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import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from contextlib import contextmanager
import loralib as lora
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from ldm.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
from ldm.util import instantiate_from_config
from ldm.modules.ema import LitEma
class VQModelTorch(nn.Module):
def __init__(self,
ddconfig,
n_embed,
embed_dim,
remap=None,
rank=8, # rank for lora
lora_alpha=1.0,
lora_tune_decoder=False,
sane_index_shape=False, # tell vector quantizer to return indices as bhw
):
super().__init__()
if lora_tune_decoder:
conv_layer = partial(lora.Conv2d, r=rank, lora_alpha=lora_alpha)
else:
conv_layer = nn.Conv2d
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(rank=rank, lora_alpha=lora_alpha, lora_tune=lora_tune_decoder, **ddconfig)
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
remap=remap, sane_index_shape=sane_index_shape)
self.quant_conv = nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
self.post_quant_conv = conv_layer(embed_dim, ddconfig["z_channels"], 1)
def encode(self, x):
h = self.encoder(x)
h = self.quant_conv(h)
return h
def decode(self, h, force_not_quantize=False):
if not force_not_quantize:
quant, emb_loss, info = self.quantize(h)
else:
quant = h
quant = self.post_quant_conv(quant)
dec = self.decoder(quant)
return dec
def decode_code(self, code_b):
quant_b = self.quantize.embed_code(code_b)
dec = self.decode(quant_b, force_not_quantize=True)
return dec
def forward(self, input, force_not_quantize=False):
h = self.encode(input)
dec = self.decode(h, force_not_quantize)
return dec
class AutoencoderKLTorch(torch.nn.Module):
def __init__(self,
ddconfig,
embed_dim,
):
super().__init__()
self.encoder = Encoder(**ddconfig)
self.decoder = Decoder(**ddconfig)
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
def encode(self, x, sample_posterior=True, return_moments=False):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
if return_moments:
return z, moments
else:
return z
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
z = self.encode(input, sample_posterior, return_moments=False)
dec = self.decode(z)
return dec
class EncoderKLTorch(torch.nn.Module):
def __init__(self,
ddconfig,
embed_dim,
):
super().__init__()
self.encoder = Encoder(**ddconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.embed_dim = embed_dim
def encode(self, x, sample_posterior=True, return_moments=False):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
if return_moments:
return z, moments
else:
return z
def forward(self, x, sample_posterior=True, return_moments=False):
return self.encode(x, sample_posterior, return_moments)
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
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