import os import random import cv2 import torch import numpy as np import torch.fft as fft from lama_cleaner.schema import Config from lama_cleaner.helper import ( load_model, get_cache_path_by_url, norm_img, boxes_from_mask, resize_max_size, ) from lama_cleaner.model.base import InpaintModel from torch import conv2d, nn import torch.nn.functional as F from lama_cleaner.model.utils import ( setup_filter, _parse_scaling, _parse_padding, Conv2dLayer, FullyConnectedLayer, MinibatchStdLayer, activation_funcs, conv2d_resample, bias_act, upsample2d, normalize_2nd_moment, downsample2d, ) def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"): assert isinstance(x, torch.Tensor) return _upfirdn2d_ref( x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain ) def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.""" # Validate arguments. assert isinstance(x, torch.Tensor) and x.ndim == 4 if f is None: f = torch.ones([1, 1], dtype=torch.float32, device=x.device) assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] assert f.dtype == torch.float32 and not f.requires_grad batch_size, num_channels, in_height, in_width = x.shape upx, upy = _parse_scaling(up) downx, downy = _parse_scaling(down) padx0, padx1, pady0, pady1 = _parse_padding(padding) # Upsample by inserting zeros. x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) # Pad or crop. x = torch.nn.functional.pad( x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)] ) x = x[ :, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0), ] # Setup filter. f = f * (gain ** (f.ndim / 2)) f = f.to(x.dtype) if not flip_filter: f = f.flip(list(range(f.ndim))) # Convolve with the filter. f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) if f.ndim == 4: x = conv2d(input=x, weight=f, groups=num_channels) else: x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) # Downsample by throwing away pixels. x = x[:, :, ::downy, ::downx] return x class EncoderEpilogue(torch.nn.Module): def __init__( self, in_channels, # Number of input channels. cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. z_dim, # Output Latent (Z) dimensionality. resolution, # Resolution of this block. img_channels, # Number of input color channels. architecture="resnet", # Architecture: 'orig', 'skip', 'resnet'. mbstd_group_size=4, # Group size for the minibatch standard deviation layer, None = entire minibatch. mbstd_num_channels=1, # Number of features for the minibatch standard deviation layer, 0 = disable. activation="lrelu", # Activation function: 'relu', 'lrelu', etc. conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. ): assert architecture in ["orig", "skip", "resnet"] super().__init__() self.in_channels = in_channels self.cmap_dim = cmap_dim self.resolution = resolution self.img_channels = img_channels self.architecture = architecture if architecture == "skip": self.fromrgb = Conv2dLayer( self.img_channels, in_channels, kernel_size=1, activation=activation ) self.mbstd = ( MinibatchStdLayer( group_size=mbstd_group_size, num_channels=mbstd_num_channels ) if mbstd_num_channels > 0 else None ) self.conv = Conv2dLayer( in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp, ) self.fc = FullyConnectedLayer( in_channels * (resolution**2), z_dim, activation=activation ) self.dropout = torch.nn.Dropout(p=0.5) def forward(self, x, cmap, force_fp32=False): _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format # FromRGB. x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.mbstd is not None: x = self.mbstd(x) const_e = self.conv(x) x = self.fc(const_e.flatten(1)) x = self.dropout(x) # Conditioning. if self.cmap_dim > 0: x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) assert x.dtype == dtype return x, const_e class EncoderBlock(torch.nn.Module): def __init__( self, in_channels, # Number of input channels, 0 = first block. tmp_channels, # Number of intermediate channels. out_channels, # Number of output channels. resolution, # Resolution of this block. img_channels, # Number of input color channels. first_layer_idx, # Index of the first layer. architecture="skip", # Architecture: 'orig', 'skip', 'resnet'. activation="lrelu", # Activation function: 'relu', 'lrelu', etc. resample_filter=[ 1, 3, 3, 1, ], # Low-pass filter to apply when resampling activations. conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. use_fp16=False, # Use FP16 for this block? fp16_channels_last=False, # Use channels-last memory format with FP16? freeze_layers=0, # Freeze-D: Number of layers to freeze. ): assert in_channels in [0, tmp_channels] assert architecture in ["orig", "skip", "resnet"] super().