codeformer support

modules/codeformer/codeformer_arch.py

@@ -0,0 +1,276 @@
+import math
+import numpy as np
+import torch
+from torch import nn, Tensor
+import torch.nn.functional as F
+from typing import Optional, List
+
+from modules.codeformer.vqgan_arch import *
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def calc_mean_std(feat, eps=1e-5):
+    """Calculate mean and std for adaptive_instance_normalization.
+
+    Args:
+        feat (Tensor): 4D tensor.
+        eps (float): A small value added to the variance to avoid
+            divide-by-zero. Default: 1e-5.
+    """
+    size = feat.size()
+    assert len(size) == 4, 'The input feature should be 4D tensor.'
+    b, c = size[:2]
+    feat_var = feat.view(b, c, -1).var(dim=2) + eps
+    feat_std = feat_var.sqrt().view(b, c, 1, 1)
+    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
+    return feat_mean, feat_std
+
+
+def adaptive_instance_normalization(content_feat, style_feat):
+    """Adaptive instance normalization.
+
+    Adjust the reference features to have the similar color and illuminations
+    as those in the degradate features.
+
+    Args:
+        content_feat (Tensor): The reference feature.
+        style_feat (Tensor): The degradate features.
+    """
+    size = content_feat.size()
+    style_mean, style_std = calc_mean_std(style_feat)
+    content_mean, content_std = calc_mean_std(content_feat)
+    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
+    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, x, mask=None):
+        if mask is None:
+            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+def _get_activation_fn(activation):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class TransformerSALayer(nn.Module):
+    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
+        # Implementation of Feedforward model - MLP
+        self.linear1 = nn.Linear(embed_dim, dim_mlp)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_mlp, embed_dim)
+
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.norm2 = nn.LayerNorm(embed_dim)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(self, tgt,
+                tgt_mask: Optional[Tensor] = None,
+                tgt_key_padding_mask: Optional[Tensor] = None,
+                query_pos: Optional[Tensor] = None):
+        
+        # self attention
+        tgt2 = self.norm1(tgt)
+        q = k = self.with_pos_embed(tgt2, query_pos)
+        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+                              key_padding_mask=tgt_key_padding_mask)[0]
+        tgt = tgt + self.dropout1(tgt2)
+
+        # ffn
+        tgt2 = self.norm2(tgt)
+        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+        tgt = tgt + self.dropout2(tgt2)
+        return tgt
+
+class Fuse_sft_block(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super().__init__()
+        self.encode_enc = ResBlock(2*in_ch, out_ch)
+
+        self.scale = nn.Sequential(
+                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+                    nn.LeakyReLU(0.2, True),
+                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+        self.shift = nn.Sequential(
+                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
+                    nn.LeakyReLU(0.2, True),
+                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
+
+    def forward(self, enc_feat, dec_feat, w=1):
+        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
+        scale = self.scale(enc_feat)
+        shift = self.shift(enc_feat)
+        residual = w * (dec_feat * scale + shift)
+        out = dec_feat + residual
+        return out
+
+
+@ARCH_REGISTRY.register()
+class CodeFormer(VQAutoEncoder):
+    def __init__(self, dim_embd=512, n_head=8, n_layers=9, 
+                codebook_size=1024, latent_size=256,
+                connect_list=['32', '64', '128', '256'],
+                fix_modules=['quantize','generator']):
+        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
+
+        if fix_modules is not None:
+            for module in fix_modules:
+                for param in getattr(self, module).parameters():
+                    param.requires_grad = False
+
+        self.connect_list = connect_list
+        self.n_layers = n_layers
+        self.dim_embd = dim_embd
+        self.dim_mlp = dim_embd*2
+
+        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
+        self.feat_emb = nn.Linear(256, self.dim_embd)
+
+        # transformer
+        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) 
+                                    for _ in range(self.n_layers)])
+
+        # logits_predict head
+        self.idx_pred_layer = nn.Sequential(
+            nn.LayerNorm(dim_embd),
+            nn.Linear(dim_embd, codebook_size, bias=False))
+        
+        self.channels = {
+            '16': 512,
+            '32': 256,
+            '64': 256,
+            '128': 128,
+            '256': 128,
+            '512': 64,
+        }
+
+        # after second residual block for > 16, before attn layer for ==16
+        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
+        # after first residual block for > 16, before attn layer for ==16
+        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
+
+        # fuse_convs_dict
+        self.fuse_convs_dict = nn.ModuleDict()
+        for f_size in self.connect_list:
+            in_ch = self.channels[f_size]
+            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
+
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=0.02)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
+        # ################### Encoder #####################
+        enc_feat_dict = {}
+        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
+        for i, block in enumerate(self.encoder.blocks):
+            x = block(x) 
+            if i in out_list:
+                enc_feat_dict[str(x.shape[-1])] = x.clone()
+
+        lq_feat = x
+        # ################# Transformer ###################
+        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
+        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
+        # BCHW -> BC(HW) -> (HW)BC
+        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
+        query_emb = feat_emb
+        # Transformer encoder
+        for layer in self.ft_layers:
+            query_emb = layer(query_emb, query_pos=pos_emb)
+
+        # output logits
+        logits = self.idx_pred_layer(query_emb) # (hw)bn
+        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
+
+        if code_only: # for training stage II
+          # logits doesn't need softmax before cross_entropy loss
+            return logits, lq_feat
+
+        # ################# Quantization ###################
+        # if self.training:
+        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
+        #     # b(hw)c -> bc(hw) -> bchw
+        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
+        # ------------
+        soft_one_hot = F.softmax(logits, dim=2)
+        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
+        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
+        # preserve gradients
+        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
+
+        if detach_16:
+            quant_feat = quant_feat.detach() # for training stage III
+        if adain:
+            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
+
+        # ################## Generator ####################
+        x = quant_feat
+        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
+
+        for i, block in enumerate(self.generator.blocks):
+            x = block(x) 
+            if i in fuse_list: # fuse after i-th block
+                f_size = str(x.shape[-1])
+                if w>0:
+                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
+        out = x
+        # logits doesn't need softmax before cross_entropy loss
+        return out, logits, lq_feat

