vae/modeling_movq.py

# Copyright 2024 Kandinsky 3.0 Model Team, AniMemory Team and The HuggingFace Team. All rights reserved.
#
# 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.

import json
import os
from types import SimpleNamespace

import torch
import torch.nn as nn
from packaging import version
from safetensors.torch import load_file

from diffusers.utils.accelerate_utils import apply_forward_hook


def nonlinearity(x):
    return x * torch.sigmoid(x)


class SpatialNorm(nn.Module):
    def __init__(
        self,
        f_channels,
        zq_channels=None,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=False,
        **norm_layer_params,
    ):
        super().__init__()
        self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
        if zq_channels is not None:
            if freeze_norm_layer:
                for p in self.norm_layer.parameters:
                    p.requires_grad = False
            self.add_conv = add_conv
            if self.add_conv:
                self.conv = nn.Conv2d(
                    zq_channels, zq_channels, kernel_size=3, stride=1, padding=1
                )
            self.conv_y = nn.Conv2d(
                zq_channels, f_channels, kernel_size=1, stride=1, padding=0
            )
            self.conv_b = nn.Conv2d(
                zq_channels, f_channels, kernel_size=1, stride=1, padding=0
            )

    def forward(self, f, zq=None):
        norm_f = self.norm_layer(f)
        if zq is not None:
            f_size = f.shape[-2:]
            if (
                version.parse(torch.__version__) < version.parse("2.1")
                and zq.dtype == torch.bfloat16
            ):
                zq = zq.to(dtype=torch.float32)
                zq = torch.nn.functional.interpolate(zq, size=f_size, mode="nearest")
                zq = zq.to(dtype=torch.bfloat16)
            else:
                zq = torch.nn.functional.interpolate(zq, size=f_size, mode="nearest")
            if self.add_conv:
                zq = self.conv(zq)
            norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
        return norm_f


def Normalize(in_channels, zq_ch=None, add_conv=None):
    return SpatialNorm(
        in_channels,
        zq_ch,
        norm_layer=nn.GroupNorm,
        freeze_norm_layer=False,
        add_conv=add_conv,
        num_groups=32,
        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):
        if (
            version.parse(torch.__version__) < version.parse("2.1")
            and x.dtype == torch.bfloat16
        ):
            x = x.to(dtype=torch.float32)
            x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
            x = x.to(dtype=torch.bfloat16)
        else:
            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:
            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,
        zq_ch=None,
        add_conv=False,
    ):
        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, zq_ch, add_conv=add_conv)
        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, zq_ch, add_conv=add_conv)
        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, zq=None):
        h = x
        h = self.norm1(h, zq)
        h = nonlinearity(h)
        h = self.conv1(h)

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

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

        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 + h


class AttnBlock(nn.Module):
    def __init__(self, in_channels, zq_ch=None, add_conv=False):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
        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, zq=None):
        h_ = x
        h_ = self.norm(h_, zq)
        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_ = torch.nn.functional.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,
        *,
        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,
        **ignore_kwargs,
    ):
        super().__init__()
        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.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(AttnBlock(block_in))
            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 = AttnBlock(block_in)
        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):
        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,
        zq_ch=None,
        add_conv=False,
        **ignorekwargs,
    ):
        super().__init__()
        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

        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 = 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,
            zq_ch=zq_ch,
            add_conv=add_conv,
        )
        self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
        self.mid.block_2 = ResnetBlock(
            in_channels=block_in,
            out_channels=block_in,
            temb_channels=self.temb_ch,
            dropout=dropout,
            zq_ch=zq_ch,
            add_conv=add_conv,
        )

        # 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,
                        temb_channels=self.temb_ch,
                        dropout=dropout,
                        zq_ch=zq_ch,
                        add_conv=add_conv,
                    )
                )
                block_in = block_out
                if curr_res in attn_resolutions:
                    attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
            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)

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

    def forward(self, z, zq):
        self.last_z_shape = z.shape
        temb = None

        h = self.conv_in(z)

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

        # 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, zq)
                if len(self.up[i_level].attn) > 0:
                    h = self.up[i_level].attn[i_block](h, zq)
            if i_level != 0:
                h = self.up[i_level].upsample(h)

        # end
        if self.give_pre_end:
            return h

        h = self.norm_out(h, zq)
        h = nonlinearity(h)
        h = self.conv_out(h)
        return h


# Modified from MoVQ in https://github.com/ai-forever/Kandinsky-3/blob/main/kandinsky3/movq.py
class MoVQ(nn.Module):
    def __init__(self, generator_params: dict):
        super().__init__()
        z_channels = generator_params["z_channels"]
        self.config = SimpleNamespace(**generator_params)
        self.encoder = Encoder(**generator_params)
        self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
        self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
        self.decoder = Decoder(zq_ch=z_channels, **generator_params)
        self.dtype = None
        self.device = None

    @staticmethod
    def get_model_config(pretrained_model_name_or_path, subfolder):
        config_path = os.path.join(
            pretrained_model_name_or_path, subfolder, "config.json"
        )
        assert os.path.exists(config_path), "config file not exists."
        with open(config_path, "r") as f:
            config = json.loads(f.read())
        return config

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path,
        subfolder="",
        torch_dtype=torch.float32,
    ):
        config = cls.get_model_config(pretrained_model_name_or_path, subfolder)
        model = cls(generator_params=config)
        ckpt_path = os.path.join(
            pretrained_model_name_or_path, subfolder, "movq_model.safetensors"
        )
        assert os.path.exists(
            ckpt_path
        ), f"ckpt path not exists, please check {ckpt_path}"
        assert torch_dtype != torch.float16, "torch_dtype doesn't support fp16"
        ckpt_weight = load_file(ckpt_path)
        model.load_state_dict(ckpt_weight, strict=True)
        model.to(dtype=torch_dtype)
        return model

    def to(self, *args, **kwargs):
        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(
            *args, **kwargs
        )
        super(MoVQ, self).to(*args, **kwargs)
        self.dtype = dtype if dtype is not None else self.dtype
        self.device = device if device is not None else self.device
        return self

    @torch.no_grad()
    @apply_forward_hook
    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    @torch.no_grad()
    @apply_forward_hook
    def decode(self, quant):
        decoder_input = self.post_quant_conv(quant)
        decoded = self.decoder(decoder_input, quant)
        return decoded