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train_mvdiffusion_joint.py
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train_mvdiffusion_joint.py
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import argparse
import datetime
import logging
import inspect
import math
import os
from typing import Dict, Optional, Tuple, List
from omegaconf import OmegaConf
from PIL import Image
import cv2
import numpy as np
from dataclasses import dataclass
from packaging import version
import shutil
from collections import defaultdict
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import torchvision.transforms.functional as TF
from torchvision.transforms import InterpolationMode
from torchvision.utils import make_grid, save_image
import transformers
import accelerate
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, StableDiffusionPipeline
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 tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel
from mvdiffusion.data.objaverse_dataset import ObjaverseDataset as MVDiffusionDataset
from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline
from einops import rearrange
import time
import pdb
logger = get_logger(__name__, log_level="INFO")
@dataclass
class TrainingConfig:
pretrained_model_name_or_path: str
pretrained_unet_path: Optional[str]
revision: Optional[str]
train_dataset: Dict
validation_dataset: Dict
validation_train_dataset: Dict
output_dir: str
seed: Optional[int]
train_batch_size: int
validation_batch_size: int
validation_train_batch_size: int
max_train_steps: int
gradient_accumulation_steps: int
gradient_checkpointing: bool
learning_rate: float
scale_lr: bool
lr_scheduler: str
lr_warmup_steps: int
snr_gamma: Optional[float]
use_8bit_adam: bool
allow_tf32: bool
use_ema: bool
dataloader_num_workers: int
adam_beta1: float
adam_beta2: float
adam_weight_decay: float
adam_epsilon: float
max_grad_norm: Optional[float]
prediction_type: Optional[str]
logging_dir: str
vis_dir: str
mixed_precision: Optional[str]
report_to: Optional[str]
local_rank: int
checkpointing_steps: int
checkpoints_total_limit: Optional[int]
resume_from_checkpoint: Optional[str]
enable_xformers_memory_efficient_attention: bool
validation_steps: int
validation_sanity_check: bool
tracker_project_name: str
trainable_modules: Optional[list]
use_classifier_free_guidance: bool
condition_drop_rate: float
scale_input_latents: bool
pipe_kwargs: Dict
pipe_validation_kwargs: Dict
unet_from_pretrained_kwargs: Dict
validation_guidance_scales: List[float]
validation_grid_nrow: int
camera_embedding_lr_mult: float
num_views: int
camera_embedding_type: str
pred_type: str
drop_type: str
last_global_step: int
def log_validation(dataloader, vae, feature_extractor, image_encoder, unet, cfg: TrainingConfig, accelerator, weight_dtype, global_step, name, save_dir):
logger.info(f"Running {name} ... ")
pipeline = MVDiffusionImagePipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, vae=vae, unet=accelerator.unwrap_model(unet), safety_checker=None,
scheduler=DDIMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
pipeline.set_progress_bar_config(disable=True)
if cfg.enable_xformers_memory_efficient_attention:
pipeline.enable_xformers_memory_efficient_attention()
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=accelerator.device).manual_seed(cfg.seed)
images_cond, images_gt, images_pred = [], [], defaultdict(list)
for i, batch in enumerate(dataloader):
# (B, Nv, 3, H, W)
imgs_in, colors_out, normals_out = batch['imgs_in'], batch['imgs_out'], batch['normals_out']
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([imgs_in]*2, dim=0)
imgs_out = torch.cat([normals_out, colors_out], dim=0)
# (2B, Nv, Nce)
camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0)
task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0)
camera_task_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1)
# (B*Nv, 3, H, W)
imgs_in, imgs_out = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W"), rearrange(imgs_out, "B Nv C H W -> (B Nv) C H W")
# (B*Nv, Nce)
camera_task_embeddings = rearrange(camera_task_embeddings, "B Nv Nce -> (B Nv) Nce")
images_cond.append(imgs_in)
images_gt.append(imgs_out)
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, camera_task_embeddings, generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
shape = out.shape
out0, out1 = out[:shape[0]//2], out[shape[0]//2:]
out = []
for ii in range(shape[0]//2):
out.append(out0[ii])
out.append(out1[ii])
out = torch.stack(out, dim=0)
images_pred[f"{name}-sample_cfg{guidance_scale:.1f}"].append(out)
images_cond_all = torch.cat(images_cond, dim=0)
images_gt_all = torch.cat(images_gt, dim=0)
images_pred_all = {}
for k, v in images_pred.items():
images_pred_all[k] = torch.cat(v, dim=0)
nrow = cfg.validation_grid_nrow
ncol = images_cond_all.shape[0] // nrow
