extensions_built_in/diffusion_models/flux2/src/autoencoder.py

from dataclasses import dataclass, field

import torch
from einops import rearrange
from torch import Tensor, nn
import math
import torch.utils.checkpoint as ckpt


@dataclass
class AutoEncoderParams:
    resolution: int = 256
    in_channels: int = 3
    ch: int = 128
    out_ch: int = 3
    ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4])
    num_res_blocks: int = 2
    z_channels: int = 32

@dataclass
class AutoEncoderSmallDecoderParams:
    resolution: int = 256
    in_channels: int = 3
    ch: int = 128
    ch_encoder: int = 96
    out_ch: int = 3
    ch_mult: list[int] = field(default_factory=lambda: [1, 2, 4, 4])
    num_res_blocks: int = 2
    z_channels: int = 32


def swish(x: Tensor) -> Tensor:
    return x * torch.sigmoid(x)


class AttnBlock(nn.Module):
    def __init__(self, in_channels: int):
        super().__init__()
        self.in_channels = in_channels

        self.norm = nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
        )

        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)

    def attention(self, h_: Tensor) -> Tensor:
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
        h_ = nn.functional.scaled_dot_product_attention(q, k, v)

        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)

    def forward(self, x: Tensor) -> Tensor:
        return x + self.proj_out(self.attention(x))


class ResnetBlock(nn.Module):
    def __init__(self, in_channels: int, out_channels: int):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels

        self.norm1 = nn.GroupNorm(
            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
        )
        self.conv1 = nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        self.norm2 = nn.GroupNorm(
            num_groups=32, num_channels=out_channels, eps=1e-6, affine=True
        )
        self.conv2 = nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if self.in_channels != self.out_channels:
            self.nin_shortcut = nn.Conv2d(
                in_channels, out_channels, kernel_size=1, stride=1, padding=0
            )

    def forward(self, x):
        h = x
        h = self.norm1(h)
        h = swish(h)
        h = self.conv1(h)

        h = self.norm2(h)
        h = swish(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            x = self.nin_shortcut(x)

        return x + h


class Downsample(nn.Module):
    def __init__(self, in_channels: int):
        super().__init__()
        # no asymmetric padding in torch conv, must do it ourselves
        self.conv = nn.Conv2d(
            in_channels, in_channels, kernel_size=3, stride=2, padding=0
        )

    def forward(self, x: Tensor):
        pad = (0, 1, 0, 1)
        x = nn.functional.pad(x, pad, mode="constant", value=0)
        x = self.conv(x)
        return x


class Upsample(nn.Module):
    def __init__(self, in_channels: int):
        super().__init__()
        self.conv = nn.Conv2d(
            in_channels, in_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x: Tensor):
        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
        x = self.conv(x)
        return x


class Encoder(nn.Module):
    def __init__(
        self,
        resolution: int,
        in_channels: int,
        ch: int,
        ch_mult: list[int],
        num_res_blocks: int,
        z_channels: int,
    ):
        super().__init__()
        self.quant_conv = torch.nn.Conv2d(2 * z_channels, 2 * z_channels, 1)
        self.ch = ch
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        # downsampling
        self.conv_in = nn.Conv2d(
            in_channels, self.ch, kernel_size=3, stride=1, padding=1
        )

        curr_res = resolution
        in_ch_mult = (1,) + tuple(ch_mult)
        self.in_ch_mult = in_ch_mult
        self.down = nn.ModuleList()
        block_in = self.ch
        for i_level in range(self.num_resolutions):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_in = ch * in_ch_mult[i_level]
            block_out = ch * ch_mult[i_level]
            for _ in range(self.num_res_blocks):
                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                block_in = block_out
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in)
                curr_res = curr_res // 2
            self.down.append(down)

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)

        # end
        self.norm_out = nn.GroupNorm(
            num_groups=32, num_channels=block_in, eps=1e-6, affine=True
        )
        self.conv_out = nn.Conv2d(
            block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1
        )
        self.gradient_checkpointing = False

    def enable_gradient_checkpointing(self):
        self.gradient_checkpointing = True

    def forward(self, x: Tensor) -> Tensor:
        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    h = ckpt.checkpoint(self.down[i_level].block[i_block], hs[-1])
                    if len(self.down[i_level].attn) > 0:
                        h = ckpt.checkpoint(self.down[i_level].attn[i_block], h)
                else:
                    h = self.down[i_level].block[i_block](hs[-1])
                    if len(self.down[i_level].attn) > 0:
                        h = self.down[i_level].attn[i_block](h)
                hs.append(h)
            if i_level != self.num_resolutions - 1:
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    hs.append(ckpt.checkpoint(self.down[i_level].downsample, hs[-1]))
                else:
                    hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            h = ckpt.checkpoint(self.mid.block_1, h)
            h = ckpt.checkpoint(self.mid.attn_1, h)
            h = ckpt.checkpoint(self.mid.block_2, h)
        else:
            h = self.mid.block_1(h)
            h = self.mid.attn_1(h)
            h = self.mid.block_2(h)
        # end
        h = self.norm_out(h)
        h = swish(h)
        h = self.conv_out(h)
        h = self.quant_conv(h)
        return h


class Decoder(nn.Module):
    def __init__(
        self,
        ch: int,
        out_ch: int,
        ch_mult: list[int],
        num_res_blocks: int,
        in_channels: int,
        resolution: int,
        z_channels: int,
    ):
        super().__init__()
        self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
        self.ch = ch
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.ffactor = 2 ** (self.num_resolutions - 1)