modules/codeformer/vqgan_arch.py

@@ -0,0 +1,435 @@
+'''
+VQGAN code, adapted from the original created by the Unleashing Transformers authors:
+https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
+
+'''
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import copy
+from basicsr.utils import get_root_logger
+from basicsr.utils.registry import ARCH_REGISTRY
+
+def normalize(in_channels):
+    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    
+
+@torch.jit.script
+def swish(x):
+    return x*torch.sigmoid(x)
+
+
+#  Define VQVAE classes
+class VectorQuantizer(nn.Module):
+    def __init__(self, codebook_size, emb_dim, beta):
+        super(VectorQuantizer, self).__init__()
+        self.codebook_size = codebook_size  # number of embeddings
+        self.emb_dim = emb_dim  # dimension of embedding
+        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
+        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
+
+    def forward(self, z):
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = z.permute(0, 2, 3, 1).contiguous()
+        z_flattened = z.view(-1, self.emb_dim)
+
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
+            2 * torch.matmul(z_flattened, self.embedding.weight.t())
+
+        mean_distance = torch.mean(d)
+        # find closest encodings
+        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
+        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
+        # [0-1], higher score, higher confidence
+        min_encoding_scores = torch.exp(-min_encoding_scores/10)
+
+        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
+        min_encodings.scatter_(1, min_encoding_indices, 1)
+
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
+        # compute loss for embedding
+        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # perplexity
+        e_mean = torch.mean(min_encodings, dim=0)
+        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
+        # reshape back to match original input shape
+        z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+        return z_q, loss, {
+            "perplexity": perplexity,
+            "min_encodings": min_encodings,
+            "min_encoding_indices": min_encoding_indices,
+            "min_encoding_scores": min_encoding_scores,
+            "mean_distance": mean_distance
+            }
+
+    def get_codebook_feat(self, indices, shape):
+        # input indices: batch*token_num -> (batch*token_num)*1
+        # shape: batch, height, width, channel
+        indices = indices.view(-1,1)
+        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
+        min_encodings.scatter_(1, indices, 1)
+        # get quantized latent vectors
+        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
+
+        if shape is not None:  # reshape back to match original input shape
+            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
+
+        return z_q
+
+
+class GumbelQuantizer(nn.Module):
+    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
+        super().__init__()
+        self.codebook_size = codebook_size  # number of embeddings
+        self.emb_dim = emb_dim  # dimension of embedding
+        self.straight_through = straight_through
+        self.temperature = temp_init
+        self.kl_weight = kl_weight
+        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
+        self.embed = nn.Embedding(codebook_size, emb_dim)
+
+    def forward(self, z):
+        hard = self.straight_through if self.training else True
+
+        logits = self.proj(z)
+
+        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
+
+        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
+
+        # + kl divergence to the prior loss
+        qy = F.softmax(logits, dim=1)
+        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
+        min_encoding_indices = soft_one_hot.argmax(dim=1)
+
+        return z_q, diff, {
+            "min_encoding_indices": min_encoding_indices
+        }
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x):
+        pad = (0, 1, 0, 1)
+        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+
+        return x
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, out_channels=None):
+        super(ResBlock, self).__init__()
+        self.in_channels = in_channels
+        self.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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x_in):
+        x = x_in
+        x = self.norm1(x)
+        x = swish(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = swish(x)
+        x = self.conv2(x)
+        if self.in_channels != self.out_channels:
+            x_in = self.conv_out(x_in)
+
+        return x + x_in
+
+
+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):
+        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, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
+        super().__init__()
+        self.nf = nf
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.attn_resolutions = attn_resolutions
+
+        curr_res = self.resolution
+        in_ch_mult = (1,)+tuple(ch_mult)
+
+        blocks = []
+        # initial convultion
+        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
+
+        # residual and downsampling blocks, with attention on smaller res (16x16)
+        for i in range(self.num_resolutions):
+            block_in_ch = nf * in_ch_mult[i]
+            block_out_ch = nf * ch_mult[i]
+            for _ in range(self.num_res_blocks):
+                blocks.append(ResBlock(block_in_ch, block_out_ch))
+                block_in_ch = block_out_ch
+                if curr_res in attn_resolutions:
+                    blocks.append(AttnBlock(block_in_ch))
+
+            if i != self.num_resolutions - 1:
+                blocks.append(Downsample(block_in_ch))
+                curr_res = curr_res // 2
