mirror of
https://github.com/Stability-AI/stablediffusion.git
synced 2024-12-22 23:55:00 +00:00
220 lines
8.4 KiB
Python
220 lines
8.4 KiB
Python
|
import torch
|
||
|
|
import pytorch_lightning as pl
|
||
|
|
import torch.nn.functional as F
|
||
|
|
from contextlib import contextmanager
|
||
|
|
|
||
|
|
from ldm.modules.diffusionmodules.model import Encoder, Decoder
|
||
|
|
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
|
||
|
|
|
||
|
|
from ldm.util import instantiate_from_config
|
||
|
|
from ldm.modules.ema import LitEma
|
||
|
|
|
||
|
|
|
||
|
|
class AutoencoderKL(pl.LightningModule):
|
||
|
|
def __init__(self,
|
||
|
|
ddconfig,
|
||
|
|
lossconfig,
|
||
|
|
embed_dim,
|
||
|
|
ckpt_path=None,
|
||
|
|
ignore_keys=[],
|
||
|
|
image_key="image",
|
||
|
|
colorize_nlabels=None,
|
||
|
|
monitor=None,
|
||
|
|
ema_decay=None,
|
||
|
|
learn_logvar=False
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.learn_logvar = learn_logvar
|
||
|
|
self.image_key = image_key
|
||
|
|
self.encoder = Encoder(**ddconfig)
|
||
|
|
self.decoder = Decoder(**ddconfig)
|
||
|
|
self.loss = instantiate_from_config(lossconfig)
|
||
|
|
assert ddconfig["double_z"]
|
||
|
|
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)
|
||
|
|
self.embed_dim = embed_dim
|
||
|
|
if colorize_nlabels is not None:
|
||
|
|
assert type(colorize_nlabels)==int
|
||
|
|
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
|
||
|
|
if monitor is not None:
|
||
|
|
self.monitor = monitor
|
||
|
|
|
||
|
|
self.use_ema = ema_decay is not None
|
||
|
|
if self.use_ema:
|
||
|
|
self.ema_decay = ema_decay
|
||
|
|
assert 0. < ema_decay < 1.
|
||
|
|
self.model_ema = LitEma(self, decay=ema_decay)
|
||
|
|
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
||
|
|
|
||
|
|
if ckpt_path is not None:
|
||
|
|
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
||
|
|
|
||
|
|
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}")
|
||
|
|
|
||
|
|
@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:
|
||
|
|
print(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:
|
||
|
|
print(f"{context}: Restored training weights")
|
||
|
|
|
||
|
|
def on_train_batch_end(self, *args, **kwargs):
|
||
|
|
if self.use_ema:
|
||
|
|
self.model_ema(self)
|
||
|
|
|
||
|
|
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 get_input(self, batch, k):
|
||
|
|
x = batch[k]
|
||
|
|
if len(x.shape) == 3:
|
||
|
|
x = x[..., None]
|
||
|
|
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
|
||
|
|
return x
|
||
|
|
|
||
|
|
def training_step(self, batch, batch_idx, optimizer_idx):
|
||
|
|
inputs = self.get_input(batch, self.image_key)
|
||
|
|
reconstructions, posterior = self(inputs)
|
||
|
|
|
||
|
|
if optimizer_idx == 0:
|
||
|
|
# train encoder+decoder+logvar
|
||
|
|
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="train")
|
||
|
|
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
||
|
|
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
||
|
|
return aeloss
|
||
|
|
|
||
|
|
if optimizer_idx == 1:
|
||
|
|
# train the discriminator
|
||
|
|
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="train")
|
||
|
|
|
||
|
|
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
||
|
|
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
||
|
|
return discloss
|
||
|
|
|
||
|
|
def validation_step(self, batch, batch_idx):
|
||
|
|
log_dict = self._validation_step(batch, batch_idx)
|
||
|
|
with self.ema_scope():
|
||
|
|
log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
|
||
|
|
return log_dict
|
||
|
|
|
||
|
|
def _validation_step(self, batch, batch_idx, postfix=""):
|
||
|
|
inputs = self.get_input(batch, self.image_key)
|
||
|
|
reconstructions, posterior = self(inputs)
|
||
|
|
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="val"+postfix)
|
||
|
|
|
||
|
|
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
|
||
|
|
last_layer=self.get_last_layer(), split="val"+postfix)
|
||
|
|
|
||
|
|
self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
|
||
|
|
self.log_dict(log_dict_ae)
|
||
|
|
self.log_dict(log_dict_disc)
|
||
|
|
return self.log_dict
|
||
|
|
|
||
|
|
def configure_optimizers(self):
|
||
|
|
lr = self.learning_rate
|
||
|
|
ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
|
||
|
|
self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
|
||
|
|
if self.learn_logvar:
|
||
|
|
print(f"{self.__class__.__name__}: Learning logvar")
|
||
|
|
ae_params_list.append(self.loss.logvar)
|
||
|
|
opt_ae = torch.optim.Adam(ae_params_list,
|
||
|
|
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
|
||
|
|
|
||
|
|
@torch.no_grad()
|
||
|
|
def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
|
||
|
|
log = dict()
|
||
|
|
x = self.get_input(batch, self.image_key)
|
||
|
|
x = x.to(self.device)
|
||
|
|
if not only_inputs:
|
||
|
|
xrec, posterior = self(x)
|
||
|
|
if x.shape[1] > 3:
|
||
|
|
# colorize with random projection
|
||
|
|
assert xrec.shape[1] > 3
|
||
|
|
x = self.to_rgb(x)
|
||
|
|
xrec = self.to_rgb(xrec)
|
||
|
|
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
|
||
|
|
log["reconstructions"] = xrec
|
||
|
|
if log_ema or self.use_ema:
|
||
|
|
with self.ema_scope():
|
||
|
|
xrec_ema, posterior_ema = self(x)
|
||
|
|
if x.shape[1] > 3:
|
||
|
|
# colorize with random projection
|
||
|
|
assert xrec_ema.shape[1] > 3
|
||
|
|
xrec_ema = self.to_rgb(xrec_ema)
|
||
|
|
log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
|
||
|
|
log["reconstructions_ema"] = xrec_ema
|
||
|
|
log["inputs"] = x
|
||
|
|
return log
|
||
|
|
|
||
|
|
def to_rgb(self, x):
|
||
|
|
assert self.image_key == "segmentation"
|
||
|
|
if not hasattr(self, "colorize"):
|
||
|
|
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
|
||
|
|
x = F.conv2d(x, weight=self.colorize)
|
||
|
|
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class IdentityFirstStage(torch.nn.Module):
|
||
|
|
def __init__(self, *args, vq_interface=False, **kwargs):
|
||
|
|
self.vq_interface = vq_interface
|
||
|
|
super().__init__()
|
||
|
|
|
||
|
|
def encode(self, x, *args, **kwargs):
|
||
|
|
return x
|
||
|
|
|
||
|
|
def decode(self, x, *args, **kwargs):
|
||
|
|
return x
|
||
|
|
|
||
|
|
def quantize(self, x, *args, **kwargs):
|
||
|
|
if self.vq_interface:
|
||
|
|
return x, None, [None, None, None]
|
||
|
|
return x
|
||
|
|
|
||
|
|
def forward(self, x, *args, **kwargs):
|
||
|
|
return x
|
||
|
|
|