FLUX.2 launch
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timudk
336
src/flux2/autoencoder.py
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336
src/flux2/autoencoder.py
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import math
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from dataclasses import dataclass, field
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import torch
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from einops import rearrange
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from torch import Tensor, nn
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@dataclass
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class AutoEncoderParams:
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resolution: int = 256
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in_channels: int = 3
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ch: int = 128
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out_ch: int = 3
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ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4])
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num_res_blocks: int = 2
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z_channels: int = 32
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def swish(x: Tensor) -> Tensor:
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return x * torch.sigmoid(x)
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class AttnBlock(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.in_channels = in_channels
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self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
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def attention(self, h_: Tensor) -> Tensor:
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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b, c, h, w = q.shape
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q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
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k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
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v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
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h_ = nn.functional.scaled_dot_product_attention(q, k, v)
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return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
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def forward(self, x: Tensor) -> Tensor:
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return x + self.proj_out(self.attention(x))
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class ResnetBlock(nn.Module):
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def __init__(self, in_channels: int, out_channels: int):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
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self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
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self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x):
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h = x
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h = self.norm1(h)
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h = swish(h)
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h = self.conv1(h)
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h = self.norm2(h)
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h = swish(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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x = self.nin_shortcut(x)
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return x + h
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class Downsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x: Tensor):
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pad = (0, 1, 0, 1)
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x = nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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return x
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class Upsample(nn.Module):
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def __init__(self, in_channels: int):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor):
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x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
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x = self.conv(x)
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return x
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class Encoder(nn.Module):
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def __init__(
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self,
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resolution: int,
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in_channels: int,
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ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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z_channels: int,
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):
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super().__init__()
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self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1)
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
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curr_res = resolution
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in_ch_mult = (1,) + tuple(ch_mult)
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self.in_ch_mult = in_ch_mult
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self.down = nn.ModuleList()
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block_in = self.ch
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x: Tensor) -> Tensor:
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1])
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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h = self.quant_conv(h)
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return h
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class Decoder(nn.Module):
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def __init__(
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self,
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ch: int,
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out_ch: int,
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ch_mult: list[int],
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num_res_blocks: int,
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in_channels: int,
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resolution: int,
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z_channels: int,
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):
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super().__init__()
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self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
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self.ch = ch
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.ffactor = 2 ** (self.num_resolutions - 1)
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# compute in_ch_mult, block_in and curr_res at lowest res
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2 ** (self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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# z to block_in
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self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for _ in range(self.num_res_blocks + 1):
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block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
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block_in = block_out
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
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self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
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def forward(self, z: Tensor) -> Tensor:
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z = self.post_quant_conv(z)
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# get dtype for proper tracing
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upscale_dtype = next(self.up.parameters()).dtype
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h)
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# cast to proper dtype
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h = h.to(upscale_dtype)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](h)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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h = self.norm_out(h)
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h = swish(h)
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h = self.conv_out(h)
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return h
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class AutoEncoder(nn.Module):
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def __init__(self, params: AutoEncoderParams):
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super().__init__()
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self.params = params
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self.encoder = Encoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.decoder = Decoder(
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resolution=params.resolution,
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in_channels=params.in_channels,
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ch=params.ch,
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out_ch=params.out_ch,
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ch_mult=params.ch_mult,
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num_res_blocks=params.num_res_blocks,
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z_channels=params.z_channels,
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)
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self.bn_eps = 1e-4
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self.bn_momentum = 0.1
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self.ps = [2, 2]
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self.bn = torch.nn.BatchNorm2d(
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math.prod(self.ps) * params.z_channels,
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eps=self.bn_eps,
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momentum=self.bn_momentum,
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affine=False,
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track_running_stats=True,
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)
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def normalize(self, z):
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self.bn.eval()
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return self.bn(z)
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def inv_normalize(self, z):
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self.bn.eval()
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s = torch.sqrt(self.bn.running_var.view(1, -1, 1, 1) + self.bn_eps)
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m = self.bn.running_mean.view(1, -1, 1, 1)
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return z * s + m
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def encode(self, x: Tensor) -> Tensor:
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moments = self.encoder(x)
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mean = torch.chunk(moments, 2, dim=1)[0]
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z = rearrange(
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mean,
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"... c (i pi) (j pj) -> ... (c pi pj) i j",
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pi=self.ps[0],
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pj=self.ps[1],
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)
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z = self.normalize(z)
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return z
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def decode(self, z: Tensor) -> Tensor:
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z = self.inv_normalize(z)
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z = rearrange(
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z,
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"... (c pi pj) i j -> ... c (i pi) (j pj)",
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pi=self.ps[0],
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pj=self.ps[1],
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)
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dec = self.decoder(z)
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return dec
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