1979 lines
93 KiB
Python
1979 lines
93 KiB
Python
"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
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"""
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import gc
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import logging
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import math
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import os
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import pickle
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import random
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import shutil
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import sys
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import accelerate
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import diffusers
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import ftfy
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import loguru
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import numpy as np
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import pyloudnorm as pyln
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import regex as re
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import scipy.signal as ss
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import torchvision.transforms.functional as TF
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import transformers
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from accelerate import Accelerator, FullyShardedDataParallelPlugin
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from accelerate.logging import get_logger
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from accelerate.state import AcceleratorState
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from accelerate.utils import ProjectConfiguration, set_seed
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from diffusers import DDIMScheduler, FlowMatchEulerDiscreteScheduler
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import (EMAModel,
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compute_density_for_timestep_sampling,
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compute_loss_weighting_for_sd3)
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from diffusers.utils import check_min_version, deprecate, is_wandb_available
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from diffusers.utils.torch_utils import is_compiled_module
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from einops import rearrange
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from omegaconf import OmegaConf
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from packaging import version
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from PIL import Image
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from torch.distributed.fsdp.fully_sharded_data_parallel import (
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FullOptimStateDictConfig, FullStateDictConfig, ShardedOptimStateDictConfig,
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ShardedStateDictConfig)
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from torch.utils.data import RandomSampler
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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from transformers import AutoTokenizer
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from transformers.utils import ContextManagers
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import datasets
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.data import (ASPECT_RATIO_512, ASPECT_RATIO_RANDOM_CROP_512,
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ASPECT_RATIO_RANDOM_CROP_PROB,
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AspectRatioBatchImageVideoSampler,
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ImageVideoDataset, ImageVideoSampler,
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RandomSampler, VideoSpeechDataset,
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get_closest_ratio, get_random_mask)
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from videox_fun.models import (AutoencoderKLLongCatVideo, AutoTokenizer,
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CLIPModel, LongCatVideoAudioEncoder,
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LongCatVideoAvatarTransformer3DModel,
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UMT5EncoderModel)
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from videox_fun.pipeline import LongCatVideoAvatarPipeline
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from videox_fun.utils.discrete_sampler import DiscreteSampling
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from videox_fun.utils.lora_utils import (convert_peft_lora_to_kohya_lora,
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create_network, merge_lora,
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unmerge_lora)
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from videox_fun.utils.tqdm_bar import PauseAwareTqdm
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from videox_fun.utils.utils import (calculate_dimensions,
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get_image_to_video_latent,
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merge_video_audio, save_videos_grid)
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if is_wandb_available():
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import wandb
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def filter_kwargs(cls, kwargs):
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import inspect
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sig = inspect.signature(cls.__init__)
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valid_params = set(sig.parameters.keys()) - {'self', 'cls'}
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filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
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return filtered_kwargs
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def get_random_downsample_ratio(sample_size, image_ratio=[],
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all_choices=False, rng=None):
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def _create_special_list(length):
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if length == 1:
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return [1.0]
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if length >= 2:
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first_element = 0.75
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remaining_sum = 1.0 - first_element
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other_elements_value = remaining_sum / (length - 1)
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special_list = [first_element] + [other_elements_value] * (length - 1)
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return special_list
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if sample_size >= 1536:
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number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio
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elif sample_size >= 1024:
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number_list = [1, 1.25, 1.5, 2] + image_ratio
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elif sample_size >= 768:
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number_list = [1, 1.25, 1.5] + image_ratio
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elif sample_size >= 512:
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number_list = [1] + image_ratio
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else:
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number_list = [1]
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if all_choices:
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return number_list
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number_list_prob = np.array(_create_special_list(len(number_list)))
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if rng is None:
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return np.random.choice(number_list, p = number_list_prob)
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else:
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return rng.choice(number_list, p = number_list_prob)
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def resize_mask(mask, latent, process_first_frame_only=True):
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latent_size = latent.size()
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batch_size, channels, num_frames, height, width = mask.shape
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if process_first_frame_only:
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target_size = list(latent_size[2:])
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target_size[0] = 1
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first_frame_resized = F.interpolate(
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mask[:, :, 0:1, :, :],
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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target_size = list(latent_size[2:])
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target_size[0] = target_size[0] - 1
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if target_size[0] != 0:
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remaining_frames_resized = F.interpolate(
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mask[:, :, 1:, :, :],
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2)
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else:
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resized_mask = first_frame_resized
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else:
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target_size = list(latent_size[2:])
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resized_mask = F.interpolate(
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mask,
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size=target_size,
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mode='trilinear',
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align_corners=False
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)
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return resized_mask
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.18.0.dev0")
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logger = get_logger(__name__, log_level="INFO")
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def log_validation(vae, text_encoder, tokenizer, audio_encoder, transformer3d, network, args, accelerator, weight_dtype, global_step):
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try:
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is_deepspeed = type(transformer3d).__name__ == 'DeepSpeedEngine'
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if is_deepspeed:
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origin_config = transformer3d.config
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transformer3d.config = accelerator.unwrap_model(transformer3d).config
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with torch.no_grad(), torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
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logger.info("Running validation... ")
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scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="scheduler"
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)
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pipeline = LongCatVideoAvatarPipeline(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=accelerator.unwrap_model(transformer3d) if type(transformer3d).__name__ == 'DistributedDataParallel' else transformer3d,
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scheduler=scheduler,
