diffusers库中stable Diffusion模块的解析

diffusers库中stable Diffusion模块的解析

diffusers中，stable Diffusion v1.5主要由以下几个部分组成

Out[3]: dict_keys(['vae', 'text_encoder', 'tokenizer', 'unet', 'scheduler', 'safety_checker', 'feature_extractor'])

下面给出具体的结构说明。

“text_encoder block”

CLIPTextModel(
  (text_model): CLIPTextTransformer(
    (embeddings): CLIPTextEmbeddings(
      (token_embedding): Embedding(49408, 768)
      (position_embedding): Embedding(77, 768)
    )
    (encoder): CLIPEncoder(
      (layers): ModuleList(
        (0-11): 12 x CLIPEncoderLayer(
          (self_attn): CLIPAttention(
            (k_proj): Linear(in_features=768, out_features=768, bias=True)
            (v_proj): Linear(in_features=768, out_features=768, bias=True)
            (q_proj): Linear(in_features=768, out_features=768, bias=True)
            (out_proj): Linear(in_features=768, out_features=768, bias=True)
          )
          (layer_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
          (mlp): CLIPMLP(
            (activation_fn): QuickGELUActivation()
            (fc1): Linear(in_features=768, out_features=3072, bias=True)
            (fc2): Linear(in_features=3072, out_features=768, bias=True)
          )
          (layer_norm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
        )
      )
    )
    (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
  )
)

“vae block”

AutoencoderKL(
  (encoder): Encoder(
    (conv_in): Conv2d(3, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (down_blocks): ModuleList(
      (0): DownEncoderBlock2D(
        (resnets): ModuleList(
          (0-1): 2 x ResnetBlock2D(
            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
        (downsamplers): ModuleList(
          (0): Downsample2D(
            (conv): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(2, 2))
          )
        )
      )
      (1): DownEncoderBlock2D(
        (resnets): ModuleList(
          (0): ResnetBlock2D(
            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
            (conv_shortcut): LoRACompatibleConv(128, 256, kernel_size=(1, 1), stride=(1, 1))
          )
          (1): ResnetBlock2D(
            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
        (downsamplers): ModuleList(
          (0): Downsample2D(
            (conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(2, 2))
          )
        )
      )
      (2): DownEncoderBlock2D(
        (resnets): ModuleList(
          (0): ResnetBlock2D(
            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
            (conv_shortcut): LoRACompatibleConv(256, 512, kernel_size=(1, 1), stride=(1, 1))
          )
          (1): ResnetBlock2D(
            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
        (downsamplers): ModuleList(
          (0): Downsample2D(
            (conv): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(2, 2))
          )
        )
      )
      (3): DownEncoderBlock2D(
        (resnets): ModuleList(
          (0-1): 2 x ResnetBlock2D(
            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
      )
    )
    (mid_block): UNetMidBlock2D(
      (attentions): ModuleList(
        (0): Attention(
          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
          (to_q): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_k): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_v): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_out): ModuleList(
            (0): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
            (1): Dropout(p=0.0, inplace=False)
          )
        )
      )
      (resnets): ModuleList(
        (0-1): 2 x ResnetBlock2D(
          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
          (conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
          (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (nonlinearity): SiLU()
        )
      )
    )
    (conv_norm_out): GroupNorm(32, 512, eps=1e-06, affine=True)
    (conv_act): SiLU()
    (conv_out): Conv2d(512, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
  (decoder): Decoder(
    (conv_in): Conv2d(4, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (up_blocks): ModuleList(
      (0-1): 2 x UpDecoderBlock2D(
        (resnets): ModuleList(
          (0-2): 3 x ResnetBlock2D(
            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
        (upsamplers): ModuleList(
          (0): Upsample2D(
            (conv): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          )
        )
      )
      (2): UpDecoderBlock2D(
        (resnets): ModuleList(
          (0): ResnetBlock2D(
            (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
            (conv_shortcut): LoRACompatibleConv(512, 256, kernel_size=(1, 1), stride=(1, 1))
          )
          (1-2): 2 x ResnetBlock2D(
            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 256, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
        (upsamplers): ModuleList(
          (0): Upsample2D(
            (conv): LoRACompatibleConv(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          )
        )
      )
      (3): UpDecoderBlock2D(
        (resnets): ModuleList(
          (0): ResnetBlock2D(
            (norm1): GroupNorm(32, 256, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
            (conv_shortcut): LoRACompatibleConv(256, 128, kernel_size=(1, 1), stride=(1, 1))
          )
          (1-2): 2 x ResnetBlock2D(
            (norm1): GroupNorm(32, 128, eps=1e-06, affine=True)
            (conv1): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (norm2): GroupNorm(32, 128, eps=1e-06, affine=True)
            (dropout): Dropout(p=0.0, inplace=False)
            (conv2): LoRACompatibleConv(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (nonlinearity): SiLU()
          )
        )
      )
    )
    (mid_block): UNetMidBlock2D(
      (attentions): ModuleList(
        (0): Attention(
          (group_norm): GroupNorm(32, 512, eps=1e-06, affine=True)
          (to_q): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_k): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_v): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
          (to_out): ModuleList(
            (0): LoRACompatibleLinear(in_features=512, out_features=512, bias=True)
            (1): Dropout(p=0.0, inplace=False)
          )
        )
      )
      (resnets): ModuleList(
        (0-1): 2 x ResnetBlock2D(
          (norm1): GroupNorm(32, 512, eps=1e-06, affine=True)
          (conv1): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (norm2): GroupNorm(32, 512, eps=1e-06, affine=True)
          (dropout): Dropout(p=0.0, inplace=False)
          (conv2): LoRACompatibleConv(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (nonlinearity): SiLU()
        )
      )
    )
    (conv_norm_out): GroupNorm(32, 128, eps=1e-06, affine=True)
    (conv_act): SiLU()
    (conv_out): Conv2d(128, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  )
  (quant_conv): Conv2d(8, 8, kernel_size=(1, 1), stride=(1, 1))
  (post_quant_conv): Conv2d(4, 4, kernel_size=(1, 1), stride=(1, 1))
)