__init__() self.in_channels = in_channels self.resolution = resolution self.img_channels = img_channels + 1 self.first_layer_idx = first_layer_idx self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = use_fp16 and fp16_channels_last self.register_buffer("resample_filter", setup_filter(resample_filter)) self.num_layers = 0 def trainable_gen(): while True: layer_idx = self.first_layer_idx + self.num_layers trainable = layer_idx >= freeze_layers self.num_layers += 1 yield trainable trainable_iter = trainable_gen() if in_channels == 0: self.fromrgb = Conv2dLayer( self.img_channels, tmp_channels, kernel_size=1, activation=activation, trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last, ) self.conv0 = Conv2dLayer( tmp_channels, tmp_channels, kernel_size=3, activation=activation, trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last, ) self.conv1 = Conv2dLayer( tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, ) if architecture == "resnet": self.skip = Conv2dLayer( tmp_channels, out_channels, kernel_size=1, bias=False, down=2, trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last, ) def forward(self, x, img, force_fp32=False): # dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 dtype = torch.float32 memory_format = ( torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format ) # Input. if x is not None: x = x.to(dtype=dtype, memory_format=memory_format) # FromRGB. if self.in_channels == 0: img = img.to(dtype=dtype, memory_format=memory_format) y = self.fromrgb(img) x = x + y if x is not None else y img = ( downsample2d(img, self.resample_filter) if self.architecture == "skip" else None ) # Main layers. if self.architecture == "resnet": y = self.skip(x, gain=np.sqrt(0.5)) x = self.conv0(x) feat = x.clone() x = self.conv1(x, gain=np.sqrt(0.5)) x = y.add_(x) else: x = self.conv0(x) feat = x.clone() x = self.conv1(x) assert x.dtype == dtype return x, img, feat class EncoderNetwork(torch.nn.Module): def __init__( self, c_dim, # Conditioning label (C) dimensionality. z_dim, # Input latent (Z) dimensionality. img_resolution, # Input resolution. img_channels, # Number of input color channels. architecture="orig", # Architecture: 'orig', 'skip', 'resnet'. channel_base=16384, # Overall multiplier for the number of channels. channel_max=512, # Maximum number of channels in any layer. num_fp16_res=0, # Use FP16 for the N highest resolutions. conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. cmap_dim=None, # Dimensionality of mapped conditioning label, None = default. block_kwargs={}, # Arguments for DiscriminatorBlock. mapping_kwargs={}, # Arguments for MappingNetwork. epilogue_kwargs={}, # Arguments for EncoderEpilogue. ): super().__init__() self.c_dim = c_dim self.z_dim = z_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.block_resolutions = [ 2**i for i in range(self.img_resolution_log2, 2, -1) ] channels_dict = { res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4] } fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) if cmap_dim is None: cmap_dim = channels_dict[4] if c_dim == 0: cmap_dim = 0 common_kwargs = dict( img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp ) cur_layer_idx = 0 for res in self.block_resolutions: in_channels = channels_dict[res] if res < img_resolution else 0 tmp_channels = channels_dict[res] out_channels = channels_dict[res // 2] use_fp16 = res >= fp16_resolution use_fp16 = False block = EncoderBlock( in_channels, tmp_channels, out_channels, resolution=res, first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs, ) setattr(self, f"b{res}", block) cur_layer_idx += block.num_layers if c_dim > 0: self.mapping = MappingNetwork( z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs, ) self.b4 = EncoderEpilogue( channels_dict[4], cmap_dim=cmap_dim, z_dim=z_dim * 2, resolution=4, **epilogue_kwargs, **common_kwargs, ) def forward(self, img, c, **block_kwargs): x = None feats = {} for res in self.block_resolutions: block = getattr(self, f"b{res}") x, img, feat = block(x, img, **block_kwargs) feats[res] = feat cmap = None if self.c_dim > 0: cmap = self.mapping(None, c) x, const_e = self.b4(x, cmap) feats[4] = const_e B, _ = x.shape z = torch.zeros( (B, self.z_dim), requires_grad=False, dtype=x.dtype, device=x.device ) ## Noise for Co-Modulation return x, z, feats def fma(a, b, c): # => a * b + c return _FusedMultiplyAdd.apply(a, b, c) class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c @staticmethod def forward(ctx, a, b, c): # pylint: disable=arguments-differ out = torch.addcmul(c, a, b) ctx.save_for_backward(a, b) ctx.c_shape = c.shape return out @staticmethod def backward(ctx, dout): # pylint: disable=arguments-differ a, b = ctx.saved_tensors c_shape = ctx.c_shape da = None db = None dc = None if ctx.needs_input_grad[0]: da = _unbroadcast(dout * b, a.shape) if ctx.needs_input_grad[1]: db = _unbroadcast(dout * a, b.shape) if ctx.needs_input_grad[2]: dc = _unbroadcast(dout, c_shape) return da, db, dc def _unbroadcast(x, shape): extra_dims = x.ndim - len(shape) assert extra_dims >= 0 dim = [ i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1) ] if