229 lines
7.5 KiB
Python
229 lines
7.5 KiB
Python
import torch
|
|
# import pytorch_lightning as pl
|
|
import torch.nn.functional as F
|
|
from contextlib import contextmanager
|
|
from typing import Any, Dict, List, Optional, Tuple, Union
|
|
|
|
from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistribution
|
|
|
|
from comfy.ldm.util import instantiate_from_config
|
|
from comfy.ldm.modules.ema import LitEma
|
|
import comfy.ops
|
|
|
|
class DiagonalGaussianRegularizer(torch.nn.Module):
|
|
def __init__(self, sample: bool = True):
|
|
super().__init__()
|
|
self.sample = sample
|
|
|
|
def get_trainable_parameters(self) -> Any:
|
|
yield from ()
|
|
|
|
def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
|
|
log = dict()
|
|
posterior = DiagonalGaussianDistribution(z)
|
|
if self.sample:
|
|
z = posterior.sample()
|
|
else:
|
|
z = posterior.mode()
|
|
kl_loss = posterior.kl()
|
|
kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
|
|
log["kl_loss"] = kl_loss
|
|
return z, log
|
|
|
|
|
|
class AbstractAutoencoder(torch.nn.Module):
|
|
"""
|
|
This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
|
|
unCLIP models, etc. Hence, it is fairly general, and specific features
|
|
(e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
ema_decay: Union[None, float] = None,
|
|
monitor: Union[None, str] = None,
|
|
input_key: str = "jpg",
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
|
|
self.input_key = input_key
|
|
self.use_ema = ema_decay is not None
|
|
if monitor is not None:
|
|
self.monitor = monitor
|
|
|
|
if self.use_ema:
|
|
self.model_ema = LitEma(self, decay=ema_decay)
|
|
logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
|
|
|
def get_input(self, batch) -> Any:
|
|
raise NotImplementedError()
|
|
|
|
def on_train_batch_end(self, *args, **kwargs):
|
|
# for EMA computation
|
|
if self.use_ema:
|
|
self.model_ema(self)
|
|
|
|
@contextmanager
|
|
def ema_scope(self, context=None):
|
|
if self.use_ema:
|
|
self.model_ema.store(self.parameters())
|
|
self.model_ema.copy_to(self)
|
|
if context is not None:
|
|
logpy.info(f"{context}: Switched to EMA weights")
|
|
try:
|
|
yield None
|
|
finally:
|
|
if self.use_ema:
|
|
self.model_ema.restore(self.parameters())
|
|
if context is not None:
|
|
logpy.info(f"{context}: Restored training weights")
|
|
|
|
def encode(self, *args, **kwargs) -> torch.Tensor:
|
|
raise NotImplementedError("encode()-method of abstract base class called")
|
|
|
|
def decode(self, *args, **kwargs) -> torch.Tensor:
|
|
raise NotImplementedError("decode()-method of abstract base class called")
|
|
|
|
def instantiate_optimizer_from_config(self, params, lr, cfg):
|
|
logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
|
|
return get_obj_from_str(cfg["target"])(
|
|
params, lr=lr, **cfg.get("params", dict())
|
|
)
|
|
|
|
def configure_optimizers(self) -> Any:
|
|
raise NotImplementedError()
|
|
|
|
|
|
class AutoencodingEngine(AbstractAutoencoder):
|
|
"""
|
|
Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
|
|
(we also restore them explicitly as special cases for legacy reasons).
