional[int] = None, text_encoder_index: int = 0, actual_max_sequence_length: int = 256, enable_text_attention_mask: bool = False, ): tokenizers = [tokenizer, tokenizer_2] text_encoders = [text_encoder, text_encoder_2] tokenizer = tokenizers[text_encoder_index] text_encoder = text_encoders[text_encoder_index] if max_sequence_length is None: if text_encoder_index == 0: max_length = min(tokenizer.model_max_length, actual_max_sequence_length) if text_encoder_index == 1: max_length = min(tokenizer_2.model_max_length, actual_max_sequence_length) else: max_length = max_sequence_length if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: text_inputs = tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids if text_input_ids.shape[-1] > actual_max_sequence_length: reprompt = tokenizer.batch_decode(text_input_ids[:, :actual_max_sequence_length], skip_special_tokens=True) text_inputs = tokenizer( reprompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): _actual_max_sequence_length = min(tokenizer.model_max_length, actual_max_sequence_length) removed_text = tokenizer.batch_decode(untruncated_ids[:, _actual_max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {_actual_max_sequence_length} tokens: {removed_text}" ) prompt_attention_mask = text_inputs.attention_mask.to(device) if enable_text_attention_mask: prompt_embeds = text_encoder( text_input_ids.to(device), attention_mask=prompt_attention_mask, ) else: prompt_embeds = text_encoder( text_input_ids.to(device) ) prompt_embeds = prompt_embeds[0] prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt max_length = prompt_embeds.shape[1] uncond_input = tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids if uncond_input_ids.shape[-1] > actual_max_sequence_length: reuncond_tokens = tokenizer.batch_decode(uncond_input_ids[:, :actual_max_sequence_length], skip_special_tokens=True) uncond_input = tokenizer( reuncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids negative_prompt_attention_mask = uncond_input.attention_mask.to(device) if enable_text_attention_mask: negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask, ) else: negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device) ) negative_prompt_embeds = negative_prompt_embeds[0] negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask # Modified from EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.prepare_extra_step_kwargs def prepare_extra_step_kwargs(scheduler, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] import inspect accepts_eta = "eta" in set(inspect.signature(scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def parse_args(): parser = argparse.ArgumentParser(description="Simple example of a training script.") 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( "--validation_prompt_path", type=str, default=None, help=("A set of prompts 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( "--validation_batch_size", type=int, default=1, help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."), ) parser.add_argument( "--validation_sample_height", type=int, default=512, help="The height of sampling videos in validation.", ) parser.add_argument( "--validation_sample_width", type=int, default=512, help="The width of sampling videos in validation.", ) 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("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--use_came", action="store_true", help="whether to use came", ) parser.add_argument( "--train_batch_size", type=int, default=16, 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( "--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( "--rank", type=int, default=128, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--network_alpha", type=int, default=64, help=("The dimension of the LoRA update matrices."), ) parser.add_argument( "--train_text_encoder", action="store_true", help="Whether to train the text encoder. If set, the text encoder should be float32 precision.", ) parser.add_argument( "--token_sample_size", type=int, default=512, help="Sample size of the token.", ) parser.add_argument( "--video_sample_n_frames", type=int, default=17, help="Num frame of 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("--save_state", action="store_true", help="Whether or not to save state.") parser.add_argument( "--use_deepspeed", action="store_true", help="Whether or not to use deepspeed." ) parser.add_argument( "--prompt_path", type=str, default="normal", help="The path to the training prompt file.", ) parser.add_argument( '--train_sample_height', type=int, default=384, help='The height of sampling videos in training' ) parser.add_argument( '--train_sample_width', type=int, default=672, help='The width of sampling videos in training' ) parser.add_argument( "--video_length", type=int, default=49, help="The number of frames to generate in training and validation." ) parser.add_argument( '--eta', type=float, default=0.0, help='eta parameter for the DDIM sampler. this controls the