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audio_encoder=audio_encoder,
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)
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pipeline = pipeline.to(accelerator.device)
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if args.seed is None:
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generator = None
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else:
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rank_seed = args.seed + accelerator.process_index
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generator = torch.Generator(device=accelerator.device).manual_seed(rank_seed)
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logger.info(f"Rank {accelerator.process_index} using seed: {rank_seed}")
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for i in range(len(args.validation_prompts)):
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start_image = Image.open(args.validation_image_paths[i])
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width, height = start_image.width, start_image.height
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width, height = calculate_dimensions(args.video_sample_size * args.video_sample_size, width / height)
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input_video, input_video_mask, clip_image = get_image_to_video_latent(args.validation_image_paths[i], None, video_length=args.video_sample_n_frames, sample_size=[height, width])
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audio_path = args.validation_audio_paths[i]
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sample = pipeline(
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prompt = args.validation_prompts[i],
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num_frames = args.video_sample_n_frames,
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negative_prompt = "Close-up, Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards",
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height = height,
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width = width,
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generator = generator,
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guidance_scale = 4.5,
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num_inference_steps = 25,
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audio_path = audio_path,
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video = input_video,
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mask_video = input_video_mask,
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fps = 16
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).videos
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os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
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save_videos_grid(
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sample,
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os.path.join(
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args.output_dir,
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f"sample/sample-{global_step}-rank{accelerator.process_index}-image-{i}.mp4"
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),
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fps=16
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)
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merge_video_audio(
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video_path=os.path.join(
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args.output_dir,
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f"sample/sample-{global_step}-rank{accelerator.process_index}-image-{i}.mp4"
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),
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audio_path=args.validation_audio_paths[i]
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)
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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transformer3d.to(accelerator.device, dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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if is_deepspeed:
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transformer3d.config = origin_config
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except Exception as e:
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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print(f"Eval error on rank {accelerator.process_index} with info {e}")
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vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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transformer3d.to(accelerator.device, dtype=weight_dtype)
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if not args.enable_text_encoder_in_dataloader:
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text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
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def linear_decay(initial_value, final_value, total_steps, current_step):
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if current_step >= total_steps:
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return final_value
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current_step = max(0, current_step)
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step_size = (final_value - initial_value) / total_steps
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current_value = initial_value + step_size * current_step
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return current_value
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def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None):
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u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator)
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t = 1 / (1 + torch.exp(-u)) * (high - low) + low
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return torch.clip(t.to(torch.int32), low, high - 1)
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a training script.")
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parser.add_argument(
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"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
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)
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--pretrained_avatar_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to pretrained model or model identifier for avatar.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help=(
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"A folder containing the training data. "
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),
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)
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parser.add_argument(
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"--train_data_meta",
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type=str,
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default=None,
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help=(
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"A csv containing the training data. "
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),
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--validation_prompts",
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type=str,
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default=None,
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nargs="+",
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_image_paths",
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type=str,
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default=None,
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nargs="+",
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help=("A set of images evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_audio_paths",
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type=str,
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default=None,
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nargs="+",
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help=("A set of audios evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="sd-model-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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)
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parser.add_argument(
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"--use_came",
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action="store_true",
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help="whether to use came",
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)
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parser.add_argument(
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"--multi_stream",
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action="store_true",
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help="whether to use cuda multi-stream",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
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)
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parser.add_argument("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-4,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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|
parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
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|
parser.add_argument(
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"--non_ema_revision",
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type=str,
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default=None,
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|
required=False,
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help=(
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"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
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" remote repository specified with --pretrained_model_name_or_path."
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),
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)
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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|
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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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(
|
|
"--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("--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(
|
|
"--use_peft_lora", action="store_true", help="Whether or not to use peft lora."
|
|
)
|
|
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(
|
|
"--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(
|
|
"--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(
|
|
"--auto_tile_batch_size", action="store_true", help="Whether to auto tile batch size.",
|
|
)
|
|
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(
|
|
"--keep_all_node_same_token_length",
|
|
action="store_true",
|
|
help="Reference of the length token.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Run train_sampling_steps.",
|
|
)
|
|
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(
|
|
"--fix_sample_size",
|
|
nargs=2, type=int, default=None,
|
|
help="Fix Sample size [height, width] when using bucket and collate_fn."
|
|
)
|
|
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(
|
|
"--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(
|
|
'--tokenizer_max_length',
|
|
type=int,
|
|
default=512,
|
|
help='Max length of tokenizer'
|
|
)
|
|
parser.add_argument(
|
|
"--use_deepspeed", action="store_true", help="Whether or not to use deepspeed."
|
|
)
|
|
parser.add_argument(
|
|
"--use_fsdp", action="store_true", help="Whether or not to use fsdp."
|
|
)
|
|
parser.add_argument(
|
|
"--low_vram", action="store_true", help="Whether enable low_vram mode."