        # compute in_ch_mult, block_in and curr_res at lowest res
        block_in = ch * ch_mult[self.num_resolutions - 1]
        curr_res = resolution // 2 ** (self.num_resolutions - 1)
        self.z_shape = (1, z_channels, curr_res, curr_res)

        # z to block_in
        self.conv_in = nn.Conv2d(
            z_channels, block_in, kernel_size=3, stride=1, padding=1
        )

        # middle
        self.mid = nn.Module()
        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = ch * ch_mult[i_level]
            for _ in range(self.num_res_blocks + 1):
                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
                block_in = block_out
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = nn.GroupNorm(
            num_groups=32, num_channels=block_in, eps=1e-6, affine=True
        )
        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
        self.gradient_checkpointing = False

    def enable_gradient_checkpointing(self):
        self.gradient_checkpointing = True

    def forward(self, z: Tensor) -> Tensor:
        z = self.post_quant_conv(z)

        # get dtype for proper tracing
        upscale_dtype = next(self.up.parameters()).dtype

        # z to block_in
        h = self.conv_in(z)

        # middle
        if torch.is_grad_enabled() and self.gradient_checkpointing:
            h = ckpt.checkpoint(self.mid.block_1, h)
            h = ckpt.checkpoint(self.mid.attn_1, h)
            h = ckpt.checkpoint(self.mid.block_2, h)
        else:
            h = self.mid.block_1(h)
            h = self.mid.attn_1(h)
            h = self.mid.block_2(h)

        # cast to proper dtype
        h = h.to(upscale_dtype)
        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    h = ckpt.checkpoint(self.up[i_level].block[i_block], h)
                    if len(self.up[i_level].attn) > 0:
                        h = ckpt.checkpoint(self.up[i_level].attn[i_block], h)
                else:
                    h = self.up[i_level].block[i_block](h)
                    if len(self.up[i_level].attn) > 0:
                        h = self.up[i_level].attn[i_block](h)
            if i_level != 0:
                if torch.is_grad_enabled() and self.gradient_checkpointing:
                    h = ckpt.checkpoint(self.up[i_level].upsample, h)
                else:
                    h = self.up[i_level].upsample(h)

        # end
        h = self.norm_out(h)
        h = swish(h)
        h = self.conv_out(h)
        return h


class AutoEncoder(nn.Module):
    def __init__(self, params: AutoEncoderParams):
        super().__init__()
        self.params = params
        self.encoder = Encoder(
            resolution=params.resolution,
            in_channels=params.in_channels,
            ch=params.ch,
            ch_mult=params.ch_mult,
            num_res_blocks=params.num_res_blocks,
            z_channels=params.z_channels,
        )
        decoder_ch = params.ch
        if hasattr(params, "ch_encoder"):
            decoder_ch = params.ch_encoder
        self.decoder = Decoder(
            resolution=params.resolution,
            in_channels=params.in_channels,
            ch=decoder_ch,
            out_ch=params.out_ch,
            ch_mult=params.ch_mult,
            num_res_blocks=params.num_res_blocks,
            z_channels=params.z_channels,
        )

        self.bn_eps = 1e-4
        self.bn_momentum = 0.1
        self.ps = [2, 2]
        self.bn = torch.nn.BatchNorm2d(
            math.prod(self.ps) * params.z_channels,
            eps=self.bn_eps,
            momentum=self.bn_momentum,
            affine=False,
            track_running_stats=True,
        )
        self._gradient_checkpointing = False

    @property
    def gradient_checkpointing(self):
        return self._gradient_checkpointing

    @gradient_checkpointing.setter
    def gradient_checkpointing(self, value: bool):
        self._gradient_checkpointing = value
        self.encoder.gradient_checkpointing = value
        self.decoder.gradient_checkpointing = value

    @property
    def device(self):
        return next(self.parameters()).device

    @property
    def dtype(self):
        return next(self.parameters()).dtype

    def enable_gradient_checkpointing(self):
        self.gradient_checkpointing = True
        self.encoder.enable_gradient_checkpointing()
        self.decoder.enable_gradient_checkpointing()

    def normalize(self, z):
        self.bn.eval()
        return self.bn(z)

    def inv_normalize(self, z):
        self.bn.eval()
        s = torch.sqrt(self.bn.running_var.view(1, -1, 1, 1) + self.bn_eps)
        m = self.bn.running_mean.view(1, -1, 1, 1)
        return z * s + m

    def encode(self, x: Tensor) -> Tensor:
        moments = self.encoder(x)
        mean = torch.chunk(moments, 2, dim=1)[0]

        z = rearrange(
            mean,
            "... c (i pi) (j pj)  -> ... (c pi pj) i j",
            pi=self.ps[0],
            pj=self.ps[1],
        )
        z = self.normalize(z)
        return z

    def decode(self, z: Tensor) -> Tensor:
        z = self.inv_normalize(z)
        z = rearrange(
            z,
            "... (c pi pj) i j -> ... c (i pi) (j pj)",
            pi=self.ps[0],
            pj=self.ps[1],
        )
        dec = self.decoder(z)
        return dec