nodes/aigc-apps-EasyAnimate
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
89cae4def04d463b2d6518e6634c70bdcc07d10c
aigc-apps-EasyAnimate/scripts/train_lora.py
T
Bubbliiiing 45c298af71 Update Readme && Update lora cache && Lora merge with CUDA (#173) 
* Update Readme

* Fix bug in training

* Update Readme

* Update Readme && Update lora cache

* Update Readme

* Update error info in sampler

* Fix Memory bug in 12b
2024-12-26 15:41:21 +08:00

2066 lines
101 KiB
Python
Raw Blame History

"""Modified from https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py
"""
#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
import argparse
import copy
import gc
import logging
import math
import os
import pickle
import shutil
import sys
import accelerate
import diffusers
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.state import AcceleratorState
from accelerate.utils import ProjectConfiguration, set_seed
from diffusers import AutoencoderKL, DDPMScheduler
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version, deprecate, is_wandb_available
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.torch_utils import is_compiled_module
from einops import rearrange
from huggingface_hub import create_repo, upload_folder
from omegaconf import OmegaConf
from packaging import version
from PIL import Image
from torch.utils.data import RandomSampler
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import (BertModel, BertTokenizer, CLIPImageProcessor,
CLIPVisionModelWithProjection,
T5EncoderModel, T5Tokenizer)
from transformers.utils import ContextManagers
import datasets
current_file_path = os.path.abspath(__file__)
project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path))]
for project_root in project_roots:
sys.path.insert(0, project_root) if project_root not in sys.path else None
from transformers import (CLIPImageProcessor, CLIPVisionModelWithProjection,
T5EncoderModel, T5Tokenizer)
from transformers.utils import ContextManagers
from easyanimate.data.bucket_sampler import (ASPECT_RATIO_512,
ASPECT_RATIO_RANDOM_CROP_512,
ASPECT_RATIO_RANDOM_CROP_PROB,
AspectRatioBatchImageSampler,
AspectRatioBatchImageVideoSampler,
AspectRatioBatchSampler,
RandomSampler, get_closest_ratio)
from easyanimate.data.dataset_image import CC15M
from easyanimate.data.dataset_image_video import (ImageVideoDataset,
ImageVideoSampler,
get_random_mask)
from easyanimate.data.dataset_video import VideoDataset, WebVid10M
from easyanimate.models import (name_to_autoencoder_magvit,
name_to_transformer3d)
from easyanimate.models.autoencoder_magvit import AutoencoderKLMagvit
from easyanimate.models.transformer2d import Transformer2DModel
from easyanimate.models.transformer3d import (HunyuanTransformer3DModel,
Transformer3DModel)
from easyanimate.pipeline.pipeline_easyanimate import EasyAnimatePipeline
from easyanimate.pipeline.pipeline_easyanimate_inpaint import \
EasyAnimateInpaintPipeline
from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder import (
EasyAnimatePipeline_Multi_Text_Encoder, get_2d_rotary_pos_embed,
get_3d_rotary_pos_embed, get_resize_crop_region_for_grid)
from easyanimate.pipeline.pipeline_easyanimate_multi_text_encoder_inpaint import (
EasyAnimatePipeline_Multi_Text_Encoder_Inpaint,
add_noise_to_reference_video, resize_mask)
from easyanimate.pipeline.pipeline_pixart_magvit import \
PixArtAlphaMagvitPipeline
from easyanimate.utils import gaussian_diffusion as gd
from easyanimate.utils.discrete_sampler import DiscreteSampling
from easyanimate.utils.lora_utils import (create_network, merge_lora,
unmerge_lora)
from easyanimate.utils.respace import SpacedDiffusion, space_timesteps
from easyanimate.utils.utils import get_image_to_video_latent, save_videos_grid
if is_wandb_available():
import wandb
rotary_pos_embed_cache = {}
def _get_2d_rotary_pos_embed_cached(embed_dim, crops_coords, grid_size):
tmp_key = (embed_dim, crops_coords, grid_size)
print('embed_dim=%d crops_coords=%s grid_size=%s in_cache=%d cache_size=%d' % (
embed_dim, crops_coords, grid_size, tmp_key in rotary_pos_embed_cache, len(rotary_pos_embed_cache)))
if tmp_key not in rotary_pos_embed_cache:
tmp_embed = get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size)
tmp_embed_gpu = (tmp_embed[0].cuda(), tmp_embed[1].cuda())
print('\tembed_dim=%d data_size=%s' % (embed_dim, tmp_embed[0].shape))
rotary_pos_embed_cache[tmp_key] = tmp_embed_gpu
return tmp_embed_gpu
else:
return rotary_pos_embed_cache[tmp_key]
def _get_3d_rotary_pos_embed_cached(embed_dim, crops_coords, grid_size, temporal_size):
tmp_key = (embed_dim, crops_coords, grid_size, temporal_size)
print('embed_dim=%d crops_coords=%s grid_size=%s in_cache=%d cache_size=%d' % (
embed_dim, crops_coords, grid_size, tmp_key in rotary_pos_embed_cache, len(rotary_pos_embed_cache)))
if tmp_key not in rotary_pos_embed_cache:
tmp_embed = get_3d_rotary_pos_embed(
embed_dim, crops_coords, grid_size=grid_size,
temporal_size=temporal_size, use_real=True,
)
tmp_embed_gpu = (tmp_embed[0].cuda(), tmp_embed[1].cuda())
print('\tembed_dim=%d data_size=%s' % (embed_dim, tmp_embed[0].shape))
rotary_pos_embed_cache[tmp_key] = tmp_embed_gpu
return tmp_embed_gpu
else:
return rotary_pos_embed_cache[tmp_key]
def encode_prompt(
tokenizer,
text_encoder,
prompt: str,
device: torch.device,
dtype: torch.dtype,
max_sequence_length = None,
tokenizer_max_length = 256,
add_special_tokens = False,
enable_text_attention_mask = True,
):
if max_sequence_length is None:
max_length = min(tokenizer.model_max_length, tokenizer_max_length)
else:
max_length = max_sequence_length
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_attention_mask=True,
add_special_tokens=add_special_tokens,
return_tensors="pt",
)
if device is not None:
text_input_ids = text_inputs.input_ids.to(device)
prompt_attention_mask = text_inputs.attention_mask.to(device)
else:
text_input_ids = text_inputs.input_ids
prompt_attention_mask = text_inputs.attention_mask
if enable_text_attention_mask:
prompt_embeds = text_encoder(
text_input_ids,
attention_mask=prompt_attention_mask,
)[0]
else:
prompt_embeds = text_encoder(
text_input_ids
)[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
prompt_attention_mask = prompt_attention_mask.to(dtype=dtype, device=device)
return prompt_embeds, prompt_attention_mask
def get_random_downsample_ratio(sample_size, image_ratio=[], all_choices=False, rng=None):
def _create_special_list(length):
if length == 1:
return [1.0]
if length >= 2:
first_element = 0.75
remaining_sum = 1.0 - first_element
other_elements_value = remaining_sum / (length - 1)
special_list = [first_element] + [other_elements_value] * (length - 1)
return special_list
if sample_size >= 1536:
number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio
elif sample_size >= 1024:
number_list = [1, 1.25, 1.5, 2] + image_ratio
elif sample_size >= 768:
number_list = [1, 1.25, 1.5] + image_ratio
elif sample_size >= 512:
number_list = [1] + image_ratio
else:
number_list = [1]
if all_choices:
return number_list