|
|
)
|
|
parser.add_argument(
|
|
"--weighting_scheme",
|
|
type=str,
|
|
default="none",
|
|
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
|
|
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
|
|
)
|
|
parser.add_argument(
|
|
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--mode_scale",
|
|
type=float,
|
|
default=1.29,
|
|
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
|
|
)
|
|
parser.add_argument(
|
|
"--lora_skip_name",
|
|
type=str,
|
|
default=None,
|
|
help=("The module is not trained in loras. "),
|
|
)
|
|
parser.add_argument(
|
|
"--target_name",
|
|
type=str,
|
|
default=None,
|
|
help=("The module is trained in loras. "),
|
|
)
|
|
|
|
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)
|
|
|
|
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,
|
|
)
|
|
|
|
deepspeed_plugin = accelerator.state.deepspeed_plugin if hasattr(accelerator.state, "deepspeed_plugin") else None
|
|
fsdp_plugin = accelerator.state.fsdp_plugin if hasattr(accelerator.state, "fsdp_plugin") else None
|
|
if deepspeed_plugin is not None:
|
|
zero_stage = int(deepspeed_plugin.zero_stage)
|
|
fsdp_stage = 0
|
|
print(f"Using DeepSpeed Zero stage: {zero_stage}")
|
|
|
|
args.use_deepspeed = True
|
|
if zero_stage == 3:
|
|
print(f"Auto set save_state to True because zero_stage == 3")
|
|
args.save_state = True
|
|
elif fsdp_plugin is not None:
|
|
from torch.distributed.fsdp import ShardingStrategy
|
|
zero_stage = 0
|
|
if fsdp_plugin.sharding_strategy is ShardingStrategy.FULL_SHARD:
|
|
fsdp_stage = 3
|
|
elif fsdp_plugin.sharding_strategy is None: # The fsdp_plugin.sharding_strategy is None in FSDP 2.
|
|
fsdp_stage = 3
|
|
elif fsdp_plugin.sharding_strategy is ShardingStrategy.SHARD_GRAD_OP:
|
|
fsdp_stage = 2
|
|
else:
|
|
fsdp_stage = 0
|
|
print(f"Using FSDP stage: {fsdp_stage}")
|
|
|
|
args.use_fsdp = True
|
|
if fsdp_stage == 3:
|
|
print(f"Auto set save_state to True because fsdp_stage == 3")
|
|
args.save_state = True
|
|
else:
|
|
zero_stage = 0
|
|
fsdp_stage = 0
|
|
print("DeepSpeed is not enabled.")
|
|
|
|
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.
|
|
noise_scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="scheduler"
|
|
)
|
|
|
|
# Get Tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "tokenizer"),
|
|
)
|
|
|
|
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()):
|
|
# Get Text encoder
|
|
text_encoder = UMT5EncoderModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, 'text_encoder'),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
text_encoder = text_encoder.eval()
|
|
# Get Vae
|
|
vae = AutoencoderKLLongCatVideo.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, 'vae'),
|
|
)
|
|
vae.eval()
|
|
|
|
# Get Audio encoder (for avatar mode)
|
|
audio_encoder = LongCatVideoAudioEncoder(
|
|
os.path.join(args.pretrained_avatar_model_name_or_path, 'chinese-wav2vec2-base')
|
|
)
|
|
audio_encoder.audio_encoder.feature_extractor._freeze_parameters()
|
|
|
|
# Get Transformer
|
|
transformer3d = LongCatVideoAvatarTransformer3DModel.from_pretrained(
|
|
os.path.join(args.pretrained_avatar_model_name_or_path, 'avatar_single'),
|
|
low_cpu_mem_usage=True,
|
|
).to(weight_dtype)
|
|
|
|
# Freeze vae and text_encoder and set transformer3d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer3d.requires_grad_(False)
|
|
|
|
# Lora will work with this...
|
|
if args.use_peft_lora:
|
|
from peft import (LoraConfig, get_peft_model_state_dict,
|
|
inject_adapter_in_model)
|
|
lora_config = LoraConfig(r=args.rank, lora_alpha=args.network_alpha, target_modules=args.target_name.split(","))
|
|
transformer3d = inject_adapter_in_model(lora_config, transformer3d)
|
|
|
|
network = None
|
|
else:
|
|
network = create_network(
|
|
1.0,
|
|
args.rank,
|
|
args.network_alpha,
|
|
text_encoder,
|
|
transformer3d,
|
|
neuron_dropout=None,
|
|
target_name=args.target_name,
|
|
skip_name=args.lora_skip_name,
|
|
)
|
|
network = network.to(weight_dtype)
|
|
network.apply_to(text_encoder, transformer3d, args.train_text_encoder and not args.training_with_video_token_length, True)
|
|
|
|
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)}")
|
|
assert len(u) == 0
|
|
|
|
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)}")
|
|
assert len(u) == 0
|
|
|
|
# `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
|
|
if fsdp_stage != 0 or zero_stage == 3:
|
|
def save_model_hook(models, weights, output_dir):
|
|
accelerate_state_dict = accelerator.get_state_dict(models[-1], unwrap=True)
|
|
if accelerator.is_main_process:
|
|
from safetensors.torch import save_file
|
|
safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors")
|
|
if args.use_peft_lora:
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(models[-1]), accelerate_state_dict)
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
safetensor_kohya_format_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model_compatible_with_comfyui.safetensors")
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
else:
|
|
network_state_dict = {}
|
|
for key in accelerate_state_dict:
|
|
if "network" in key:
|
|
network_state_dict[key.replace("network.", "")] = accelerate_state_dict[key].to(weight_dtype)
|
|
save_file(network_state_dict, safetensor_save_path, metadata={"format": "pt"})
|
|
|
|
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):
|
|
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}.")