images_cond_grid = make_grid(images_cond_all, nrow=nrow, ncol=ncol, padding=0, value_range=(0, 1))
images_gt_grid = make_grid(images_gt_all, nrow=nrow, ncol=ncol, padding=0, value_range=(0, 1))
images_pred_grid = {}
for k, v in images_pred_all.items():
images_pred_grid[k] = make_grid(v, nrow=nrow, ncol=ncol, padding=0, value_range=(0, 1))
save_image(images_cond_grid, os.path.join(save_dir, f"{global_step}-{name}-cond.jpg"))
save_image(images_gt_grid, os.path.join(save_dir, f"{global_step}-{name}-gt.jpg"))
for k, v in images_pred_grid.items():
save_image(v, os.path.join(save_dir, f"{global_step}-{k}.jpg"))
torch.cuda.empty_cache()
def main(
cfg: TrainingConfig
):
# override local_rank with envvar
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != cfg.local_rank:
cfg.local_rank = env_local_rank
vis_dir = os.path.join(cfg.output_dir, cfg.vis_dir)
logging_dir = os.path.join(cfg.output_dir, cfg.logging_dir)
accelerator_project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=logging_dir)
accelerator = Accelerator(
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
mixed_precision=cfg.mixed_precision,
log_with=cfg.report_to,
project_config=accelerator_project_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if cfg.seed is not None:
set_seed(cfg.seed)
generator = torch.Generator(device=accelerator.device).manual_seed(cfg.seed)
# Handle the repository creation
if accelerator.is_main_process:
os.makedirs(cfg.output_dir, exist_ok=True)
os.makedirs(vis_dir, exist_ok=True)
OmegaConf.save(cfg, os.path.join(cfg.output_dir, 'config.yaml'))
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_encoder", revision=cfg.revision)
feature_extractor = CLIPImageProcessor.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="feature_extractor", revision=cfg.revision)
vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision)
if cfg.pretrained_unet_path is None:
unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
else:
print("load pre-trained unet from ", cfg.pretrained_unet_path)
unet = UNetMV2DConditionModel.from_pretrained(cfg.pretrained_unet_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
if cfg.use_ema:
ema_unet = EMAModel(unet.parameters(), model_cls=UNetMV2DConditionModel, model_config=unet.config)
def compute_snr(timesteps):
"""
Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
"""
alphas_cumprod = noise_scheduler.alphas_cumprod
sqrt_alphas_cumprod = alphas_cumprod**0.5
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
# Expand the tensors.
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
# Compute SNR.
snr = (alpha / sigma) ** 2
return snr
# Freeze vae and text_encoder
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
if cfg.trainable_modules is None:
unet.requires_grad_(True)
else:
unet.requires_grad_(False)
for name, module in unet.named_modules():
if name.endswith(tuple(cfg.trainable_modules)):
for params in module.parameters():
# print("trainable: ", params)
params.requires_grad = True
if cfg.enable_xformers_memory_efficient_attention:
if is_xformers_available():
import xformers
xformers_version = version.parse(xformers.__version__)
if xformers_version == version.parse("0.0.16"):
logger.warn(
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
)
unet.enable_xformers_memory_efficient_attention()
print("use xformers to speed up")
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 cfg.use_ema:
ema_unet.save_pretrained(os.path.join(output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(output_dir, "unet"))
# make sure to pop weight so that corresponding model is not saved again
weights.pop()
def load_model_hook(models, input_dir):
if cfg.use_ema:
load_model = EMAModel.from_pretrained(os.path.join(input_dir, "unet_ema"), UNetMV2DConditionModel)
ema_unet.load_state_dict(load_model.state_dict())
ema_unet.to(accelerator.device)
del load_model
for i in range(len(models)):
# pop models so that they are not loaded again
model = models.pop()
# load diffusers style into model
load_model = UNetMV2DConditionModel.from_pretrained(input_dir, subfolder="unet")
model.register_to_config(**load_model.config)
model.load_state_dict(load_model.state_dict())
del load_model
accelerator.register_save_state_pre_hook(save_model_hook)
accelerator.register_load_state_pre_hook(load_model_hook)
if cfg.gradient_checkpointing:
unet.enable_gradient_checkpointing()
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if cfg.allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if cfg.scale_lr:
cfg.learning_rate = (
cfg.learning_rate * cfg.gradient_accumulation_steps * cfg.train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if cfg.use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
params, params_class_embedding = [], []
for name, param in unet.named_parameters():
if 'class_embedding' in name:
params_class_embedding.append(param)
else:
params.append(param)