“unet block”

UNet2DConditionModel(
  (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)
  )
  (down_blocks): ModuleList(
    (0): CrossAttnDownBlock2D(
      (attentions): ModuleList(
        (0-1): 2 x Transformer2DModel(
          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)
          (proj_in): LoRACompatibleConv(320, 320, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): BasicTransformerBlock(
              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (attn1): Attention(
                (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_out): ModuleList(
                  (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
                  (1): Dropout(p=0.0, inplace=False)
                )
              )
              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (attn2): Attention(
                (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_k): LoRACompatibleLinear(in_features=768, out_features=320, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, out_features=320, bias=False)
                (to_out): ModuleList(
                  (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
                  (1): Dropout(p=0.0, inplace=False)
                )
              )
              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (ff): FeedForward(
                (net): ModuleList(
                  (0): GEGLU(
                    (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True)
                  )
                  (1): Dropout(p=0.0, inplace=False)
                  (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
                )
              )
            )
          )
          (proj_out): LoRACompatibleConv(320, 320, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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): LoRACompatibleConv(640, 640, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): 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=768, out_features=640, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, 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): LoRACompatibleConv(640, 640, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): 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=768, out_features=1280, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, 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): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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()
        )
      )
      (downsamplers): ModuleList(
        (0): Downsample2D(
          (conv): LoRACompatibleConv(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        )
      )
    )
    (3): DownBlock2D(
      (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()
        )
      )
    )
  )
  (up_blocks): ModuleList(
    (0): UpBlock2D(
      (resnets): ModuleList(
        (0-2): 3 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))
        )
      )
      (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, 1280, eps=1e-06, affine=True)
          (proj_in): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): 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=768, out_features=1280, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, 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): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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))
        )
      )
    )
    (2): CrossAttnUpBlock2D(
      (attentions): ModuleList(
        (0-2): 3 x Transformer2DModel(
          (norm): GroupNorm(32, 640, eps=1e-06, affine=True)
          (proj_in): LoRACompatibleConv(640, 640, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): 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=768, out_features=640, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, 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): LoRACompatibleConv(640, 640, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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))
        )
      )
    )
    (3): CrossAttnUpBlock2D(
      (attentions): ModuleList(
        (0-2): 3 x Transformer2DModel(
          (norm): GroupNorm(32, 320, eps=1e-06, affine=True)
          (proj_in): LoRACompatibleConv(320, 320, kernel_size=(1, 1), stride=(1, 1))
          (transformer_blocks): ModuleList(
            (0): BasicTransformerBlock(
              (norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (attn1): Attention(
                (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_k): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_v): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_out): ModuleList(
                  (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
                  (1): Dropout(p=0.0, inplace=False)
                )
              )
              (norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (attn2): Attention(
                (to_q): LoRACompatibleLinear(in_features=320, out_features=320, bias=False)
                (to_k): LoRACompatibleLinear(in_features=768, out_features=320, bias=False)
                (to_v): LoRACompatibleLinear(in_features=768, out_features=320, bias=False)
                (to_out): ModuleList(
                  (0): LoRACompatibleLinear(in_features=320, out_features=320, bias=True)
                  (1): Dropout(p=0.0, inplace=False)
                )
              )
              (norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)
              (ff): FeedForward(
                (net): ModuleList(
                  (0): GEGLU(
                    (proj): LoRACompatibleLinear(in_features=320, out_features=2560, bias=True)
                  )
                  (1): Dropout(p=0.0, inplace=False)
                  (2): LoRACompatibleLinear(in_features=1280, out_features=320, bias=True)
                )
              )
            )
          )
          (proj_out): LoRACompatibleConv(320, 320, kernel_size=(1, 1), stride=(1, 1))
        )
      )
      (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): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
        (transformer_blocks): ModuleList(
          (0): 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=768, out_features=1280, bias=False)
              (to_v): LoRACompatibleLinear(in_features=768, 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): LoRACompatibleConv(1280, 1280, kernel_size=(1, 1), stride=(1, 1))
      )
    )
    (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))
)

