fixes

Author: asomoza

src/diffusers/models/controlnets/controlnet_union.py

@@ -752,7 +752,7 @@ def forward(
             condition = self.controlnet_cond_embedding(cond)
             feat_seq = torch.mean(condition, dim=(2, 3))
             feat_seq = feat_seq + self.task_embedding[control_idx]
-            if from_multi:
+            if from_multi or len(control_type_idx) == 1:
                 inputs.append(feat_seq.unsqueeze(1))
                 condition_list.append(condition)
             else:
@@ -772,7 +772,7 @@ def forward(
         for (idx, condition), scale in zip(enumerate(condition_list[:-1]), conditioning_scale):
             alpha = self.spatial_ch_projs(x[:, idx])
             alpha = alpha.unsqueeze(-1).unsqueeze(-1)
-            if from_multi:
+            if from_multi or len(control_type_idx) == 1:
                 controlnet_cond_fuser += condition + alpha
             else:
                 controlnet_cond_fuser += condition + alpha * scale
@@ -819,11 +819,11 @@ def forward(
         # 6. scaling
         if guess_mode and not self.config.global_pool_conditions:
             scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
-            if from_multi:
+            if from_multi or len(control_type_idx) == 1:
                 scales = scales * conditioning_scale[0]
             down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales)]
             mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
-        elif from_multi:
+        elif from_multi or len(control_type_idx) == 1:
             down_block_res_samples = [sample * conditioning_scale[0] for sample in down_block_res_samples]
             mid_block_res_sample = mid_block_res_sample * conditioning_scale[0]