optimizer = optimizer_cls(
[
{"params": params, "lr": cfg.learning_rate},
{"params": params_class_embedding, "lr": cfg.learning_rate * cfg.camera_embedding_lr_mult}
],
betas=(cfg.adam_beta1, cfg.adam_beta2),
weight_decay=cfg.adam_weight_decay,
eps=cfg.adam_epsilon,
)
lr_scheduler = get_scheduler(
cfg.lr_scheduler,
optimizer=optimizer,
num_warmup_steps=cfg.lr_warmup_steps * accelerator.num_processes,
num_training_steps=cfg.max_train_steps * accelerator.num_processes,
)
# Get the training dataset
train_dataset = MVDiffusionDataset(
**cfg.train_dataset
)
validation_dataset = MVDiffusionDataset(
**cfg.validation_dataset
)
validation_train_dataset = MVDiffusionDataset(
**cfg.validation_train_dataset
)
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=cfg.train_batch_size, shuffle=True, num_workers=cfg.dataloader_num_workers,
)
validation_dataloader = torch.utils.data.DataLoader(
validation_dataset, batch_size=cfg.validation_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
validation_train_dataloader = torch.utils.data.DataLoader(
validation_train_dataset, batch_size=cfg.validation_train_batch_size, shuffle=False, num_workers=cfg.dataloader_num_workers
)
# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
if cfg.use_ema:
ema_unet.to(accelerator.device)
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) 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
cfg.mixed_precision = accelerator.mixed_precision
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
cfg.mixed_precision = accelerator.mixed_precision
# Move text_encode and vae to gpu and cast to weight_dtype
image_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
clip_image_mean = torch.as_tensor(feature_extractor.image_mean)[:,None,None].to(accelerator.device, dtype=torch.float32)
clip_image_std = torch.as_tensor(feature_extractor.image_std)[:,None,None].to(accelerator.device, dtype=torch.float32)
# 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) / cfg.gradient_accumulation_steps)
num_train_epochs = math.ceil(cfg.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(cfg))
tracker_config = {}
accelerator.init_trackers(cfg.tracker_project_name, tracker_config)
# Train!
total_batch_size = cfg.train_batch_size * accelerator.num_processes * cfg.gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {cfg.train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {cfg.gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {cfg.max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if cfg.resume_from_checkpoint:
if cfg.resume_from_checkpoint != "latest":
path = os.path.basename(cfg.resume_from_checkpoint)
else:
# Get the most recent checkpoint
if os.path.exists(os.path.join(cfg.output_dir, "checkpoint")):
path = "checkpoint"
else:
dirs = os.listdir(cfg.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 '{cfg.resume_from_checkpoint}' does not exist. Starting a new training run."
)
cfg.resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(cfg.output_dir, path))
# global_step = int(path.split("-")[1])
global_step = cfg.last_global_step
resume_global_step = global_step * cfg.gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * cfg.gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, cfg.max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, num_train_epochs):
unet.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if cfg.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % cfg.gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# (B, Nv, 3, H, W)
imgs_in, colors_out, normals_out = batch['imgs_in'], batch['imgs_out'], batch['normals_out']
bnm, Nv = imgs_in.shape[:2]
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([imgs_in]*2, dim=0)
imgs_out = torch.cat([normals_out, colors_out], dim=0)
# (2B, Nv, Nce)
camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0)
task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0)
camera_task_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1)
# (B*Nv, 3, H, W)
imgs_in, imgs_out = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W"), rearrange(imgs_out, "B Nv C H W -> (B Nv) C H W")
# (B*Nv, Nce)
camera_task_embeddings = rearrange(camera_task_embeddings, "B Nv Nce -> (B Nv) Nce")
# (B*Nv, Nce')
if cfg.camera_embedding_type == 'e_de_da_sincos':
camera_task_embeddings = torch.cat([
torch.sin(camera_task_embeddings),
torch.cos(camera_task_embeddings)
], dim=-1)
else:
raise NotImplementedError
imgs_in, imgs_out, camera_task_embeddings = imgs_in.to(weight_dtype), imgs_out.to(weight_dtype), camera_task_embeddings.to(weight_dtype)
# (B*Nv, 4, Hl, Wl)
# pdb.set_trace()
cond_vae_embeddings = vae.encode(imgs_in * 2.0 - 1.0).latent_dist.mode()
if cfg.scale_input_latents:
cond_vae_embeddings = cond_vae_embeddings * vae.config.scaling_factor
latents = vae.encode(imgs_out * 2.0 - 1.0).latent_dist.sample() * vae.config.scaling_factor