number_list_prob = np.array(_create_special_list(len(number_list)))
if rng is None:
return np.random.choice(number_list, p = number_list_prob)
else:
return rng.choice(number_list, p = number_list_prob)
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.18.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def log_validation(
vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, transformer3d, image_encoder, image_processor, network,
config, args, accelerator, weight_dtype, global_step
):
try:
logger.info("Running validation... ")
# Get New Transformer
Choosen_Transformer3DModel = name_to_transformer3d[
config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel')
]
transformer3d_val = Choosen_Transformer3DModel.from_pretrained_2d(
args.pretrained_model_name_or_path, subfolder="transformer",
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
).to(weight_dtype)
transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict())
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
if args.train_mode != "normal":
pipeline = EasyAnimatePipeline_Multi_Text_Encoder_Inpaint.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae).to(weight_dtype),
text_encoder=accelerator.unwrap_model(text_encoder),
text_encoder_2=accelerator.unwrap_model(text_encoder_2),
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer3d_val,
torch_dtype=weight_dtype,
clip_image_encoder=image_encoder,
clip_image_processor=image_processor,
)
else:
pipeline = EasyAnimatePipeline_Multi_Text_Encoder.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae).to(weight_dtype),
text_encoder=accelerator.unwrap_model(text_encoder),
text_encoder_2=accelerator.unwrap_model(text_encoder_2),
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer3d_val,
torch_dtype=weight_dtype
)
else:
if args.train_mode != "normal":
pipeline = EasyAnimateInpaintPipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae).to(weight_dtype),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
transformer=transformer3d_val,
torch_dtype=weight_dtype,
clip_image_encoder=image_encoder,
clip_image_processor=image_processor,
)
else:
pipeline = EasyAnimatePipeline.from_pretrained(
args.pretrained_model_name_or_path,
vae=accelerator.unwrap_model(vae).to(weight_dtype),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
transformer=transformer3d_val,
torch_dtype=weight_dtype
)
pipeline = pipeline.to(accelerator.device)
pipeline = merge_lora(
pipeline, None, 1, accelerator.device, state_dict=accelerator.unwrap_model(network).state_dict(), transformer_only=True
)
if args.enable_xformers_memory_efficient_attention \
and config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') == 'Transformer3DModel':
pipeline.enable_xformers_memory_efficient_attention()
if args.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
for i in range(len(args.validation_prompts)):
with torch.no_grad():
if args.train_mode != "normal":
with torch.autocast("cuda", dtype=weight_dtype):
if vae.cache_mag_vae:
video_length = int((args.video_sample_n_frames - 1) // vae.mini_batch_encoder * vae.mini_batch_encoder) + 1 if args.video_sample_n_frames != 1 else 1
else:
video_length = int(args.video_sample_n_frames // vae.mini_batch_encoder * vae.mini_batch_encoder) if args.video_sample_n_frames != 1 else 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size])
sample = pipeline(
args.validation_prompts[i],
video_length = video_length,
negative_prompt = "bad detailed",
height = args.video_sample_size,
width = args.video_sample_size,
generator = generator,
video = input_video,
mask_video = input_video_mask,
clip_image = clip_image,
).videos
os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif"))
video_length = 1
input_video, input_video_mask, clip_image = get_image_to_video_latent(None, None, video_length=video_length, sample_size=[args.video_sample_size, args.video_sample_size])
sample = pipeline(
args.validation_prompts[i],
video_length = 1,
negative_prompt = "bad detailed",
height = args.video_sample_size,
width = args.video_sample_size,
generator = generator,
video = input_video,
mask_video = input_video_mask,
clip_image = clip_image,
).videos
os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif"))
else:
with torch.autocast("cuda", dtype=weight_dtype):
sample = pipeline(
args.validation_prompts[i],
video_length = video_length,
negative_prompt = "bad detailed",
height = args.video_sample_size,
width = args.video_sample_size,
generator = generator
).videos
os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-{i}.gif"))
sample = pipeline(
args.validation_prompts[i],
video_length = 1,
negative_prompt = "bad detailed",
height = args.video_sample_size,
width = args.video_sample_size,
generator = generator
).videos
os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
save_videos_grid(sample, os.path.join(args.output_dir, f"sample/sample-{global_step}-image-{i}.gif"))
del pipeline
del transformer3d_val
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return None
except Exception as e:
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
print(f"Eval error with info {e}")
return None
def linear_decay(initial_value, final_value, total_steps, current_step):
if current_step >= total_steps:
return final_value
current_step = max(0, current_step)
step_size = (final_value - initial_value) / total_steps
current_value = initial_value + step_size * current_step
return current_value
def generate_timestep_with_lognorm(low, high, shape, device="cpu", generator=None):
u = torch.normal(mean=0.0, std=1.0, size=shape, device=device, generator=generator)
t = 1 / (1 + torch.exp(-u)) * (high - low) + low
return torch.clip(t.to(torch.int32), low, high - 1)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--input_perturbation", type=float, default=0, help="The scale of input perturbation. Recommended 0.1."
)
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default=None,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
parser.add_argument(
"--variant",
type=str,
default=None,
help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. "
),
)
parser.add_argument(
"--train_data_meta",
type=str,
default=None,
help=(
"A csv containing the training data. "
),
)
parser.add_argument(
"--max_train_samples",
type=int,
default=None,
help=(
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
),
)
parser.add_argument(
"--validation_prompts",
type=str,
default=None,
nargs="+",
help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
)
parser.add_argument(
"--output_dir",
type=str,
default="sd-model-finetuned",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument(
"--random_flip",
action="store_true",
help="whether to randomly flip images horizontally",
)
parser.add_argument(
"--use_came",
action="store_true",
help="whether to use came",
)
parser.add_argument(
"--multi_stream",
action="store_true",
help="whether to use cuda multi-stream",
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--vae_mini_batch", type=int, default=32, help="mini batch size for vae."
