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Adapted from CompVis/latent-diffusion
https://github.com/CompVis/stable-diffusion
"""
import types
from typing import Tuple, Callable
from functools import partial
from omegaconf import OmegaConf, DictConfig
import torch
import lightning.pytorch as pl
from einops import rearrange
from diffusers import AutoencoderKL as DiffuserImageVAE
from torchmetrics.image import FrechetInceptionDistance
from utils.logging_utils import log_video
from utils.logging_utils import get_validation_metrics_for_videos
from utils.ckpt_utils import (
is_wandb_run_path,
is_hf_path,
wandb_to_local_path,
download_pretrained as hf_to_local_path,
)
from ..common.distribution import DiagonalGaussianDistribution
from ..common.base_vae import VAE
from ..common.losses import LPIPSWithDiscriminator, warmup
from .model import Encoder, Decoder
class ImageVAETrainer(pl.LightningModule):
def __init__(
self,
cfg: DictConfig,
):
super().__init__()
self.cfg = cfg
self.learning_rate = cfg.lr
self.automatic_optimization = False
ddconfig, lossconfig = cfg.ddconfig, cfg.lossconfig
self.embed_dim = cfg.embed_dim
self.warmup_steps = cfg.warmup_steps
self.gradient_clip_val = cfg.gradient_clip_val
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.loss = LPIPSWithDiscriminator(**lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(
2 * ddconfig["z_channels"], 2 * self.embed_dim, 1
)
self.post_quant_conv = torch.nn.Conv2d(
self.embed_dim, ddconfig["z_channels"], 1
)
if cfg.ckpt_path is not None:
self.init_from_ckpt(cfg.ckpt_path)
self.fid_model = None
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
print(f"Restored from {path}")
def on_save_checkpoint(self, checkpoint):
"""
save cfgs together to enable easily loading the pretrained model
"""
checkpoint["cfg"] = self.cfg
return checkpoint
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
def on_after_batch_transfer(
self, batch: tuple, dataloader_idx: int = 0
) -> torch.Tensor:
x = batch["videos"]
x = 2.0 * x - 1.0 # normalize to [-1, 1]
return x
def training_step(self, batch, batch_idx):
# pylint: disable=unpacking-non-sequence
opt_ae, opt_disc = self.optimizers()
batch = rearrange(batch, "b t c h w -> (b t) c h w")
reconstructions, posterior = self(batch)
log_loss = partial(
self.log,
prog_bar=True,
logger=True,
on_step=True,
on_epoch=True,
)
log_loss_dict = partial(
self.log_dict,
prog_bar=False,
logger=True,
on_step=True,
on_epoch=False,
)
# Warm-up: at the beginning of training / after GAN loss starts being used
# compute lr_scale
should_warmup, lr_scale = False, 1.0
if self.global_step < self.warmup_steps:
should_warmup = True
lr_scale = float(self.global_step + 1) / self.warmup_steps
elif (
self.global_step >= self.cfg.lossconfig.disc_start - 1
and self.global_step < self.cfg.lossconfig.disc_start + self.warmup_steps
):
should_warmup = True
lr_scale = (
float(self.global_step - self.cfg.lossconfig.disc_start + 1)
/ self.warmup_steps
)
lr_scale = min(1.0, lr_scale)
# Optimize the autoencoder
aeloss, log_dict_ae = self.loss(
batch,
reconstructions,
posterior,
0,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
opt_ae.zero_grad()
self.manual_backward(aeloss)
self.clip_gradients(opt_ae, gradient_clip_val=self.gradient_clip_val)
if should_warmup:
opt_ae = warmup(opt_ae, self.learning_rate, lr_scale)
opt_ae.step()
log_loss(
"aeloss",
aeloss,
)
log_loss_dict(log_dict_ae)
# Optimize the discriminator
discloss, log_dict_disc = self.loss(
batch,
reconstructions,
posterior,
1,
self.global_step,
last_layer=self.get_last_layer(),
split="train",
)
opt_disc.zero_grad()
self.manual_backward(discloss)
self.clip_gradients(opt_disc, gradient_clip_val=self.gradient_clip_val)
if should_warmup:
opt_disc = warmup(opt_disc, self.learning_rate, lr_scale)
opt_disc.step()
log_loss(
"discloss",
discloss,
)
log_loss_dict(log_dict_disc)
def on_validation_epoch_start(self):
self.fid_model = FrechetInceptionDistance(feature=64).to(self.device)
def on_validation_epoch_end(self):
self.fid_model = None
def validation_step(self, batch, batch_idx):
batch_size = batch.size(0)
batch = rearrange(batch, "b t c h w -> (b t) c h w")
reconstructions, posterior = self(batch)
aeloss, log_dict_ae = self.loss(
batch,
reconstructions,
posterior,
0,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
