首先根据传入的 cross_attention_kwargs 参数,通过 create_controller 方法 实例化对应的注意力图修改类。然后通过 prompt2prompt pipeline 的 register_attention_control 方法 controller 实例化对应的 P2PAttnProcessor 类 ,并遍历替换掉 UNet 各层的原AttnProcessor 类,这里其实只需要增加一行,在此处使用 controller 对...
self.load_state_dict(state_dict)# 实例化模型defresnet50(pretrained=False, **kwargs):"""Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """# [3,4,6,3]对应block_num,残差块的数量model = ResNet(Bottleneck, [3,4,6,3], num...
[bs,1,num_query,num_cams,num_points,num_levels] attention_weights = self.attention_weights(query).view( bs, 1, num_query, self.num_cams, self.num_points, self.num_levels) reference_points_3d, output, mask = feature_sampling( value, reference_points, self.pc_range, kwargs['img_metas...
to["patches_replace"]["attn2"][key].add(ipadapter_attention, **patch_kwargs) AttributeError: 'CrossAttentionPatch' object has no attribute 'add' anyone can fixed it ?? cubiqclosed this ascompletedMay 19, 2024
(*args, **kwargs) File "/sgl-workspace/sglang/python/sglang/srt/managers/tp_worker_overlap_thread.py", line 134, in forward_thread_func_ logits_output, next_token_ids = self.worker.forward_batch_generation( File "/sgl-workspace/sglang/python/sglang/srt/managers/tp_worker.py", line ...
虽然在某些特定的场景下计算机可以比人类更快、更精准的识别出目标，但实际上，由于各类物体在不同的观测...
class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., window_size=None, attn_head_dim=None, xattn=False, rope=None, subln=False, norm_layer=nn.LayerNorm): ...
这样类n°1被分配值0，类n°2被分配值1。反过来，您打印的批次的标签将如下所示：
从文章中所提供的桥接模式方法来看,可以将其总结为信息加性融合(如 Pointwise Addition、 Concatenation 和 Attention Pooling)和信息乘性融合(如 Hadamard Product)。然而当原始网络出现梯度偏差的时候(比如一个正常、一个梯度爆炸或者消失),直观来看桥接模块是将信息进行了整合,但从数据集梯度传播的角度来看,其直接进行...
**kwargs, ): super().__init__() self.ln_1 = norm_layer(hidden_dim) self.self_attention = SS2D(d_model=hidden_dim, dropout=attn_drop_rate, d_state=d_state, **kwargs) self.drop_path = DropPath(drop_path) self.CDC_block = LDC(hidden_dim, hidden_dim) def forward(self, input...