import logging
import warnings
from typing import Callable, List, Optional, Union, Dict, Any
import PIL
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from packaging import version
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPFeatureExtractor, CLIPTokenizer, CLIPTextModel
from diffusers.utils.import_utils import is_accelerate_available
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models.embeddings import get_timestep_embedding
from diffusers.schedulers import KarrasDiffusionSchedulers, PNDMScheduler, DDIMScheduler, DDPMScheduler
from diffusers.utils import deprecate
from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from accelerate.utils import ProjectConfiguration, set_seed
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
import transformers
import diffusers
import accelerate
from accelerate import Accelerator
from torchvision.transforms import InterpolationMode
import argparse
from omegaconf import OmegaConf
from mvdiffusion.models_unclip.unet_mv2d_condition import UNetMV2DConditionModel
# from mvdiffusion.data.objaverse_dataset_unclip_xxdata import ObjaverseDataset as MVDiffusionDataset
from mvdiffusion.data.dreamdata import ObjaverseDataset as MVDiffusionDataset
from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
from accelerate.logging import get_logger
import os
import numpy as np
from PIL import Image
import math
from tqdm import tqdm
from einops import rearrange, repeat
from torchvision.transforms import InterpolationMode
from einops import rearrange, repeat
from diffusers.schedulers import PNDMScheduler
from collections import defaultdict
from torchvision.utils import make_grid, save_image
from mvdiffusion.pipelines.pipeline_mvdiffusion_unclip import StableUnCLIPImg2ImgPipeline
from dataclasses import dataclass
import json
import shutil
from mvdiffusion.models_unclip.face_networks import prepare_face_proj_model
logger = get_logger(__name__, log_level="INFO")
@dataclass
class TrainingConfig:
pretrained_model_name_or_path: str
pretrained_unet_path: Optional[str]
clip_path: str
revision: Optional[str]
data_common: Optional[dict]
train_dataset: Dict
validation_dataset: Dict
validation_train_dataset: Dict
output_dir: str
checkpoint_prefix: 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
step_rules: Optional[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
regress_elevation: bool
regress_focal_length: bool
elevation_loss_weight: float
focal_loss_weight: float
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
plot_pose_acc: bool
num_views: int
data_view_num: Optional[int]
pred_type: str
drop_type: str
with_smpl: Optional[bool]
@torch.no_grad()
def convert_image(
tensor,
fp,
format: Optional[str] = None,
**kwargs,
) -> None:
"""
Save a given Tensor into an image file.
Args:
tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
saves the tensor as a grid of images by calling ``make_grid``.
fp (string or file object): A filename or a file object
format(Optional): If omitted, the format to use is determined from the filename extension.
If a file object was used instead of a filename, this parameter should always be used.
**kwargs: Other arguments are documented in ``make_grid``.
"""
grid = make_grid(tensor, **kwargs)
# Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
im = Image.fromarray(ndarr)
im.save(fp, format=format)
def log_validation_joint(dataloader, vae, feature_extractor, image_encoder, image_normlizer, image_noising_scheduler, tokenizer, text_encoder,
unet, face_proj_model, cfg:TrainingConfig, accelerator, weight_dtype, global_step, name, save_dir):
pipeline = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=accelerator.unwrap_model(unet),
scheduler=DDIMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
pipeline.set_progress_bar_config(disable=True)
if cfg.seed is None:
generator = None
else:
generator = torch.Generator(device=unet.device).manual_seed(cfg.seed)
images_cond, pred_cat = [], defaultdict(list)
for i, batch in tqdm(enumerate(dataloader)):
images_cond.append(batch['imgs_in'][:, 0])
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(device=accelerator.device, dtype=weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
# if dino_encoder:
# dino_input = TF.resize(batch['imgs_in'][:, 0], (224, 224)).float().to(accelerator.device)
# dino_feature = dino_encoder(dino_input)
# dino_feature = repeat(dino_feature, "B N C -> (B V) N C", V=cfg.num_views*2)
# else:
# dino_feature = None
imgs_in = torch.cat([batch['imgs_in']]*2, dim=0)
num_views = imgs_in.shape[1]
imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")# (B*Nv, 3, H, W)
if cfg.with_smpl:
smpl_in = torch.cat([batch['smpl_imgs_in']]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W")
else:
smpl_in = None
normal_prompt_embeddings, clr_prompt_embeddings = batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']
prompt_embeddings = torch.cat([normal_prompt_embeddings, clr_prompt_embeddings], dim=0)
prompt_embeddings = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, None, prompt_embeds=prompt_embeddings,
dino_feature=face_embeds, smpl_in=smpl_in,
generator=generator, guidance_scale=guidance_scale, output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs
).images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
# print(normals_pred.shape, images_pred.shape)
pred_cat[f"cfg{guidance_scale:.1f}"].append(torch.cat([normals_pred, images_pred], dim=-1)) # b, 3, h, w
# from icecream import ic
images_cond_all = torch.cat(images_cond, dim=0)
images_pred_all = {}
for k, v in pred_cat.items():
images_pred_all[k] = torch.cat(v, dim=0).cpu()
# print(images_pred_all[k].shape)
# import pdb;pdb.set_trace()
nrow = cfg.validation_grid_nrow
# ncol = images_cond_all.shape[0] // nrow
images_cond_grid = make_grid(images_cond_all, nrow=1, padding=0, value_range=(0, 1))
edge_pad = torch.zeros(list(images_cond_grid.shape[:2]) + [3], dtype=torch.float32)
images_vis = torch.cat([images_cond_grid, edge_pad], -1)
for k, v in images_pred_all.items():
images_vis = torch.cat([images_vis, make_grid(v, nrow=nrow, padding=0, value_range=(0, 1)), edge_pad], -1)
save_image(images_vis, os.path.join(save_dir, f"{name}-{global_step}.jpg"))
torch.cuda.empty_cache()
def log_validation(dataloader, vae, feature_extractor, image_encoder, image_normlizer, image_noising_scheduler, tokenizer, text_encoder,
unet, face_proj_model, cfg:TrainingConfig, accelerator, weight_dtype, global_step, name, save_dir):
logger.info(f"Running {name} ... ")
pipeline = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=accelerator.unwrap_model(unet),
scheduler=DDIMScheduler.from_pretrained_linear(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']
images_cond.append(imgs_in[:, 0, :, :, :])
# 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)
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")
images_gt.append(imgs_out)
if cfg.with_smpl:
smpl_in = torch.cat([batch['smpl_imgs_in']]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W")
else:
smpl_in = None
prompt_embeddings = torch.cat([batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']], dim=0)
# (B*Nv, N, C)
prompt_embeds = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
prompt_embeds = prompt_embeds.to(weight_dtype)
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(device=accelerator.device, dtype=weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
with torch.autocast("cuda"):
# B*Nv images
for guidance_scale in cfg.validation_guidance_scales:
out = pipeline(
imgs_in, None, prompt_embeds=prompt_embeds, smpl_in=smpl_in, dino_feature=face_embeds, 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).cpu()
nrow = cfg.validation_grid_nrow * 2
images_cond_grid = make_grid(images_cond_all, nrow=1, padding=0, value_range=(0, 1))
images_gt_grid = make_grid(images_gt_all, nrow=nrow, padding=0, value_range=(0, 1))
edge_pad = torch.zeros(list(images_cond_grid.shape[:2]) + [3], dtype=torch.float32)
images_vis = torch.cat([images_cond_grid.cpu(), edge_pad], -1)
for k, v in images_pred_all.items():
images_vis = torch.cat([images_vis, make_grid(v, nrow=nrow, padding=0, value_range=(0, 1)), edge_pad], -1)
# images_pred_grid = {}
# for k, v in images_pred_all.items():
# images_pred_grid[k] = make_grid(v, nrow=nrow, padding=0, value_range=(0, 1))
save_image(images_vis, 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"))
torch.cuda.empty_cache()
def noise_image_embeddings(
image_embeds: torch.Tensor,
noise_level: int,
noise: Optional[torch.FloatTensor] = None,
generator: Optional[torch.Generator] = None,
image_normalizer: Optional[StableUnCLIPImageNormalizer] = None,
image_noising_scheduler: Optional[DDPMScheduler] = None,
):
"""
Add noise to the image embeddings. The amount of noise is controlled by a `noise_level` input. A higher
`noise_level` increases the variance in the final un-noised images.
The noise is applied in two ways
1. A noise schedule is applied directly to the embeddings
2. A vector of sinusoidal time embeddings are appended to the output.
In both cases, the amount of noise is controlled by the same `noise_level`.
