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optionally tile images in token space

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This commit is contained in:
Brett Kuprel
2022-07-17 07:33:31 -04:00
parent 39376c9cf2
commit 798e6ac5a3
6 changed files with 145 additions and 107 deletions
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94
min_dalle/models/vqgan_detokenizer.py
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@@ -1,19 +1,20 @@
import torch
from torch import nn
from torch import FloatTensor, LongTensor
from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding
from math import sqrt
class ResnetBlock(Module):
class ResnetBlock(nn.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)
self.norm1 = nn.GroupNorm(2 ** 5, m)
self.conv1 = nn.Conv2d(m, n, 3, padding=1)
self.norm2 = nn.GroupNorm(2 ** 5, n)
self.conv2 = nn.Conv2d(n, n, 3, padding=1)
if not self.is_middle:
self.nin_shortcut = Conv2d(m, n, 1)
self.nin_shortcut = nn.Conv2d(m, n, 1)
def forward(self, x: FloatTensor) -> FloatTensor:
h = x
@@ -28,38 +29,39 @@ class ResnetBlock(Module):
return x + h
class AttentionBlock(Module):
class AttentionBlock(nn.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)
self.norm = nn.GroupNorm(2 ** 5, n)
self.q = nn.Conv2d(n, n, 1)
self.k = nn.Conv2d(n, n, 1)
self.v = nn.Conv2d(n, n, 1)
self.proj_out = nn.Conv2d(n, n, 1)
def forward(self, x: FloatTensor) -> FloatTensor:
n, m = 2 ** 9, x.shape[0]
h = x
h = self.norm(h)
q = self.q.forward(h)
k = self.k.forward(h)
v = self.v.forward(h)
q = q.reshape(m, n, 2 ** 8)
q = self.q.forward(h)
k = k.reshape(m, n, -1)
v = v.reshape(m, n, -1)
q = q.reshape(m, n, -1)
q = q.permute(0, 2, 1)
k = k.reshape(m, n, 2 ** 8)
w = torch.bmm(q, k)
w /= n ** 0.5
w = torch.softmax(w, dim=2)
v = v.reshape(m, n, 2 ** 8)
w = w.permute(0, 2, 1)
h = torch.bmm(v, w)
h = h.reshape(m, n, 2 ** 4, 2 ** 4)
token_count = int(sqrt(h.shape[-1]))
h = h.reshape(m, n, token_count, token_count)
h = self.proj_out.forward(h)
return x + h
class MiddleLayer(Module):
class MiddleLayer(nn.Module):
def __init__(self):
super().__init__()
self.block_1 = ResnetBlock(9, 9)
@@ -73,12 +75,12 @@ class MiddleLayer(Module):
return h
class Upsample(Module):
class Upsample(nn.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)
self.conv = nn.Conv2d(n, n, 3, padding=1)
def forward(self, x: FloatTensor) -> FloatTensor:
x = self.upsample.forward(x.to(torch.float32))
@@ -86,7 +88,7 @@ class Upsample(Module):
return x
class UpsampleBlock(Module):
class UpsampleBlock(nn.Module):
def __init__(
self,
log2_count_in: int,
@@ -97,19 +99,19 @@ class UpsampleBlock(Module):
super().__init__()
self.has_attention = has_attention
self.has_upsample = has_upsample
self.block = ModuleList([
self.block = nn.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([
self.attn = nn.ModuleList([
AttentionBlock(),
AttentionBlock(),
AttentionBlock()
])
else:
self.attn = ModuleList()
if has_upsample:
self.upsample = Upsample(log2_count_out)
@@ -125,14 +127,14 @@ class UpsampleBlock(Module):
return h
class Decoder(Module):
class Decoder(nn.Module):
def __init__(self):
super().__init__()
self.conv_in = Conv2d(2 ** 8, 2 ** 9, 3, padding=1)
self.conv_in = nn.Conv2d(2 ** 8, 2 ** 9, 3, padding=1)
self.mid = MiddleLayer()
self.up = ModuleList([
self.up = nn.ModuleList([
UpsampleBlock(7, 7, False, False),
UpsampleBlock(8, 7, False, True),
UpsampleBlock(8, 8, False, True),
@@ -140,8 +142,8 @@ class Decoder(Module):
UpsampleBlock(9, 9, True, True)
])
self.norm_out = GroupNorm(2 ** 5, 2 ** 7)
self.conv_out = Conv2d(2 ** 7, 3, 3, padding=1)
self.norm_out = nn.GroupNorm(2 ** 5, 2 ** 7)
self.conv_out = nn.Conv2d(2 ** 7, 3, 3, padding=1)
def forward(self, z: FloatTensor) -> FloatTensor:
z = self.conv_in.forward(z)
@@ -156,22 +158,40 @@ class Decoder(Module):
return z
class VQGanDetokenizer(Module):
class VQGanDetokenizer(nn.Module):
def __init__(self):
super().__init__()
vocab_count, embed_count = 2 ** 14, 2 ** 8
self.vocab_count = vocab_count
self.embedding = Embedding(vocab_count, embed_count)
self.post_quant_conv = Conv2d(embed_count, embed_count, 1)
self.embedding = nn.Embedding(vocab_count, embed_count)
self.post_quant_conv = nn.Conv2d(embed_count, embed_count, 1)
self.decoder = Decoder()
def forward(self, z: LongTensor) -> FloatTensor:
def forward(self, is_seamless: bool, z: LongTensor) -> FloatTensor:
z.clamp_(0, self.vocab_count - 1)
z = self.embedding.forward(z)
z = z.view((z.shape[0], 2 ** 4, 2 ** 4, 2 ** 8))
grid_size = int(sqrt(z.shape[0]))
token_count = grid_size * 2 ** 4
if is_seamless:
z = z.view([grid_size, grid_size, 2 ** 4, 2 ** 4])
z = z.flatten(1, 2).transpose(1, 0).flatten(1, 2)
z = z.flatten().unsqueeze(1)
z = self.embedding.forward(z)
z = z.view((1, token_count, token_count, 2 ** 8))
else:
z = self.embedding.forward(z)
z = z.view((z.shape[0], 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
if is_seamless:
z = z[0]
else:
z = z.view([grid_size, grid_size, 2 ** 8, 2 ** 8, 3])
z = z.flatten(1, 2).transpose(1, 0).flatten(1, 2)
return z