imaginAIry/imaginairy/modules/diffusion/model.py

# pytorch_diffusion + derived encoder decoder
import gc
import math
from typing import Any, Optional

import numpy as np
import torch
from einops import rearrange
from torch import nn

from imaginairy.modules.attention import MemoryEfficientCrossAttention
from imaginairy.utils import get_device

try:
    import xformers  # noqa
    import xformers.ops  # noqa

    XFORMERS_IS_AVAILABLE = True
except ImportError:
    XFORMERS_IS_AVAILABLE = False
    # print("No module 'xformers'. Proceeding without it.")


def get_timestep_embedding(timesteps, embedding_dim):
    """
    Build sinusoidal embeddings.

    This matches the implementation in Denoising Diffusion Probabilistic Models:
    From Fairseq.

    This matches the implementation in tensor2tensor, but differs slightly
    from the description in Section 3.5 of "Attention Is All You Need".
    """
    assert len(timesteps.shape) == 1

    half_dim = embedding_dim // 2
    emb = math.log(10000) / (half_dim - 1)
    emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
    emb = emb.to(device=timesteps.device)
    emb = timesteps.float()[:, None] * emb[None, :]
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
    if embedding_dim % 2 == 1:  # zero pad
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


# context manager that prints execution time


def nonlinearity(x):
    # swish
    t = torch.sigmoid(x)
    x *= t
    del t

    return x


def Normalize(in_channels, num_groups=32):
    return torch.nn.GroupNorm(
        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
    )


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

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


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

    def forward(self, x):
        if self.with_conv:
            pad = (0, 1, 0, 1)
            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
        return x


class ResnetBlock(nn.Module):
    def __init__(
        self,
        *,
        in_channels,
        out_channels=None,
        conv_shortcut=False,
        dropout,
        temb_channels=512,
    ):
        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.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels)
        self.conv1 = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
        self.norm2 = Normalize(out_channels)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = torch.nn.Conv2d(
            out_channels, out_channels, kernel_size=3, stride=1, padding=1
        )
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
                )
            else:
                self.nin_shortcut = torch.nn.Conv2d(
                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
                )

    def forward(self, x, temb):
        h1 = x
        h2 = self.norm1(h1)
        del h1

        h3 = nonlinearity(h2)
        del h2

        h4 = self.conv1(h3)
        del h3

        if temb is not None:
            h4 = h4 + self.temb_proj(nonlinearity(temb))[:, :, None, None]

        h5 = self.norm2(h4)
        del h4

        h6 = nonlinearity(h5)
        del h5

        h7 = self.dropout(h6)
        del h6

        h8 = self.conv2(h7)
        del h7

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

        return x + h8


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

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )

    def forward(self, x):
        if get_device() == "cuda":
            return self.forward_cuda(x)

        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = q.reshape(b, c, h * w)
        q = q.permute(0, 2, 1)  # b,hw,c
        k = k.reshape(b, c, h * w)  # b,c,hw
        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x + h_

    def forward_cuda(self, x):
        h_ = x
        h_ = self.norm(h_)
        q1 = self.q(h_)
        k1 = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q1.shape

        q2 = q1.reshape(b, c, h * w)
        del q1

        q = q2.permute(0, 2, 1)  # b,hw,c
        del q2

        k = k1.reshape(b, c, h * w)  # b,c,hw
        del k1

        h_ = torch.zeros_like(k, device=q.device)

        stats = torch.cuda.memory_stats(q.device)
        mem_active = stats["active_bytes.all.current"]
        mem_reserved = stats["reserved_bytes.all.current"]
        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
        mem_free_torch = mem_reserved - mem_active
        mem_free_total = mem_free_cuda + mem_free_torch

        tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
        mem_required = tensor_size * 2.5
        steps = 1

        if mem_required > mem_free_total:
            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))

        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
        for i in range(0, q.shape[1], slice_size):
            end = i + slice_size

            w1 = torch.bmm(q[:, i:end], k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
            w2 = w1 * (int(c) ** (-0.5))
            del w1
            w3 = torch.nn.functional.softmax(w2, dim=2, dtype=q.dtype)
            del w2

            # attend to values
            v1 = v.reshape(b, c, h * w)
            w4 = w3.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
            del w3

            h_[:, :, i:end] = torch.bmm(
                v1, w4
            )  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
            del v1, w4

        h2 = h_.reshape(b, c, h, w)
        del h_

        h3 = self.proj_out(h2)
        del h2

        h3 += x

        return h3


class MemoryEfficientAttnBlock(nn.Module):
    """
    Uses xformers efficient implementation,
    see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
    Note: this is a single-head self-attention operation.
    """

