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Zero
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import argparse
import torch
from models.bsq_vae.flux_vqgan import AutoEncoder
def load_cnn(model, state_dict, prefix, expand=False, use_linear=False):
delete_keys = []
loaded_keys = []
for key in state_dict:
if key.startswith(prefix):
_key = key[len(prefix):]
if _key in model.state_dict():
# load nn.Conv2d or nn.Linear to nn.Linear
if use_linear and (".q.weight" in key or ".k.weight" in key or ".v.weight" in key or ".proj_out.weight" in key):
load_weights = state_dict[key].squeeze()
elif _key.endswith(".conv.weight") and expand:
if model.state_dict()[_key].shape == state_dict[key].shape:
# 2D cnn to 2D cnn
load_weights = state_dict[key]
else:
# 2D cnn to 3D cnn
_expand_dim = model.state_dict()[_key].shape[2]
load_weights = state_dict[key].unsqueeze(2).repeat(1, 1, _expand_dim, 1, 1)
else:
load_weights = state_dict[key]
model.state_dict()[_key].copy_(load_weights)
delete_keys.append(key)
loaded_keys.append(prefix+_key)
# load nn.Conv2d to Conv class
conv_list = ["conv"] if use_linear else ["conv", ".q.", ".k.", ".v.", ".proj_out.", ".nin_shortcut."]
if any(k in _key for k in conv_list):
if _key.endswith(".weight"):
conv_key = _key.replace(".weight", ".conv.weight")
if conv_key and conv_key in model.state_dict():
if model.state_dict()[conv_key].shape == state_dict[key].shape:
# 2D cnn to 2D cnn
load_weights = state_dict[key]
else:
# 2D cnn to 3D cnn
_expand_dim = model.state_dict()[conv_key].shape[2]
load_weights = state_dict[key].unsqueeze(2).repeat(1, 1, _expand_dim, 1, 1)
model.state_dict()[conv_key].copy_(load_weights)
delete_keys.append(key)
loaded_keys.append(prefix+conv_key)
if _key.endswith(".bias"):
conv_key = _key.replace(".bias", ".conv.bias")
if conv_key and conv_key in model.state_dict():
model.state_dict()[conv_key].copy_(state_dict[key])
delete_keys.append(key)
loaded_keys.append(prefix+conv_key)
# load nn.GroupNorm to Normalize class
if "norm" in _key:
if _key.endswith(".weight"):
norm_key = _key.replace(".weight", ".norm.weight")
if norm_key and norm_key in model.state_dict():
model.state_dict()[norm_key].copy_(state_dict[key])
delete_keys.append(key)
loaded_keys.append(prefix+norm_key)
if _key.endswith(".bias"):
norm_key = _key.replace(".bias", ".norm.bias")
if norm_key and norm_key in model.state_dict():
model.state_dict()[norm_key].copy_(state_dict[key])
delete_keys.append(key)
loaded_keys.append(prefix+norm_key)
for key in delete_keys:
del state_dict[key]
return model, state_dict, loaded_keys
def vae_model(vqgan_ckpt, schedule_mode, codebook_dim, codebook_size, test_mode=True, patch_size=16, encoder_ch_mult=[1, 2, 4, 4, 4], decoder_ch_mult=[1, 2, 4, 4, 4],):
args=argparse.Namespace(
vqgan_ckpt=vqgan_ckpt,
sd_ckpt=None,
inference_type='image',
save='./imagenet_val_bsq',
save_prediction=True,
image_recon4video=False,
junke_old=False,
device='cuda',
max_steps=1000000.0,
log_every=1,
visu_every=1000,
ckpt_every=1000,
default_root_dir='',
compile='no',
ema='no',
lr=0.0001,
beta1=0.9,
beta2=0.95,
warmup_steps=0,
optim_type='Adam',
disc_optim_type=None,
lr_min=0.0,
warmup_lr_init=0.0,
max_grad_norm=1.0,
max_grad_norm_disc=1.0,
disable_sch=False,
patch_size=patch_size,
temporal_patch_size=4,
embedding_dim=256,
codebook_dim=codebook_dim,