The embeddings are normalized before the noise is applied and un-normalized after the noise is applied.
"""
if noise is None:
noise = randn_tensor(
image_embeds.shape, generator=generator, device=image_embeds.device, dtype=image_embeds.dtype
)
noise_level = torch.tensor([noise_level] * image_embeds.shape[0], device=image_embeds.device)
image_embeds = image_normalizer.scale(image_embeds)
image_embeds = image_noising_scheduler.add_noise(image_embeds, timesteps=noise_level, noise=noise)
image_embeds = image_normalizer.unscale(image_embeds)
noise_level = get_timestep_embedding(
timesteps=noise_level, embedding_dim=image_embeds.shape[-1], flip_sin_to_cos=True, downscale_freq_shift=0
)
# `get_timestep_embeddings` does not contain any weights and will always return f32 tensors,
# but we might actually be running in fp16. so we need to cast here.
# there might be better ways to encapsulate this.
noise_level = noise_level.to(image_embeds.dtype)
image_embeds = torch.cat((image_embeds, noise_level), 1)
return image_embeds
def main(cfg: TrainingConfig):
# -------------------------------------------prepare custom log and accelaeator --------------------------------
# override local_rank with envvar
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank not in [-1, cfg.local_rank]:
cfg.local_rank = env_local_rank
logging_dir = os.path.join(cfg.output_dir, cfg.logging_dir)
model_dir = os.path.join(cfg.checkpoint_prefix, cfg.output_dir)
vis_dir = os.path.join(cfg.output_dir, cfg.vis_dir)
accelerator_project_config = ProjectConfiguration(project_dir=cfg.output_dir, logging_dir=logging_dir)
# print(os.getenv("SLURM_PROCID"), os.getenv("SLURM_LOCALID"), os.getenv("SLURM_NODEID"), os.getenv('GLOBAL_RANK'), os.getenv('LOCAL_RANK'), os.getenv('RNAK'), os.getenv('MASTER_ADDR'))
# exit()
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)
# Handle the repository creation
if accelerator.is_main_process:
os.makedirs(model_dir, exist_ok=True)
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 models --------------------------------
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)
image_noising_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_noising_scheduler")
image_normlizer = StableUnCLIPImageNormalizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_normalizer")
tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="tokenizer", revision=cfg.revision)
text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder='text_encoder', revision=cfg.revision)
# note: official code use PNDMScheduler
noise_scheduler = DDPMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler")
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:
logger.info(f'laod pretrained model from {cfg.pretrained_unet_path}')
unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_unet_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
# unet = UNet2DConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision)
if cfg.unet_from_pretrained_kwargs.use_dino:
from models.dinov2_wrapper import Dinov2Wrapper
dino_encoder = Dinov2Wrapper(model_name='dinov2_vitb14', freeze=True)
else:
dino_encoder = None
# TODO: extract face projection model weights
if cfg.unet_from_pretrained_kwargs.use_face_adapter:
face_proj_model = prepare_face_proj_model('models/image_proj_model.pth', cross_attention_dim=1024, pretrain=False)
else:
face_proj_model = None
if cfg.use_ema:
ema_unet = EMAModel(unet.parameters(), model_cls=UNetMV2DConditionModel, model_config=unet.config)
# ema_unet = EMAModel(unet.parameters(), model_cls=UNet2DConditionModel, 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, image_encoder, text_encoder
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
image_normlizer.requires_grad_(False)
text_encoder.requires_grad_(False)
if face_proj_model is not None: face_proj_model.requires_grad_(True)
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():
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(cfg.checkpoint_prefix, output_dir, "unet_ema"))
for i, model in enumerate(models):
model.save_pretrained(os.path.join(cfg.checkpoint_prefix, 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(cfg.checkpoint_prefix, 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(os.path.join(cfg.checkpoint_prefix, 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
# -------------------------------------- optimizer and lr --------------------------------
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, params_rowwise_layers = [], [], []