len(dim): x = x.sum(dim=dim, keepdim=True) if extra_dims: x = x.reshape(-1, *x.shape[extra_dims + 1 :]) assert x.shape == shape return x def modulated_conv2d( x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. styles, # Modulation coefficients of shape [batch_size, in_channels]. noise=None, # Optional noise tensor to add to the output activations. up=1, # Integer upsampling factor. down=1, # Integer downsampling factor. padding=0, # Padding with respect to the upsampled image. resample_filter=None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). demodulate=True, # Apply weight demodulation? flip_weight=True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). fused_modconv=True, # Perform modulation, convolution, and demodulation as a single fused operation? ): batch_size = x.shape[0] out_channels, in_channels, kh, kw = weight.shape # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == torch.float16 and demodulate: weight = weight * ( 1 / np.sqrt(in_channels * kh * kw) / weight.norm(float("inf"), dim=[1, 2, 3], keepdim=True) ) # max_Ikk styles = styles / styles.norm(float("inf"), dim=1, keepdim=True) # max_I # Calculate per-sample weights and demodulation coefficients. w = None dcoefs = None if demodulate or fused_modconv: w = weight.unsqueeze(0) # [NOIkk] w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] if demodulate: dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] if demodulate and fused_modconv: w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) x = conv2d_resample.conv2d_resample( x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight, ) if demodulate and noise is not None: x = fma( x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype) ) elif demodulate: x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) elif noise is not None: x = x.add_(noise.to(x.dtype)) return x # Execute as one fused op using grouped convolution. batch_size = int(batch_size) x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) x = conv2d_resample( x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight, ) x = x.reshape(batch_size, -1, *x.shape[2:]) if noise is not None: x = x.add_(noise) return x class SynthesisLayer(torch.nn.Module): def __init__( self, in_channels, # Number of input channels. out_channels, # Number of output channels. w_dim, # Intermediate latent (W) dimensionality. resolution, # Resolution of this layer. kernel_size=3, # Convolution kernel size. up=1, # Integer upsampling factor. use_noise=True, # Enable noise input? activation="lrelu", # Activation function: 'relu', 'lrelu', etc. resample_filter=[ 1, 3, 3, 1, ], # Low-pass filter to apply when resampling activations. conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. channels_last=False, # Use channels_last format for the weights? ): super().__init__() self.resolution = resolution self.up = up self.use_noise = use_noise self.activation = activation self.conv_clamp = conv_clamp self.register_buffer("resample_filter", setup_filter(resample_filter)) self.padding = kernel_size // 2 self.act_gain = activation_funcs[activation].def_gain self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = ( torch.channels_last if channels_last else torch.contiguous_format ) self.weight = torch.nn.Parameter( torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( memory_format=memory_format ) ) if use_noise: self.register_buffer("noise_const", torch.randn([resolution, resolution])) self.noise_strength = torch.nn.Parameter(torch.zeros([])) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) def forward(self, x, w, noise_mode="none", fused_modconv=True, gain=1): assert noise_mode in ["random", "const", "none"] in_resolution = self.resolution // self.up styles = self.affine(w) noise = None if self.use_noise and noise_mode == "random": noise = ( torch.randn( [x.shape[0], 1, self.resolution, self.resolution], device=x.device ) * self.noise_strength ) if self.use_noise and noise_mode == "const": noise = self.noise_const * self.noise_strength flip_weight = self.up == 1 # slightly faster x = modulated_conv2d( x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv, ) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None x = F.leaky_relu(x, negative_slope=0.2, inplace=False) if act_gain != 1: x = x * act_gain if act_clamp is not None: x = x.clamp(-act_clamp, act_clamp) return x class ToRGBLayer(torch.nn.Module): def __init__( self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False, ): super().