|
|
|
|
else:
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
accelerate_state_dict = accelerator.get_state_dict(models[-1], unwrap=True)
|
|
if accelerator.is_main_process:
|
|
from safetensors.torch import save_file
|
|
safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors")
|
|
if args.use_peft_lora:
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(models[-1]), accelerate_state_dict)
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
safetensor_kohya_format_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model_compatible_with_comfyui.safetensors")
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
else:
|
|
network_state_dict = {}
|
|
for key in accelerate_state_dict:
|
|
if "network" in key:
|
|
network_state_dict[key.replace("network.", "")] = accelerate_state_dict[key].to(weight_dtype)
|
|
save_file(network_state_dict, safetensor_save_path, metadata={"format": "pt"})
|
|
|
|
if not args.use_deepspeed:
|
|
for _ in range(len(weights)):
|
|
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):
|
|
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 Exception:
|
|
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
|
|
|
|
if args.use_peft_lora:
|
|
logging.info("Add peft parameters")
|
|
trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
trainable_params_optim = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
else:
|
|
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)
|
|
|
|
if args.use_came:
|
|
optimizer = optimizer_cls(
|
|
trainable_params_optim,
|
|
lr=args.learning_rate,
|
|
# weight_decay=args.adam_weight_decay,
|
|
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.config.scale_factor_temporal
|
|
|
|
if args.fix_sample_size is not None and args.enable_bucket:
|
|
args.video_sample_size = max(max(args.fix_sample_size), args.video_sample_size)
|
|
args.training_with_video_token_length = False
|
|
args.random_hw_adapt = False
|
|
|
|
# Get the dataset
|
|
train_dataset = VideoSpeechDataset(
|
|
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,
|
|
enable_bucket=args.enable_bucket, enable_inpaint=True,
|
|
)
|
|
|
|
def worker_init_fn(_seed):
|
|
_seed = _seed * 256
|
|
def _worker_init_fn(worker_id):
|
|
print(f"worker_init_fn with {_seed + worker_id}")
|
|
np.random.seed(_seed + worker_id)
|
|
random.seed(_seed + worker_id)
|
|
return _worker_init_fn
|
|
|
|
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,
|
|
)
|
|
|
|
def collate_fn(examples):
|
|
def get_length_to_frame_num(token_length):
|
|
if args.video_sample_size > 256:
|
|
sample_sizes = list(range(256, args.video_sample_size + 1, 128))
|
|
|
|
if sample_sizes[-1] != args.video_sample_size:
|
|
sample_sizes.append(args.video_sample_size)
|
|
else:
|
|
sample_sizes = [args.video_sample_size]
|
|
|
|
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
|
|
}
|
|
|
|
return length_to_frame_num
|
|
|
|
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 = 0.90
|
|
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)
|
|
|
|
# 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"] = []
|
|
new_examples["audio"] = []
|
|
new_examples["sample_rate"] = []
|
|
|
|
# Used in Inpaint mode
|
|
new_examples["mask_pixel_values"] = []
|
|
new_examples["mask"] = []
|
|
new_examples["clip_pixel_values"] = []
|
|
|
|
# Get downsample ratio in image and videos
|
|
pixel_value = examples[0]["pixel_values"]
|
|
f, h, w, c = np.shape(pixel_value)
|
|
|
|
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 * 1.25]
|
|
if len(choice_list) == 0:
|
|
choice_list = list(length_to_frame_num.keys())
|
|
local_video_sample_size = np.random.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)
|
|
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()}
|
|
|
|
if args.fix_sample_size is not None:
|
|
fix_sample_size = [int(x / 16) * 16 for x in args.fix_sample_size]
|
|
elif 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]
|
|
else:
|
|
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]
|
|
|
|
min_example_length = min(
|
|
[example["pixel_values"].shape[0] for example in examples]
|
|
)
|
|
batch_video_length = int(min(batch_video_length, min_example_length))
|
|
|
|
# Magvae needs the number of frames to be 4n + 1.
|
|
batch_video_length = (batch_video_length - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
|
|
|
|
if batch_video_length <= 0:
|
|
batch_video_length = 1
|
|
|
|
for example in examples:
|
|
if args.fix_sample_size is not None:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
fix_sample_size = list(map(lambda x: int(x), fix_sample_size))
|
|
transform = transforms.Compose([
|
|
transforms.Resize(fix_sample_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(fix_sample_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
elif 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.
