mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
241 lines
8.5 KiB
Python
241 lines
8.5 KiB
Python
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
from einops import repeat
|
|
|
|
from taming.modules.discriminator.model import (
|
|
NLayerDiscriminator,
|
|
weights_init,
|
|
)
|
|
from taming.modules.losses.lpips import LPIPS
|
|
from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss
|
|
|
|
|
|
def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights):
|
|
assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0]
|
|
loss_real = torch.mean(F.relu(1.0 - logits_real), dim=[1, 2, 3])
|
|
loss_fake = torch.mean(F.relu(1.0 + logits_fake), dim=[1, 2, 3])
|
|
loss_real = (weights * loss_real).sum() / weights.sum()
|
|
loss_fake = (weights * loss_fake).sum() / weights.sum()
|
|
d_loss = 0.5 * (loss_real + loss_fake)
|
|
return d_loss
|
|
|
|
|
|
def adopt_weight(weight, global_step, threshold=0, value=0.0):
|
|
if global_step < threshold:
|
|
weight = value
|
|
return weight
|
|
|
|
|
|
def measure_perplexity(predicted_indices, n_embed):
|
|
# src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py
|
|
# eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally
|
|
encodings = (
|
|
F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed)
|
|
)
|
|
avg_probs = encodings.mean(0)
|
|
perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp()
|
|
cluster_use = torch.sum(avg_probs > 0)
|
|
return perplexity, cluster_use
|
|
|
|
|
|
def l1(x, y):
|
|
return torch.abs(x - y)
|
|
|
|
|
|
def l2(x, y):
|
|
return torch.pow((x - y), 2)
|
|
|
|
|
|
class VQLPIPSWithDiscriminator(nn.Module):
|
|
def __init__(
|
|
self,
|
|
disc_start,
|
|
codebook_weight=1.0,
|
|
pixelloss_weight=1.0,
|
|
disc_num_layers=3,
|
|
disc_in_channels=3,
|
|
disc_factor=1.0,
|
|
disc_weight=1.0,
|
|
perceptual_weight=1.0,
|
|
use_actnorm=False,
|
|
disc_conditional=False,
|
|
disc_ndf=64,
|
|
disc_loss='hinge',
|
|
n_classes=None,
|
|
perceptual_loss='lpips',
|
|
pixel_loss='l1',
|
|
):
|
|
super().__init__()
|
|
assert disc_loss in ['hinge', 'vanilla']
|
|
assert perceptual_loss in ['lpips', 'clips', 'dists']
|
|
assert pixel_loss in ['l1', 'l2']
|
|
self.codebook_weight = codebook_weight
|
|
self.pixel_weight = pixelloss_weight
|
|
if perceptual_loss == 'lpips':
|
|
print(f'{self.__class__.__name__}: Running with LPIPS.')
|
|
self.perceptual_loss = LPIPS().eval()
|
|
else:
|
|
raise ValueError(
|
|
f'Unknown perceptual loss: >> {perceptual_loss} <<'
|
|
)
|
|
self.perceptual_weight = perceptual_weight
|
|
|
|
if pixel_loss == 'l1':
|
|
self.pixel_loss = l1
|
|
else:
|
|
self.pixel_loss = l2
|
|
|
|
self.discriminator = NLayerDiscriminator(
|
|
input_nc=disc_in_channels,
|
|
n_layers=disc_num_layers,
|
|
use_actnorm=use_actnorm,
|
|
ndf=disc_ndf,
|
|
).apply(weights_init)
|
|
self.discriminator_iter_start = disc_start
|
|
if disc_loss == 'hinge':
|
|
self.disc_loss = hinge_d_loss
|
|
elif disc_loss == 'vanilla':
|
|
self.disc_loss = vanilla_d_loss
|
|
else:
|
|
raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
|
|
print(f'VQLPIPSWithDiscriminator running with {disc_loss} loss.')