)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument(
"--max_train_steps",
type=int,
default=None,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
parser.add_argument(
"--non_ema_revision",
type=str,
default=None,
required=False,
help=(
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
" remote repository specified with --pretrained_model_name_or_path."
),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help=(
"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
),
)
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--prediction_type",
type=str,
default=None,
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
)
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--report_model_info", action="store_true", help="Whether or not to report more info about model (such as norm, grad)."
)
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
parser.add_argument(
"--checkpoints_total_limit",
type=int,
default=None,
help=("Max number of checkpoints to store."),
)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
parser.add_argument(
"--validation_epochs",
type=int,
default=5,
help="Run validation every X epochs.",
)
parser.add_argument(
"--validation_steps",
type=int,
default=2000,
help="Run validation every X steps.",
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="text2image-fine-tune",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
parser.add_argument(
"--rank",
type=int,
default=128,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--network_alpha",
type=int,
default=64,
help=("The dimension of the LoRA update matrices."),
)
parser.add_argument(
"--train_text_encoder",
action="store_true",
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
)
parser.add_argument(
"--snr_loss", action="store_true", help="Whether or not to use snr_loss."
)
parser.add_argument(
"--uniform_sampling", action="store_true", help="Whether or not to use uniform_sampling."
)
parser.add_argument(
"--not_sigma_loss", action="store_true", help="Whether or not to not use sigma_loss."
)
parser.add_argument(
"--enable_text_encoder_in_dataloader", action="store_true", help="Whether or not to use text encoder in dataloader."
)
parser.add_argument(
"--enable_bucket", action="store_true", help="Whether enable bucket sample in datasets."
)
parser.add_argument(
"--random_ratio_crop", action="store_true", help="Whether enable random ratio crop sample in datasets."
)
parser.add_argument(
"--random_frame_crop", action="store_true", help="Whether enable random frame crop sample in datasets."
)
parser.add_argument(
"--random_hw_adapt", action="store_true", help="Whether enable random adapt height and width in datasets."
)
parser.add_argument(
"--training_with_video_token_length", action="store_true", help="The training stage of the model in training.",
)
parser.add_argument(
"--noise_share_in_frames", action="store_true", help="Whether enable noise share in frames."
)
parser.add_argument(
"--noise_share_in_frames_ratio", type=float, default=0.5, help="Noise share ratio.",
)
parser.add_argument(
"--motion_sub_loss", action="store_true", help="Whether enable motion sub loss."
)
parser.add_argument(
"--motion_sub_loss_ratio", type=float, default=0.25, help="The ratio of motion sub loss."
)
parser.add_argument(
"--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(
"--image_sample_size",
type=int,
default=512,
help="Sample size of the video.",
)
parser.add_argument(
"--video_sample_stride",
type=int,
default=4,
help="Sample stride of the video.",
)
parser.add_argument(
"--video_sample_n_frames",
type=int,
default=17,
help="Num frame of video.",
)
parser.add_argument(
"--video_repeat",
type=int,
default=0,
help="Num of repeat video.",
)
parser.add_argument(
"--image_repeat_in_forward",
type=int,
default=0,
help="Num of repeat image in forward.",
)
parser.add_argument(
"--config_path",
type=str,
default=None,
help=(
"The config of the model in training."
),
)
parser.add_argument(
"--transformer_path",
type=str,
default=None,
help=("If you want to load the weight from other transformers, input its path."),
)
parser.add_argument(
"--vae_path",
type=str,
default=None,
help=("If you want to load the weight from other vaes, input its path."),
)
parser.add_argument("--save_state", action="store_true", help="Whether or not to save state.")
parser.add_argument(
'--tokenizer_max_length',
type=int,
default=256,
help='Max length of tokenizer'
)
parser.add_argument(
"--use_deepspeed", action="store_true", help="Whether or not to use deepspeed."
)
parser.add_argument(
"--low_vram", action="store_true", help="Whether enable low_vram mode."
)
parser.add_argument(
"--train_mode",
type=str,
default="normal",
help=(
'The format of training data. Support `"normal"`'
' (default), `"inpaint"`.'
),
)
parser.add_argument(
"--abnormal_norm_clip_start",
type=int,
default=1000,
help=(
'When do we start doing additional processing on abnormal gradients. '
),
)
parser.add_argument(
"--initial_grad_norm_ratio",
type=int,
default=5,
help=(
'The initial gradient is relative to the multiple of the max_grad_norm. '
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
# default to using the same revision for the non-ema model if not specified
if args.non_ema_revision is None:
args.non_ema_revision = args.revision
return args
def main():
args = parse_args()
if args.report_to == "wandb" and args.hub_token is not None:
raise ValueError(
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
" Please use `huggingface-cli login` to authenticate with the Hub."
)
if args.non_ema_revision is not None:
deprecate(
"non_ema_revision!=None",
"0.15.0",
message=(
"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
" use `--variant=non_ema` instead."
),
)
logging_dir = os.path.join(args.output_dir, args.logging_dir)
config = OmegaConf.load(args.config_path)
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=args.gradient_accumulation_steps,
mixed_precision=args.mixed_precision,
log_with=args.report_to,
project_config=accelerator_project_config,
)
if accelerator.is_main_process:
writer = SummaryWriter(log_dir=logging_dir)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
rng = np.random.default_rng(np.random.PCG64(args.seed + accelerator.process_index))
torch_rng = torch.Generator(accelerator.device).manual_seed(args.seed + accelerator.process_index)
else:
rng = None
torch_rng = None
index_rng = np.random.default_rng(np.random.PCG64(43))
print(f"Init rng with seed {args.seed + accelerator.process_index}. Process_index is {accelerator.process_index}")
# Handle the repository creation
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora transformer3d) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
args.mixed_precision = accelerator.mixed_precision
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
args.mixed_precision = accelerator.mixed_precision
# Load scheduler, tokenizer and models.
if args.not_sigma_loss:
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
else:
train_diffusion = SpacedDiffusion(
use_timesteps=space_timesteps(1000, str(args.train_sampling_steps)), betas=gd.get_named_beta_schedule("linear", 1000),
model_mean_type=(gd.ModelMeanType.EPSILON), model_var_type=((gd.ModelVarType.LEARNED_RANGE)),
loss_type=gd.LossType.MSE, snr=args.snr_loss, return_startx=False,
)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
print("Init BertTokenizer")
tokenizer = BertTokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
print("Init T5Tokenizer")
tokenizer_2 = T5Tokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer_2", revision=args.revision
)
else:
print("Init T5Tokenizer")
tokenizer = T5Tokenizer.from_pretrained(
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
)
tokenizer_2 = None
def deepspeed_zero_init_disabled_context_manager():
"""
returns either a context list that includes one that will disable zero.Init or an empty context list
"""
deepspeed_plugin = AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
if deepspeed_plugin is None:
return []