+
+        # non-local attention block
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+        blocks.append(AttnBlock(block_in_ch))
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+        # normalise and convert to latent size
+        blocks.append(normalize(block_in_ch))
+        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
+        self.blocks = nn.ModuleList(blocks)
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+            
+        return x
+
+
+class Generator(nn.Module):
+    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
+        super().__init__()
+        self.nf = nf 
+        self.ch_mult = ch_mult 
+        self.num_resolutions = len(self.ch_mult)
+        self.num_res_blocks = res_blocks
+        self.resolution = img_size 
+        self.attn_resolutions = attn_resolutions
+        self.in_channels = emb_dim
+        self.out_channels = 3
+        block_in_ch = self.nf * self.ch_mult[-1]
+        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
+
+        blocks = []
+        # initial conv
+        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
+
+        # non-local attention block
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+        blocks.append(AttnBlock(block_in_ch))
+        blocks.append(ResBlock(block_in_ch, block_in_ch))
+
+        for i in reversed(range(self.num_resolutions)):
+            block_out_ch = self.nf * self.ch_mult[i]
+
+            for _ in range(self.num_res_blocks):
+                blocks.append(ResBlock(block_in_ch, block_out_ch))
+                block_in_ch = block_out_ch
+
+                if curr_res in self.attn_resolutions:
+                    blocks.append(AttnBlock(block_in_ch))
+
+            if i != 0:
+                blocks.append(Upsample(block_in_ch))
+                curr_res = curr_res * 2
+
+        blocks.append(normalize(block_in_ch))
+        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
+
+        self.blocks = nn.ModuleList(blocks)
+   
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+            
+        return x
+
+  
+@ARCH_REGISTRY.register()
+class VQAutoEncoder(nn.Module):
+    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256,
+                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
+        super().__init__()
+        logger = get_root_logger()
+        self.in_channels = 3 
+        self.nf = nf 
+        self.n_blocks = res_blocks 
+        self.codebook_size = codebook_size
+        self.embed_dim = emb_dim
+        self.ch_mult = ch_mult
+        self.resolution = img_size
+        self.attn_resolutions = attn_resolutions
+        self.quantizer_type = quantizer
+        self.encoder = Encoder(
+            self.in_channels,
+            self.nf,
+            self.embed_dim,
+            self.ch_mult,
+            self.n_blocks,
+            self.resolution,
+            self.attn_resolutions
+        )
+        if self.quantizer_type == "nearest":
+            self.beta = beta #0.25
+            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
+        elif self.quantizer_type == "gumbel":
+            self.gumbel_num_hiddens = emb_dim
+            self.straight_through = gumbel_straight_through
+            self.kl_weight = gumbel_kl_weight
+            self.quantize = GumbelQuantizer(
+                self.codebook_size,
+                self.embed_dim,
+                self.gumbel_num_hiddens,
+                self.straight_through,
+                self.kl_weight
+            )
+        self.generator = Generator(
+            self.nf, 
+            self.embed_dim,
+            self.ch_mult, 
+            self.n_blocks, 
+            self.resolution, 
+            self.attn_resolutions
+        )
+
+        if model_path is not None:
+            chkpt = torch.load(model_path, map_location='cpu')
+            if 'params_ema' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
+                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
+            elif 'params' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+                logger.info(f'vqgan is loaded from: {model_path} [params]')
+            else:
+                raise ValueError(f'Wrong params!')
+
+
+    def forward(self, x):
+        x = self.encoder(x)
+        quant, codebook_loss, quant_stats = self.quantize(x)
+        x = self.generator(quant)
+        return x, codebook_loss, quant_stats
+
+
+
+# patch based discriminator
+@ARCH_REGISTRY.register()
+class VQGANDiscriminator(nn.Module):
+    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
+        super().__init__()
+
+        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
+        ndf_mult = 1
+        ndf_mult_prev = 1
+        for n in range(1, n_layers):  # gradually increase the number of filters
+            ndf_mult_prev = ndf_mult
+            ndf_mult = min(2 ** n, 8)
+            layers += [
+                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(ndf * ndf_mult),
+                nn.LeakyReLU(0.2, True)
+            ]
+
+        ndf_mult_prev = ndf_mult
+        ndf_mult = min(2 ** n_layers, 8)
+
+        layers += [
+            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
+            nn.BatchNorm2d(ndf * ndf_mult),
+            nn.LeakyReLU(0.2, True)
+        ]
+
+        layers += [
+            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
+        self.main = nn.Sequential(*layers)
+
+        if model_path is not None:
+            chkpt = torch.load(model_path, map_location='cpu')
+            if 'params_d' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
+            elif 'params' in chkpt:
+                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
+            else:
+                raise ValueError(f'Wrong params!')
+
+    def forward(self, x):
+        return self.main(x)