“feature extractor block”

CLIPImageProcessor {
  "crop_size": {
    "height": 224,
    "width": 224
  },
  "do_center_crop": true,
  "do_convert_rgb": true,
  "do_normalize": true,
  "do_rescale": true,
  "do_resize": true,
  "feature_extractor_type": "CLIPFeatureExtractor",
  "image_mean": [
    0.48145466,
    0.4578275,
    0.40821073
  ],
  "image_processor_type": "CLIPImageProcessor",
  "image_std": [
    0.26862954,
    0.26130258,
    0.27577711
  ],
  "resample": 3,
  "rescale_factor": 0.00392156862745098,
  "size": {
    "shortest_edge": 224
  },
  "use_square_size": false
}

“tokenizer block”

CLIPTokenizer(name_or_path='/home/tiger/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/1d0c4ebf6ff58a5caecab40fa1406526bca4b5b9/tokenizer', vocab_size=49408, model_max_length=77, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<|startoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>', 'pad_token': '<|endoftext|>'}, clean_up_tokenization_spaces=True),  added_tokens_decoder={
        49406: AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),
        49407: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),
}

“safety_checker block”

StableDiffusionSafetyChecker(
  (vision_model): CLIPVisionModel(
    (vision_model): CLIPVisionTransformer(
      (embeddings): CLIPVisionEmbeddings(
        (patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
        (position_embedding): Embedding(257, 1024)
      )
      (pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
      (encoder): CLIPEncoder(
        (layers): ModuleList(
          (0-23): 24 x CLIPEncoderLayer(
            (self_attn): CLIPAttention(
              (k_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (v_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (q_proj): Linear(in_features=1024, out_features=1024, bias=True)
              (out_proj): Linear(in_features=1024, out_features=1024, bias=True)
            )
            (layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
            (mlp): CLIPMLP(
              (activation_fn): QuickGELUActivation()
              (fc1): Linear(in_features=1024, out_features=4096, bias=True)
              (fc2): Linear(in_features=4096, out_features=1024, bias=True)
            )
            (layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
          )
        )
      )
      (post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
    )
  )
  (visual_projection): Linear(in_features=1024, out_features=768, bias=False)
)

“scheduler block”

PNDMScheduler {
  "_class_name": "PNDMScheduler",
  "_diffusers_version": "0.22.3",
  "beta_end": 0.012,
  "beta_schedule": "scaled_linear",
  "beta_start": 0.00085,
  "clip_sample": false,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "set_alpha_to_one": false,
  "skip_prk_steps": true,
  "steps_offset": 1,
  "timestep_spacing": "leading",
  "trained_betas": null
}