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
# For this to work properly all models must be run through `accelerate.prepare`. But accelerate
# will try to assign the same optimizer with the same weights to all models during
# `deepspeed.initialize`, which of course doesn't work.
#
# For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
# frozen models from being partitioned during `zero.Init` which gets called during
# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
text_encoder = BertModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
torch_dtype=weight_dtype
)
text_encoder_2 = T5EncoderModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder_2", revision=args.revision, variant=args.variant,
torch_dtype=weight_dtype
)
else:
text_encoder = T5EncoderModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
torch_dtype=weight_dtype
)
text_encoder_2 = None
# Get Vae
Choosen_AutoencoderKL = name_to_autoencoder_magvit[
config['vae_kwargs'].get('vae_type', 'AutoencoderKL')
]
vae = Choosen_AutoencoderKL.from_pretrained(
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant,
vae_additional_kwargs=OmegaConf.to_container(config['vae_kwargs'])
)
# Get Transformer
Choosen_Transformer3DModel = name_to_transformer3d[
config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel')
]
transformer3d = Choosen_Transformer3DModel.from_pretrained_2d(
args.pretrained_model_name_or_path, subfolder="transformer",
transformer_additional_kwargs=OmegaConf.to_container(config['transformer_additional_kwargs'])
)
# Get Image encoder
if args.train_mode != "normal" and config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
args.pretrained_model_name_or_path, subfolder="image_encoder"
)
image_processor = CLIPImageProcessor.from_pretrained(
args.pretrained_model_name_or_path, subfolder="image_encoder"
)
else:
image_encoder = None
image_processor = None
# Freeze vae and text_encoder and set transformer3d to trainable
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
text_encoder_2.requires_grad_(False)
transformer3d.requires_grad_(False)
if args.train_mode != "normal" and config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
image_encoder.requires_grad_(False)
# Lora will work with this...
network = create_network(
1.0,
args.rank,
args.network_alpha,
text_encoder,
transformer3d,
neuron_dropout=None,
add_lora_in_attn_temporal=True,
)
network.apply_to(text_encoder, transformer3d, args.train_text_encoder and not args.training_with_video_token_length, True)
# Load transformer and vae from path if it needs.
if args.transformer_path is not None:
print(f"From checkpoint: {args.transformer_path}")
if args.transformer_path.endswith("safetensors"):
from safetensors.torch import load_file, safe_open
state_dict = load_file(args.transformer_path)
else:
state_dict = torch.load(args.transformer_path, map_location="cpu")
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
m, u = transformer3d.load_state_dict(state_dict, strict=False)
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
if args.vae_path is not None:
print(f"From checkpoint: {args.vae_path}")
if args.vae_path.endswith("safetensors"):
from safetensors.torch import load_file, safe_open
state_dict = load_file(args.vae_path)
else:
state_dict = torch.load(args.vae_path, map_location="cpu")
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
m, u = vae.load_state_dict(state_dict, strict=False)
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
if args.enable_xformers_memory_efficient_attention \
and config['transformer_additional_kwargs'].get('transformer_type', 'Transformer3DModel') == 'Transformer3DModel':
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
transformer3d.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# `accelerate` 0.16.0 will have better support for customized saving
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
def save_model_hook(models, weights, output_dir):
if accelerator.is_main_process:
safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors")
save_model(safetensor_save_path, accelerator.unwrap_model(models[-1]))
if not args.use_deepspeed:
for _ in range(len(weights)):
weights.pop()
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:
raise ImportError(
"Please install came_pytorch to use CAME. You can do so by running `pip install came_pytorch`"
)
optimizer_cls = CAME
else:
optimizer_cls = torch.optim.AdamW
logging.info("Add network parameters")
trainable_params = list(filter(lambda p: p.requires_grad, network.parameters()))
trainable_params_optim = network.prepare_optimizer_params(args.learning_rate / 2, args.learning_rate, args.learning_rate)
# Init optimizer
if args.use_came:
optimizer = optimizer_cls(
trainable_params_optim,
lr=args.learning_rate,
betas=(0.9, 0.999, 0.9999),
eps=(1e-30, 1e-16)
)
else:
optimizer = optimizer_cls(
trainable_params_optim,
lr=args.learning_rate,
betas=(args.adam_beta1, args.adam_beta2),
weight_decay=args.adam_weight_decay,
eps=args.adam_epsilon,
)
# Get the training dataset
sample_n_frames_bucket_interval = vae.mini_batch_encoder if vae.quant_conv is None or vae.quant_conv.weight.ndim==5 else 4
# Get the dataset
train_dataset = ImageVideoDataset(
args.train_data_meta, args.train_data_dir,
video_sample_size=args.video_sample_size, video_sample_stride=args.video_sample_stride, video_sample_n_frames=args.video_sample_n_frames,
video_repeat=args.video_repeat,
image_sample_size=args.image_sample_size,
enable_bucket=args.enable_bucket, enable_inpaint=True if args.train_mode != "normal" else False,
)
if args.enable_bucket:
aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
batch_sampler_generator = torch.Generator().manual_seed(args.seed)
batch_sampler = AspectRatioBatchImageVideoSampler(
sampler=RandomSampler(train_dataset, generator=batch_sampler_generator), dataset=train_dataset.dataset,
batch_size=args.train_batch_size, train_folder = args.train_data_dir, drop_last=True,
aspect_ratios=aspect_ratio_sample_size,
)
# Get the frame length at different resolutions according to token_length
def get_length_to_frame_num(token_length):
if args.image_sample_size > args.video_sample_size:
sample_sizes = list(range(args.video_sample_size, args.image_sample_size + 1, 128))
if sample_sizes[-1] != args.image_sample_size:
sample_sizes.append(args.image_sample_size)
else:
sample_sizes = [args.image_sample_size]
if vae.cache_mag_vae:
length_to_frame_num = {
sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1 for sample_size in sample_sizes
}
else:
length_to_frame_num = {
sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval for sample_size in sample_sizes
}
return length_to_frame_num
def collate_fn(examples):
# Get token length
target_token_length = args.video_sample_n_frames * args.token_sample_size * args.token_sample_size
length_to_frame_num = get_length_to_frame_num(target_token_length)
# Create new output
new_examples = {}
new_examples["target_token_length"] = target_token_length
new_examples["pixel_values"] = []
new_examples["text"] = []
# Used in Inpaint mode
if args.train_mode != "normal":
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"]
data_type = examples[0]["data_type"]
f, h, w, c = np.shape(pixel_value)
if data_type == 'image':
random_downsample_ratio = 1 if not args.random_hw_adapt else get_random_downsample_ratio(args.image_sample_size, image_ratio=[args.image_sample_size / args.video_sample_size], rng=rng)
aspect_ratio_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
else:
if args.random_hw_adapt:
if args.training_with_video_token_length:
local_min_size = np.min(np.array([np.mean(np.array([np.shape(example["pixel_values"])[1], np.shape(example["pixel_values"])[2]])) for example in examples]))
