min-dalle-test/min_dalle/models/vqgan_detokenizer.py
2022-06-28 21:28:36 -04:00

174 lines
5.0 KiB
Python

import torch
from torch import Tensor
from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding
torch.set_grad_enabled(False)
BATCH_COUNT: int = 1
class ResnetBlock(Module):
def __init__(self, log2_count_in: int, log2_count_out: int):
super().__init__()
m, n = 2 ** log2_count_in, 2 ** log2_count_out
self.is_middle = m == n
self.norm1 = GroupNorm(2 ** 5, m)
self.conv1 = Conv2d(m, n, 3, padding=1)
self.norm2 = GroupNorm(2 ** 5, n)
self.conv2 = Conv2d(n, n, 3, padding=1)
if not self.is_middle:
self.nin_shortcut = Conv2d(m, n, 1)
def forward(self, x: Tensor) -> Tensor:
h = x
h = self.norm1.forward(h)
h *= torch.sigmoid(h)
h = self.conv1.forward(h)
h = self.norm2.forward(h)
h *= torch.sigmoid(h)
h = self.conv2(h)
if not self.is_middle:
x = self.nin_shortcut.forward(x)
return x + h
class AttentionBlock(Module):
def __init__(self):
super().__init__()
n = 2 ** 9
self.norm = GroupNorm(2 ** 5, n)
self.q = Conv2d(n, n, 1)
self.k = Conv2d(n, n, 1)
self.v = Conv2d(n, n, 1)
self.proj_out = Conv2d(n, n, 1)
def forward(self, x: Tensor) -> Tensor:
n = 2 ** 9
h = x
h = self.norm(h)
q = self.q.forward(h)
k = self.k.forward(h)
v = self.v.forward(h)
q = q.reshape(BATCH_COUNT, n, 2 ** 8)
q = q.permute(0, 2, 1)
k = k.reshape(BATCH_COUNT, n, 2 ** 8)
w = torch.bmm(q, k)
w /= n ** 0.5
w = torch.softmax(w, dim=2)
v = v.reshape(BATCH_COUNT, n, 2 ** 8)
w = w.permute(0, 2, 1)
h = torch.bmm(v, w)
h = h.reshape(BATCH_COUNT, n, 2 ** 4, 2 ** 4)
h = self.proj_out.forward(h)
return x + h
class MiddleLayer(Module):
def __init__(self):
super().__init__()
self.block_1 = ResnetBlock(9, 9)
self.attn_1 = AttentionBlock()
self.block_2 = ResnetBlock(9, 9)
def forward(self, h: Tensor) -> Tensor:
h = self.block_1.forward(h)
h = self.attn_1.forward(h)
h = self.block_2.forward(h)
return h
class Upsample(Module):
def __init__(self, log2_count):
super().__init__()
n = 2 ** log2_count
self.upsample = torch.nn.UpsamplingNearest2d(scale_factor=2)
self.conv = Conv2d(n, n, 3, padding=1)
def forward(self, x: Tensor) -> Tensor:
x = self.upsample.forward(x)
x = self.conv.forward(x)
return x
class UpsampleBlock(Module):
def __init__(
self,
log2_count_in: int,
log2_count_out: int,
has_attention: bool,
has_upsample: bool
):
super().__init__()
self.has_attention = has_attention
self.has_upsample = has_upsample
self.block = ModuleList([
ResnetBlock(log2_count_in, log2_count_out),
ResnetBlock(log2_count_out, log2_count_out),
ResnetBlock(log2_count_out, log2_count_out)
])
if has_attention:
self.attn = ModuleList([
AttentionBlock(),
AttentionBlock(),
AttentionBlock()
])
else:
self.attn = ModuleList()
if has_upsample:
self.upsample = Upsample(log2_count_out)
def forward(self, h: Tensor) -> Tensor:
for j in range(3):
h = self.block[j].forward(h)
if self.has_attention:
h = self.attn[j].forward(h)
if self.has_upsample:
h = self.upsample.forward(h)
return h
class Decoder(Module):
def __init__(self):
super().__init__()
self.conv_in = Conv2d(2 ** 8, 2 ** 9, 3, padding=1)
self.mid = MiddleLayer()
self.up = ModuleList([
UpsampleBlock(7, 7, False, False),
UpsampleBlock(8, 7, False, True),
UpsampleBlock(8, 8, False, True),
UpsampleBlock(9, 8, False, True),
UpsampleBlock(9, 9, True, True)
])
self.norm_out = GroupNorm(2 ** 5, 2 ** 7)
self.conv_out = Conv2d(2 ** 7, 3, 3, padding=1)
def forward(self, z: Tensor) -> Tensor:
z = self.conv_in.forward(z)
z = self.mid.forward(z)
for i in reversed(range(5)):
z = self.up[i].forward(z)
z = self.norm_out.forward(z)
z *= torch.sigmoid(z)
z = self.conv_out.forward(z)
return z
class VQGanDetokenizer(Module):
def __init__(self):
super().__init__()
m, n = 2 ** 14, 2 ** 8
self.embedding = Embedding(m, n)
self.post_quant_conv = Conv2d(n, n, 1)
self.decoder = Decoder()
def forward(self, z: Tensor) -> Tensor:
z = self.embedding.forward(z)
z = z.view((BATCH_COUNT, 2 ** 4, 2 ** 4, 2 ** 8))
z = z.permute(0, 3, 1, 2).contiguous()
z = self.post_quant_conv.forward(z)
z = self.decoder.forward(z)
z = z.permute(0, 2, 3, 1)
z = z.clip(0.0, 1.0) * 255
return z[0]