# DO NOT use this! Very slow!
# imgs_in_pil = [TF.to_pil_image(img) for img in imgs_in]
# imgs_in_proc = feature_extractor(images=imgs_in_pil, return_tensors='pt').pixel_values.to(dtype=latents.dtype, device=latents.device)
imgs_in_proc = TF.resize(imgs_in, (feature_extractor.crop_size['height'], feature_extractor.crop_size['width']), interpolation=InterpolationMode.BICUBIC)
# do the normalization in float32 to preserve precision
imgs_in_proc = ((imgs_in_proc.float() - clip_image_mean) / clip_image_std).to(weight_dtype)
# (B*Nv, 1, 768)
image_embeddings = image_encoder(imgs_in_proc).image_embeds.unsqueeze(1)
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# same noise for different views of the same object
timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz // cfg.num_views,), device=latents.device).repeat_interleave(cfg.num_views)
timesteps = timesteps.long()
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Conditioning dropout to support classifier-free guidance during inference. For more details
# check out the section 3.2.1 of the original paper https://arxiv.org/abs/2211.09800.
if cfg.use_classifier_free_guidance and cfg.condition_drop_rate > 0.:
# assert cfg.drop_type == 'drop_as_a_whole'
if cfg.drop_type == 'drop_as_a_whole':
# drop a group of normals and colors as a whole
random_p = torch.rand(bnm, device=latents.device, generator=generator)
# Sample masks for the conditioning images.
image_mask_dtype = cond_vae_embeddings.dtype
image_mask = 1 - (
(random_p >= cfg.condition_drop_rate).to(image_mask_dtype)
* (random_p < 3 * cfg.condition_drop_rate).to(image_mask_dtype)
)
image_mask = image_mask.reshape(bnm, 1, 1, 1, 1).repeat(1, Nv, 1, 1, 1)
image_mask = rearrange(image_mask, "B Nv C H W -> (B Nv) C H W")
image_mask = torch.cat([image_mask]*2, dim=0)
# Final image conditioning.
cond_vae_embeddings = image_mask * cond_vae_embeddings
# Sample masks for the conditioning images.
clip_mask_dtype = image_embeddings.dtype
clip_mask = 1 - (
(random_p < 2 * cfg.condition_drop_rate).to(clip_mask_dtype)
)
clip_mask = clip_mask.reshape(bnm, 1, 1, 1).repeat(1, Nv, 1, 1)
clip_mask = rearrange(clip_mask, "B Nv M C -> (B Nv) M C")
clip_mask = torch.cat([clip_mask]*2, dim=0)
# Final image conditioning.
image_embeddings = clip_mask * image_embeddings
elif cfg.drop_type == 'drop_independent':
# randomly drop all independently
random_p = torch.rand(bsz, device=latents.device, generator=generator)
# Sample masks for the conditioning images.
image_mask_dtype = cond_vae_embeddings.dtype
image_mask = 1 - (
(random_p >= cfg.condition_drop_rate).to(image_mask_dtype)
* (random_p < 3 * cfg.condition_drop_rate).to(image_mask_dtype)
)
image_mask = image_mask.reshape(bsz, 1, 1, 1)