# The video will be resized to a lower resolution than its own.
choice_list = [length for length in list(length_to_frame_num.keys()) if length < local_min_size * 1.25]
if len(choice_list) == 0:
choice_list = list(length_to_frame_num.keys())
if rng is None:
local_video_sample_size = np.random.choice(choice_list)
else:
local_video_sample_size = rng.choice(choice_list)
batch_video_length = length_to_frame_num[local_video_sample_size]
random_downsample_ratio = args.video_sample_size / local_video_sample_size
else:
random_downsample_ratio = get_random_downsample_ratio(
args.video_sample_size, rng=rng)
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
else:
random_downsample_ratio = 1
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
aspect_ratio_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
aspect_ratio_random_crop_sample_size = {key : [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size)
closest_size = [int(x / 16) * 16 for x in closest_size]
if args.random_ratio_crop:
if rng is None:
random_sample_size = aspect_ratio_random_crop_sample_size[
np.random.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB)
]
else:
random_sample_size = aspect_ratio_random_crop_sample_size[
rng.choice(list(aspect_ratio_random_crop_sample_size.keys()), p = ASPECT_RATIO_RANDOM_CROP_PROB)
]
random_sample_size = [int(x / 16) * 16 for x in random_sample_size]
for example in examples:
if args.random_ratio_crop:
# To 0~1
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
pixel_values = pixel_values / 255.
# 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))
new_examples["text"].append(example["text"])
# Magvae needs the number of frames to be 4n + 1.
if vae.cache_mag_vae:
batch_video_length = int(
min(
batch_video_length,
(len(pixel_values) - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1,
)
)
else:
batch_video_length = int(
min(
batch_video_length,
len(pixel_values) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval,
)
)
if batch_video_length == 0:
batch_video_length = 1
# Used in Inpaint mode
if args.train_mode != "normal":
mask = get_random_mask(new_examples["pixel_values"][-1].size())
mask_pixel_values = new_examples["pixel_values"][-1] * (1 - mask) + torch.ones_like(new_examples["pixel_values"][-1]) * -1 * mask
new_examples["mask_pixel_values"].append(mask_pixel_values)
new_examples["mask"].append(mask)
def get_random_clip_index(low, high):
if high - low <= 1.1:
return low
values = np.arange(low, high)
probabilities = np.ones(len(values)) * 0.5 / (len(values) - 1)
probabilities[0] = 0.5
return np.random.choice(values, p=probabilities)
clip_index = get_random_clip_index(0, len(new_examples["pixel_values"][-1]))
clip_pixel_values = new_examples["pixel_values"][-1][clip_index].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[:batch_video_length] for example in new_examples["pixel_values"]])
if args.train_mode != "normal":
new_examples["mask_pixel_values"] = torch.stack([example[:batch_video_length] for example in new_examples["mask_pixel_values"]])
new_examples["mask"] = torch.stack([example[:batch_video_length] for example in new_examples["mask"]])
new_examples["clip_pixel_values"] = torch.stack([example for example in new_examples["clip_pixel_values"]])
# Encode prompts when enable_text_encoder_in_dataloader=True
if args.enable_text_encoder_in_dataloader:
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
prompt_embeds, prompt_attention_mask = \
encode_prompt(
tokenizer, text_encoder, new_examples['text'], None,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True)
)
prompt_embeds_2, prompt_attention_mask_2 = \
encode_prompt(
tokenizer_2, text_encoder_2, new_examples['text'], None,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True)
)
new_examples['prompt_embeds'] = prompt_embeds
new_examples['prompt_attention_mask'] = prompt_attention_mask
new_examples['prompt_embeds_2'] = prompt_embeds_2
new_examples['prompt_attention_mask_2'] = prompt_attention_mask_2
else:
prompt_embeds, prompt_attention_mask = \
encode_prompt(
tokenizer, text_encoder, new_examples['text'], None,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
add_special_tokens=True,
enable_text_attention_mask=transformer3d.config.get("enable_text_attention_mask", True)
)
new_examples['prompt_embeds'] = prompt_embeds
new_examples['prompt_attention_mask'] = prompt_attention_mask
return new_examples
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_sampler=batch_sampler,
collate_fn=collate_fn,
persistent_workers=True if args.dataloader_num_workers != 0 else False,
num_workers=args.dataloader_num_workers,
)
else:
# DataLoaders creation:
batch_sampler_generator = torch.Generator().manual_seed(args.seed)
batch_sampler = ImageVideoSampler(RandomSampler(train_dataset, generator=batch_sampler_generator), train_dataset, args.train_batch_size)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_sampler=batch_sampler,
persistent_workers=True if args.dataloader_num_workers != 0 else False,
num_workers=args.dataloader_num_workers,
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if args.max_train_steps is None:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
lr_scheduler = get_scheduler(
args.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
num_training_steps=args.max_train_steps * accelerator.num_processes,
)
# Prepare everything with our `accelerator`.
network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
network, optimizer, train_dataloader, lr_scheduler
)
# Move text_encode and vae to gpu and cast to weight_dtype
vae.to(accelerator.device, dtype=weight_dtype)
if args.train_mode != "normal" and config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
image_encoder.to(accelerator.device, dtype=weight_dtype)
transformer3d.to(accelerator.device, dtype=weight_dtype)
if not args.enable_text_encoder_in_dataloader:
text_encoder.to(accelerator.device)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
text_encoder_2.to(accelerator.device)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
if overrode_max_train_steps:
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
tracker_config = dict(vars(args))
tracker_config.pop("validation_prompts")
accelerator.init_trackers(args.tracker_project_name, tracker_config)
# Function for unwrapping if model was compiled with `torch.compile`.
def unwrap_model(model):
model = accelerator.unwrap_model(model)
model = model._orig_mod if is_compiled_module(model) else model
return model
# Train!
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {args.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint != "latest":
path = os.path.basename(args.resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(args.output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
)
args.resume_from_checkpoint = None
initial_global_step = 0
else:
global_step = int(path.split("-")[1])
initial_global_step = global_step
pkl_path = os.path.join(os.path.join(args.output_dir, path), "sampler_pos_start.pkl")
if os.path.exists(pkl_path):
with open(pkl_path, 'rb') as file:
_, first_epoch = pickle.load(file)
else:
first_epoch = global_step // num_update_steps_per_epoch
print(f"Load pkl from {pkl_path}. Get first_epoch = {first_epoch}.")