    #
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.k = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.v = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.proj_out = torch.nn.Conv2d(
            in_channels, in_channels, kernel_size=1, stride=1, padding=0
        )
        self.attention_op: Optional[Any] = None

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        B, C, H, W = q.shape
        q, k, v = (rearrange(x, "b c h w -> b (h w) c") for x in (q, k, v))
        q, k, v = (
            t.unsqueeze(3)
            .reshape(B, t.shape[1], 1, C)
            .permute(0, 2, 1, 3)
            .reshape(B * 1, t.shape[1], C)
            .contiguous()
            for t in (q, k, v)
        )

        out = xformers.ops.memory_efficient_attention(
            q, k, v, attn_bias=None, op=self.attention_op
        )

        out = (
            out.unsqueeze(0)
            .reshape(B, 1, out.shape[1], C)
            .permute(0, 2, 1, 3)
            .reshape(B, out.shape[1], C)
        )
        out = rearrange(out, "b (h w) c -> b c h w", b=B, h=H, w=W, c=C)
        out = self.proj_out(out)
        return x + out


class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
    def forward(self, x, context=None, mask=None):
        b, c, h, w = x.shape
        x = rearrange(x, "b c h w -> b (h w) c")
        out = super().forward(x, context=context, mask=mask)
        out = rearrange(out, "b (h w) c -> b c h w", h=h, w=w, c=c)
        return x + out


def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
    assert attn_type in [
        "vanilla",
        "vanilla-xformers",
        "memory-efficient-cross-attn",
        "linear",
        "none",
    ], f"attn_type {attn_type} unknown"
    if XFORMERS_IS_AVAILABLE and attn_type == "vanilla":
        attn_type = "vanilla-xformers"
    # print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
    if attn_type == "vanilla":
        assert attn_kwargs is None
        return AttnBlock(in_channels)
    if attn_type == "vanilla-xformers":
        # print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
        return MemoryEfficientAttnBlock(in_channels)
    if type == "memory-efficient-cross-attn":
        attn_kwargs["query_dim"] = in_channels
        return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
    if attn_type == "none":
        return nn.Identity(in_channels)

    raise NotImplementedError()


class Model(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        use_timestep=True,
        use_linear_attn=False,
        attn_type="vanilla",
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = self.ch * 4
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels

        self.use_timestep = use_timestep
        if self.use_timestep:
            # timestep embedding
            self.temb = nn.Module()
            self.temb.dense = nn.ModuleList(
                [
                    torch.nn.Linear(self.ch, self.temb_ch),
                    torch.nn.Linear(self.temb_ch, self.temb_ch),
                ]
            )

        # downsampling
        self.conv_in = torch.nn.Conv2d(
            in_channels, self.ch, kernel_size=3, stride=1, padding=1
        )

        curr_res = resolution
        in_ch_mult = (1,) + tuple(ch_mult)
        self.down = nn.ModuleList()
        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 i_block in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                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,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # 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]
            skip_in = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                if i_block == self.num_res_blocks:
                    skip_in = ch * in_ch_mult[i_level]
                block.append(
                    ResnetBlock(
                        in_channels=block_in + skip_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_ch, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x, t=None, context=None):
        # assert x.shape[2] == x.shape[3] == self.resolution
        if context is not None:
            # assume aligned context, cat along channel axis
            x = torch.cat((x, context), dim=1)
        if self.use_timestep:
            # timestep embedding
            assert t is not None
            temb = get_timestep_embedding(t, self.ch)
            temb = self.temb.dense[0](temb)
            temb = nonlinearity(temb)
            temb = self.temb.dense[1](temb)
        else:
            temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                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:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](
                    torch.cat([h, hs.pop()], dim=1), temb
                )
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

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

    def get_last_layer(self):
        return self.conv_out.weight


class Encoder(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        double_z=True,
        use_linear_attn=False,
        attn_type="vanilla",
        **ignore_kwargs,
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        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 = torch.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()
        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 i_block in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level != self.num_resolutions - 1:
                down.downsample = Downsample(block_in, resamp_with_conv)
                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,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in,
            2 * z_channels if double_z else z_channels,
            kernel_size=3,
            stride=1,
            padding=1,
        )

    def forward(self, x):
        # timestep embedding
        temb = None

        # downsampling
        hs = [self.conv_in(x)]
        for i_level in range(self.num_resolutions):
            for i_block in range(self.num_res_blocks):
                h = self.down[i_level].block[i_block](hs[-1], temb)
                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:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        h = self.mid.block_1(h, temb)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h, temb)