for name, param in unet.named_parameters():
if ('class_embedding' in name) or ('camera_embedding' in name):
params_class_embedding.append(param)
elif ('attn_mv' in name) or ('norm_mv' in name):
# print('Find mv attn block')
params_rowwise_layers.append(param)
else:
params.append(param)
opti_params = [{"params": params, "lr": cfg.learning_rate}]
if len(params_class_embedding) > 0:
opti_params.append({"params": params_class_embedding, "lr": cfg.learning_rate * cfg.camera_embedding_lr_mult})
if len(params_rowwise_layers) > 0:
opti_params.append({"params": params_rowwise_layers, "lr": cfg.learning_rate * cfg.camera_embedding_lr_mult})
optimizer = optimizer_cls(
opti_params,
betas=(cfg.adam_beta1, cfg.adam_beta2),
weight_decay=cfg.adam_weight_decay,
eps=cfg.adam_epsilon,
)
lr_scheduler = get_scheduler(
cfg.lr_scheduler,
step_rules=cfg.step_rules,
optimizer=optimizer,
num_warmup_steps=cfg.lr_warmup_steps * accelerator.num_processes,
num_training_steps=cfg.max_train_steps * accelerator.num_processes,
)
# -------------------------------------- load dataset --------------------------------
# Get the training dataset
train_dataset = MVDiffusionDataset(
**cfg.train_dataset
)
if cfg.with_smpl:
from mvdiffusion.data.testdata_with_smpl import SingleImageDataset
else:
from mvdiffusion.data.single_image_dataset import SingleImageDataset
validation_dataset = SingleImageDataset(
**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)
# -------------------------------------- cast dtype and 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)
image_normlizer.to(accelerator.device, weight_dtype)
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
if face_proj_model: face_proj_model.to(accelerator.device, dtype=weight_dtype)
if dino_encoder: dino_encoder.to(accelerator.device)
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(
project_name= cfg.tracker_project_name,
config= tracker_config,
init_kwargs={"wandb":
{"entity": "lpstarry",
"notes": cfg.output_dir.split('/')[-1],
# "tags": [cfg.output_dir.split('/')[-1]],
}},)
# -------------------------------------- load pipeline --------------------------------
# pipe = StableUnCLIPImg2ImgPipeline(feature_extractor=feature_extractor,
# image_encoder=image_encoder,
# image_normalizer=image_normlizer,
# image_noising_scheduler= image_noising_scheduler,
# tokenizer=tokenizer,
# text_encoder=text_encoder,
# unet=unet,
# scheduler=noise_scheduler,
# vae=vae).to('cuda')
# -------------------------------------- train --------------------------------
total_batch_size = cfg.train_batch_size * accelerator.num_processes * cfg.gradient_accumulation_steps
generator = torch.Generator(device=accelerator.device).manual_seed(cfg.seed)
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(model_dir, "checkpoint")):
path = "checkpoint"
else:
dirs = os.listdir(model_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
initial_global_step = 0
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(model_dir, path))
global_step = int(path.split("-")[1])
initial_global_step = global_step
first_epoch = global_step // num_update_steps_per_epoch
if False:
# log_validation_joint(
# validation_dataloader,
# vae,
# feature_extractor,
# image_encoder,
# image_normlizer,
# image_noising_scheduler,
# tokenizer,
# text_encoder,
# unet,
# dino_encoder,
# cfg,
# accelerator,
# weight_dtype,
# global_step,
# 'validation',
# vis_dir
# )
log_validation(
validation_train_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
cfg,
accelerator,
weight_dtype,
global_step,
'validation-train',
vis_dir
)
exit()
progress_bar = tqdm(
range(0, cfg.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,
)
new_layer_norm = {}
# Main training loop
for epoch in range(first_epoch, num_train_epochs):
unet.train()
train_mse_loss, train_ele_loss, train_focal_loss = 0.0, 0.0, 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']
ids = batch['id']
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)
# (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")
imgs_in, imgs_out = imgs_in.to(weight_dtype), imgs_out.to(weight_dtype)
if cfg.with_smpl:
smpl_in = batch['smpl_imgs_in']
smpl_in = torch.cat([smpl_in]*2, dim=0)
smpl_in = rearrange(smpl_in, "B Nv C H W -> (B Nv) C H W").to(weight_dtype)
else:
smpl_in = None
prompt_embeddings = torch.cat([batch['normal_prompt_embeddings'], batch['color_prompt_embeddings']], dim=0)