from safetensors.torch import load_file, safe_open
state_dict = load_file(os.path.join(os.path.join(args.output_dir, path), "lora_diffusion_pytorch_model.safetensors"))
m, u = accelerator.unwrap_model(network).load_state_dict(state_dict, strict=False)
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(args.output_dir, path))
else:
initial_global_step = 0
# function for saving/removing
def save_model(ckpt_file, unwrapped_nw):
os.makedirs(args.output_dir, exist_ok=True)
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
unwrapped_nw.save_weights(ckpt_file, weight_dtype, None)
progress_bar = tqdm(
range(0, args.max_train_steps),
initial=initial_global_step,
desc="Steps",
# Only show the progress bar once on each machine.
disable=not accelerator.is_local_main_process,
)
if args.multi_stream and args.train_mode != "normal":
# 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")
if pixel_values.ndim==4:
pixel_values = pixel_values.unsqueeze(2)
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]}.gif", rescale=True)
if args.train_mode != "normal":
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}'}.gif", rescale=True)
with accelerator.accumulate(transformer3d):
# Convert images to latent space
pixel_values = batch["pixel_values"].to(weight_dtype)
# Increase the batch size when the length of the latent sequence of the current sample is small
if args.training_with_video_token_length:
if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]:
pixel_values = torch.tile(pixel_values, (4, 1, 1, 1, 1))
if args.enable_text_encoder_in_dataloader:
batch['prompt_embeds'] = torch.tile(batch['prompt_embeds'], (4, 1, 1))
batch['prompt_attention_mask'] = torch.tile(batch['prompt_attention_mask'], (4, 1))
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
batch['prompt_embeds_2'] = torch.tile(batch['prompt_embeds_2'], (4, 1, 1))
batch['prompt_attention_mask_2'] = torch.tile(batch['prompt_attention_mask_2'], (4, 1))
else:
batch['text'] = batch['text'] * 4
elif args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 4 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]:
pixel_values = torch.tile(pixel_values, (2, 1, 1, 1, 1))
if args.enable_text_encoder_in_dataloader:
batch['prompt_embeds'] = torch.tile(batch['prompt_embeds'], (2, 1, 1))
batch['prompt_attention_mask'] = torch.tile(batch['prompt_attention_mask'], (2, 1))
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
batch['prompt_embeds_2'] = torch.tile(batch['prompt_embeds_2'], (2, 1, 1))
batch['prompt_attention_mask_2'] = torch.tile(batch['prompt_attention_mask_2'], (2, 1))
else:
batch['text'] = batch['text'] * 2
if args.train_mode != "normal":
clip_pixel_values = batch["clip_pixel_values"]
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.training_with_video_token_length:
if args.video_sample_n_frames * args.token_sample_size * args.token_sample_size // 16 >= pixel_values.size()[1] * pixel_values.size()[3] * pixel_values.size()[4]:
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))
# Random crop number of frames to adapt different frames of video.
if args.random_frame_crop:
def _create_special_list(length):
if length == 1:
return [1.0]
if length >= 2:
last_element = 0.90
remaining_sum = 1.0 - last_element
other_elements_value = remaining_sum / (length - 1)
special_list = [other_elements_value] * (length - 1) + [last_element]
return special_list
if vae.cache_mag_vae:
select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval + 1, args.video_sample_n_frames + sample_n_frames_bucket_interval, sample_n_frames_bucket_interval))]
else:
select_frames = [_tmp for _tmp in list(range(sample_n_frames_bucket_interval, args.video_sample_n_frames + sample_n_frames_bucket_interval, sample_n_frames_bucket_interval))]
select_frames_prob = np.array(_create_special_list(len(select_frames)))
if rng is None:
temp_n_frames = np.random.choice(select_frames, p = select_frames_prob)
else:
temp_n_frames = rng.choice(select_frames, p = select_frames_prob)
pixel_values = pixel_values[:, :temp_n_frames, :, :]
if args.train_mode != "normal":
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)
if vae.cache_mag_vae:
actual_video_length = (min(
actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames
) - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
else:
actual_video_length = min(
actual_token_length / pixel_values.size()[-1] / pixel_values.size()[-2], args.video_sample_n_frames
) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval
actual_video_length = int(max(actual_video_length, 1))
pixel_values = pixel_values[:, :actual_video_length, :, :]
if args.train_mode != "normal":
mask_pixel_values = mask_pixel_values[:, :actual_video_length, :, :]
mask = mask[:, :actual_video_length, :, :]
# Make the inpaint latents to be zeros.
if args.train_mode != "normal":
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)
# Reduce the vram by offload vae and text encoders
if args.low_vram:
torch.cuda.empty_cache()
vae.to(accelerator.device)
if not args.enable_text_encoder_in_dataloader:
text_encoder.to('cpu')
if text_encoder_2 is not None:
text_encoder_2.to('cpu')
with torch.no_grad():
video_length = pixel_values.shape[1]
# This way is quicker when batch grows up
def _batch_encode_vae(pixel_values):
if vae.quant_conv is None or vae.quant_conv.weight.ndim==5:
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
bs = args.vae_mini_batch
new_pixel_values = []
for i in range(0, pixel_values.shape[0], bs):
pixel_values_bs = pixel_values[i : i + bs]
pixel_values_bs = vae.encode(pixel_values_bs)[0]
pixel_values_bs = pixel_values_bs.sample()
new_pixel_values.append(pixel_values_bs)
latents = torch.cat(new_pixel_values, dim = 0)
else:
pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
bs = args.vae_mini_batch
new_pixel_values = []
for i in range(0, pixel_values.shape[0], bs):
pixel_values_bs = pixel_values[i : i + bs]
pixel_values_bs = vae.encode(pixel_values_bs.to(dtype=weight_dtype)).latent_dist
pixel_values_bs = pixel_values_bs.sample()
new_pixel_values.append(pixel_values_bs)
latents = torch.cat(new_pixel_values, dim = 0)
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
return latents
# Encode latents.
if vae_stream_1 is not None:
vae_stream_1.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(vae_stream_1):
latents = _batch_encode_vae(pixel_values)
else:
latents = _batch_encode_vae(pixel_values)
latents = latents * vae.config.scaling_factor
if args.train_mode != "normal":