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


class Decoder(nn.Module):
    def __init__(
        self,
        *,
        ch,
        out_ch,
        ch_mult=(1, 2, 4, 8),
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        in_channels,
        resolution,
        z_channels,
        give_pre_end=False,
        tanh_out=False,
        use_linear_attn=False,
        attn_type="vanilla",
        **ignore_kwargs,
    ):
        super().__init__()
        if use_linear_attn:
            attn_type = "linear"
        self.ch = ch
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        self.resolution = resolution
        self.in_channels = in_channels
        self.give_pre_end = give_pre_end
        self.tanh_out = tanh_out

        # compute 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)
        # print(
        #     "Working with z of shape {} = {} dimensions.".format(
        #         self.z_shape, np.prod(self.z_shape)
        #     )
        # )

        # z to block_in
        self.conv_in = torch.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,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )
        self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
        )

        # 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 i_block in range(self.num_res_blocks + 1):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(make_attn(block_in, attn_type=attn_type))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level != 0:
                up.upsample = Upsample(block_in, resamp_with_conv)
                curr_res = curr_res * 2
            self.up.insert(0, up)  # prepend to get consistent order

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_ch, kernel_size=3, stride=1, padding=1
        )

        self.last_z_shape = None

    def forward(self, z):
        # assert z.shape[1:] == self.z_shape[1:]
        self.last_z_shape = z.shape

        # timestep embedding
        temb = None

        # z to block_in
        h1 = self.conv_in(z)

        # middle
        h2 = self.mid.block_1(h1, temb)
        del h1

        h3 = self.mid.attn_1(h2)
        del h2

        h = self.mid.block_2(h3, temb)
        del h3

        # prepare for up sampling
        gc.collect()
        torch.cuda.empty_cache()

        # upsampling
        for i_level in reversed(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.up[i_level].block[i_block](h, temb)
                if len(self.up[i_level].attn) > 0:
                    t = h
                    h = self.up[i_level].attn[i_block](t)
                    del t
            if i_level != 0:
                t = h
                h = self.up[i_level].upsample(t)
                del t

        # end
        if self.give_pre_end:
            return h

        h1 = self.norm_out(h)
        del h

        h2 = nonlinearity(h1)
        del h1

        h = self.conv_out(h2)
        del h2

        if self.tanh_out:
            t = h
            h = torch.tanh(t)
            del t
        return h


class SimpleDecoder(nn.Module):
    def __init__(self, in_channels, out_channels, *args, **kwargs):
        super().__init__()
        self.model = nn.ModuleList(
            [
                nn.Conv2d(in_channels, in_channels, 1),
                ResnetBlock(
                    in_channels=in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=2 * in_channels,
                    out_channels=4 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=4 * in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                nn.Conv2d(2 * in_channels, in_channels, 1),
                Upsample(in_channels, with_conv=True),
            ]
        )
        # end
        self.norm_out = Normalize(in_channels)
        self.conv_out = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        for i, layer in enumerate(self.model):
            if i in [1, 2, 3]:
                x = layer(x, None)
            else:
                x = layer(x)

        h = self.norm_out(x)
        h = nonlinearity(h)
        x = self.conv_out(h)
        return x


class UpsampleDecoder(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        ch,
        num_res_blocks,
        resolution,
        ch_mult=(2, 2),
        dropout=0.0,
    ):
        super().__init__()
        # upsampling
        self.temb_ch = 0
        self.num_resolutions = len(ch_mult)
        self.num_res_blocks = num_res_blocks
        block_in = in_channels
        curr_res = resolution // 2 ** (self.num_resolutions - 1)
        self.res_blocks = nn.ModuleList()
        self.upsample_blocks = nn.ModuleList()
        for i_level in range(self.num_resolutions):
            res_block = []
            block_out = ch * ch_mult[i_level]
            for i_block in range(self.num_res_blocks + 1):
                res_block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                    )
                )
                block_in = block_out
            self.res_blocks.append(nn.ModuleList(res_block))
            if i_level != self.num_resolutions - 1:
                self.upsample_blocks.append(Upsample(block_in, True))
                curr_res = curr_res * 2

        # end
        self.norm_out = Normalize(block_in)
        self.conv_out = torch.nn.Conv2d(
            block_in, out_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        # upsampling
        h = x
        for k, i_level in enumerate(range(self.num_resolutions)):
            for i_block in range(self.num_res_blocks + 1):
                h = self.res_blocks[i_level][i_block](h, None)
            if i_level != self.num_resolutions - 1:
                h = self.upsample_blocks[k](h)
        h = self.norm_out(h)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


class LatentRescaler(nn.Module):
    def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
        super().__init__()
        # residual block, interpolate, residual block
        self.factor = factor
        self.conv_in = nn.Conv2d(
            in_channels, mid_channels, kernel_size=3, stride=1, padding=1
        )
        self.res_block1 = nn.ModuleList(
            [
                ResnetBlock(
                    in_channels=mid_channels,
                    out_channels=mid_channels,
                    temb_channels=0,
                    dropout=0.0,
                )
                for _ in range(depth)
            ]
        )
        self.attn = AttnBlock(mid_channels)
        self.res_block2 = nn.ModuleList(
            [
                ResnetBlock(
                    in_channels=mid_channels,
                    out_channels=mid_channels,
                    temb_channels=0,
                    dropout=0.0,
                )
                for _ in range(depth)
            ]
        )