__init__() self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = ( torch.channels_last if channels_last else torch.contiguous_format ) self.weight = torch.nn.Parameter( torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( memory_format=memory_format ) ) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2)) def forward(self, x, w, fused_modconv=True): styles = self.affine(w) * self.weight_gain x = modulated_conv2d( x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv, ) x = bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) return x class SynthesisForeword(torch.nn.Module): def __init__( self, z_dim, # Output Latent (Z) dimensionality. resolution, # Resolution of this block. in_channels, img_channels, # Number of input color channels. architecture="skip", # Architecture: 'orig', 'skip', 'resnet'. activation="lrelu", # Activation function: 'relu', 'lrelu', etc. ): super().__init__() self.in_channels = in_channels self.z_dim = z_dim self.resolution = resolution self.img_channels = img_channels self.architecture = architecture self.fc = FullyConnectedLayer( self.z_dim, (self.z_dim // 2) * 4 * 4, activation=activation ) self.conv = SynthesisLayer( self.in_channels, self.in_channels, w_dim=(z_dim // 2) * 3, resolution=4 ) if architecture == "skip": self.torgb = ToRGBLayer( self.in_channels, self.img_channels, kernel_size=1, w_dim=(z_dim // 2) * 3, ) def forward(self, x, ws, feats, img, force_fp32=False): _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format x_global = x.clone() # ToRGB. x = self.fc(x) x = x.view(-1, self.z_dim // 2, 4, 4) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. x_skip = feats[4].clone() x = x + x_skip mod_vector = [] mod_vector.append(ws[:, 0]) mod_vector.append(x_global.clone()) mod_vector = torch.cat(mod_vector, dim=1) x = self.conv(x, mod_vector) mod_vector = [] mod_vector.append(ws[:, 2 * 2 - 3]) mod_vector.append(x_global.clone()) mod_vector = torch.cat(mod_vector, dim=1) if self.architecture == "skip": img = self.torgb(x, mod_vector) img = img.to(dtype=torch.float32, memory_format=torch.contiguous_format) assert x.dtype == dtype return x, img class SELayer(nn.Module): def __init__(self, channel, reduction=16): super(SELayer, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=False), nn.Linear(channel // reduction, channel, bias=False), nn.Sigmoid(), ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) res = x * y.expand_as(x) return res class FourierUnit(nn.Module): def __init__( self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode="bilinear", spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm="ortho", ): # bn_layer not used super(FourierUnit, self).__init__() self.groups = groups self.conv_layer = torch.nn.Conv2d( in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), out_channels=out_channels * 2, kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False, ) self.relu = torch.nn.ReLU(inplace=False) # squeeze and excitation block self.use_se = use_se if use_se: if se_kwargs is None: se_kwargs = {} self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) self.spatial_scale_factor = spatial_scale_factor self.spatial_scale_mode = spatial_scale_mode self.spectral_pos_encoding = spectral_pos_encoding self.ffc3d = ffc3d self.fft_norm = fft_norm def forward(self, x): batch = x.shape[0] if self.spatial_scale_factor is not None: orig_size = x.shape[-2:] x = F.interpolate( x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False, ) r_size = x.size() # (batch, c, h, w/2+1, 2) fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) ffted = fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) ffted = torch.stack((ffted.real, ffted.imag), dim=-1) ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) ffted = ffted.view( ( batch, -1, ) + ffted.size()[3:] ) if self.spectral_pos_encoding: height, width = ffted.shape[-2:] coords_vert = ( torch.linspace(0, 1, height)[None, None, :, None] .expand(batch, 1, height, width) .to(ffted) ) coords_hor = ( torch.linspace(0, 1, width)[None, None, None, :] .expand(batch, 1, height, width) .to(ffted) ) ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) if self.use_se: ffted = self.se(ffted) ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1) ffted = self.relu(ffted) ffted = ( ffted.view( ( batch, -1, 2, ) + ffted.size()[2:] ) .permute(0, 1, 3, 4, 2) .contiguous() ) # (batch,c, t, h, w/2+1, 2) ffted = torch.complex(ffted[..., 0], ffted[..., 1]) ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] output = torch.fft.irfftn( ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm ) if self.spatial_scale_factor is not None: output = F.interpolate( output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False, ) return output class SpectralTransform(nn.Module): def __init__( self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs, ): # bn_layer not used super(SpectralTransform, self).