|
|
|
|
# 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.
|
|
|
|
# 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)[:batch_video_length])
|
|
new_examples["text"].append(example["text"])
|
|
|
|
audio_length = np.shape(example["audio"])[0]
|
|
batch_audio_length = int(audio_length / pixel_values.size()[0] * batch_video_length)
|
|
new_examples["audio"].append(example["audio"][:batch_audio_length])
|
|
new_examples["sample_rate"].append(example["sample_rate"])
|
|
|
|
mask = get_random_mask(new_examples["pixel_values"][-1].size(), image_start_only=True)
|
|
mask_pixel_values = new_examples["pixel_values"][-1] * (1 - mask)
|
|
# Wan 2.1 use 0 for masked pixels
|
|
# + torch.ones_like(new_examples["pixel_values"][-1]) * -1 * mask
|
|
new_examples["mask_pixel_values"].append(mask_pixel_values)
|
|
new_examples["mask"].append(mask)
|
|
|
|
clip_pixel_values = new_examples["pixel_values"][-1][0].permute(1, 2, 0).contiguous()
|
|
clip_pixel_values = (clip_pixel_values * 0.5 + 0.5) * 255
|
|
new_examples["clip_pixel_values"].append(clip_pixel_values)
|
|
|
|
# Limit the number of frames to the same
|
|
new_examples["pixel_values"] = torch.stack([example for example in new_examples["pixel_values"]])
|
|
new_examples["mask_pixel_values"] = torch.stack([example for example in new_examples["mask_pixel_values"]])
|
|
new_examples["mask"] = torch.stack([example for example in new_examples["mask"]])
|
|
new_examples["clip_pixel_values"] = torch.stack([example for example in new_examples["clip_pixel_values"]])
|
|
new_examples["audio"] = torch.stack([example for example in new_examples["audio"]])
|
|
|
|
# Encode prompts when enable_text_encoder_in_dataloader=True
|
|
if args.enable_text_encoder_in_dataloader:
|
|
prompt_ids = tokenizer(
|
|
new_examples['text'],
|
|
max_length=args.tokenizer_max_length,
|
|
padding="max_length",
|
|
add_special_tokens=True,
|
|
truncation=True,
|
|
return_tensors="pt"
|
|
)
|
|
encoder_hidden_states = text_encoder(
|
|
prompt_ids.input_ids,
|
|
attention_mask=prompt_ids.attention_mask
|
|
)[0]
|
|
batch_size_embed, seq_len, hidden_dim = encoder_hidden_states.shape
|
|
encoder_hidden_states = encoder_hidden_states.view(batch_size_embed, 1, seq_len, hidden_dim)
|
|
new_examples['encoder_attention_mask'] = prompt_ids.attention_mask
|
|
new_examples['encoder_hidden_states'] = encoder_hidden_states
|
|
|
|
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,
|
|
worker_init_fn=worker_init_fn(args.seed + accelerator.process_index)
|
|
)
|
|
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,
|
|
worker_init_fn=worker_init_fn(args.seed + accelerator.process_index)
|
|
)
|
|
|
|
# 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`.
|
|
if args.use_peft_lora:
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
else:
|
|
transformer3d.network = network
|
|
transformer3d = transformer3d.to(dtype=weight_dtype)
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
if fsdp_stage != 0 or zero_stage != 0:
|
|
from functools import partial
|
|
|
|
from videox_fun.dist import set_multi_gpus_devices, shard_model
|
|
shard_fn = partial(shard_model, device_id=accelerator.device, param_dtype=weight_dtype, module_to_wrapper=text_encoder.encoder.block)
|
|
text_encoder = shard_fn(text_encoder)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
transformer3d.to(accelerator.device, dtype=weight_dtype)
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
audio_encoder.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
|
|
# 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))
|
|
keys_to_pop = [k for k, v in tracker_config.items() if isinstance(v, list)]
|
|
for k in keys_to_pop:
|
|
tracker_config.pop(k)
|
|
print(f"Removed tracker_config['{k}']")
|
|
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
|
|
|
|
checkpoint_folder_path = os.path.join(args.output_dir, path)
|
|
pkl_path = os.path.join(checkpoint_folder_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}.")
|
|
|
|
if zero_stage != 3 and not args.use_fsdp:
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(os.path.join(checkpoint_folder_path, "lora_diffusion_pytorch_model.safetensors"), device=str(accelerator.device))
|
|
m, u = accelerator.unwrap_model(network).load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
|
|
optimizer_file_pt = os.path.join(checkpoint_folder_path, "optimizer.pt")
|
|
optimizer_file_bin = os.path.join(checkpoint_folder_path, "optimizer.bin")
|
|
optimizer_file_to_load = None
|
|
|
|
if os.path.exists(optimizer_file_pt):
|
|
optimizer_file_to_load = optimizer_file_pt
|
|
elif os.path.exists(optimizer_file_bin):
|
|
optimizer_file_to_load = optimizer_file_bin
|
|
|
|
if optimizer_file_to_load:
|
|
try:
|
|
accelerator.print(f"Loading optimizer state from {optimizer_file_to_load}")
|
|
optimizer_state = torch.load(optimizer_file_to_load, map_location=accelerator.device)
|
|
optimizer.load_state_dict(optimizer_state)
|
|
accelerator.print("Optimizer state loaded successfully.")
