791 lines
26 KiB
Python
791 lines
26 KiB
Python
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"""
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Modified from https://github.com/sczhou/CodeFormer
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VQGAN code, adapted from the original created by the Unleashing Transformers authors:
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https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
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This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
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"""
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import math
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from typing import Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import logging as logger
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from torch import Tensor
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class VectorQuantizer(nn.Module):
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def __init__(self, codebook_size, emb_dim, beta):
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super(VectorQuantizer, self).__init__()
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self.codebook_size = codebook_size # number of embeddings
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self.emb_dim = emb_dim # dimension of embedding
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self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
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self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
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self.embedding.weight.data.uniform_(
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-1.0 / self.codebook_size, 1.0 / self.codebook_size
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)
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def forward(self, z):
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# reshape z -> (batch, height, width, channel) and flatten
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z = z.permute(0, 2, 3, 1).contiguous()
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z_flattened = z.view(-1, self.emb_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = (
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(z_flattened**2).sum(dim=1, keepdim=True)
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+ (self.embedding.weight**2).sum(1)
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- 2 * torch.matmul(z_flattened, self.embedding.weight.t())
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)
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mean_distance = torch.mean(d)
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# find closest encodings
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# min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
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min_encoding_scores, min_encoding_indices = torch.topk(
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d, 1, dim=1, largest=False
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)
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# [0-1], higher score, higher confidence
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min_encoding_scores = torch.exp(-min_encoding_scores / 10)
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min_encodings = torch.zeros(
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min_encoding_indices.shape[0], self.codebook_size
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).to(z)
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min_encodings.scatter_(1, min_encoding_indices, 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
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# compute loss for embedding
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loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
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(z_q - z.detach()) ** 2
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)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# perplexity
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e_mean = torch.mean(min_encodings, dim=0)
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perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return (
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z_q,
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loss,
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{
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"perplexity": perplexity,
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"min_encodings": min_encodings,
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"min_encoding_indices": min_encoding_indices,
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"min_encoding_scores": min_encoding_scores,
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"mean_distance": mean_distance,
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},
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)
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def get_codebook_feat(self, indices, shape):
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# input indices: batch*token_num -> (batch*token_num)*1
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# shape: batch, height, width, channel
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indices = indices.view(-1, 1)
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min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
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min_encodings.scatter_(1, indices, 1)
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# get quantized latent vectors
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z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
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if shape is not None: # reshape back to match original input shape
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z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
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return z_q
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class GumbelQuantizer(nn.Module):
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def __init__(
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self,
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codebook_size,
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emb_dim,
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num_hiddens,
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straight_through=False,
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kl_weight=5e-4,
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temp_init=1.0,
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):
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super().__init__()
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self.codebook_size = codebook_size # number of embeddings
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self.emb_dim = emb_dim # dimension of embedding
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self.straight_through = straight_through
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self.temperature = temp_init
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self.kl_weight = kl_weight
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self.proj = nn.Conv2d(
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num_hiddens, codebook_size, 1
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) # projects last encoder layer to quantized logits
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self.embed = nn.Embedding(codebook_size, emb_dim)
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def forward(self, z):
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hard = self.straight_through if self.training else True
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logits = self.proj(z)
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soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
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z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
