Attention couple: remove base mask parameter

- first mask is first element in the list
- first conditioning is ignored (ksampler input is used instead)

region.py

@@ -48,7 +48,6 @@ class AttentionCouple:
         return {
             "required": {
                 "model": ("MODEL",),
-                "base_mask": ("MASK",),
                 "regions": ("LIST",),
             }
         }
@@ -61,16 +60,16 @@ class AttentionCouple:
     conds: list[Tensor]
     batch_size: int
 
-    def attention_couple(self, model: ModelPatcher, base_mask: Tensor, regions: ListWrapper):
+    def attention_couple(self, model: ModelPatcher, regions: ListWrapper):
         new_model = model.clone()
-        num_conds = len(regions.content) + 1
+        num_conds = len(regions.content)
 
-        mask = torch.stack([base_mask] + [r["mask"] for r in regions.content], dim=0)
+        mask = torch.stack([r["mask"] for r in regions.content], dim=0)
         mask_sum = mask.sum(dim=0, keepdim=True)
         assert mask_sum.sum() > 0, "There are areas that are zero in all masks."
         self.mask = mask / mask_sum
 
-        self.conds = [r["conditioning"][0][0] for r in regions.content]
+        self.conds = [r["conditioning"][0][0] for r in regions.content[1:]]
         num_tokens = [cond.shape[1] for cond in self.conds]
 
         def attn2_patch(q: Tensor, k: Tensor, v: Tensor, extra_options: dict):