amount of noise injected into the sampling process, ' 'with 0.0 being fully deterministic and 1.0 being equivalent to the DDPM sampler.' ) parser.add_argument( "--guidance_scale", type=float, default=6.0, help="The classifier-free diffusion guidance." ) parser.add_argument( "--num_inference_steps", type=int, default=50, help="The number of denoising steps in training and validation." ) parser.add_argument( "--num_decoded_latents", type=int, default=3, help="The number of latents to be decoded." ) parser.add_argument( "--num_sampled_frames", type=int, default=None, help="The number of sampled frames for the reward function." ) parser.add_argument( "--reward_fn", type=str, default="aesthetic_loss_fn", help='The reward function.' ) parser.add_argument( "--reward_fn_kwargs", type=str, default=None, help='The keyword arguments of the reward function.' ) parser.add_argument( "--backprop", action="store_true", default=False, help="Whether to use the backprop training mode.", ) 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, ) # Sanity check for validation do_validation = (args.validation_prompt_path is not None or args.validation_prompts is not None) if do_validation: if not (os.path.exists(args.validation_prompt_path) or args.validation_prompt_path.endswith(".txt")): raise ValueError("The `--validation_prompt_path` must be a txt file containing prompts.") if args.validation_batch_size < accelerator.num_processes or args.validation_batch_size % accelerator.num_processes != 0: raise ValueError("The `--validation_batch_size` must be divisible by the number of processes.") # 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, device_specific=True) # 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. # Use DDIM instead of DDPM to sample training videos. noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler") noise_scheduler.set_timesteps(args.num_inference_steps, device=accelerator.device) 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) vae.eval() 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) # Lora will work with this... network = create_network( 1.0, args.rank, args.network_alpha, text_encoder, transformer3d, neuron_dropout=None, add_lora_in_attn_temporal=True, ) network.apply_to(text_encoder, transformer3d, args.train_text_encoder and not args.training_with_video_token_length, True) # 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)}") 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: safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors") save_model(safetensor_save_path, accelerator.unwrap_model(models[-1])) if not args.use_deepspeed: for _ in range(len(weights)): weights.pop() 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 logging.info("Add network parameters") trainable_params = list(filter(lambda p: p.requires_grad, network.parameters())) trainable_params_optim = network.prepare_optimizer_params(args.learning_rate / 2, args.learning_rate, args.learning_rate) # 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, ) # loss function reward_fn_kwargs = {} if args.reward_fn_kwargs is not None: reward_fn_kwargs = json.loads(args.reward_fn_kwargs) if accelerator.is_main_process: # Check if the model is downloaded in the main process. loss_fn = getattr(reward_fn, args.reward_fn)(device="cpu", dtype=weight_dtype, **reward_fn_kwargs) accelerator.wait_for_everyone() loss_fn = getattr(reward_fn, args.reward_fn)(device=accelerator.device, dtype=weight_dtype, **reward_fn_kwargs) # Get RL training prompts prompt_list = load_prompts(args.prompt_path) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(prompt_list) / 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`. network, optimizer, lr_scheduler = accelerator.prepare(network, optimizer, lr_scheduler) # Move text_encode and vae to gpu and cast to weight_dtype vae.to(accelerator.device, dtype=weight_dtype) transformer3d.to(accelerator.device, dtype=weight_dtype) 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(prompt_list) / 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") 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(prompt_list)}") 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}.") from safetensors.torch import load_file, safe_open state_dict = load_file(os.path.join(os.path.join(args.output_dir, path), "lora_diffusion_pytorch_model.safetensors")) m, u = accelerator.unwrap_model(network).load_state_dict(state_dict, strict=False) print(f"missing keys: {len(m)}, unexpected keys: {len(u)}") accelerator.print(f"Resuming from checkpoint {path}") accelerator.load_state(os.path.join(args.output_dir, path)) else: initial_global_step = 0 # function for saving/removing def save_model(ckpt_file, unwrapped_nw): os.makedirs(args.output_dir, exist_ok=True) accelerator.print(f"\nsaving checkpoint: {ckpt_file}") unwrapped_nw.save_weights(ckpt_file, weight_dtype, None) 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, ) for epoch in range(first_epoch, args.num_train_epochs): train_loss = 0.0 train_reward = 0.0 # In the following training loop, randomly select training prompts and use the # `EasyAnimatePipeline_Multi_Text_Encoder_Inpaint` to sample videos, calculate rewards, and update the network. for _ in range(num_update_steps_per_epoch): # train_prompt = random.sample(prompt_list, args.train_batch_size) train_prompt = random.choices(prompt_list, k=args.train_batch_size) logger.info(f"train_prompt: {train_prompt}") # default height and width height = int(args.train_sample_height // 16 * 16) width = int(args.train_sample_width // 16 * 16) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = args.guidance_scale > 1.0 # Encode input prompt ( prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask, ) = encode_prompt( tokenizer, tokenizer_2, text_encoder, text_encoder_2, train_prompt, device=accelerator.device, dtype=weight_dtype, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=[""] * len(train_prompt), text_encoder_index=0, enable_text_attention_mask=transformer3d.config.enable_text_attention_mask, ) ( prompt_embeds_2, negative_prompt_embeds_2, prompt_attention_mask_2, negative_prompt_attention_mask_2, ) = encode_prompt( tokenizer, tokenizer_2, text_encoder, text_encoder_2, train_prompt, device=accelerator.device, dtype=weight_dtype, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=[""] * len(train_prompt), text_encoder_index=1, enable_text_attention_mask=transformer3d.config.enable_text_attention_mask, ) # Prepare timesteps timesteps = noise_scheduler.timesteps # Prepare latent variables num_channels_latents = vae.config.latent_channels num_channels_transformer = transformer3d.config.in_channels vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1) latent_shape = [ args.train_batch_size, vae.config.latent_channels, int((args.video_length - 1) // vae.mini_batch_encoder * vae.mini_batch_decoder + 1) if args.video_length != 1 else 1, args.train_sample_height // vae_scale_factor, args.train_sample_width // vae_scale_factor, ] with accelerator.accumulate(transformer3d): with accelerator.autocast(): latents = torch.randn(*latent_shape, device=accelerator.device, dtype=weight_dtype) latents = latents * noise_scheduler.init_noise_sigma # Prepare inpaint latents if it needs. # Use zero latents if we want to t2v. mask_latents = torch.zeros_like(latents)[:, :1].to(latents.device, latents.dtype) masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype) # Check that sizes of mask, masked image and latents match if num_channels_transformer != num_channels_latents: num_channels_mask = mask_latents.shape[1] num_channels_masked_image = masked_video_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != transformer3d.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.transformer`: {transformer3d.config} expects" f" {transformer3d.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.transformer` or your `mask_image` or `image` input." ) generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) # Prepare extra step kwargs. extra_step_kwargs = prepare_extra_step_kwargs(noise_scheduler, generator, args.eta) # Create image_rotary_emb, style embedding & time ids grid_height = height // 8 // transformer3d.config.patch_size grid_width = width // 8 // transformer3d.config.patch_size base_size_width = 720 // 8 // transformer3d.config.patch_size base_size_height = 480 // 8 // 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( transformer3d.config.attention_head_dim, grid_crops_coords, grid_size=(grid_height, grid_width), temporal_size=latents.size(2), use_real=True, ) # Get other hunyuan params style = torch.tensor([0], device=accelerator.device) original_size = (1024, 1024) crops_coords_top_left = (0, 0) target_size = (height, width) add_time_ids = list(original_size + target_size + crops_coords_top_left) add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask]) prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2]) prompt_attention_mask_2 = torch.cat([negative_prompt_attention_mask_2, prompt_attention_mask_2]) add_time_ids = torch.cat([add_time_ids] * 2, dim=0) style = torch.cat([style] * 2, dim=0) prompt_embeds = prompt_embeds.to(device=accelerator.device) prompt_attention_mask = prompt_attention_mask.to(device=accelerator.device) prompt_embeds_2 = prompt_embeds_2.to(device=accelerator.device) prompt_attention_mask_2 = prompt_attention_mask_2.to(device=accelerator.device) add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=accelerator.device).repeat( args.train_batch_size, 1 ) style = style.to(device=accelerator.device).repeat(args.train_batch_size) # Denoising loop for i, t in enumerate(tqdm(timesteps)): # the reward gradient is back propagated only for the last K steps. if args.backprop: # backprop_cutoff_idx = random.randint(0, args.num_sampling_steps - 1) # random backprop_cutoff_idx = args.num_inference_steps - 1 # last if i >= backprop_cutoff_idx: for param in network.parameters(): param.requires_grad = True else: for param in network.parameters(): param.requires_grad = False # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = noise_scheduler.scale_model_input(latent_model_input, t) # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input t_expand = torch.tensor([t] * latent_model_input.shape[0], device=accelerator.device).to( dtype=latent_model_input.dtype ) # predict the noise residual noise_pred = transformer3d( latent_model_input, t_expand, 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, inpaint_latents=inpaint_latents, clip_encoder_hidden_states=None, clip_attention_mask=None, return_dict=False, )[0] # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + args.