        self.conv_out = nn.Conv2d(
            mid_channels,
            out_channels,
            kernel_size=1,
        )

    def forward(self, x):
        x = self.conv_in(x)
        for block in self.res_block1:
            x = block(x, None)
        x = torch.nn.functional.interpolate(
            x,
            size=(
                int(round(x.shape[2] * self.factor)),
                int(round(x.shape[3] * self.factor)),
            ),
        )
        x = self.attn(x)
        for block in self.res_block2:
            x = block(x, None)
        x = self.conv_out(x)
        return x


class MergedRescaleEncoder(nn.Module):
    def __init__(
        self,
        in_channels,
        ch,
        resolution,
        out_ch,
        num_res_blocks,
        attn_resolutions,
        dropout=0.0,
        resamp_with_conv=True,
        ch_mult=(1, 2, 4, 8),
        rescale_factor=1.0,
        rescale_module_depth=1,
    ):
        super().__init__()
        intermediate_chn = ch * ch_mult[-1]
        self.encoder = Encoder(
            in_channels=in_channels,
            num_res_blocks=num_res_blocks,
            ch=ch,
            ch_mult=ch_mult,
            z_channels=intermediate_chn,
            double_z=False,
            resolution=resolution,
            attn_resolutions=attn_resolutions,
            dropout=dropout,
            resamp_with_conv=resamp_with_conv,
            out_ch=None,
        )
        self.rescaler = LatentRescaler(
            factor=rescale_factor,
            in_channels=intermediate_chn,
            mid_channels=intermediate_chn,
            out_channels=out_ch,
            depth=rescale_module_depth,
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.rescaler(x)
        return x


class MergedRescaleDecoder(nn.Module):
    def __init__(
        self,
        z_channels,
        out_ch,
        resolution,
        num_res_blocks,
        attn_resolutions,
        ch,
        ch_mult=(1, 2, 4, 8),
        dropout=0.0,
        resamp_with_conv=True,
        rescale_factor=1.0,
        rescale_module_depth=1,
    ):
        super().__init__()
        tmp_chn = z_channels * ch_mult[-1]
        self.decoder = Decoder(
            out_ch=out_ch,
            z_channels=tmp_chn,
            attn_resolutions=attn_resolutions,
            dropout=dropout,
            resamp_with_conv=resamp_with_conv,
            in_channels=None,
            num_res_blocks=num_res_blocks,
            ch_mult=ch_mult,
            resolution=resolution,
            ch=ch,
        )
        self.rescaler = LatentRescaler(
            factor=rescale_factor,
            in_channels=z_channels,
            mid_channels=tmp_chn,
            out_channels=tmp_chn,
            depth=rescale_module_depth,
        )

    def forward(self, x):
        x = self.rescaler(x)
        x = self.decoder(x)
        return x


class Upsampler(nn.Module):
    def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
        super().__init__()
        assert out_size >= in_size
        num_blocks = int(np.log2(out_size // in_size)) + 1
        factor_up = 1.0 + (out_size % in_size)
        # print(
        #     f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}"
        # )
        self.rescaler = LatentRescaler(
            factor=factor_up,
            in_channels=in_channels,
            mid_channels=2 * in_channels,
            out_channels=in_channels,
        )
        self.decoder = Decoder(
            out_ch=out_channels,
            resolution=out_size,
            z_channels=in_channels,
            num_res_blocks=2,
            attn_resolutions=[],
            in_channels=None,
            ch=in_channels,
            ch_mult=[ch_mult for _ in range(num_blocks)],
        )

    def forward(self, x):
        x = self.rescaler(x)
        x = self.decoder(x)
        return x


class Resize(nn.Module):
    def __init__(self, in_channels=None, learned=False, mode="bilinear"):
        super().__init__()
        self.with_conv = learned
        self.mode = mode
        if self.with_conv:
            # print(
            #     f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode"
            # )
            raise NotImplementedError()
            # assert in_channels is not None
            # # no asymmetric padding in torch conv, must do it ourselves
            # self.conv = torch.nn.Conv2d(
            #     in_channels, in_channels, kernel_size=4, stride=2, padding=1
            # )

    def forward(self, x, scale_factor=1.0):
        if scale_factor == 1.0:
            return x
        x = torch.nn.functional.interpolate(
            x, mode=self.mode, align_corners=False, scale_factor=scale_factor
        )
        return x