modules/codeformer_model.py

@@ -0,0 +1,108 @@
+import os
+import sys
+import traceback
+import torch
+
+from modules import shared
+from modules.paths import script_path
+import modules.shared
+import modules.face_restoration
+from importlib import reload
+
+# codeformer people made a choice to include modified basicsr librry to their projectwhich makes
+# it utterly impossiblr to use it alongside with other libraries that also use basicsr, like GFPGAN.
+# I am making a choice to include some files from codeformer to work around this issue.
+
+pretrain_model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+
+have_codeformer = False
+
+def setup_codeformer():
+    path = modules.paths.paths.get("CodeFormer", None)
+    if path is None:
+        return
+
+
+    # both GFPGAN and CodeFormer use bascisr, one has it installed from pip the other uses its own
+    #stored_sys_path = sys.path
+    #sys.path = [path] + sys.path
+
+    try:
+        from torchvision.transforms.functional import normalize
+        from modules.codeformer.codeformer_arch import CodeFormer
+        from basicsr.utils.download_util import load_file_from_url
+        from basicsr.utils import imwrite, img2tensor, tensor2img
+        from facelib.utils.face_restoration_helper import FaceRestoreHelper
+        from modules.shared import cmd_opts
+
+        net_class = CodeFormer
+
+        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
+            def name(self):
+                return "CodeFormer"
+
+            def __init__(self):
+                self.net = None
+                self.face_helper = None
+
+            def create_models(self):
+
+                if self.net is not None and self.face_helper is not None:
+                    return self.net, self.face_helper
+
+                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(shared.device)
+                ckpt_path = load_file_from_url(url=pretrain_model_url, model_dir=os.path.join(path, 'weights/CodeFormer'), progress=True)
+                checkpoint = torch.load(ckpt_path)['params_ema']
+                net.load_state_dict(checkpoint)
+                net.eval()
+
+                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=shared.device)
+
+                if not cmd_opts.unload_gfpgan:
+                    self.net = net
+                    self.face_helper = face_helper
+
+                return net, face_helper
+
+            def restore(self, np_image):
+                np_image = np_image[:, :, ::-1]
+
+                net, face_helper = self.create_models()
+                face_helper.clean_all()
+                face_helper.read_image(np_image)
+                face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+                face_helper.align_warp_face()
+
+                for idx, cropped_face in enumerate(face_helper.cropped_faces):
+                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                    cropped_face_t = cropped_face_t.unsqueeze(0).to(shared.device)
+
+                    try:
+                        with torch.no_grad():
+                            output = net(cropped_face_t, w=shared.opts.code_former_weight, adain=True)[0]
+                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+                        del output
+                        torch.cuda.empty_cache()
+                    except Exception as error:
+                        print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
+                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+
+                    restored_face = restored_face.astype('uint8')
+                    face_helper.add_restored_face(restored_face)
+
+                face_helper.get_inverse_affine(None)
+
+                restored_img = face_helper.paste_faces_to_input_image()
+                restored_img = restored_img[:, :, ::-1]
+                return restored_img
+
+        global have_codeformer
+        have_codeformer = True
+        shared.face_restorers.append(FaceRestorerCodeFormer())
+
+    except Exception:
+        print("Error setting up CodeFormer:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+   # sys.path = stored_sys_path