# Encode masks.
if config['transformer_additional_kwargs'].get('resize_inpaint_mask_directly', False):
mask = rearrange(mask, "b f c h w -> b c f h w")
mask = 1 - mask
mask = resize_mask(mask, latents, vae.cache_mag_vae)
else:
mask = torch.tile(mask, [1, 1, 3, 1, 1])
if vae_stream_2 is not None:
vae_stream_2.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(vae_stream_2):
mask = _batch_encode_vae(mask)
else:
mask = _batch_encode_vae(mask)
if unwrap_model(transformer3d).config.add_noise_in_inpaint_model:
mask_pixel_values = add_noise_to_reference_video(mask_pixel_values)
# Encode inpaint latents.
mask_latents = _batch_encode_vae(mask_pixel_values)
if vae_stream_2 is not None:
torch.cuda.current_stream().wait_stream(vae_stream_2)
inpaint_latents = torch.concat([mask, mask_latents], dim=1)
inpaint_latents = t2v_flag[:, None, None, None, None] * inpaint_latents
inpaint_latents = inpaint_latents * vae.config.scaling_factor
# Encode Vision CLIP latents
with torch.no_grad():
if config['transformer_additional_kwargs'].get('enable_clip_in_inpaint', True):
clip_encoder_hidden_states = []
for clip_pixel_value in clip_pixel_values:
image = Image.fromarray(np.uint8(clip_pixel_value.cpu().numpy()))
inputs = image_processor(images=image, return_tensors="pt")
inputs["pixel_values"] = inputs["pixel_values"].to(accelerator.device, dtype=weight_dtype)
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
outputs = image_encoder(**inputs).last_hidden_state[0, 1:]
else:
outputs = image_encoder(**inputs).image_embeds[0]
clip_encoder_hidden_states.append(outputs)
clip_encoder_hidden_states = torch.stack(clip_encoder_hidden_states)
bsc = clip_encoder_hidden_states.shape[0]
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
clip_attention_mask = torch.ones([bsc, unwrap_model(transformer3d).n_query]) if np.random.rand() < 0.50 else torch.zeros([bsc, unwrap_model(transformer3d).n_query])
else:
clip_attention_mask = torch.ones([bsc, 8]) if np.random.rand() < 0.50 else torch.zeros([bsc, 8])
clip_attention_mask = clip_attention_mask.to(accelerator.device, dtype=weight_dtype)
else:
clip_encoder_hidden_states = None
clip_attention_mask = None
# wait for latents = vae.encode(pixel_values) to complete
if vae_stream_1 is not None:
torch.cuda.current_stream().wait_stream(vae_stream_1)
# Reduce the vram by offload vae and text encoders
if args.low_vram:
vae.to('cpu')
torch.cuda.empty_cache()
if not args.enable_text_encoder_in_dataloader:
text_encoder.to(accelerator.device)
if text_encoder_2 is not None:
text_encoder_2.to(accelerator.device)
# Get text embeds
if args.enable_text_encoder_in_dataloader:
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
prompt_embeds = batch['prompt_embeds'].to(device=latents.device)
prompt_attention_mask = batch['prompt_attention_mask'].to(device=latents.device)
prompt_embeds_2 = batch['prompt_embeds_2'].to(device=latents.device)
prompt_attention_mask_2 = batch['prompt_attention_mask_2'].to(device=latents.device)
else:
prompt_embeds = batch['prompt_embeds'].to(device=latents.device)
prompt_attention_mask = batch['prompt_attention_mask'].to(device=latents.device)
prompt_embeds_2 = None
prompt_attention_mask_2 = None
else:
if config['text_encoder_kwargs'].get('enable_multi_text_encoder', False):
with torch.no_grad():
prompt_embeds, prompt_attention_mask = \
encode_prompt(
tokenizer, text_encoder, batch['text'], latents.device,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True)
)
prompt_embeds_2, prompt_attention_mask_2 = \
encode_prompt(
tokenizer_2, text_encoder_2, batch['text'], latents.device,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True)
)
else:
with torch.no_grad():
prompt_embeds, prompt_attention_mask = \
encode_prompt(
tokenizer, text_encoder, batch['text'], latents.device,
tokenizer_max_length=args.tokenizer_max_length, dtype=weight_dtype,
add_special_tokens=True,
enable_text_attention_mask=unwrap_model(transformer3d).config.get("enable_text_attention_mask", True)
)
prompt_embeds_2 = None
prompt_attention_mask_2 = None
# Reduce the vram by offload vae and text encoders
if args.low_vram and not args.enable_text_encoder_in_dataloader:
text_encoder.to('cpu')
if text_encoder_2 is not None:
text_encoder_2.to('cpu')
torch.cuda.empty_cache()
# Get Noise
bsz = latents.shape[0]
if args.noise_share_in_frames:
def generate_noise(bs, channel, length, height, width, ratio=0.5, generator=None, device="cuda", dtype=None):
noise = torch.randn(bs, channel, length, height, width, generator=generator, device=device, dtype=dtype)
for i in range(1, length):
noise[:, :, i, :, :] = ratio * noise[:, :, i - 1, :, :] + (1 - ratio) * noise[:, :, i, :, :]
return noise
noise = generate_noise(*latents.size(), ratio=args.noise_share_in_frames_ratio, device=latents.device, generator=torch_rng, dtype=weight_dtype)
else:
noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype)
# Sample a random timestep for each image
# timesteps = generate_timestep_with_lognorm(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng)
# timesteps = torch.randint(0, args.train_sampling_steps, (bsz,), device=latents.device, generator=torch_rng)
timesteps = idx_sampling(bsz, generator=torch_rng, device=latents.device)
timesteps = timesteps.long()
if args.not_sigma_loss:
# Create image_rotary_emb, style embedding & time ids
height, width = batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1]
# Get 3D rope or 2d rope
grid_height = height // 8 // unwrap_model(transformer3d).config.patch_size
grid_width = width // 8 // unwrap_model(transformer3d).config.patch_size
if unwrap_model(transformer3d).config.get("time_position_encoding_type", "2d_rope") == "3d_rope":
base_size_width = 720 // 8 // unwrap_model(transformer3d).config.patch_size
base_size_height = 480 // 8 // unwrap_model(transformer3d).config.patch_size
grid_crops_coords = get_resize_crop_region_for_grid(
(grid_height, grid_width), base_size_width, base_size_height
)
image_rotary_emb = _get_3d_rotary_pos_embed_cached(
unwrap_model(transformer3d).config.attention_head_dim,
grid_crops_coords, (grid_height, grid_width), latents.size()[2],
)
else:
base_size = 512 // 8 // unwrap_model(transformer3d).config.patch_size
grid_crops_coords = get_resize_crop_region_for_grid(
(grid_height, grid_width), base_size, base_size
)
image_rotary_emb = _get_2d_rotary_pos_embed_cached(
unwrap_model(transformer3d).config.attention_head_dim,
grid_crops_coords, (grid_height, grid_width)
)
# Get other hunyuan params
style = torch.tensor([0], device=latents.device)
target_size = (height, width)