# (B*Nv, N, C)
prompt_embeds = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
prompt_embeds = prompt_embeds.to(weight_dtype) # BV, L, C
# ------------------------------------project face embed --------------------------------
if face_proj_model is not None:
face_embeds = batch['face_embed']
face_embeds = torch.cat([face_embeds]*2, dim=0)
face_embeds = rearrange(face_embeds, "B Nv L C -> (B Nv) L C")
face_embeds = face_embeds.to(weight_dtype)
face_embeds = face_proj_model(face_embeds)
else:
face_embeds = None
# ------------------------------------Encoder input image --------------------------------
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, 1024)
image_embeddings = image_encoder(imgs_in_proc).image_embeds
noise_level = torch.tensor([0], device=accelerator.device)
# (B*Nv, 2048)
image_embeddings = noise_image_embeddings(image_embeddings, noise_level, generator=generator, image_normalizer=image_normlizer,
image_noising_scheduler= image_noising_scheduler).to(weight_dtype)
#--------------------------------------vae input and output latents ---------------------------------------
cond_vae_embeddings = vae.encode(imgs_in * 2.0 - 1.0).latent_dist.mode() #
if cfg.scale_input_latents:
cond_vae_embeddings *= vae.config.scaling_factor
if cfg.with_smpl:
cond_smpl_embeddings = vae.encode(smpl_in * 2.0 - 1.0).latent_dist.mode()
if cfg.scale_input_latents:
cond_smpl_embeddings *= vae.config.scaling_factor
cond_vae_embeddings = torch.cat([cond_vae_embeddings, cond_smpl_embeddings], dim=1)
# sample outputs latent
latents = vae.encode(imgs_out * 2.0 - 1.0).latent_dist.sample() * vae.config.scaling_factor
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)
timesteps = repeat(timesteps, "b -> (b v)", v=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.:
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).repeat(1, Nv, 1)
clip_mask = rearrange(clip_mask, "B Nv C -> (B Nv) 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':
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
# (B*Nv, 8, Hl, Wl)
latent_model_input = torch.cat([noisy_latents, cond_vae_embeddings], dim=1)
model_out = unet(
latent_model_input,
timesteps,
encoder_hidden_states=prompt_embeds,
class_labels=image_embeddings,
dino_feature=face_embeds,
vis_max_min=False
)
if cfg.regress_elevation or cfg.regress_focal_length:
model_pred = model_out[0].sample
pose_pred = model_out[1]
else:
model_pred = model_out[0].sample
pose_pred = None
# 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)
# target = noise_scheduler._get_prev_sample(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
if cfg.snr_gamma is None:
loss_mse = F.mse_loss(model_pred.float(), target.float(), reduction="mean").to(weight_dtype)
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_mse = loss.mean().to(weight_dtype)
# Gather the losses across all processes for logging (if we use distributed training).
avg_mse_loss = accelerator.gather(loss_mse.repeat(cfg.train_batch_size)).mean()
train_mse_loss += avg_mse_loss.item() / cfg.gradient_accumulation_steps
if cfg.regress_elevation:
loss_ele = F.mse_loss(pose_pred[:, 0:1], batch['elevations_cond'].to(accelerator.device).float(), reduction="mean").to(weight_dtype)
avg_ele_loss = accelerator.gather(loss_ele.repeat(cfg.train_batch_size)).mean()
train_ele_loss += avg_ele_loss.item() / cfg.gradient_accumulation_steps
if cfg.plot_pose_acc:
ele_acc = torch.sum(torch.abs(pose_pred[:, 0:1] - torch.cat([batch['elevations_cond']]*2)) < 0.01) / pose_pred.shape[0]
else:
loss_ele = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
train_ele_loss += torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.plot_pose_acc:
ele_acc = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.regress_focal_length:
loss_focal = F.mse_loss(pose_pred[:, 1:], batch['focal_cond'].to(accelerator.device).float(), reduction="mean").to(weight_dtype)
avg_focal_loss = accelerator.gather(loss_focal.repeat(cfg.train_batch_size)).mean()
train_focal_loss += avg_focal_loss.item() / cfg.gradient_accumulation_steps
if cfg.plot_pose_acc:
focal_acc = torch.sum(torch.abs(pose_pred[:, 1:] - torch.cat([batch['focal_cond']]*2)) < 0.01) / pose_pred.shape[0]
else:
loss_focal = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
train_focal_loss += torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
if cfg.plot_pose_acc:
focal_acc = torch.tensor(0.0, device=accelerator.device, dtype=weight_dtype)
