ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py

# pylint: skip-file
# type: ignore
import math
import random

import torch
from torch import nn
from torch.nn import functional as F

from .stylegan2_clean_arch import StyleGAN2GeneratorClean


class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
    Args:
        out_size (int): The spatial size of outputs.
        num_style_feat (int): Channel number of style features. Default: 512.
        num_mlp (int): Layer number of MLP style layers. Default: 8.
        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
        narrow (float): The narrow ratio for channels. Default: 1.
        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
    """

    def __init__(
        self,
        out_size,
        num_style_feat=512,
        num_mlp=8,
        channel_multiplier=2,
        narrow=1,
        sft_half=False,
    ):
        super(StyleGAN2GeneratorCSFT, self).__init__(
            out_size,
            num_style_feat=num_style_feat,
            num_mlp=num_mlp,
            channel_multiplier=channel_multiplier,
            narrow=narrow,
        )
        self.sft_half = sft_half

    def forward(
        self,
        styles,
        conditions,
        input_is_latent=False,
        noise=None,
        randomize_noise=True,
        truncation=1,
        truncation_latent=None,
        inject_index=None,
        return_latents=False,
    ):
        """Forward function for StyleGAN2GeneratorCSFT.
        Args:
            styles (list[Tensor]): Sample codes of styles.
            conditions (list[Tensor]): SFT conditions to generators.
            input_is_latent (bool): Whether input is latent style. Default: False.
            noise (Tensor | None): Input noise or None. Default: None.
            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
            truncation (float): The truncation ratio. Default: 1.
            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
            inject_index (int | None): The injection index for mixing noise. Default: None.
            return_latents (bool): Whether to return style latents. Default: False.
        """
        # style codes -> latents with Style MLP layer
        if not input_is_latent:
            styles = [self.style_mlp(s) for s in styles]
        # noises
        if noise is None:
            if randomize_noise:
                noise = [None] * self.num_layers  # for each style conv layer
            else:  # use the stored noise
                noise = [
                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
                ]
        # style truncation
        if truncation < 1:
            style_truncation = []
            for style in styles:
                style_truncation.append(
                    truncation_latent + truncation * (style - truncation_latent)
                )
            styles = style_truncation
        # get style latents with injection
        if len(styles) == 1:
            inject_index = self.num_latent

            if styles[0].ndim < 3:
                # repeat latent code for all the layers
                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            else:  # used for encoder with different latent code for each layer
                latent = styles[0]
        elif len(styles) == 2:  # mixing noises
            if inject_index is None:
                inject_index = random.randint(1, self.num_latent - 1)
            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
            latent2 = (
                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
            )
            latent = torch.cat([latent1, latent2], 1)

        # main generation
        out = self.constant_input(latent.shape[0])
        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
        skip = self.to_rgb1(out, latent[:, 1])

        i = 1
        for conv1, conv2, noise1, noise2, to_rgb in zip(
            self.style_convs[::2],
            self.style_convs[1::2],
            noise[1::2],
            noise[2::2],
            self.to_rgbs,
        ):
            out = conv1(out, latent[:, i], noise=noise1)

            # the conditions may have fewer levels
            if i < len(conditions):
                # SFT part to combine the conditions
                if self.sft_half:  # only apply SFT to half of the channels
                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
                    out_sft = out_sft * conditions[i - 1] + conditions[i]
                    out = torch.cat([out_same, out_sft], dim=1)
                else:  # apply SFT to all the channels
                    out = out * conditions[i - 1] + conditions[i]

            out = conv2(out, latent[:, i + 1], noise=noise2)
            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
            i += 2

        image = skip

        if return_latents:
            return image, latent
        else:
            return image, None


class ResBlock(nn.Module):
    """Residual block with bilinear upsampling/downsampling.
    Args:
        in_channels (int): Channel number of the input.
        out_channels (int): Channel number of the output.
        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
    """

    def __init__(self, in_channels, out_channels, mode="down"):
        super(ResBlock, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
        if mode == "down":
            self.scale_factor = 0.5
        elif mode == "up":
            self.scale_factor = 2

    def forward(self, x):
        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
        # upsample/downsample
        out = F.interpolate(
            out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
        )
        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
        # skip
        x = F.interpolate(
            x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
        )
        skip = self.skip(x)
        out = out + skip
        return out


class GFPGANv1Clean(nn.Module):
    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
    Args:
        out_size (int): The spatial size of outputs.
        num_style_feat (int): Channel number of style features. Default: 512.
        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
        fix_decoder (bool): Whether to fix the decoder. Default: True.
        num_mlp (int): Layer number of MLP style layers. Default: 8.
        input_is_latent (bool): Whether input is latent style. Default: False.
        different_w (bool): Whether to use different latent w for different layers. Default: False.
        narrow (float): The narrow ratio for channels. Default: 1.
        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
    """