__init__() self.enable_lfu = enable_lfu if stride == 2: self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) else: self.downsample = nn.Identity() self.stride = stride self.conv1 = nn.Sequential( nn.Conv2d( in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False ), # nn.BatchNorm2d(out_channels // 2), nn.ReLU(inplace=True), ) self.fu = FourierUnit(out_channels // 2, out_channels // 2, groups, **fu_kwargs) if self.enable_lfu: self.lfu = FourierUnit(out_channels // 2, out_channels // 2, groups) self.conv2 = torch.nn.Conv2d( out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False ) def forward(self, x): x = self.downsample(x) x = self.conv1(x) output = self.fu(x) if self.enable_lfu: n, c, h, w = x.shape split_no = 2 split_s = h // split_no xs = torch.cat( torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 ).contiguous() xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() xs = self.lfu(xs) xs = xs.repeat(1, 1, split_no, split_no).contiguous() else: xs = 0 output = self.conv2(x + output + xs) return output class FFC(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride=1, padding=0, dilation=1, groups=1, bias=False, enable_lfu=True, padding_type="reflect", gated=False, **spectral_kwargs, ): super(FFC, self).__init__() assert stride == 1 or stride == 2, "Stride should be 1 or 2." self.stride = stride in_cg = int(in_channels * ratio_gin) in_cl = in_channels - in_cg out_cg = int(out_channels * ratio_gout) out_cl = out_channels - out_cg # groups_g = 1 if groups == 1 else int(groups * ratio_gout) # groups_l = 1 if groups == 1 else groups - groups_g self.ratio_gin = ratio_gin self.ratio_gout = ratio_gout self.global_in_num = in_cg module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d self.convl2l = module( in_cl, out_cl, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d self.convl2g = module( in_cl, out_cg, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d self.convg2l = module( in_cg, out_cl, kernel_size, stride, padding, dilation, groups, bias, padding_mode=padding_type, ) module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform self.convg2g = module( in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs, ) self.gated = gated module = ( nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d ) self.gate = module(in_channels, 2, 1) def forward(self, x, fname=None): x_l, x_g = x if type(x) is tuple else (x, 0) out_xl, out_xg = 0, 0 if self.gated: total_input_parts = [x_l] if torch.is_tensor(x_g): total_input_parts.append(x_g) total_input = torch.cat(total_input_parts, dim=1) gates = torch.sigmoid(self.gate(total_input)) g2l_gate, l2g_gate = gates.chunk(2, dim=1) else: g2l_gate, l2g_gate = 1, 1 spec_x = self.convg2g(x_g) if self.ratio_gout != 1: out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate if self.ratio_gout != 0: out_xg = self.convl2g(x_l) * l2g_gate + spec_x return out_xl, out_xg class FFC_BN_ACT(nn.Module): def __init__( self, in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride=1, padding=0, dilation=1, groups=1, bias=False, norm_layer=nn.SyncBatchNorm, activation_layer=nn.Identity, padding_type="reflect", enable_lfu=True, **kwargs, ): super(FFC_BN_ACT, self).__init__() self.ffc = FFC( in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride, padding, dilation, groups, bias, enable_lfu, padding_type=padding_type, **kwargs, ) lnorm = nn.Identity if ratio_gout == 1 else norm_layer gnorm = nn.Identity if ratio_gout == 0 else norm_layer global_channels = int(out_channels * ratio_gout) # self.bn_l = lnorm(out_channels - global_channels) # self.bn_g = gnorm(global_channels) lact = nn.Identity if ratio_gout == 1 else activation_layer gact = nn.Identity if ratio_gout == 0 else activation_layer self.act_l = lact(inplace=True) self.act_g = gact(inplace=True) def forward(self, x, fname=None): x_l, x_g = self.ffc( x, fname=fname, ) x_l = self.act_l(x_l) x_g = self.act_g(x_g) return x_l, x_g class FFCResnetBlock(nn.Module): def __init__( self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, spatial_transform_kwargs=None, inline=False, ratio_gin=0.75, ratio_gout=0.75, ): super().__init__() self.conv1 = FFC_BN_ACT( dim, dim, kernel_size=3, padding=dilation, dilation=dilation, norm_layer=norm_layer, activation_layer=activation_layer, padding_type=padding_type, ratio_gin=ratio_gin, ratio_gout=ratio_gout, ) self.conv2 = FFC_BN_ACT( dim, dim, kernel_size=3, padding=dilation, dilation=dilation, norm_layer=norm_layer, activation_layer=activation_layer, padding_type=padding_type, ratio_gin=ratio_gin, ratio_gout=ratio_gout, ) self.inline = inline def forward(self, x, fname=None): if self.inline: x_l, x_g = ( x[:, : -self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num :], ) else: x_l, x_g = x if type(x) is tuple else (x, 0) id_l, id_g = x_l, x_g x_l, x_g = self.conv1((x_l, x_g), fname=fname) x_l, x_g = self.conv2((x_l, x_g), fname=fname) x_l, x_g = id_l + x_l, id_g + x_g out = x_l, x_g if self.inline: out = torch.cat(out, dim=1) return out class ConcatTupleLayer(nn.Module): def forward(self, x): assert isinstance(x, tuple) x_l, x_g = x assert torch.is_tensor(x_l) or torch.is_tensor(x_g) if not torch.is_tensor(x_g): return x_l return torch.cat(x, dim=1) class FFCBlock(torch.nn.Module): def __init__( self, dim, # Number of output/input channels. kernel_size, # Width and height of the convolution kernel. padding, ratio_gin=0.75, ratio_gout=0.75, activation="linear", # Activation function: 'relu', 'lrelu', etc. ): super().