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sdxl结构分析

时间:2023-11-27 19:13:00浏览次数:29  
标签:分析 1280 LoRACompatibleLinear 640 features sdxl 结构 True out

目录

text encoder

unet

conv_in
Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
time_proj
Timesteps()
time_embedding
TimestepEmbedding(
  (linear_1): LoRACompatibleLinear(in_features=320, out_features=1280, bias=True)
  (act): SiLU()
  (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
add_time_proj
Timesteps()
add_embedding
TimestepEmbedding(
  (linear_1): LoRACompatibleLinear(in_features=2816, out_features=1280, bias=True)
  (act): SiLU()
  (linear_2): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
)
down_blocks
ModuleList(
  (0): DownBlock2D(
    (resnets): ModuleList(
      (0-1): 2 x ResnetBlock2D(
        (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
        (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
      )
    )
    (downsamplers): ModuleList(
      (0): Downsample2D(
        (conv): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      )
    )
  )
  (1): CrossAttnDownBlock2D(
    (attentions): ModuleList(
      (0-1): 2 x Transformer2DModel(
        (norm): GroupNorm(32, 640, eps=1e-06, affine=True)
        (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
        (transformer_blocks): ModuleList(
          (0-1): 2 x BasicTransformerBlock(
            (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (attn1): Attention(
              (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (attn2): Attention(
              (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
              (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (ff): FeedForward(
              (net): ModuleList(
                (0): GEGLU(
                  (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
                )
                (1): Dropout(p=0.0, inplace=False)
                (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
              )
            )
          )
        )
        (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
      )
    )
    (resnets): ModuleList(
      (0): ResnetBlock2D(
        (norm1): GroupNorm(32, 320, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
        (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(320, 640, kernel_size=(1, 1), stride=(1, 1))
      )
      (1): ResnetBlock2D(
        (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
        (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
      )
    )
    (downsamplers): ModuleList(
      (0): Downsample2D(
        (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      )
    )
  )
  (2): CrossAttnDownBlock2D(
    (attentions): ModuleList(
      (0-1): 2 x Transformer2DModel(
        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
        (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (transformer_blocks): ModuleList(
          (0-9): 10 x BasicTransformerBlock(
            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (attn1): Attention(
              (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (attn2): Attention(
              (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
              (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (ff): FeedForward(
              (net): ModuleList(
                (0): GEGLU(
                  (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
                )
                (1): Dropout(p=0.0, inplace=False)
                (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
              )
            )
          )
        )
        (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
      )
    )
    (resnets): ModuleList(
      (0): ResnetBlock2D(
        (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(640, 1280, kernel_size=(1, 1), stride=(1, 1))
      )
      (1): ResnetBlock2D(
        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
      )
    )
  )
)
up_blocks
ModuleList(
  (0): CrossAttnUpBlock2D(
    (attentions): ModuleList(
      (0-2): 3 x Transformer2DModel(
        (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
        (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (transformer_blocks): ModuleList(
          (0-9): 10 x BasicTransformerBlock(
            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (attn1): Attention(
              (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (attn2): Attention(
              (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
              (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
              (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
            (ff): FeedForward(
              (net): ModuleList(
                (0): GEGLU(
                  (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
                )
                (1): Dropout(p=0.0, inplace=False)
                (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
              )
            )
          )
        )
        (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
      )
    )
    (resnets): ModuleList(
      (0-1): 2 x ResnetBlock2D(
        (norm1): GroupNorm(32, 2560, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(2560, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(2560, 1280, kernel_size=(1, 1), stride=(1, 1))
      )
      (2): ResnetBlock2D(
        (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(1920, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
        (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(1920, 1280, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (upsamplers): ModuleList(
      (0): Upsample2D(
        (conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
    )
  )
  (1): CrossAttnUpBlock2D(
    (attentions): ModuleList(
      (0-2): 3 x Transformer2DModel(
        (norm): GroupNorm(32, 640, eps=1e-06, affine=True)