num_quantizers=8,
quantizer_type='MultiScaleBSQ',
use_vae=False,
use_freq_enc=False,
use_freq_dec=False,
preserve_norm=False,
ln_before_quant=False,
ln_init_by_sqrt=False,
use_pxsf=False,
new_quant=True,
use_decay_factor=False,
mask_out=False,
use_stochastic_depth=False,
drop_rate=0.0,
schedule_mode=schedule_mode,
lr_drop=None,
lr_drop_rate=0.1,
keep_first_quant=False,
keep_last_quant=False,
remove_residual_detach=False,
use_out_phi=False,
use_out_phi_res=False,
use_lecam_reg=False,
lecam_weight=0.05,
perceptual_model='vgg16',
base_ch_disc=64,
random_flip=False,
flip_prob=0.5,
flip_mode='stochastic',
max_flip_lvl=1,
not_load_optimizer=False,
use_lecam_reg_zero=False,
freeze_encoder=False,
rm_downsample=False,
random_flip_1lvl=False,
flip_lvl_idx=0,
drop_when_test=False,
drop_lvl_idx=0,
drop_lvl_num=1,
disc_version='v1',
magvit_disc=False,
sigmoid_in_disc=False,
activation_in_disc='leaky_relu',
apply_blur=False,
apply_noise=False,
dis_warmup_steps=0,
dis_lr_multiplier=1.0,
dis_minlr_multiplier=False,
disc_channels=64,
disc_layers=3,
discriminator_iter_start=0,
disc_pretrain_iter=0,
disc_optim_steps=1,
disc_warmup=0,
disc_pool='no',
disc_pool_size=1000,
advanced_disc=False,
recon_loss_type='l1',
video_perceptual_weight=0.0,
image_gan_weight=1.0,
video_gan_weight=1.0,
image_disc_weight=0.0,
video_disc_weight=0.0,
l1_weight=4.0,
gan_feat_weight=0.0,
perceptual_weight=0.0,
kl_weight=0.0,
lfq_weight=0.0,
entropy_loss_weight=0.1,
commitment_loss_weight=0.25,
diversity_gamma=1,
norm_type='group',
disc_loss_type='hinge',
use_checkpoint=False,
precision='fp32',
encoder_dtype='fp32',
upcast_attention='',
upcast_tf32=False,
tokenizer='flux',
pretrained=None,
pretrained_mode='full',
inflation_pe=False,
init_vgen='no',
no_init_idis=False,
init_idis='keep',
init_vdis='no',
enable_nan_detector=False,
turn_on_profiler=False,
profiler_scheduler_wait_steps=10,
debug=True,
video_logger=False,
bytenas='',
username='',
seed=1234,
vq_to_vae=False,
load_not_strict=False,
zero=0,
bucket_cap_mb=40,
manual_gc_interval=1000,
data_path=[''],
data_type=[''],
dataset_list=['imagenet'],
fps=-1,
dataaug='resizecrop',
multi_resolution=False,
random_bucket_ratio=0.0,
sequence_length=16,
resolution=[256, 256],
batch_size=[1],
num_workers=0,
image_channels=3,
codebook_size=codebook_size,
codebook_l2_norm=True,
codebook_show_usage=True,
commit_loss_beta=0.25,
entropy_loss_ratio=0.0,
base_ch=128,
num_res_blocks=2,
encoder_ch_mult=encoder_ch_mult,
decoder_ch_mult=decoder_ch_mult,
dropout_p=0.0,
cnn_type='2d',
cnn_version='v1',
conv_in_out_2d='no',
conv_inner_2d='no',
res_conv_2d='no',
cnn_attention='no',
cnn_norm_axis='spatial',
flux_weight=0,
cycle_weight=0,
cycle_feat_weight=0,
cycle_gan_weight=0,
cycle_loop=0,
z_drop=0.0)
vae = AutoEncoder(args)
use_vae = vae.use_vae
if not use_vae:
num_codes = args.codebook_size
if isinstance(vqgan_ckpt, str):
state_dict = torch.load(args.vqgan_ckpt, map_location=torch.device("cpu"), weights_only=True)
else:
state_dict = args.vqgan_ckpt
if state_dict:
if args.ema == "yes":
vae, new_state_dict, loaded_keys = load_cnn(vae, state_dict["ema"], prefix="", expand=False)
else:
vae, new_state_dict, loaded_keys = load_cnn(vae, state_dict["vae"], prefix="", expand=False)
if test_mode:
vae.eval()
[p.requires_grad_(False) for p in vae.parameters()]
return vae |