__init__() if activation == "linear": self.activation = nn.Identity else: self.activation = nn.ReLU self.padding = padding self.kernel_size = kernel_size self.ffc_block = FFCResnetBlock( dim=dim, padding_type="reflect", norm_layer=nn.SyncBatchNorm, activation_layer=self.activation, dilation=1, ratio_gin=ratio_gin, ratio_gout=ratio_gout, ) self.concat_layer = ConcatTupleLayer() def forward(self, gen_ft, mask, fname=None): x = gen_ft.float() x_l, x_g = ( x[:, : -self.ffc_block.conv1.ffc.global_in_num], x[:, -self.ffc_block.conv1.ffc.global_in_num :], ) id_l, id_g = x_l, x_g x_l, x_g = self.ffc_block((x_l, x_g), fname=fname) x_l, x_g = id_l + x_l, id_g + x_g x = self.concat_layer((x_l, x_g)) return x + gen_ft.float() class FFCSkipLayer(torch.nn.Module): def __init__( self, dim, # Number of input/output channels. kernel_size=3, # Convolution kernel size. ratio_gin=0.75, ratio_gout=0.75, ): super().__init__() self.padding = kernel_size // 2 self.ffc_act = FFCBlock( dim=dim, kernel_size=kernel_size, activation=nn.ReLU, padding=self.padding, ratio_gin=ratio_gin, ratio_gout=ratio_gout, ) def forward(self, gen_ft, mask, fname=None): x = self.ffc_act(gen_ft, mask, fname=fname) return x class SynthesisBlock(torch.nn.Module): def __init__( self, in_channels, # Number of input channels, 0 = first block. out_channels, # Number of output channels. w_dim, # Intermediate latent (W) dimensionality. resolution, # Resolution of this block. img_channels, # Number of output color channels. is_last, # Is this the last block? architecture="skip", # Architecture: 'orig', 'skip', 'resnet'. resample_filter=[ 1, 3, 3, 1, ], # Low-pass filter to apply when resampling activations. conv_clamp=None, # Clamp the output of convolution layers to +-X, None = disable clamping. use_fp16=False, # Use FP16 for this block? fp16_channels_last=False, # Use channels-last memory format with FP16? **layer_kwargs, # Arguments for SynthesisLayer. ): assert architecture in ["orig", "skip", "resnet"] super().__init__() self.in_channels = in_channels self.w_dim = w_dim self.resolution = resolution self.img_channels = img_channels self.is_last = is_last self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = use_fp16 and fp16_channels_last self.register_buffer("resample_filter", setup_filter(resample_filter)) self.num_conv = 0 self.num_torgb = 0 self.res_ffc = {4: 0, 8: 0, 16: 0, 32: 1, 64: 1, 128: 1, 256: 1, 512: 1} if in_channels != 0 and resolution >= 8: self.ffc_skip = nn.ModuleList() for _ in range(self.res_ffc[resolution]): self.ffc_skip.append(FFCSkipLayer(dim=out_channels)) if in_channels == 0: self.const = torch.nn.Parameter( torch.randn([out_channels, resolution, resolution]) ) if in_channels != 0: self.conv0 = SynthesisLayer( in_channels, out_channels, w_dim=w_dim * 3, resolution=resolution, up=2, resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs, ) self.num_conv += 1 self.conv1 = SynthesisLayer( out_channels, out_channels, w_dim=w_dim * 3, resolution=resolution, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs, ) self.num_conv += 1 if is_last or architecture == "skip": self.torgb = ToRGBLayer( out_channels, img_channels, w_dim=w_dim * 3, conv_clamp=conv_clamp, channels_last=self.channels_last, ) self.num_torgb += 1 if in_channels != 0 and architecture == "resnet": self.skip = Conv2dLayer( in_channels, out_channels, kernel_size=1, bias=False, up=2, resample_filter=resample_filter, channels_last=self.channels_last, ) def forward( self, x, mask, feats, img, ws, fname=None, force_fp32=False, fused_modconv=None, **layer_kwargs, ): dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 dtype = torch.float32 memory_format = ( torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format ) if fused_modconv is None: fused_modconv = (not self.training) and ( dtype == torch.float32 or int(x.shape[0]) == 1 ) x = x.to(dtype=dtype, memory_format=memory_format) x_skip = ( feats[self.resolution].clone().to(dtype=dtype, memory_format=memory_format) ) # Main layers. if self.in_channels == 0: x = self.conv1(x, ws[1], fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == "resnet": y = self.skip(x, gain=np.sqrt(0.5)) x = self.conv0( x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs ) if len(self.ffc_skip) > 0: mask = F.interpolate( mask, size=x_skip.shape[2:], ) z = x + x_skip for fres in self.ffc_skip: z = fres(z, mask) x = x + z else: x = x + x_skip x = self.conv1( x, ws[1].clone(), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs, ) x = y.add_(x) else: x = self.conv0( x, ws[0].clone(), fused_modconv=fused_modconv, **layer_kwargs ) if len(self.ffc_skip) > 0: mask = F.interpolate( mask, size=x_skip.shape[2:], ) z = x + x_skip for fres in self.ffc_skip: z = fres(z, mask) x = x + z else: x = x + x_skip x = self.conv1( x, ws[1].clone(), fused_modconv=fused_modconv, **layer_kwargs ) # ToRGB. if img is not None: img = upsample2d(img, self.resample_filter) if self.is_last or self.architecture == "skip": y = self.torgb(x, ws[2].clone(), fused_modconv=fused_modconv) y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y x = x.to(dtype=dtype) assert x.dtype == dtype assert img is None or img.dtype == torch.float32 return x, img class SynthesisNetwork(torch.nn.Module): def __init__( self, w_dim, # Intermediate latent (W) dimensionality. z_dim, # Output Latent (Z) dimensionality. img_resolution, # Output image resolution. img_channels, # Number of color channels. channel_base=16384, # Overall multiplier for the number of channels. channel_max=512, # Maximum number of channels in any layer. num_fp16_res=0, # Use FP16 for the N highest resolutions. **block_kwargs, # Arguments for SynthesisBlock. ): assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 super().__init__() self.w_dim = w_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.block_resolutions = [ 2**i for i in range(3, self.img_resolution_log2 + 1) ] channels_dict = { res: min(channel_base // res, channel_max) for res in self.block_resolutions } fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) self.foreword = SynthesisForeword( img_channels=img_channels, in_channels=min(channel_base // 4, channel_max), z_dim=z_dim * 2, resolution=4, ) self.num_ws = self.img_resolution_log2 * 2 - 2 for res in self.block_resolutions: if res // 2 in channels_dict.keys(): in_channels = channels_dict[res // 2] if res > 4 else 0 else: in_channels = min(channel_base // (res // 2), channel_max) out_channels = channels_dict[res] use_fp16 = res >= fp16_resolution use_fp16 = False is_last = res == self.img_resolution block = SynthesisBlock( in_channels, out_channels, w_dim=w_dim, resolution=res, img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs, ) setattr(self, f"b{res}", block) def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs): img = None x, img = self.foreword(x_global, ws, feats, img) for res in self.block_resolutions: block = getattr(self, f"b{res}") mod_vector0 = [] mod_vector0.append(ws[:, int(np.log2(res)) * 2 - 5]) mod_vector0.append(x_global.clone()) mod_vector0 = torch.cat(mod_vector0, dim=1) mod_vector1 = [] mod_vector1.append(ws[:, int(np.log2(res)) * 2 - 4]) mod_vector1.append(x_global.clone()) mod_vector1 = torch.cat(mod_vector1, dim=1) mod_vector_rgb = [] mod_vector_rgb.append(ws[:, int(np.log2(res)) * 2 - 3]) mod_vector_rgb.append(x_global.clone()) mod_vector_rgb = torch.cat(mod_vector_rgb, dim=1) x, img = block( x, mask, feats, img, (mod_vector0, mod_vector1, mod_vector_rgb), fname=fname, **block_kwargs, ) return img class MappingNetwork(torch.nn.Module): def __init__( self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim, # Conditioning label (C) dimensionality, 0 = no label. w_dim, # Intermediate latent (W) dimensionality. num_ws, # Number of intermediate latents to output, None = do not broadcast. num_layers=8, # Number of mapping layers. embed_features=None, # Label embedding dimensionality, None = same as w_dim. layer_features=None, # Number of intermediate features in the mapping layers, None = same as w_dim. activation="lrelu", # Activation function: 'relu', 'lrelu', etc. lr_multiplier=0.01, # Learning rate multiplier for the mapping layers. w_avg_beta=0.995, # Decay for tracking the moving average of W during training, None = do not track. ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.num_ws = num_ws self.num_layers = num_layers self.w_avg_beta = w_avg_beta if embed_features is None: embed_features = w_dim if c_dim == 0: embed_features = 0 if layer_features is None: layer_features = w_dim features_list = ( [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] ) if c_dim > 0: self.embed = FullyConnectedLayer(c_dim, embed_features) for idx in range(num_layers): in_features = features_list[idx] out_features = features_list[idx + 1] layer = FullyConnectedLayer( in_features, out_features, activation=activation, lr_multiplier=lr_multiplier, ) setattr(self, f"fc{idx}", layer) if num_ws is not None and w_avg_beta is not None: self.register_buffer("w_avg", torch.zeros([w_dim])) def forward( self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False ): # Embed, normalize, and concat inputs. x = None with torch.autograd.profiler.record_function("input"): if self.z_dim > 0: x = normalize_2nd_moment(z.to(torch.float32)) if self.c_dim > 0: y = normalize_2nd_moment(self.embed(c.to(torch.float32))) x = torch.cat([x, y], dim=1) if x is not None else y # Main layers. for idx in