    def __init__(
        self,
        state_dict,
    ):
        super(GFPGANv1Clean, self).__init__()

        out_size = 512
        num_style_feat = 512
        channel_multiplier = 2
        decoder_load_path = None
        fix_decoder = False
        num_mlp = 8
        input_is_latent = True
        different_w = True
        narrow = 1
        sft_half = True

        self.model_arch = "GFPGAN"
        self.sub_type = "Face SR"
        self.scale = 8
        self.in_nc = 3
        self.out_nc = 3
        self.state = state_dict

        self.supports_fp16 = False
        self.supports_bf16 = True
        self.min_size_restriction = 512

        self.input_is_latent = input_is_latent
        self.different_w = different_w
        self.num_style_feat = num_style_feat

        unet_narrow = narrow * 0.5  # by default, use a half of input channels
        channels = {
            "4": int(512 * unet_narrow),
            "8": int(512 * unet_narrow),
            "16": int(512 * unet_narrow),
            "32": int(512 * unet_narrow),
            "64": int(256 * channel_multiplier * unet_narrow),
            "128": int(128 * channel_multiplier * unet_narrow),
            "256": int(64 * channel_multiplier * unet_narrow),
            "512": int(32 * channel_multiplier * unet_narrow),
            "1024": int(16 * channel_multiplier * unet_narrow),
        }

        self.log_size = int(math.log(out_size, 2))
        first_out_size = 2 ** (int(math.log(out_size, 2)))

        self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1)

        # downsample
        in_channels = channels[f"{first_out_size}"]
        self.conv_body_down = nn.ModuleList()
        for i in range(self.log_size, 2, -1):
            out_channels = channels[f"{2**(i - 1)}"]
            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down"))
            in_channels = out_channels

        self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1)

        # upsample
        in_channels = channels["4"]
        self.conv_body_up = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            out_channels = channels[f"{2**i}"]
            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up"))
            in_channels = out_channels

        # to RGB
        self.toRGB = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1))

        if different_w:
            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
        else:
            linear_out_channel = num_style_feat

        self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel)

        # the decoder: stylegan2 generator with SFT modulations
        self.stylegan_decoder = StyleGAN2GeneratorCSFT(
            out_size=out_size,
            num_style_feat=num_style_feat,
            num_mlp=num_mlp,
            channel_multiplier=channel_multiplier,
            narrow=narrow,
            sft_half=sft_half,
        )

        # load pre-trained stylegan2 model if necessary
        if decoder_load_path:
            self.stylegan_decoder.load_state_dict(
                torch.load(
                    decoder_load_path, map_location=lambda storage, loc: storage
                )["params_ema"]
            )
        # fix decoder without updating params
        if fix_decoder:
            for _, param in self.stylegan_decoder.named_parameters():
                param.requires_grad = False

        # for SFT modulations (scale and shift)
        self.condition_scale = nn.ModuleList()
        self.condition_shift = nn.ModuleList()
        for i in range(3, self.log_size + 1):
            out_channels = channels[f"{2**i}"]
            if sft_half:
                sft_out_channels = out_channels
            else:
                sft_out_channels = out_channels * 2
            self.condition_scale.append(
                nn.Sequential(
                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
                )
            )
            self.condition_shift.append(
                nn.Sequential(
                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
                    nn.LeakyReLU(0.2, True),
                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
                )
            )
        self.load_state_dict(state_dict)

    def forward(
        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
    ):
        """Forward function for GFPGANv1Clean.
        Args:
            x (Tensor): Input images.
            return_latents (bool): Whether to return style latents. Default: False.
            return_rgb (bool): Whether return intermediate rgb images. Default: True.
            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
        """
        conditions = []
        unet_skips = []
        out_rgbs = []

        # encoder
        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
        for i in range(self.log_size - 2):
            feat = self.conv_body_down[i](feat)
            unet_skips.insert(0, feat)
        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)

        # style code
        style_code = self.final_linear(feat.view(feat.size(0), -1))
        if self.different_w:
            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)

        # decode
        for i in range(self.log_size - 2):
            # add unet skip
            feat = feat + unet_skips[i]
            # ResUpLayer
            feat = self.conv_body_up[i](feat)
            # generate scale and shift for SFT layers
            scale = self.condition_scale[i](feat)
            conditions.append(scale.clone())
            shift = self.condition_shift[i](feat)
            conditions.append(shift.clone())
            # generate rgb images
            if return_rgb:
                out_rgbs.append(self.toRGB[i](feat))

        # decoder
        image, _ = self.stylegan_decoder(
            [style_code],
            conditions,
            return_latents=return_latents,
            input_is_latent=self.input_is_latent,
            randomize_noise=randomize_noise,
        )

        return image, out_rgbs