文章目录
1. 前言
同学们，抖音买家秀中的那些极具设计感的作品是怎么来的？
你还在羡慕别人的绘画天赋，对着空白的画纸发呆吗？
有没有想过将生硬的代码和灵动的艺术完美融合？
有没有感到现代技术带来了戏剧性改变，从替代马车的汽车，到取代传统影像的数字摄影，再到今天的AI美术创作？
是的，“AI绘画”，它来了。
AI绘画，如雷贯耳，无处不在。
曾几何时，人们认为艺术需要人工创作，需要才情与灵感的瞬间迸发。
然而，技术的高速发展，打破了这种观念，AI绘画正在逐渐崭露头角，成为新的画坛巨擘。
但聊一聊看似高大上的AI技术可能让你觉得高不可攀。那就跟我一起，用Stable Diffusion挑战无限可能，一窥AI绘画的花花世界！
Stable Diffusion WebUI 中的核心组件，人脸图像面部画面修复模型 CodeFormer的核心代码如下所示：
import math
import torch
from torch import nn, Tensor
import torch.nn.functional as F
from typing import Optional
from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
from basicsr.utils.registry import ARCH_REGISTRY
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
2. 书籍推荐《AI魔法绘画：用Stable Diffusion挑战无限可能》
俗话说，知识就是力量，掌握AI绘画技术，你不仅可以创造出令人眼花缭乱的艺术作品，还可能在众多同行中脱颖而出，走上职业生涯的新高峰。
看看那些因为AI的到来，开始大规模裁员的公司，你是否意识到了自己该学习AI绘画的重要性？答案是明显的。掌握潮流，主宰未来，你准备好了吗？
这里，向大家强烈推荐一本书——《AI魔法绘画：用Stable Diffusion挑战无限可能》。
它是一本以实际操作为导向的入门级AI绘画图书。
书中详细讲解了基于Stable Diffusion进行AI绘画的完整学习路线，包括绘画技巧、图片生成、提示词编写、ControlNet插件、模型训练等等。