|
|
self.disc_factor = disc_factor
|
|
self.discriminator_weight = disc_weight
|
|
self.disc_conditional = disc_conditional
|
|
self.n_classes = n_classes
|
|
|
|
def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
|
|
if last_layer is not None:
|
|
nll_grads = torch.autograd.grad(
|
|
nll_loss, last_layer, retain_graph=True
|
|
)[0]
|
|
g_grads = torch.autograd.grad(
|
|
g_loss, last_layer, retain_graph=True
|
|
)[0]
|
|
else:
|
|
nll_grads = torch.autograd.grad(
|
|
nll_loss, self.last_layer[0], retain_graph=True
|
|
)[0]
|
|
g_grads = torch.autograd.grad(
|
|
g_loss, self.last_layer[0], retain_graph=True
|
|
)[0]
|
|
|
|
d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
|
|
d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
|
|
d_weight = d_weight * self.discriminator_weight
|
|
return d_weight
|
|
|
|
def forward(
|
|
self,
|
|
codebook_loss,
|
|
inputs,
|
|
reconstructions,
|
|
optimizer_idx,
|
|
global_step,
|
|
last_layer=None,
|
|
cond=None,
|
|
split='train',
|
|
predicted_indices=None,
|
|
):
|
|
if not exists(codebook_loss):
|
|
codebook_loss = torch.tensor([0.0]).to(inputs.device)
|
|
# rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
|
|
rec_loss = self.pixel_loss(
|
|
inputs.contiguous(), reconstructions.contiguous()
|
|
)
|
|
if self.perceptual_weight > 0:
|
|
p_loss = self.perceptual_loss(
|
|
inputs.contiguous(), reconstructions.contiguous()
|
|
)
|
|
rec_loss = rec_loss + self.perceptual_weight * p_loss
|
|
else:
|
|
p_loss = torch.tensor([0.0])
|
|
|
|
nll_loss = rec_loss
|
|
# nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
|
|
nll_loss = torch.mean(nll_loss)
|
|
|
|
# now the GAN part
|
|
if optimizer_idx == 0:
|
|
# generator update
|
|
if cond is None:
|
|
assert not self.disc_conditional
|
|
logits_fake = self.discriminator(reconstructions.contiguous())
|
|
else:
|
|
assert self.disc_conditional
|
|
logits_fake = self.discriminator(
|
|
torch.cat((reconstructions.contiguous(), cond), dim=1)
|
|
)
|
|
g_loss = -torch.mean(logits_fake)
|
|
|
|
try:
|
|
d_weight = self.calculate_adaptive_weight(
|
|
nll_loss, g_loss, last_layer=last_layer
|
|
)
|
|
except RuntimeError:
|
|
assert not self.training
|
|
d_weight = torch.tensor(0.0)
|
|
|
|
disc_factor = adopt_weight(
|
|
self.disc_factor,
|
|
global_step,
|
|
threshold=self.discriminator_iter_start,
|
|
)
|
|
loss = (
|
|
nll_loss
|
|
+ d_weight * disc_factor * g_loss
|
|
+ self.codebook_weight * codebook_loss.mean()
|
|
)
|
|
|
|
log = {
|
|
'{}/total_loss'.format(split): loss.clone().detach().mean(),
|
|
'{}/quant_loss'.format(split): codebook_loss.detach().mean(),
|
|
'{}/nll_loss'.format(split): nll_loss.detach().mean(),
|
|
'{}/rec_loss'.format(split): rec_loss.detach().mean(),
|
|
'{}/p_loss'.format(split): p_loss.detach().mean(),
|
|
'{}/d_weight'.format(split): d_weight.detach(),
|
|
'{}/disc_factor'.format(split): torch.tensor(disc_factor),
|
|
'{}/g_loss'.format(split): g_loss.detach().mean(),
|
|
}
|
|
if predicted_indices is not None:
|
|
assert self.n_classes is not None
|
|
with torch.no_grad():
|
|
perplexity, cluster_usage = measure_perplexity(
|
|
predicted_indices, self.n_classes
|
|
)
|
|
log[f'{split}/perplexity'] = perplexity
|
|
log[f'{split}/cluster_usage'] = cluster_usage
|
|
return loss, log
|
|
|
|
if optimizer_idx == 1:
|
|
# second pass for discriminator update
|
|
if cond is None:
|
|
logits_real = self.discriminator(inputs.contiguous().detach())
|
|
logits_fake = self.discriminator(
|
|
reconstructions.contiguous().detach()
|
|
)
|
|
else:
|
|
logits_real = self.discriminator(
|
|
torch.cat((inputs.contiguous().detach(), cond), dim=1)
|
|
)
|
|
logits_fake = self.discriminator(
|
|
torch.cat(
|
|
(reconstructions.contiguous().detach(), cond), dim=1
|
|
)
|
|
)
|
|
|
|
disc_factor = adopt_weight(
|
|
self.disc_factor,
|
|
global_step,
|
|
threshold=self.discriminator_iter_start,
|
|
)
|
|
d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
|
|
|
|
log = {
|
|
'{}/disc_loss'.format(split): d_loss.clone().detach().mean(),
|
|
'{}/logits_real'.format(split): logits_real.detach().mean(),
|
|
'{}/logits_fake'.format(split): logits_fake.detach().mean(),
|
|
}
|
|
return d_loss, log
|