cosmos_transfer1/auxiliary/tokenizer/modules/layers2d.py

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""The model definition for Continuous 2D layers

Adapted from: https://github.com/CompVis/stable-diffusion/blob/
21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/ldm/modules/diffusionmodules/model.py

[Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors]
https://github.com/CompVis/stable-diffusion/blob/
21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/LICENSE
"""

import math

import numpy as np

# pytorch_diffusion + derived encoder decoder
import torch
import torch.nn as nn
import torch.nn.functional as F
from loguru import logger as logging

from cosmos_transfer1.auxiliary.tokenizer.modules.patching import Patcher, UnPatcher
from cosmos_transfer1.auxiliary.tokenizer.modules.utils import Normalize, nonlinearity


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: torch.Tensor) -> torch.Tensor:
        x = x.repeat_interleave(2, dim=2).repeat_interleave(2, dim=3)
        return self.conv(x)


class Downsample(nn.Module):
    def __init__(self, in_channels: int):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        pad = (0, 1, 0, 1)
        x = F.pad(x, pad, mode="constant", value=0)
        return self.conv(x)


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

        self.norm1 = Normalize(in_channels)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.norm2 = Normalize(out_channels)
        self.dropout = nn.Dropout(dropout)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.nin_shortcut = (
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
            if in_channels != out_channels
            else nn.Identity()
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        h = x
        h = self.norm1(h)
        h = nonlinearity(h)
        h = self.conv1(h)

        h = self.norm2(h)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        x = self.nin_shortcut(x)

        return x + h


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

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

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # TODO (freda): Consider reusing implementations in Attn `imaginaire`,
        # since than one is gonna be based on TransformerEngine's attn op,
        # w/c could ease CP implementations.
        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)
        k = k.reshape(b, c, h * w)
        w_ = torch.bmm(q, k)
        w_ = w_ * (int(c) ** (-0.5))
        w_ = F.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b, c, h * w)
        w_ = w_.permute(0, 2, 1)
        h_ = torch.bmm(v, w_)
        h_ = h_.reshape(b, c, h, w)

        h_ = self.proj_out(h_)

        return x + h_


class Encoder(nn.Module):
    def __init__(
        self,
        in_channels: int,
        channels: int,
        channels_mult: list[int],
        num_res_blocks: int,
        attn_resolutions: list[int],
        dropout: float,
        resolution: int,
        z_channels: int,
        spatial_compression: int,
        **ignore_kwargs,
    ):
        super().__init__()
        self.num_resolutions = len(channels_mult)
        self.num_res_blocks = num_res_blocks

        # Patcher.
        patch_size = ignore_kwargs.get("patch_size", 1)
        self.patcher = Patcher(patch_size, ignore_kwargs.get("patch_method", "rearrange"))
        in_channels = in_channels * patch_size * patch_size

        # calculate the number of downsample operations
        self.num_downsamples = int(math.log2(spatial_compression)) - int(math.log2(patch_size))
        assert (
            self.num_downsamples <= self.num_resolutions
        ), f"we can only downsample {self.num_resolutions} times at most"

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

        curr_res = resolution // patch_size
        in_ch_mult = (1,) + tuple(channels_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 = channels * in_ch_mult[i_level]
            block_out = channels * channels_mult[i_level]
            for _ in range(self.num_res_blocks):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in))
            down = nn.Module()
            down.block = block
            down.attn = attn
            if i_level < self.num_downsamples:
                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, dropout=dropout)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout)

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

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.patcher(x)

        # 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])
                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_downsamples:
                hs.append(self.down[i_level].downsample(hs[-1]))

        # middle
        h = hs[-1]
        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 = nonlinearity(h)
        h = self.conv_out(h)
        return h


class Decoder(nn.Module):
    def __init__(
        self,
        out_channels: int,
        channels: int,
        channels_mult: list[int],
        num_res_blocks: int,
        attn_resolutions: int,
        dropout: float,
        resolution: int,
        z_channels: int,
        spatial_compression: int,
        **ignore_kwargs,
    ):
        super().__init__()
        self.num_resolutions = len(channels_mult)
        self.num_res_blocks = num_res_blocks

        # UnPatcher.
        patch_size = ignore_kwargs.get("patch_size", 1)
        self.unpatcher = UnPatcher(patch_size, ignore_kwargs.get("patch_method", "rearrange"))
        out_ch = out_channels * patch_size * patch_size

        # calculate the number of upsample operations
        self.num_upsamples = int(math.log2(spatial_compression)) - int(math.log2(patch_size))
        assert self.num_upsamples <= self.num_resolutions, f"we can only upsample {self.num_resolutions} times at most"

        block_in = channels * channels_mult[self.num_resolutions - 1]
        curr_res = (resolution // patch_size) // 2 ** (self.num_resolutions - 1)
        self.z_shape = (1, z_channels, curr_res, curr_res)
        logging.info("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, dropout=dropout)
        self.mid.attn_1 = AttnBlock(block_in)
        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dropout=dropout)

        # upsampling
        self.up = nn.ModuleList()
        for i_level in reversed(range(self.num_resolutions)):
            block = nn.ModuleList()
            attn = nn.ModuleList()
            block_out = channels * channels_mult[i_level]
            for _ in range(self.num_res_blocks + 1):
                block.append(
                    ResnetBlock(
                        in_channels=block_in,
                        out_channels=block_out,
                        dropout=dropout,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in))
            up = nn.Module()
            up.block = block
            up.attn = attn
            if i_level >= (self.num_resolutions - self.num_upsamples):
                up.upsample = Upsample(block_in)
                curr_res = curr_res * 2
            self.up.insert(0, up)

        # 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, z: torch.Tensor) -> torch.Tensor:
        h = self.conv_in(z)

        # middle
        h = self.mid.block_1(h)
        h = self.mid.attn_1(h)
        h = self.mid.block_2(h)

        # 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)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h)
            if i_level >= (self.num_resolutions - self.num_upsamples):
                h = self.up[i_level].upsample(h)

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