add_time_ids = list((1024, 1024) + target_size + (0, 0))
add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype)
# To latents.device
prompt_embeds = prompt_embeds.to(device=latents.device)
prompt_attention_mask = prompt_attention_mask.to(device=latents.device)
if prompt_embeds_2 is not None:
prompt_embeds_2 = prompt_embeds_2.to(device=latents.device)
prompt_attention_mask_2 = prompt_attention_mask_2.to(device=latents.device)
add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=latents.device).repeat(
bsz, 1
)
style = style.to(device=latents.device).repeat(bsz)
# Add noise
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
# Predict the noise residual
noise_pred = transformer3d(
noisy_latents,
timesteps.to(noisy_latents.dtype),
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=prompt_embeds_2,
text_embedding_mask_t5=prompt_attention_mask_2,
image_meta_size=add_time_ids,
style=style,
image_rotary_emb=image_rotary_emb,
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
clip_encoder_hidden_states=clip_encoder_hidden_states if args.train_mode != "normal" else None,
clip_attention_mask=clip_attention_mask if args.train_mode != "normal" else None,
return_dict=False
)[0]
if noise_pred.size()[1] != vae.config.latent_channels:
noise_pred, _ = noise_pred.chunk(2, dim=1)
def custom_mse_loss(noise_pred, target, threshold=50):
noise_pred = noise_pred.float()
target = target.float()
diff = noise_pred - target
mse_loss = F.mse_loss(noise_pred, target, reduction='none')
mask = (diff.abs() <= threshold).float()
masked_loss = mse_loss * mask
final_loss = masked_loss.mean()
return final_loss
loss = custom_mse_loss(noise_pred.float(), target.float())
if args.motion_sub_loss and noise_pred.size()[2] > 2:
gt_sub_noise = noise_pred[:, :, 1:].float() - noise_pred[:, :, :-1].float()
pre_sub_noise = target[:, :, 1:].float() - target[:, :, :-1].float()
sub_loss = F.mse_loss(gt_sub_noise, pre_sub_noise, reduction="mean")
loss = loss * (1 - args.motion_sub_loss_ratio) + sub_loss * args.motion_sub_loss_ratio
else:
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
if unwrap_model(transformer3d).config.sample_size == 128:
bs, height, width = bsz, batch["pixel_values"].size()[-2], batch["pixel_values"].size()[-1]
resolution = torch.tensor([height, width]).repeat(bs, 1)
aspect_ratio = torch.tensor([float(height / width)]).repeat(bs, 1)
resolution = resolution.to(accelerator.device, dtype=weight_dtype)
aspect_ratio = aspect_ratio.to(accelerator.device, dtype=weight_dtype)
added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
loss_term = train_diffusion.training_losses(
transformer3d,
latents,
timesteps,
noise=noise,
model_kwargs=dict(
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
added_cond_kwargs=added_cond_kwargs,
inpaint_latents=inpaint_latents if args.train_mode != "normal" else None,
clip_encoder_hidden_states=clip_encoder_hidden_states if args.train_mode != "normal" else None,
clip_attention_mask=clip_attention_mask if args.train_mode != "normal" else None,
return_dict=False
)
)
loss = loss_term['loss'].mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
train_loss += avg_loss.item() / args.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
if not args.use_deepspeed:
trainable_params_grads = [p.grad for p in trainable_params if p.grad is not None]
trainable_params_total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2) for g in trainable_params_grads]), 2)
max_grad_norm = linear_decay(args.max_grad_norm * args.initial_grad_norm_ratio, args.max_grad_norm, args.abnormal_norm_clip_start, global_step)
if trainable_params_total_norm / max_grad_norm > 5 and global_step > args.abnormal_norm_clip_start:
actual_max_grad_norm = max_grad_norm / min((trainable_params_total_norm / max_grad_norm), 10)
else:
actual_max_grad_norm = max_grad_norm
else:
actual_max_grad_norm = args.max_grad_norm
if not args.use_deepspeed and args.report_model_info and accelerator.is_main_process:
if trainable_params_total_norm > 1 and global_step > args.abnormal_norm_clip_start:
for name, param in transformer3d.named_parameters():
if param.requires_grad:
writer.add_scalar(f'gradients/before_clip_norm/{name}', param.grad.norm(), global_step=global_step)
norm_sum = accelerator.clip_grad_norm_(trainable_params, actual_max_grad_norm)
if not args.use_deepspeed and args.report_model_info and accelerator.is_main_process:
writer.add_scalar(f'gradients/norm_sum', norm_sum, global_step=global_step)
writer.add_scalar(f'gradients/actual_max_grad_norm', actual_max_grad_norm, global_step=global_step)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % args.checkpointing_steps == 0:
if args.use_deepspeed or accelerator.is_main_process:
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
if args.checkpoints_total_limit is not None:
checkpoints = os.listdir(args.output_dir)
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
if len(checkpoints) >= args.checkpoints_total_limit:
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
removing_checkpoints = checkpoints[0:num_to_remove]
logger.info(
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
)
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
for removing_checkpoint in removing_checkpoints:
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
if not args.save_state:
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
save_model(safetensor_save_path, accelerator.unwrap_model(network))
logger.info(f"Saved safetensor to {safetensor_save_path}")
else:
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
accelerator.save_state(accelerator_save_path)
logger.info(f"Saved state to {accelerator_save_path}")
if accelerator.is_main_process:
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
log_validation(
vae,
text_encoder,
text_encoder_2,
tokenizer,
tokenizer_2,
transformer3d,
image_encoder,
image_processor,
network,
config,
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 accelerator.is_main_process:
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
log_validation(
vae,
text_encoder,
text_encoder_2,
tokenizer,
tokenizer_2,
transformer3d,
image_encoder,
image_processor,
network,
config,
args,
accelerator,
weight_dtype,
global_step,
)
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
save_model(safetensor_save_path, accelerator.unwrap_model(network))
if args.save_state:
accelerator.save_state(accelerator_save_path)
logger.info(f"Saved state to {accelerator_save_path}")
accelerator.end_training()
if __name__ == "__main__":
main()
Reference in New Issue View Git Blame Copy Permalink