range(self.num_layers): layer = getattr(self, f"fc{idx}") x = layer(x) # Update moving average of W. if self.w_avg_beta is not None and self.training and not skip_w_avg_update: with torch.autograd.profiler.record_function("update_w_avg"): self.w_avg.copy_( x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta) ) # Broadcast. if self.num_ws is not None: with torch.autograd.profiler.record_function("broadcast"): x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) # Apply truncation. if truncation_psi != 1: with torch.autograd.profiler.record_function("truncate"): assert self.w_avg_beta is not None if self.num_ws is None or truncation_cutoff is None: x = self.w_avg.lerp(x, truncation_psi) else: x[:, :truncation_cutoff] = self.w_avg.lerp( x[:, :truncation_cutoff], truncation_psi ) return x class Generator(torch.nn.Module): def __init__( self, z_dim, # Input latent (Z) dimensionality. c_dim, # Conditioning label (C) dimensionality. w_dim, # Intermediate latent (W) dimensionality. img_resolution, # Output resolution. img_channels, # Number of output color channels. encoder_kwargs={}, # Arguments for EncoderNetwork. mapping_kwargs={}, # Arguments for MappingNetwork. synthesis_kwargs={}, # Arguments for SynthesisNetwork. ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.img_resolution = img_resolution self.img_channels = img_channels self.encoder = EncoderNetwork( c_dim=c_dim, z_dim=z_dim, img_resolution=img_resolution, img_channels=img_channels, **encoder_kwargs, ) self.synthesis = SynthesisNetwork( z_dim=z_dim, w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs, ) self.num_ws = self.synthesis.num_ws self.mapping = MappingNetwork( z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs ) def forward( self, img, c, fname=None, truncation_psi=1, truncation_cutoff=None, **synthesis_kwargs, ): mask = img[:, -1].unsqueeze(1) x_global, z, feats = self.encoder(img, c) ws = self.mapping( z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff ) img = self.synthesis(x_global, mask, feats, ws, fname=fname, **synthesis_kwargs) return img FCF_MODEL_URL = os.environ.get( "FCF_MODEL_URL", "https://github.com/Sanster/models/releases/download/add_fcf/places_512_G.pth", ) FCF_MODEL_MD5 = os.environ.get("FCF_MODEL_MD5", "3323152bc01bf1c56fd8aba74435a211") class FcF(InpaintModel): name = "fcf" min_size = 512 pad_mod = 512 pad_to_square = True def init_model(self, device, **kwargs): seed = 0 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False kwargs = { "channel_base": 1 * 32768, "channel_max": 512, "num_fp16_res": 4, "conv_clamp": 256, } G = Generator( z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3, synthesis_kwargs=kwargs, encoder_kwargs=kwargs, mapping_kwargs={"num_layers": 2}, ) self.model = load_model(G, FCF_MODEL_URL, device, FCF_MODEL_MD5) self.label = torch.zeros([1, self.model.c_dim], device=device) @staticmethod def is_downloaded() -> bool: return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL)) @torch.no_grad() def __call__(self, image, mask, config: Config): """ images: [H, W, C] RGB, not normalized masks: [H, W] return: BGR IMAGE """ if image.shape[0] == 512 and image.shape[1] == 512: return self._pad_forward(image, mask, config) boxes = boxes_from_mask(mask) crop_result = [] config.hd_strategy_crop_margin = 128 for box in boxes: crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config) origin_size = crop_image.shape[:2] resize_image = resize_max_size(crop_image, size_limit=512) resize_mask = resize_max_size(crop_mask, size_limit=512) inpaint_result = self._pad_forward(resize_image, resize_mask, config) # only paste masked area result inpaint_result = cv2.resize( inpaint_result, (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC, ) original_pixel_indices = crop_mask < 127 inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][ original_pixel_indices ] crop_result.append((inpaint_result, crop_box)) inpaint_result = image[:, :, ::-1] for crop_image, crop_box in crop_result: x1, y1, x2, y2 = crop_box inpaint_result[y1:y2, x1:x2, :] = crop_image return inpaint_result def forward(self, image, mask, config: Config): """Input images and output images have same size images: [H, W, C] RGB masks: [H, W] mask area == 255 return: BGR IMAGE """ image = norm_img(image) # [0, 1] image = image * 2 - 1 # [0, 1] -> [-1, 1] mask = (mask > 120) * 255 mask = norm_img(mask) image = torch.from_numpy(image).unsqueeze(0).to(self.device) mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) erased_img = image * (1 - mask) input_image = torch.cat([0.5 - mask, erased_img], dim=1) output = self.model( input_image, self.label, truncation_psi=0.1, noise_mode="none" ) output = ( (output.permute(0, 2, 3, 1) * 127.5 + 127.5) .round() .clamp(0, 255) .to(torch.uint8) ) output = output[0].cpu().numpy() cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return cur_res