使用html做网站的网页培训网络营销机构
1--前言
以论文《High-Resolution Image Synthesis with Latent Diffusion Models》 开源的项目为例，剖析Stable Diffusion经典组成部分，巩固学习加深印象。
2--UNetModel
一个可以debug的小demo：SD_UNet
以文生图为例，剖析UNetModel核心组成模块。
2-1--Forward总揽
提供的文生图Demo中，实际传入的参数只有x、timesteps和context三个，其中：
x 表示随机初始化的噪声Tensor（shape: [B*2, 4, 64, 64]，*2表示使用Classifier-Free Diffusion Guidance）。
timesteps 表示去噪过程中每一轮传入的timestep（shape: [B*2]）。
context表示经过CLIP编码后对应的文本Prompt（shape: [B*2, 77, 768]）。
def forward(self, x, timesteps=None, context=None, y=None,**kwargs):"""Apply the model to an input batch.:param x: an [N x C x ...] Tensor of inputs.:param timesteps: a 1-D batch of timesteps.:param context: conditioning plugged in via crossattn:param y: an [N] Tensor of labels, if class-conditional.:return: an [N x C x ...] Tensor of outputs."""assert (y is not None) == (self.num_classes is not None), "must specify y if and only if the model is class-conditional"hs = []t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) # Create sinusoidal timestep embeddings.emb = self.time_embed(t_emb) # MLPif self.num_classes is not None:assert y.shape == (x.shape[0],)emb = emb + self.label_emb(y)h = x.type(self.dtype)for module in self.input_blocks:h = module(h, emb, context)hs.append(h)h = self.middle_block(h, emb, context)for module in self.output_blocks:h = th.cat([h, hs.pop()], dim=1)h = module(h, emb, context)h = h.type(x.dtype)if self.predict_codebook_ids:return self.id_predictor(h)else:return self.out(h)2-2--timestep embedding生成
使用函数 timestep_embedding() 和 self.time_embed() 对传入的timestep进行位置编码，生成sinusoidal timestep embeddings。
其中 timestep_embedding() 函数定义如下，而self.time_embed()是一个MLP函数。
def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):"""Create sinusoidal timestep embeddings.:param timesteps: a 1-D Tensor of N indices, one per batch element.These may be fractional.:param dim: the dimension of the output.:param max_period: controls the minimum frequency of the embeddings.:return: an [N x dim] Tensor of positional embeddings."""if not repeat_only:half = dim // 2freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(device=timesteps.device)args = timesteps[:, None].float() * freqs[None]embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)if dim % 2:embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)else:embedding = repeat(timesteps, 'b -> b d', d=dim)return embeddingself.time_embed = nn.Sequential(linear(model_channels, time_embed_dim),nn.SiLU(),linear(time_embed_dim, time_embed_dim),
)2-3--self.input_blocks下采样
在 Forward() 中，使用 self.input_blocks 将输入噪声进行分辨率下采样，经过下采样具体维度变化为：[B*2, 4, 64, 64] > [B*2, 1280, 8, 8]；
下采样模块共有12个 module，其组成如下：
ModuleList((0): TimestepEmbedSequential((0): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(1-2): 2 x TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=320, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())(1): SpatialTransformer((norm): GroupNorm(32, 320, eps=1e-06, affine=True)(proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=320, out_features=320, bias=False)(to_v): Linear(in_features=320, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=320, out_features=2560, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=1280, out_features=320, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=768, out_features=320, bias=False)(to_v): Linear(in_features=768, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))))(3): TimestepEmbedSequential((0): Downsample((op): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))))(4): TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(320, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=640, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 640, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Conv2d(320, 640, kernel_size=(1, 1), stride=(1, 1)))(1): SpatialTransformer((norm): GroupNorm(32, 640, eps=1e-06, affine=True)(proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=640, out_features=640, bias=False)(to_k): Linear(in_features=640, out_features=640, bias=False)(to_v): Linear(in_features=640, out_features=640, bias=False)(to_out): Sequential((0): Linear(in_features=640, out_features=640, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=640, out_features=5120, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=2560, out_features=640, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=640, out_features=640, bias=False)(to_k): Linear(in_features=768, out_features=640, bias=False)(to_v): Linear(in_features=768, out_features=640, bias=False)(to_out): Sequential((0): Linear(in_features=640, out_features=640, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))))(5): TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 640, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=640, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 640, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())(1): SpatialTransformer((norm): GroupNorm(32, 640, eps=1e-06, affine=True)(proj_in): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=640, out_features=640, bias=False)(to_k): Linear(in_features=640, out_features=640, bias=False)(to_v): Linear(in_features=640, out_features=640, bias=False)(to_out): Sequential((0): Linear(in_features=640, out_features=640, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=640, out_features=5120, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=2560, out_features=640, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=640, out_features=640, bias=False)(to_k): Linear(in_features=768, out_features=640, bias=False)(to_v): Linear(in_features=768, out_features=640, bias=False)(to_out): Sequential((0): Linear(in_features=640, out_features=640, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((640,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((640,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((640,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(640, 640, kernel_size=(1, 1), stride=(1, 1))))(6): TimestepEmbedSequential((0): Downsample((op): Conv2d(640, 640, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))))(7): TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 640, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(640, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=1280, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Conv2d(640, 1280, kernel_size=(1, 1), stride=(1, 1)))(1): SpatialTransformer((norm): GroupNorm(32, 1280, eps=1e-06, affine=True)(proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=1280, out_features=1280, bias=False)(to_k): Linear(in_features=1280, out_features=1280, bias=False)(to_v): Linear(in_features=1280, out_features=1280, bias=False)(to_out): Sequential((0): Linear(in_features=1280, out_features=1280, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=1280, out_features=10240, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=5120, out_features=1280, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=1280, out_features=1280, bias=False)(to_k): Linear(in_features=768, out_features=1280, bias=False)(to_v): Linear(in_features=768, out_features=1280, bias=False)(to_out): Sequential((0): Linear(in_features=1280, out_features=1280, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))))(8): TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=1280, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())(1): SpatialTransformer((norm): GroupNorm(32, 1280, eps=1e-06, affine=True)(proj_in): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=1280, out_features=1280, bias=False)(to_k): Linear(in_features=1280, out_features=1280, bias=False)(to_v): Linear(in_features=1280, out_features=1280, bias=False)(to_out): Sequential((0): Linear(in_features=1280, out_features=1280, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=1280, out_features=10240, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=5120, out_features=1280, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=1280, out_features=1280, bias=False)(to_k): Linear(in_features=768, out_features=1280, bias=False)(to_v): Linear(in_features=768, out_features=1280, bias=False)(to_out): Sequential((0): Linear(in_features=1280, out_features=1280, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(1280, 1280, kernel_size=(1, 1), stride=(1, 1))))(9): TimestepEmbedSequential((0): Downsample((op): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))))(10-11): 2 x TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=1280, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity()))
)12个 module 都使用了 TimestepEmbedSequential 类进行封装，根据不同的网络层，将输入噪声x与timestep embedding和prompt context进行运算。
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):"""A sequential module that passes timestep embeddings to the children thatsupport it as an extra input."""def forward(self, x, emb, context=None):for layer in self:if isinstance(layer, TimestepBlock):x = layer(x, emb)elif isinstance(layer, SpatialTransformer):x = layer(x, context)else:x = layer(x)return x2-3-1--Module0
Module 0 是一个2D卷积层，主要对输入噪声进行特征提取；
