# 组装升维模块:self.output_blocks. for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ResBlock(ch + ich,time_embed_dim,dropout,model_channels * mult,dims=dims,use_checkpoint,use_scale...
CONV,即初始卷积 最开始有一个基础通道数model\_channels=320,以及通道数倍数channel\_mult=[1,2,4,4]。后者控制每经过一个第一种编码块,通道数翻的倍数,其长度也决定了有多少个第一种编码块。 前两种类型的编码块是交替堆叠的,根据默认的配置文件,一般是n=2个第一种类型的编码块和 1 个第二种类型的编码...
for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] 1. 2. 3. 4. 5. 6. 7....
1117 + 'attention_resolutions': [1, 2, 4], 'transformer_depth': [1, 1, 1, 0], 'channel_mult': [1, 2, 4, 4], 1118 + 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768} 1119 + 1120 + supported_models = [SDXL, SDXL_refiner, SD...
self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(3, input_channel, stride=2)] for t, c, n, s in inverted_residual_setting: output_channel = _make_divisible(c * width_mult, round_nearest) ...
channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, num_classes=None, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, transformer_depth=1, context_dim=None, n_embed=None, num_attention_blocks=No...
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channel_mult [1, 2, 4, 4] conv_resample True dims 2 num_classes None use_checkpoint True use_fp16 False use_bf16 False num_heads 8 num_head_channels -1 num_heads_upsample -1 use_scale_shift_norm False resblock_updown False
last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest) features = [ConvBNReLU(3, input_channel, stride=2)] current_stride *= 2 dilation=1 previous_dilation = 1 # building inverted residual blocks for t, c, n, s in inverted_residual_setting: output_channel...
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