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文章来源：https://www.toymoban.com/news/detail-773729.html
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。文章来源地址https://www.toymoban.com/news/detail-773729.html
“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
}
到了这里，关于diffusers库中stable Diffusion模块的解析的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