|
|
except Exception as e:
|
|
accelerator.print(f"Failed to load optimizer state from {optimizer_file_to_load}: {e}")
|
|
|
|
scheduler_file_pt = os.path.join(checkpoint_folder_path, "scheduler.pt")
|
|
scheduler_file_bin = os.path.join(checkpoint_folder_path, "scheduler.bin")
|
|
scheduler_file_to_load = None
|
|
|
|
if os.path.exists(scheduler_file_pt):
|
|
scheduler_file_to_load = scheduler_file_pt
|
|
elif os.path.exists(scheduler_file_bin):
|
|
scheduler_file_to_load = scheduler_file_bin
|
|
|
|
if scheduler_file_to_load:
|
|
try:
|
|
accelerator.print(f"Loading scheduler state from {scheduler_file_to_load}")
|
|
scheduler_state = torch.load(scheduler_file_to_load, map_location=accelerator.device)
|
|
lr_scheduler.load_state_dict(scheduler_state)
|
|
accelerator.print("Scheduler state loaded successfully.")
|
|
except Exception as e:
|
|
accelerator.print(f"Failed to load scheduler state from {scheduler_file_to_load}: {e}")
|
|
|
|
if hasattr(accelerator, 'scaler') and accelerator.scaler is not None:
|
|
scaler_file = os.path.join(checkpoint_folder_path, "scaler.pt")
|
|
if os.path.exists(scaler_file):
|
|
try:
|
|
accelerator.print(f"Loading GradScaler state from {scaler_file}")
|
|
scaler_state = torch.load(scaler_file, map_location=accelerator.device)
|
|
accelerator.scaler.load_state_dict(scaler_state)
|
|
accelerator.print("GradScaler state loaded successfully.")
|
|
except Exception as e:
|
|
accelerator.print(f"Failed to load GradScaler state: {e}")
|
|
|
|
else:
|
|
accelerator.load_state(checkpoint_folder_path)
|
|
accelerator.print("accelerator.load_state() completed for zero_stage 3.")
|
|
|
|
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}")
|
|
if isinstance(unwrapped_nw, dict):
|
|
from safetensors.torch import save_file
|
|
save_file(unwrapped_nw, ckpt_file, metadata={"format": "pt"})
|
|
return ckpt_file
|
|
unwrapped_nw.save_weights(ckpt_file, weight_dtype, None)
|
|
|
|
progress_bar = PauseAwareTqdm(
|
|
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']
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
audio = batch["audio"].cpu()
|
|
|
|
os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True)
|
|
for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
|
|
pixel_value = 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]}.mp4", rescale=True)
|
|
import importlib
|
|
if importlib.util.find_spec("soundfile") is not None:
|
|
import soundfile as sf
|
|
sf.write(f"{args.output_dir}/sanity_check/{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.wav", audio[idx], 16000)
|
|
clip_pixel_values, mask_pixel_values, texts = batch['clip_pixel_values'].cpu(), batch['mask_pixel_values'].cpu(), batch['text']
|
|
mask_pixel_values = rearrange(mask_pixel_values, "b f c h w -> b c f h w")
|
|
for idx, (clip_pixel_value, pixel_value, text) in enumerate(zip(clip_pixel_values, mask_pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
Image.fromarray(np.uint8(clip_pixel_value)).save(f"{args.output_dir}/sanity_check/clip_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.png")
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/mask_{gif_name[:10] if not text == '' else f'{global_step}-{idx}'}.mp4", rescale=True)
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
# Convert images to latent space
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
audio = batch["audio"]
|
|
sample_rate = batch["sample_rate"]
|
|
|
|
# Increase the batch size when the length of the latent sequence of the current sample is small
|
|
if args.auto_tile_batch_size and args.training_with_video_token_length and zero_stage != 3:
|
|
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))
|
|
if args.enable_text_encoder_in_dataloader:
|
|
batch['encoder_hidden_states'] = torch.tile(batch['encoder_hidden_states'], (4, 1, 1))
|
|
batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (4, 1))
|
|
else:
|
|
batch['text'] = batch['text'] * 4
|
|
audio = audio * 4
|
|
sample_rate = sample_rate * 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))
|
|
if args.enable_text_encoder_in_dataloader:
|
|
batch['encoder_hidden_states'] = torch.tile(batch['encoder_hidden_states'], (2, 1, 1))
|
|
batch['encoder_attention_mask'] = torch.tile(batch['encoder_attention_mask'], (2, 1))
|
|
else:
|
|
batch['text'] = batch['text'] * 2
|
|
audio = audio * 2
|
|
sample_rate = sample_rate * 2
|
|
|
|
clip_pixel_values = batch["clip_pixel_values"].to(weight_dtype)
|
|
mask_pixel_values = batch["mask_pixel_values"].to(weight_dtype)
|
|
mask = batch["mask"].to(weight_dtype)
|
|
# Increase the batch size when the length of the latent sequence of the current sample is small
|
|
if args.auto_tile_batch_size and args.training_with_video_token_length and zero_stage != 3:
|
|
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]:
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (4, 1, 1, 1))
|
|
mask_pixel_values = torch.tile(mask_pixel_values, (4, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (4, 1, 1, 1, 1))
|
|
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]:
|
|
clip_pixel_values = torch.tile(clip_pixel_values, (2, 1, 1, 1))
|
|
mask_pixel_values = torch.tile(mask_pixel_values, (2, 1, 1, 1, 1))
|
|
mask = torch.tile(mask, (2, 1, 1, 1, 1))
|
|
|
|
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
|
|
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))]
|
|
select_frames_prob = np.array(_create_special_list(len(select_frames)))
|
|
|
|
if len(select_frames) != 0:
|
|
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)
|
|
else:
|
|
temp_n_frames = 1
|
|
|
|
# Magvae needs the number of frames to be 4n + 1.
|
|
temp_n_frames = (temp_n_frames - 1) // sample_n_frames_bucket_interval + 1
|
|
|
|
pixel_values = pixel_values[:, :temp_n_frames, :, :]
|
|
|
|
mask_pixel_values = mask_pixel_values[:, :temp_n_frames, :, :]
|
|
mask = mask[:, :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)
|
|
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
|
|
actual_video_length = int(max(actual_video_length, 1))
|
|
|
|
# Magvae needs the number of frames to be 4n + 1.
|
|
actual_video_length = (actual_video_length - 1) // sample_n_frames_bucket_interval + 1
|
|
|
|
pixel_values = pixel_values[:, :actual_video_length, :, :]
|
|
mask_pixel_values = mask_pixel_values[:, :actual_video_length, :, :]
|
|
mask = mask[:, :actual_video_length, :, :]
|
|
|
|
# Make the inpaint latents to be zeros.
|
|
t2v_flag = [(_mask == 1).all() for _mask in mask]
|
|
new_t2v_flag = []
|
|
for _mask in t2v_flag:
|
|
if _mask and np.random.rand() < 0.90:
|
|
new_t2v_flag.append(0)
|
|
else:
|
|
new_t2v_flag.append(1)
|
|
t2v_flag = torch.from_numpy(np.array(new_t2v_flag)).to(accelerator.device, dtype=weight_dtype)
|
|
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
vae.to(accelerator.device)
|
|
audio_encoder.to(accelerator.device)
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to("cpu")
|
|
|
|
with torch.no_grad():
|
|
# This way is quicker when batch grows up
|
|
def _batch_encode_vae(pixel_values):
|
|
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)
|
|
return torch.cat(new_pixel_values, dim = 0)
|
|
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_mean = (
|
|
torch.tensor(vae.config.latents_mean)
|
|
.view(1, vae.config.z_dim, 1, 1, 1)
|
|
.to(latents.device, latents.dtype)
|
|
)
|
|
latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1).to(
|
|
latents.device, latents.dtype
|
|
)
|
|
latents = (latents - latents_mean) * latents_std
|
|
|
|
# Encode inpaint latents.
|
|
inpaint_latents = _batch_encode_vae(mask_pixel_values[:, :1])
|
|
if vae_stream_2 is not None:
|
|
torch.cuda.current_stream().wait_stream(vae_stream_2)
|
|
inpaint_latents = (inpaint_latents - latents_mean) * latents_std
|
|
|
|
with torch.no_grad():
|
|
audio_stride = 2
|
|
num_frames = pixel_values.size()[1]
|
|
audio_cond_embs = []
|
|
for index, speech_array in enumerate(audio):
|
|
audio_emb = audio_encoder.extract_audio_feat_without_file_load(
|
|
audio_segment=speech_array.cpu().numpy(),
|
|
sample_rate=sample_rate[index],
|
|
num_frames=num_frames,
|
|
audio_stride=audio_stride
|
|
).to(accelerator.device)
|
|
audio_cond_embs.append(audio_emb)
|
|
audio_cond_embs = torch.cat(audio_cond_embs, dim=0)
|
|
|
|
# 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)
|
|
|
|
if args.low_vram:
|
|
vae.to('cpu')
|
|
audio_encoder.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
if not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to(accelerator.device)
|
|
|
|
if args.enable_text_encoder_in_dataloader:
|
|
prompt_embeds = batch['encoder_hidden_states'].to(device=latents.device)
|
|
prompt_attention_mask = batch['encoder_attention_mask'].to(device=latents.device)
|
|
else:
|
|
with torch.no_grad():
|
|
prompt_ids = tokenizer(
|
|
batch['text'],
|
|
padding="max_length",
|
|
max_length=args.tokenizer_max_length,
|
|
truncation=True,
|
|
add_special_tokens=True,
|
|
return_attention_mask=True,
|
|
return_tensors="pt"
|
|
)
|
|
text_input_ids = prompt_ids.input_ids.to(latents.device)
|
|
prompt_attention_mask = prompt_ids.attention_mask.to(latents.device)
|
|
|
|
prompt_embeds = text_encoder(text_input_ids, attention_mask=prompt_attention_mask).last_hidden_state
|
|
batch_size_embed, seq_len, hidden_dim = prompt_embeds.shape
|
|
prompt_embeds = prompt_embeds.view(batch_size_embed, 1, seq_len, hidden_dim)
|
|
|
|
if args.low_vram and not args.enable_text_encoder_in_dataloader:
|
|
text_encoder.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
bsz, channel, num_frames, height, width = latents.size()
|
|
noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype)
|
|
|
|
if not args.uniform_sampling:
|
|
u = compute_density_for_timestep_sampling(
|
|
weighting_scheme=args.weighting_scheme,
|
|
batch_size=bsz,
|
|
logit_mean=args.logit_mean,
|
|
logit_std=args.logit_std,
|
|
mode_scale=args.mode_scale,
|
|
)
|
|
indices = (u * noise_scheduler.config.num_train_timesteps).long()
|
|
else:
|
|
# 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)
|
|
indices = idx_sampling(bsz, generator=torch_rng, device=latents.device)
|
|
indices = indices.long().cpu()
|
|
timesteps = noise_scheduler.timesteps[indices].to(device=latents.device)