modules/face_restoration.py

@@ -0,0 +1,19 @@
+from modules import shared
+
+
+class FaceRestoration:
+    def name(self):
+        return "None"
+
+    def restore(self, np_image):
+        return np_image
+
+
+def restore_faces(np_image):
+    face_restorers = [x for x in shared.face_restorers if x.name() == shared.opts.face_restoration_model or shared.opts.face_restoration_model is None]
+    if len(face_restorers) == 0:
+        return np_image
+
+    face_restorer = face_restorers[0]
+
+    return face_restorer.restore(np_image)

modules/gfpgan_model.py

@@ -2,12 +2,15 @@ import os
 import sys
 import traceback
 
-from modules.paths import script_path
+from modules import shared
 from modules.shared import cmd_opts
-import modules.shared
+from modules.paths import script_path
+import modules.face_restoration
 
 
 def gfpgan_model_path():
+    from modules.shared import cmd_opts
+
     places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
     files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
     found = [x for x in files if os.path.exists(x)]
@@ -62,6 +65,19 @@ def setup_gfpgan():
 
         global gfpgan_constructor
         gfpgan_constructor = GFPGANer
+
+        class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
+            def name(self):
+                return "GFPGAN"
+
+            def restore(self, np_image):
+                np_image_bgr = np_image[:, :, ::-1]
+                cropped_faces, restored_faces, gfpgan_output_bgr = gfpgan().enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
+                np_image = gfpgan_output_bgr[:, :, ::-1]
+
+                return np_image
+
+        shared.face_restorers.append(FaceRestorerGFPGAN())
     except Exception:
         print("Error setting up GFPGAN:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)

modules/img2img.py

@@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html
 import modules.images as images
 import modules.scripts
 
-def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
+def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args):
     is_inpaint = mode == 1
     is_loopback = mode == 2
     is_upscale = mode == 3
@@ -36,7 +36,7 @@ def img2img(prompt: str, init_img, init_img_with_mask, steps: int, sampler_index
         cfg_scale=cfg_scale,
         width=width,
         height=height,
-        use_GFPGAN=use_GFPGAN,
+        restore_faces=restore_faces,
         tiling=tiling,
         init_images=[image],
         mask=mask,

modules/paths.py

@@ -5,7 +5,7 @@ import sys
 script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
 sys.path.insert(0, script_path)
 
-# use current directory as SD dir if it has related files, otherwise parent dir of script as stated in guide
+# search for directory of stable diffsuion in following palces
 sd_path = None
 possible_sd_paths = ['.', os.path.dirname(script_path), os.path.join(script_path, 'repositories/stable-diffusion')]
 for possible_sd_path in possible_sd_paths:
@@ -14,14 +14,19 @@ for possible_sd_path in possible_sd_paths:
 
 assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + possible_sd_paths
 
-# add parent directory to path; this is where Stable diffusion repo should be
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion'),
-    (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers')
+    (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers'),
+    (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer'),
 ]
+
+paths = {}
+
 for d, must_exist, what in path_dirs:
     must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
     if not os.path.exists(must_exist_path):
         print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
     else:
-        sys.path.append(os.path.join(script_path, d))
+        d = os.path.abspath(d)
+        sys.path.append(d)
+        paths[what] = d

modules/processing.py

@@ -14,7 +14,7 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
-import modules.gfpgan_model as gfpgan
+import modules.face_restoration
 import modules.images as images
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
@@ -29,7 +29,7 @@ def torch_gc():
 