# Final image conditioning.
cond_vae_embeddings = image_mask * cond_vae_embeddings
# Sample masks for the conditioning images.
clip_mask_dtype = image_embeddings.dtype
clip_mask = 1 - (
(random_p < 2 * cfg.condition_drop_rate).to(clip_mask_dtype)
)
clip_mask = clip_mask.reshape(bsz, 1, 1)
# Final image conditioning.
image_embeddings = clip_mask * image_embeddings
elif cfg.drop_type == 'drop_joint':
# randomly drop all independently
random_p = torch.rand(bsz//2, device=latents.device, generator=generator)
# Sample masks for the conditioning images.
image_mask_dtype = cond_vae_embeddings.dtype
image_mask = 1 - (
(random_p >= cfg.condition_drop_rate).to(image_mask_dtype)
* (random_p < 3 * cfg.condition_drop_rate).to(image_mask_dtype)
)
image_mask = torch.cat([image_mask]*2, dim=0)
image_mask = image_mask.reshape(bsz, 1, 1, 1)
# Final image conditioning.
cond_vae_embeddings = image_mask * cond_vae_embeddings
# Sample masks for the conditioning images.
clip_mask_dtype = image_embeddings.dtype
clip_mask = 1 - (
(random_p < 2 * cfg.condition_drop_rate).to(clip_mask_dtype)
)
clip_mask = torch.cat([clip_mask]*2, dim=0)
clip_mask = clip_mask.reshape(bsz, 1, 1)
# Final image conditioning.
image_embeddings = clip_mask * image_embeddings
# (B*Nv, 8, Hl, Wl)
latent_model_input = torch.cat([noisy_latents, cond_vae_embeddings], dim=1)
model_pred = unet(
latent_model_input,
timesteps,
encoder_hidden_states=image_embeddings,
class_labels=camera_task_embeddings
).sample
# Get the target for loss depending on the prediction type
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}")
if cfg.snr_gamma is None:
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
else:
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
# This is discussed in Section 4.2 of the same paper.
snr = compute_snr(timesteps)
mse_loss_weights = (
torch.stack([snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
)
# We first calculate the original loss. Then we mean over the non-batch dimensions and
# rebalance the sample-wise losses with their respective loss weights.
# Finally, we take the mean of the rebalanced loss.
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
loss = loss.mean()
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(cfg.train_batch_size)).mean()
train_loss += avg_loss.item() / cfg.gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients and cfg.max_grad_norm is not None:
accelerator.clip_grad_norm_(unet.parameters(), cfg.max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
if cfg.use_ema:
ema_unet.step(unet.parameters())
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % cfg.checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(cfg.output_dir, f"checkpoint")
accelerator.save_state(save_path)
try:
unet.module.save_pretrained(os.path.join(cfg.output_dir, f"unet-{global_step}/unet"))
except:
unet.save_pretrained(os.path.join(cfg.output_dir, f"unet-{global_step}/unet"))
logger.info(f"Saved state to {save_path}")
if global_step % cfg.validation_steps == 0 or (cfg.validation_sanity_check and global_step == 1):
if accelerator.is_main_process:
if cfg.use_ema:
# Store the UNet parameters temporarily and load the EMA parameters to perform inference.
ema_unet.store(unet.parameters())
ema_unet.copy_to(unet.parameters())
log_validation(
validation_dataloader,
vae,
feature_extractor,
image_encoder,
unet,
cfg,
accelerator,
weight_dtype,
global_step,
'validation',
vis_dir
)
log_validation(
validation_train_dataloader,
vae,
feature_extractor,
image_encoder,
unet,
cfg,
accelerator,
weight_dtype,
global_step,
'validation_train',
vis_dir
)
if cfg.use_ema:
# Switch back to the original UNet parameters.
ema_unet.restore(unet.parameters())
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step >= cfg.max_train_steps:
break
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
if cfg.use_ema:
ema_unet.copy_to(unet.parameters())
pipeline = MVDiffusionImagePipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, vae=vae, unet=unet, safety_checker=None,
scheduler=DDIMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
os.makedirs(os.path.join(cfg.output_dir, "pipeckpts"), exist_ok=True)
pipeline.save_pretrained(os.path.join(cfg.output_dir, "pipeckpts"))
accelerator.end_training()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, required=True)
args = parser.parse_args()
schema = OmegaConf.structured(TrainingConfig)
cfg = OmegaConf.load(args.config)
cfg = OmegaConf.merge(schema, cfg)
main(cfg)