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# + kl divergence to the prior loss
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qy = F.softmax(logits, dim=1)
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diff = (
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self.kl_weight
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* torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
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)
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min_encoding_indices = soft_one_hot.argmax(dim=1)
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return z_q, diff, {"min_encoding_indices": min_encoding_indices}
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class Downsample(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0
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)
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def forward(self, x):
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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return x
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class Upsample(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.conv = nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=1, padding=1
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)
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def forward(self, x):
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x = F.interpolate(x, scale_factor=2.0, mode="nearest")
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x = self.conv(x)
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return x
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class AttnBlock(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0
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)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, c, h * w)
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q = q.permute(0, 2, 1)
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k = k.reshape(b, c, h * w)
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w_ = torch.bmm(q, k)
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w_ = w_ * (int(c) ** (-0.5))
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w_ = F.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h * w)
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w_ = w_.permute(0, 2, 1)
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h_ = torch.bmm(v, w_)
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x + h_
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels,
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nf,
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out_channels,
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ch_mult,
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num_res_blocks,
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resolution,
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attn_resolutions,
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):
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super().__init__()
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self.nf = nf
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.attn_resolutions = attn_resolutions
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curr_res = self.resolution
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in_ch_mult = (1,) + tuple(ch_mult)
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blocks = []
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# initial convultion
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blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
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# residual and downsampling blocks, with attention on smaller res (16x16)
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for i in range(self.num_resolutions):
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block_in_ch = nf * in_ch_mult[i]
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block_out_ch = nf * ch_mult[i]
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for _ in range(self.num_res_blocks):
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blocks.append(ResBlock(block_in_ch, block_out_ch))
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block_in_ch = block_out_ch
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if curr_res in attn_resolutions:
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blocks.append(AttnBlock(block_in_ch))
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if i != self.num_resolutions - 1:
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blocks.append(Downsample(block_in_ch))
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curr_res = curr_res // 2
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# non-local attention block
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blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore
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blocks.append(AttnBlock(block_in_ch)) # type: ignore
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blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore
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# normalise and convert to latent size
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blocks.append(normalize(block_in_ch)) # type: ignore
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blocks.append(
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nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore
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)
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self.blocks = nn.ModuleList(blocks)
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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return x
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class Generator(nn.Module):
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def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim):
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super().__init__()
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self.nf = nf
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self.ch_mult = ch_mult
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self.num_resolutions = len(self.ch_mult)
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self.num_res_blocks = res_blocks
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self.resolution = img_size
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self.attn_resolutions = attn_resolutions
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self.in_channels = emb_dim
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self.out_channels = 3
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block_in_ch = self.nf * self.ch_mult[-1]
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curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
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blocks = []
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# initial conv
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blocks.append(
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nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
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)
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# non-local attention block
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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blocks.append(AttnBlock(block_in_ch))
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blocks.append(ResBlock(block_in_ch, block_in_ch))
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for i in reversed(range(self.num_resolutions)):
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block_out_ch = self.nf * self.ch_mult[i]
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for _ in range(self.num_res_blocks):
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blocks.append(ResBlock(block_in_ch, block_out_ch))
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block_in_ch = block_out_ch
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if curr_res in self.attn_resolutions:
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blocks.append(AttnBlock(block_in_ch))
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if i != 0:
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blocks.append(Upsample(block_in_ch))
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curr_res = curr_res * 2
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blocks.append(normalize(block_in_ch))
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blocks.append(
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nn.Conv2d(
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block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
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)
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)
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self.blocks = nn.ModuleList(blocks)
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def forward(self, x):
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for block in self.blocks:
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x = block(x)
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return x
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class VQAutoEncoder(nn.Module):
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def __init__(
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self,
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img_size,
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nf,
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ch_mult,
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quantizer="nearest",
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res_blocks=2,
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attn_resolutions=[16],
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codebook_size=1024,
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emb_dim=256,
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beta=0.25,
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gumbel_straight_through=False,
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gumbel_kl_weight=1e-8,
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model_path=None,
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):
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super().__init__()
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self.in_channels = 3
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self.nf = nf
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self.n_blocks = res_blocks
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self.codebook_size = codebook_size
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self.embed_dim = emb_dim
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self.ch_mult = ch_mult
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self.resolution = img_size
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self.attn_resolutions = attn_resolutions
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self.quantizer_type = quantizer
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self.encoder = Encoder(
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self.in_channels,
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self.nf,
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self.embed_dim,
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self.ch_mult,
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self.n_blocks,
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self.resolution,
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self.attn_resolutions,
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)
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if self.quantizer_type == "nearest":
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self.beta = beta # 0.25
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self.quantize = VectorQuantizer(
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self.codebook_size, self.embed_dim, self.beta
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|
)
|
||
|
|
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(
|
||
|
|
nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim
|
||
|
|
)
|
||
|
|
|
||
|
|
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("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
|
||
|
|
|
||
|
|
|
||
|
|
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 # pylint: disable=invalid-unary-operand-type
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
def normalize(in_channels):
|
||
|
|
return torch.nn.GroupNorm(
|
||
|
|
num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@torch.jit.script # type: ignore
|
||
|
|
def swish(x):
|
||
|
|
return x * torch.sigmoid(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 # type: ignore
|
||
|
|
)
|
||
|
|
self.norm2 = normalize(out_channels)
|
||
|
|
self.conv2 = nn.Conv2d(
|
||
|
|
out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore
|
||
|
|
)
|
||
|
|
if self.in_channels != self.out_channels:
|
||
|
|
self.conv_out = nn.Conv2d(
|
||
|
|
in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore
|
||
|
|
)
|
||
|
|
|
||
|
|
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 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
|
||
|
|
|
||
|
|
|
||
|
|
class CodeFormer(VQAutoEncoder):
|
||
|
|
def __init__(self, state_dict):
|
||
|
|
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"]
|
||
|
|
|
||
|
|
# This is just a guess as I only have one model to look at
|
||
|
|
position_emb = state_dict["position_emb"]
|
||
|
|
dim_embd = position_emb.shape[1]
|
||
|
|
latent_size = position_emb.shape[0]
|
||
|
|
|
||
|
|
try:
|
||
|
|
n_layers = len(
|
||
|
|
set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x])
|
||
|
|
)
|
||
|
|
except:
|
||
|
|
pass
|
||
|
|
|
||
|
|
codebook_size = state_dict["quantize.embedding.weight"].shape[0]
|
||
|
|
|
||
|
|
# This is also just another guess
|
||
|
|
n_head_exp = (
|
||
|
|
state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd
|
||
|
|
)
|
||
|
|
n_head = 2**n_head_exp
|
||
|
|
|
||
|
|
in_nc = state_dict["encoder.blocks.0.weight"].shape[1]
|
||
|
|
|
||
|
|
self.model_arch = "CodeFormer"
|
||
|
|
self.sub_type = "Face SR"
|
||
|
|
self.scale = 8
|
||
|
|
self.in_nc = in_nc
|
||
|
|
self.out_nc = in_nc
|
||
|
|
|
||
|
|
self.state = state_dict
|
||
|
|
|
||
|
|
self.supports_fp16 = False
|
||
|
|
self.supports_bf16 = True
|
||
|
|
self.min_size_restriction = 16
|
||
|
|
|
||
|
|
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)) # type: ignore
|
||
|
|
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)
|
||
|
|
|
||
|
|
self.load_state_dict(state_dict)
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
if isinstance(module, (nn.Linear, nn.Embedding)):
|
||
|
|
module.weight.data.normal_(mean=0.0, std=0.02)
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if isinstance(module, nn.Linear) and module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.LayerNorm):
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||
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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||
|
|
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|
def forward(self, x, weight=0.5, **kwargs):
|
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|
detach_16 = True
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|
|
code_only = False
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|
|
adain = True
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||
|
|
# ################### Encoder #####################
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||
|
|
enc_feat_dict = {}
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||
|
|
out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
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||
|
|
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] # type: ignore
|
||
|
|
)
|
||
|
|
# 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 weight > 0:
|
||
|
|
x = self.fuse_convs_dict[f_size](
|
||
|
|
enc_feat_dict[f_size].detach(), x, weight
|
||
|
|
)
|
||
|
|
out = x
|
||
|
|
# logits doesn't need softmax before cross_entropy loss
|
||
|
|
# return out, logits, lq_feat
|
||
|
|
return out, logits
|