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, use_GFPGAN=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", seed=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -44,7 +44,7 @@ class StableDiffusionProcessing:
         self.cfg_scale: float = cfg_scale
         self.width: int = width
         self.height: int = height
-        self.use_GFPGAN: bool = use_GFPGAN
+        self.restore_faces: bool = restore_faces
         self.tiling: bool = tiling
         self.do_not_save_samples: bool = do_not_save_samples
         self.do_not_save_grid: bool = do_not_save_grid
@@ -136,7 +136,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             "Sampler": samplers[p.sampler_index].name,
             "CFG scale": p.cfg_scale,
             "Seed": all_seeds[position_in_batch + iteration * p.batch_size],
-            "GFPGAN": ("GFPGAN" if p.use_GFPGAN else None),
+            "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
             "Batch size": (None if p.batch_size < 2 else p.batch_size),
             "Batch pos": (None if p.batch_size < 2 else position_in_batch),
         }
@@ -193,10 +193,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
 
-                if p.use_GFPGAN:
+                if p.restore_faces:
                     torch_gc()
 
-                    x_sample = gfpgan.gfpgan_fix_faces(x_sample)
+                    x_sample = modules.face_restoration.restore_faces(x_sample)
 
                 image = Image.fromarray(x_sample)
 

modules/shared.py

@@ -1,11 +1,13 @@
 import argparse
 import json
 import os
+
 import gradio as gr
 import torch
 
 import modules.artists
 from modules.paths import script_path, sd_path
+import modules.codeformer_model
 
 config_filename = "config.json"
 
@@ -40,6 +42,7 @@ device = gpu if torch.cuda.is_available() else cpu
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 
+
 class State:
     interrupted = False
     job = ""
@@ -65,6 +68,7 @@ state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
 
+face_restorers = []
 
 def find_any_font():
     fonts = ['/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf']
@@ -116,6 +120,8 @@ class Options:
         "upscale_at_full_resolution_padding": OptionInfo(16, "Inpainting at full resolution: padding, in pixels, for the masked region.", gr.Slider, {"minimum": 0, "maximum": 128, "step": 4}),
         "show_progressbar": OptionInfo(True, "Show progressbar"),
         "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+        "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+        "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     }
 
     def __init__(self):

modules/txt2img.py

@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, use_GFPGAN: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
+def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, height: int, width: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -21,7 +21,7 @@ def txt2img(prompt: str, negative_prompt: str, steps: int, sampler_index: int, u
         cfg_scale=cfg_scale,
         width=width,
         height=height,
-        use_GFPGAN=use_GFPGAN,
+        restore_faces=restore_faces,
         tiling=tiling,
     )
 

modules/ui.py

@@ -206,7 +206,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
 
                 with gr.Row():
-                    use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
 
                 with gr.Row():
@@ -253,7 +253,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     negative_prompt,
                     steps,
                     sampler_index,
-                    use_gfpgan,
+                    restore_faces,
                     tiling,
                     batch_count,
                     batch_size,
@@ -335,7 +335,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     inpainting_mask_invert = gr.Radio(label='Masking mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", visible=False)
 
                 with gr.Row():
-                    use_gfpgan = gr.Checkbox(label='GFPGAN', value=False, visible=gfpgan.have_gfpgan)
+                    restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
                     tiling = gr.Checkbox(label='Tiling', value=False)
                     sd_upscale_overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, visible=False)
 
@@ -425,7 +425,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     sampler_index,
                     mask_blur,
                     inpainting_fill,
-                    use_gfpgan,
+                    restore_faces,
                     tiling,
                     switch_mode,
                     batch_count,
@@ -521,7 +521,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
 
                 with gr.Group():
-                    gfpgan_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN strength", value=0, interactive=gfpgan.have_gfpgan)
+                    face_restoration_blending = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Faces restoration visibility", value=0, interactive=len(shared.face_restorers) > 1)
 