discloss, log_dict_disc = self.loss(
batch,
reconstructions,
posterior,
1,
self.global_step,
last_layer=self.get_last_layer(),
split="val",
)
self.log("val/rec_loss", log_dict_ae["val/rec_loss"], sync_dist=True)
self.log_dict(log_dict_ae, sync_dist=True)
self.log_dict(log_dict_disc, sync_dist=True)
validation_metrics = get_validation_metrics_for_videos(
*map(
lambda x: rearrange(x, "(b t) c h w -> t b c h w", b=batch_size)
.contiguous()
.detach(),
(batch, reconstructions),
),
fid_model=self.fid_model,
)
self.log_dict(
{f"val/{k}": v for k, v in validation_metrics.items()},
prog_bar=True,
sync_dist=True,
)
if batch_idx == 0: # log visualizations
batch, reconstructions = (
self._rearrange_and_unnormalize(x, batch_size).detach().cpu()
for x in (batch, reconstructions)
)
if self.logger is not None:
log_video(
reconstructions,
batch,
step=self.global_step,
namespace="reconstruction_vis",
logger=self.logger.experiment,
)
def _rearrange_and_unnormalize(
self, batch: torch.Tensor, batch_size: int
) -> torch.Tensor:
batch = rearrange(batch, "(b t) c h w -> t b c h w", b=batch_size)
batch = 0.5 * batch + 0.5
return batch
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(
list(self.encoder.parameters())
+ list(self.decoder.parameters())
+ list(self.quant_conv.parameters())
+ list(self.post_quant_conv.parameters()),
lr=lr,
betas=(0.5, 0.9),
)
opt_disc = torch.optim.Adam(
self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
)
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
class ImageVAE(VAE):
"""
Pretrained ImageVAE model that can be used to encode and decode images.
Can be used to load pretrained models from custom checkpoints or huggingface repository.
"""
def __init__(
self,
cfg: DictConfig,
):
super().__init__()
ddconfig, embed_dim = cfg.ddconfig, cfg.embed_dim
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
self.quant_conv = torch.nn.Conv2d(2 * ddconfig["z_channels"], 2 * embed_dim, 1)
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
@classmethod
def from_pretrained(cls, path: str, **kwargs) -> VAE:
if path.startswith("diffuser:"):
# e.g. diffuser:madebyollin/sdxl-vae-fp16-fix (from HuggingFace)
path = path.replace("diffuser:", "")
return cls._from_pretrained_diffuser(path, **kwargs)
return cls._from_pretrained_custom(path)
@classmethod
def _from_pretrained_custom(cls, path: str) -> "ImageVAE":
if is_wandb_run_path(path):
path = wandb_to_local_path(path)
elif is_hf_path(path):
path = hf_to_local_path(path)
checkpoint = torch.load(path, map_location="cpu")
# FIXME: temporary fix for vaes trained with older versions of the code (for minecraft VAE)
if "cfg" not in checkpoint:
checkpoint["cfg"] = OmegaConf.load(
"configurations/algorithm/image_vae.yaml"
)
checkpoint["cfg"].ddconfig.resolution = 256
cfg = checkpoint["cfg"]
model = cls(cfg)
# filter out checkpoint state_dict
state_dict = checkpoint["state_dict"]
for k in list(state_dict.keys()):
if k.startswith("loss"):
del state_dict[k]
model.load_state_dict(state_dict)
return model
@classmethod
def _from_pretrained_diffuser(cls, path: str, **kwargs) -> VAE:
vae = DiffuserImageVAE.from_pretrained(path, **kwargs)
return diffuser_to_custom(vae)
def encode(self, x: torch.Tensor) -> DiagonalGaussianDistribution:
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z: torch.Tensor) -> torch.Tensor:
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def diffuser_to_custom(vae: DiffuserImageVAE) -> VAE:
"""
Modify DiffuserImageVAE to be compatible with VAE abstract class
"""
def wrap_encode(encode: Callable) -> Callable:
def wrapped_encode(self, x: torch.Tensor) -> DiagonalGaussianDistribution:
return encode(x).latent_dist
return wrapped_encode
def wrap_decode(decode: Callable) -> Callable:
def wrapped_decode(self, z: torch.Tensor) -> torch.Tensor:
return decode(z).sample
return wrapped_decode
def wrapped_forward(
self, sample: torch.Tensor, sample_posterior: bool = True
) -> Tuple[torch.Tensor, DiagonalGaussianDistribution]:
posterior = self.encode(sample)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
vae.encode = types.MethodType(wrap_encode(vae.encode), vae)
vae.decode = types.MethodType(wrap_decode(vae.decode), vae)
vae.forward = types.MethodType(wrapped_forward, vae)
return vae
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