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents = noise_scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] # decode latents (tensor) # latents = latents.permute(0, 2, 1, 3, 4) # [B, C, T, H, W] # Since the casual VAE decoding consumes a large amount of VRAM, and we need to keep the decoding # operation within the computational graph. Thus, we only decode the first args.num_decoded_latents # to calculate the reward. # TODO: Decode all latents but keep a portion of the decoding operation within the computational graph. sampled_latent_indices = list(range(args.num_decoded_latents)) sampled_latents = latents[:, :, sampled_latent_indices, :, :] sampled_latents = 1 / vae.config.scaling_factor * sampled_latents sampled_frames = vae.decode(sampled_latents)[0] sampled_frames = sampled_frames.clamp(-1, 1) sampled_frames = (sampled_frames / 2 + 0.5).clamp(0, 1) # [-1, 1] -> [0, 1] if global_step % args.checkpointing_steps == 0: saved_file = f"sample-{global_step}-{accelerator.process_index}.mp4" save_videos_grid( sampled_frames.to(torch.float32).detach().cpu(), os.path.join(args.output_dir, "train_sample", saved_file), fps=8 ) if args.num_sampled_frames is not None: num_frames = sampled_frames.size(2) - 1 sampled_frames_indices = torch.linspace(0, num_frames, steps=args.num_sampled_frames).long() sampled_frames = sampled_frames[:, :, sampled_frames_indices, :, :] # compute loss and reward loss, reward = loss_fn(sampled_frames, train_prompt) # Gather the losses and rewards across all processes for logging (if we use distributed training). avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() avg_reward = accelerator.gather(reward.repeat(args.train_batch_size)).mean() train_loss += avg_loss.item() / args.gradient_accumulation_steps train_reward += avg_reward.item() / args.gradient_accumulation_steps # Backpropagate accelerator.backward(loss) if accelerator.sync_gradients: total_norm = accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm) # If `args.use_deepspeed` is enabled, `total_norm` cannot be logged by accelerator. if not args.use_deepspeed: accelerator.log({"total_norm": total_norm}, 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: progress_bar.update(1) global_step += 1 accelerator.log({"train_loss": train_loss, "train_reward": train_reward}, step=global_step) train_loss = 0.0 train_reward = 0.0 if global_step % args.checkpointing_steps == 0: # DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues. 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) if not args.save_state: safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors") save_model(safetensor_save_path, accelerator.unwrap_model(network)) logger.info(f"Saved safetensor to {safetensor_save_path}") else: accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") accelerator.save_state(accelerator_save_path) logger.info(f"Saved state to {accelerator_save_path}") # Validation (distributed) if do_validation and (global_step % args.validation_steps) == 0: if args.validation_prompts is None and args.validation_prompt_path.endswith(".txt"): validation_prompts = [] with open(args.validation_prompt_path, "r") as f: for line in f: validation_prompts.append(line.strip()) # Do not select randomly to ensure that `args.validation_prompts` is the same for each process. args.validation_prompts = validation_prompts[:args.validation_batch_size] validation_prompts_idx = [(i, p) for i, p in enumerate(args.validation_prompts)] accelerator.wait_for_everyone() with accelerator.split_between_processes(validation_prompts_idx) as splitted_prompts_idx: validation_loss, validation_reward = log_validation( vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, network, loss_fn, config, args, accelerator, weight_dtype, global_step, splitted_prompts_idx ) avg_validation_loss = accelerator.gather(validation_loss).mean() avg_validation_reward = accelerator.gather(validation_reward).mean() accelerator.print(avg_validation_loss, avg_validation_reward) if accelerator.is_main_process: accelerator.log({"validation_loss": avg_validation_loss, "validation_reward": avg_validation_reward}, step=global_step) accelerator.wait_for_everyone() logs = {"step_loss": loss.detach().item(), "step_reward": reward.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break if __name__ == "__main__": main()