# Backpropagate
loss = loss_mse + cfg.elevation_loss_weight * loss_ele + cfg.focal_loss_weight * loss_focal
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)
progress_bar.update(1)
global_step += 1
# accelerator.log({"train_loss": train_loss}, step=global_step)
accelerator.log({"train_mse_loss": train_mse_loss}, step=global_step)
accelerator.log({"train_ele_loss": train_ele_loss}, step=global_step)
if cfg.plot_pose_acc:
accelerator.log({"ele_acc": ele_acc}, step=global_step)
accelerator.log({"focal_acc": focal_acc}, step=global_step)
accelerator.log({"train_focal_loss": train_focal_loss}, step=global_step)
train_ele_loss, train_mse_loss, train_focal_loss = 0.0, 0.0, 0.0
if global_step % cfg.checkpointing_steps == 0:
if accelerator.is_main_process:
if cfg.checkpoints_total_limit is not None:
checkpoints = os.listdir(model_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) >= cfg.checkpoints_total_limit:
num_to_remove = len(checkpoints) - cfg.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(model_dir, removing_checkpoint)
shutil.rmtree(removing_checkpoint)
save_path = os.path.join(model_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
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())
torch.cuda.empty_cache()
log_validation_joint(
validation_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
face_proj_model,
cfg,
accelerator,
weight_dtype,
global_step,
'validation',
vis_dir
)
log_validation(
validation_train_dataloader,
vae,
feature_extractor,
image_encoder,
image_normlizer,
image_noising_scheduler,
tokenizer,
text_encoder,
unet,
face_proj_model,
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 = StableUnCLIPImg2ImgPipeline(
image_encoder=image_encoder, feature_extractor=feature_extractor, image_normalizer=image_normlizer,
image_noising_scheduler=image_noising_scheduler, tokenizer=tokenizer, text_encoder=text_encoder,
vae=vae, unet=unet,
scheduler=DDIMScheduler.from_pretrained_linear(cfg.pretrained_model_name_or_path, subfolder="scheduler"),
**cfg.pipe_kwargs
)
os.makedirs(os.path.join(model_dir, "ckpts"), exist_ok=True)
pipeline.save_pretrained(os.path.join(model_dir, "ckpts"))
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)
# device = 'cuda'
# ## -------------------------------------- load models --------------------------------
# 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)
# image_noising_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_noising_scheduler")
# image_normlizer = StableUnCLIPImageNormalizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_normalizer")
# tokenizer = CLIPTokenizer.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="tokenizer", revision=cfg.revision)
# text_encoder = CLIPTextModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder='text_encoder', revision=cfg.revision)
# noise_scheduler = PNDMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler")
# vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision)
# unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs)
# # unet = UNetMV2DConditionModel.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="unet", revision=cfg.revision,
# # **cfg.unet_from_pretrained_kwargs
# # )
# 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"):
# print(
# "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.")
# # from diffusers import StableUnCLIPImg2ImgPipeline
# # -------------------------------------- load pipeline --------------------------------
# pipe = StableUnCLIPImg2ImgPipeline(feature_extractor=feature_extractor,
# image_encoder=image_encoder,
# image_normalizer=image_normlizer,
# image_noising_scheduler= image_noising_scheduler,
# tokenizer=tokenizer,
# text_encoder=text_encoder,
# unet=unet,
# scheduler=noise_scheduler,
# vae=vae).to('cuda')
# # -------------------------------------- input --------------------------------
# # image = Image.open('test/woman.jpg')
# # w, h = image.size
# # image = np.asarray(image)[:w, :w, :]
# # image_in = Image.fromarray(image).resize((768, 768))
# im_path = '/mnt/pfs/users/longxiaoxiao/data/test_images/syncdreamer_testset/box.png'
# rgba = np.array(Image.open(im_path)) / 255.0
# rgb = rgba[:,:,:3]
# alpha = rgba[:,:,3:4]
# bg_color = np.array([1., 1., 1.])
# image_in = rgb * alpha + (1 - alpha) * bg_color[None,None,:]
# image_in = Image.fromarray((image_in * 255).astype(np.uint8)).resize((768, 768))
# res = pipe(image_in, 'a rendering image of 3D models, left view, normal map.').images[0]
# res.save("unclip.png")