import torch from torch import Tensor from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding torch.set_grad_enabled(False) 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, 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 = 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) 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.to(torch.float32)) 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((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 return z