def METHOD_NAME(model, editor: AttentionBase):
    """
    Register a attention editor to Diffuser Pipeline, refer from [Prompt-to-Prompt]
    """
    def ca_forward(self, place_in_unet):
        def forward(x,
                    encoder_hidden_states=None,
                    attention_mask=None,
                    context=None,
                    mask=None):
            """
            The attention is similar to the original implementation of LDM CrossAttention class
            except adding some modifications on the attention
            """
            if encoder_hidden_states is not None:
                context = encoder_hidden_states
            if attention_mask is not None:
                mask = attention_mask
            to_out = self.to_out
            if isinstance(to_out, nn.modules.container.ModuleList):
                to_out = self.to_out[0]
            else:
                to_out = self.to_out
            h = self.heads
            q = self.to_q(x)
            is_cross = context is not None
            context = context if is_cross else x
            k = self.to_k(context)
            v = self.to_v(context)
            q, k, v = map(
                lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h),
                (q, k, v))
            sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
            if mask is not None:
                mask = rearrange(mask, 'b ... -> b (...)')
                max_neg_value = -torch.finfo(sim.dtype).max
                mask = repeat(mask, 'b j -> (b h) () j', h=h)
                mask = mask[:, None, :].repeat(h, 1, 1)
                sim.masked_fill_(~mask, max_neg_value)
            attn = sim.softmax(dim=-1)
            # the only difference
            out = editor(
                q,
                k,
                v,
                sim,
                attn,
                is_cross,
                place_in_unet,
                self.heads,
                scale=self.scale)
            return to_out(out)
        return forward
    def register_editor(net, count, place_in_unet):
        for name, subnet in net.named_children():
            if net.__class__.__name__ == 'Attention':  # spatial Transformer layer
                net.forward = ca_forward(net, place_in_unet)
                return count + 1
            elif hasattr(net, 'children'):
                count = register_editor(subnet, count, place_in_unet)
        return count
    cross_att_count = 0
    for net_name, net in model.unet.named_children():
        if 'down' in net_name:
            cross_att_count += register_editor(net, 0, 'down')
        elif 'mid' in net_name:
            cross_att_count += register_editor(net, 0, 'mid')
        elif 'up' in net_name:
            cross_att_count += register_editor(net, 0, 'up')
    editor.num_att_layers = cross_att_count