        (proj_in): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
        (transformer_blocks): ModuleList(
          (0-1): 2 x BasicTransformerBlock(
            (norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (attn1): Attention(
              (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_k): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_v): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (attn2): Attention(
              (to_q): LoRACompatibleLinear(in_features=640, out_features=640, bias=False)
              (to_k): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
              (to_v): LoRACompatibleLinear(in_features=2048, out_features=640, bias=False)
              (to_out): ModuleList(
                (0): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
                (1): Dropout(p=0.0, inplace=False)
              )
            )
            (norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)
            (ff): FeedForward(
              (net): ModuleList(
                (0): GEGLU(
                  (proj): LoRACompatibleLinear(in_features=640, out_features=5120, bias=True)
                )
                (1): Dropout(p=0.0, inplace=False)
                (2): LoRACompatibleLinear(in_features=2560, out_features=640, bias=True)
              )
            )
          )
        )
        (proj_out): LoRACompatibleLinear(in_features=640, out_features=640, bias=True)
      )
    )
    (resnets): ModuleList(
      (0): ResnetBlock2D(
        (norm1): GroupNorm(32, 1920, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(1920, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
        (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(1920, 640, kernel_size=(1, 1), stride=(1, 1))
      )
      (1): ResnetBlock2D(
        (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(1280, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
        (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(1280, 640, kernel_size=(1, 1), stride=(1, 1))
      )
      (2): ResnetBlock2D(
        (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(960, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=640, bias=True)
        (norm2): GroupNorm(32, 640, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(960, 640, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (upsamplers): ModuleList(
      (0): Upsample2D(
        (conv): LoRACompatibleConv(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      )
    )
  )
  (2): UpBlock2D(
    (resnets): ModuleList(
      (0): ResnetBlock2D(
        (norm1): GroupNorm(32, 960, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(960, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
        (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(960, 320, kernel_size=(1, 1), stride=(1, 1))
      )
      (1-2): 2 x ResnetBlock2D(
        (norm1): GroupNorm(32, 640, eps=1e-05, affine=True)
        (conv1): LoRACompatibleConv(640, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
        (norm2): GroupNorm(32, 320, eps=1e-05, affine=True)
        (dropout): Dropout(p=0.0, inplace=False)
        (conv2): LoRACompatibleConv(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (nonlinearity): SiLU()
        (conv_shortcut): LoRACompatibleConv(640, 320, kernel_size=(1, 1), stride=(1, 1))
      )
    )
  )
)
mid_block
UNetMidBlock2DCrossAttn(
  (attentions): ModuleList(
    (0): Transformer2DModel(
      (norm): GroupNorm(32, 1280, eps=1e-06, affine=True)
      (proj_in): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
      (transformer_blocks): ModuleList(
        (0-9): 10 x BasicTransformerBlock(
          (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (attn1): Attention(
            (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
            (to_k): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
            (to_v): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
            (to_out): ModuleList(
              (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
              (1): Dropout(p=0.0, inplace=False)
            )
          )
          (norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (attn2): Attention(
            (to_q): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=False)
            (to_k): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
            (to_v): LoRACompatibleLinear(in_features=2048, out_features=1280, bias=False)
            (to_out): ModuleList(
              (0): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
              (1): Dropout(p=0.0, inplace=False)
            )
          )
          (norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (ff): FeedForward(
            (net): ModuleList(
              (0): GEGLU(
                (proj): LoRACompatibleLinear(in_features=1280, out_features=10240, bias=True)
              )
              (1): Dropout(p=0.0, inplace=False)
              (2): LoRACompatibleLinear(in_features=5120, out_features=1280, bias=True)
            )
          )
        )
      )
      (proj_out): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
    )
  )
  (resnets): ModuleList(
    (0-1): 2 x ResnetBlock2D(
      (norm1): GroupNorm(32, 1280, eps=1e-05, affine=True)
      (conv1): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (time_emb_proj): LoRACompatibleLinear(in_features=1280, out_features=1280, bias=True)
      (norm2): GroupNorm(32, 1280, eps=1e-05, affine=True)
      (dropout): Dropout(p=0.0, inplace=False)
      (conv2): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (nonlinearity): SiLU()
    )
  )
)
conv_norm_out
GroupNorm(32, 320, eps=1e-05, affine=True)
conv_act
SiLU()
conv_out
Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))

vae

标签:分析,1280,LoRACompatibleLinear,640,features,sdxl,结构,True,out
From: https://www.cnblogs.com/FrostyForest/p/17860155.html

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