|
|
Regularizations such as KL or VQ are moved to the regularizer class.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
encoder_config: Dict,
|
|
decoder_config: Dict,
|
|
regularizer_config: Dict,
|
|
**kwargs,
|
|
):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
|
|
self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
|
|
self.regularization: AbstractRegularizer = instantiate_from_config(
|
|
regularizer_config
|
|
)
|
|
|
|
def get_last_layer(self):
|
|
return self.decoder.get_last_layer()
|
|
|
|
def encode(
|
|
self,
|
|
x: torch.Tensor,
|
|
return_reg_log: bool = False,
|
|
unregularized: bool = False,
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
|
|
z = self.encoder(x)
|
|
if unregularized:
|
|
return z, dict()
|
|
z, reg_log = self.regularization(z)
|
|
if return_reg_log:
|
|
return z, reg_log
|
|
return z
|
|
|
|
def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
|
|
x = self.decoder(z, **kwargs)
|
|
return x
|
|
|
|
def forward(
|
|
self, x: torch.Tensor, **additional_decode_kwargs
|
|
) -> Tuple[torch.Tensor, torch.Tensor, dict]:
|
|
z, reg_log = self.encode(x, return_reg_log=True)
|
|
dec = self.decode(z, **additional_decode_kwargs)
|
|
return z, dec, reg_log
|
|
|
|
|
|
class AutoencodingEngineLegacy(AutoencodingEngine):
|
|
def __init__(self, embed_dim: int, **kwargs):
|
|
self.max_batch_size = kwargs.pop("max_batch_size", None)
|
|
ddconfig = kwargs.pop("ddconfig")
|
|
super().__init__(
|
|
encoder_config={
|
|
"target": "comfy.ldm.modules.diffusionmodules.model.Encoder",
|
|
"params": ddconfig,
|
|
},
|
|
decoder_config={
|
|
"target": "comfy.ldm.modules.diffusionmodules.model.Decoder",
|
|
"params": ddconfig,
|
|
},
|
|
**kwargs,
|
|
)
|
|
self.quant_conv = comfy.ops.disable_weight_init.Conv2d(
|
|
(1 + ddconfig["double_z"]) * ddconfig["z_channels"],
|
|
(1 + ddconfig["double_z"]) * embed_dim,
|
|
1,
|
|
)
|
|
self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
|
|
self.embed_dim = embed_dim
|
|
|
|
def get_autoencoder_params(self) -> list:
|
|
params = super().get_autoencoder_params()
|
|
return params
|
|
|
|
def encode(
|
|
self, x: torch.Tensor, return_reg_log: bool = False
|
|
) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
|
|
if self.max_batch_size is None:
|
|
z = self.encoder(x)
|
|
z = self.quant_conv(z)
|
|
else:
|
|
N = x.shape[0]
|
|
bs = self.max_batch_size
|
|
n_batches = int(math.ceil(N / bs))
|
|
z = list()
|
|
for i_batch in range(n_batches):
|
|
z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
|
|
z_batch = self.quant_conv(z_batch)
|
|
z.append(z_batch)
|
|
z = torch.cat(z, 0)
|
|
|
|
z, reg_log = self.regularization(z)
|
|
if return_reg_log:
|
|
return z, reg_log
|
|
return z
|
|
|
|
def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
|
|
if self.max_batch_size is None:
|
|
dec = self.post_quant_conv(z)
|
|
dec = self.decoder(dec, **decoder_kwargs)
|
|
else:
|
|
N = z.shape[0]
|
|
bs = self.max_batch_size
|
|
n_batches = int(math.ceil(N / bs))
|
|
dec = list()
|
|
for i_batch in range(n_batches):
|
|
dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
|
|
dec_batch = self.decoder(dec_batch, **decoder_kwargs)
|
|
dec.append(dec_batch)
|
|
dec = torch.cat(dec, 0)
|
|
|
|
return dec
|
|
|
|
|
|
class AutoencoderKL(AutoencodingEngineLegacy):
|
|
def __init__(self, **kwargs):
|
|
if "lossconfig" in kwargs:
|
|
kwargs["loss_config"] = kwargs.pop("lossconfig")
|
|
super().__init__(
|
|
regularizer_config={
|
|
"target": (
|
|
"comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"
|
|
)
|
|
},
|
|
**kwargs,
|
|
)
|