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
@@ -534,7 +534,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             fn=run_extras,
             inputs=[
                 image,
-                gfpgan_strength,
+                face_restoration_blending,
                 upscaling_resize,
                 extras_upscaler_1,
                 extras_upscaler_2,
@@ -585,7 +585,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
         t = type(info.default)
 
         if info.component is not None:
-            item = info.component(label=info.label, value=fun, **(info.component_args or {}))
+            args = info.component_args() if callable(info.component_args) else info.component_args
+            item = info.component(label=info.label, value=fun, **(args or {}))
         elif t == str:
             item = gr.Textbox(label=info.label, value=fun, lines=1)
         elif t == int:

webui.bat

@@ -92,6 +92,7 @@ echo Installing requirements...
 %PYTHON% -m pip install -r %REQS_FILE% --prefer-binary >tmp/stdout.txt 2>tmp/stderr.txt
 if %ERRORLEVEL% == 0 goto :update_numpy
 goto :show_stdout_stderr
+
 :update_numpy
 %PYTHON% -m pip install -U numpy --prefer-binary >tmp/stdout.txt 2>tmp/stderr.txt
 
@@ -105,12 +106,28 @@ if %ERRORLEVEL% == 0 goto :clone_transformers
 goto :show_stdout_stderr
 
 :clone_transformers
-if exist repositories\taming-transformers goto :check_model
+if exist repositories\taming-transformers goto :clone_codeformer
 echo Cloning Taming Transforming repository...
 %GIT% clone https://github.com/CompVis/taming-transformers.git repositories\taming-transformers >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :clone_codeformer
+goto :show_stdout_stderr
+
+:clone_codeformer
+if exist repositories\CodeFormer goto :install_codeformer_reqs
+echo Cloning CodeFormer repository...
+%GIT% clone https://github.com/sczhou/CodeFormer.git repositories\CodeFormer >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :install_codeformer_reqs
+goto :show_stdout_stderr
+
+:install_codeformer_reqs
+%PYTHON% -c "import lpips" >tmp/stdout.txt 2>tmp/stderr.txt
+if %ERRORLEVEL% == 0 goto :check_model
+echo Installing requirements for CodeFormer...
+%PYTHON% -m pip install -r repositories\CodeFormer\requirements.txt --prefer-binary >tmp/stdout.txt 2>tmp/stderr.txt
 if %ERRORLEVEL% == 0 goto :check_model
 goto :show_stdout_stderr
 
+
 :check_model
 dir model.ckpt >tmp/stdout.txt 2>tmp/stderr.txt
 if %ERRORLEVEL% == 0 goto :check_gfpgan

webui.py

@@ -19,7 +19,9 @@ from modules.ui import plaintext_to_html
 import modules.scripts
 import modules.processing as processing
 import modules.sd_hijack
-import modules.gfpgan_model as gfpgan
+import modules.codeformer_model
+import modules.gfpgan_model
+import modules.face_restoration
 import modules.realesrgan_model as realesrgan
 import modules.esrgan_model as esrgan
 import modules.images as images
@@ -28,10 +30,12 @@ import modules.txt2img
 import modules.img2img
 
 
+modules.codeformer_model.setup_codeformer()
+modules.gfpgan_model.setup_gfpgan()
+shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+
 esrgan.load_models(cmd_opts.esrgan_models_path)
 realesrgan.setup_realesrgan()
-gfpgan.setup_gfpgan()
-
 
 def load_model_from_config(config, ckpt, verbose=False):
     print(f"Loading model from {ckpt}")
@@ -54,19 +58,19 @@ def load_model_from_config(config, ckpt, verbose=False):
 cached_images = {}
 
 
-def run_extras(image, gfpgan_strength, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(image, face_restoration_blending, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     processing.torch_gc()
 
     image = image.convert("RGB")
 
     outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    if gfpgan.have_gfpgan is not None and gfpgan_strength > 0:
-        restored_img = gfpgan.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+    if face_restoration_blending > 0:
+        restored_img = modules.face_restoration.restore_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
 
-        if gfpgan_strength < 1.0:
-            res = Image.blend(image, res, gfpgan_strength)
+        if face_restoration_blending < 1.0:
+            res = Image.blend(image, res, face_restoration_blending)
 
         image = res