|
|
|
|
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
|
|
sigmas = noise_scheduler.sigmas.to(device=accelerator.device, dtype=dtype)
|
|
schedule_timesteps = noise_scheduler.timesteps.to(accelerator.device)
|
|
timesteps = timesteps.to(accelerator.device)
|
|
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
|
|
|
|
sigma = sigmas[step_indices].flatten()
|
|
while len(sigma.shape) < n_dim:
|
|
sigma = sigma.unsqueeze(-1)
|
|
return sigma
|
|
|
|
# Add noise according to flow matching.
|
|
# zt = (1 - texp) * x + texp * z1
|
|
sigmas = get_sigmas(timesteps, n_dim=latents.ndim, dtype=latents.dtype)
|
|
noisy_latents = (1.0 - sigmas) * latents + sigmas * noise
|
|
|
|
# Add noise
|
|
target = noise - latents
|
|
|
|
# Adapt i2v
|
|
noisy_latents[:, :, :1] = inpaint_latents
|
|
timesteps = timesteps.unsqueeze(-1).repeat(1, noisy_latents.shape[2])
|
|
timesteps[:, :1] = 0
|
|
|
|
# Predict the noise residual
|
|
with torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
|
|
noise_pred = transformer3d(
|
|
hidden_states=noisy_latents,
|
|
timestep=timesteps,
|
|
encoder_hidden_states=prompt_embeds,
|
|
encoder_attention_mask=prompt_attention_mask,
|
|
audio_embs=audio_cond_embs,
|
|
num_cond_latents=1,
|
|
)
|
|
|
|
def custom_mse_loss(noise_pred, target, weighting=None, 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
|
|
if weighting is not None:
|
|
masked_loss = masked_loss * weighting
|
|
final_loss = masked_loss.mean()
|
|
return final_loss
|
|
|
|
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
|
|
loss = custom_mse_loss(-noise_pred.float(), target.float(), weighting.float())
|
|
loss = loss.mean()
|
|
|
|
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
|
|
|
|
# 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:
|
|
accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
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 args.use_fsdp 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)
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
# Keep the checkpoint out of the progress bar rate: a minute-long save would
|
|
# otherwise land in the next step's interval and be shown as a slow step. The
|
|
# save also stages the whole state in host RAM (safetensors materializes every
|
|
# tensor as bytes) and leaves the freed blocks in the allocator caches, so the
|
|
# cache flushes run inside the same window.
|
|
with progress_bar.paused():
|
|
if not args.save_state:
|
|
if args.use_peft_lora:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(transformer3d))
|
|
save_model(safetensor_save_path, network_state_dict)
|
|
|
|
safetensor_kohya_format_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}-compatible_with_comfyui.safetensors")
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
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}")
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
with progress_bar.paused():
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
audio_encoder,
|
|
transformer3d,
|
|
network,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
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 args.validation_prompts is not None and epoch % args.validation_epochs == 0:
|
|
with progress_bar.paused():
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
audio_encoder,
|
|
transformer3d,
|
|
network,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
# Close the bar before the end-of-run checkpoint: tqdm keeps redrawing a live bar whenever
|
|
# something else writes to the console. PauseAwareTqdm.close() rebases the closing line onto
|
|
# the smoothed rate, so the worker warm-up and the first dataloader fetch do not dilute it.
|
|
progress_bar.close()
|
|
|
|
# Create the pipeline using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if args.use_deepspeed or args.use_fsdp or accelerator.is_main_process:
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
if not args.save_state:
|
|
if args.use_peft_lora:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
network_state_dict = get_peft_model_state_dict(accelerator.unwrap_model(transformer3d))
|
|
save_model(safetensor_save_path, network_state_dict)
|
|
|
|
safetensor_kohya_format_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}-compatible_with_comfyui.safetensors")
|
|
network_state_dict_kohya = convert_peft_lora_to_kohya_lora(network_state_dict)
|
|
save_model(safetensor_kohya_format_save_path, network_state_dict_kohya)
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
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}")
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|