# init 初始化
self.input_blocks = nn.ModuleList([TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))]
)# 打印 self.input_blocks[0]
TimestepEmbedSequential((0): Conv2d(4, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)2-3-2--Module1和Module2
Module1和Module2的结构相同，都由一个ResBlock和一个SpatialTransformer组成；
# init 初始化
for _ in range(num_res_blocks):layers = [ResBlock(ch,time_embed_dim,dropout,out_channels=mult * model_channels,dims=dims,use_checkpoint=use_checkpoint,use_scale_shift_norm=use_scale_shift_norm,)]ch = mult * model_channelsif ds in attention_resolutions:if num_head_channels == -1:dim_head = ch // num_headselse:num_heads = ch // num_head_channelsdim_head = num_head_channelsif legacy:#num_heads = 1dim_head = ch // num_heads if use_spatial_transformer else num_head_channelslayers.append(AttentionBlock(ch,use_checkpoint=use_checkpoint,num_heads=num_heads,num_head_channels=dim_head,use_new_attention_order=use_new_attention_order,) if not use_spatial_transformer else SpatialTransformer(ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim))self.input_blocks.append(TimestepEmbedSequential(*layers))self._feature_size += chinput_block_chans.append(ch)# 打印 self.input_blocks[1]
TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=320, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())(1): SpatialTransformer((norm): GroupNorm(32, 320, eps=1e-06, affine=True)(proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=320, out_features=320, bias=False)(to_v): Linear(in_features=320, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=320, out_features=2560, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=1280, out_features=320, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=768, out_features=320, bias=False)(to_v): Linear(in_features=768, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1)))
)# 打印 self.input_blocks[2]
TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=320, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 320, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())(1): SpatialTransformer((norm): GroupNorm(32, 320, eps=1e-06, affine=True)(proj_in): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1))(transformer_blocks): ModuleList((0): BasicTransformerBlock((attn1): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=320, out_features=320, bias=False)(to_v): Linear(in_features=320, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(ff): FeedForward((net): Sequential((0): GEGLU((proj): Linear(in_features=320, out_features=2560, bias=True))(1): Dropout(p=0.0, inplace=False)(2): Linear(in_features=1280, out_features=320, bias=True)))(attn2): CrossAttention((to_q): Linear(in_features=320, out_features=320, bias=False)(to_k): Linear(in_features=768, out_features=320, bias=False)(to_v): Linear(in_features=768, out_features=320, bias=False)(to_out): Sequential((0): Linear(in_features=320, out_features=320, bias=True)(1): Dropout(p=0.0, inplace=False)))(norm1): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm2): LayerNorm((320,), eps=1e-05, elementwise_affine=True)(norm3): LayerNorm((320,), eps=1e-05, elementwise_affine=True)))(proj_out): Conv2d(320, 320, kernel_size=(1, 1), stride=(1, 1)))
)2-3-3--Module3
Module3是一个下采样2D卷积层。
# init 初始化
if level != len(channel_mult) - 1:out_ch = chself.input_blocks.append(TimestepEmbedSequential(ResBlock(ch,time_embed_dim,dropout,out_channels=out_ch,dims=dims,use_checkpoint=use_checkpoint,use_scale_shift_norm=use_scale_shift_norm,down=True,)if resblock_updownelse Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)))# 打印 self.input_blocks[3]
TimestepEmbedSequential((0): Downsample((op): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)))
)2-3-4--Module4、Module5、Module7和Module8
与Module1和Module2的结构相同，都由一个ResBlock和一个SpatialTransformer组成，只有特征维度上的区别；
2-3-4--Module6和Module9
与Module3的结构相同，是一个下采样2D卷积层。
2-3--5--Module10和Module11
Module10和Module12的结构相同，只由一个ResBlock组成。
# 打印 self.input_blocks[10]
TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=1280, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())
)# 打印 self.input_blocks[11]
TimestepEmbedSequential((0): ResBlock((in_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(h_upd): Identity()(x_upd): Identity()(emb_layers): Sequential((0): SiLU()(1): Linear(in_features=1280, out_features=1280, bias=True))(out_layers): Sequential((0): GroupNorm32(32, 1280, eps=1e-05, affine=True)(1): SiLU()(2): Dropout(p=0, inplace=False)(3): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))(skip_connection): Identity())
)